silenceper – silenceper | AI Agents, Developer Tools, Open Source & Tech Notes

html-video: Generate Real MP4s Locally with HTML, CSS, and Coding Agents

Fri, 05 Jun 2026 23:34:00 +0800

One-line Positioning

html-video is a local video generation workflow for coding agents: give it a prompt, an article link, or a GitHub repository, and it generates animated multi-frame HTML that can be rendered into a real MP4.

Basic Information

Item	Information
Project	html-video
GitHub	https://github.com/nexu-io/html-video
Website	https://open-design.ai/html-video
Team	Open Design / nexu-io
License	Apache-2.0
Main technologies	HTML, CSS, TypeScript, Headless Chromium, ffmpeg
Default render engine	Hyperframes
Templates	21 built-in templates
Supported agents	Open Design, Claude Code, Cursor, Codex, Gemini, Qwen, OpenCode, Copilot, Aider, Hermes, and more
Positioning	Agent-driven local HTML-to-video workflow

What Problem It Solves

Programmatic video is not a new idea. Tools such as Remotion, Motion Canvas, and Manim can all turn code into video, but each comes with its own authoring model: some are React-oriented, some are canvas-oriented, and some are built primarily for mathematical animation.

That creates a practical barrier. You are not just “making a video”; you first need to choose a framework, learn its model, understand its rendering pipeline, and then manually break the content into scenes.

html-video tries to move that complexity behind a higher-level workflow. The user interacts with a local Studio or CLI, while the connected coding agent handles content analysis, template selection, and frame generation.

flowchart LR
A[Prompt / Article Link / GitHub Repository] --> B[Fetch and Prepare Source Material]
B --> C[Coding Agent Builds a Storyboard]
C --> D[Generate Animated HTML Per Frame]
D --> E[Record with Headless Chromium]
E --> F[Encode and Compose with ffmpeg]
F --> G[Export MP4]

The key idea is to separate content understanding from rendering. The agent decides what to say and how to structure the scenes; the rendering engine decides how those scenes become video frames.

Core Features

1. Generate Video from Links or Repositories

html-video is not limited to a single prompt. You can paste a web article, a WeChat article, or a GitHub repository URL. The Studio fetches and flattens the source content into Markdown, then passes it to the agent as material for the video.

This is especially useful for technical content. For example, if you provide an open-source repository, it can read the project description, README, and top-level structure, then generate a structured project introduction video.

2. Multi-frame Storyboards

The project uses a content-graph to organize multi-scene videos. In practice, the agent breaks the source material into nodes; each node becomes a frame, while the sequence, contrast, and dependency relationships between nodes define the pacing of the final video.

Single-frame videos can take a fast path. Multi-scene videos generate HTML frame by frame, then compose those frames into one MP4.

3. Express Frames with HTML and CSS

Compared with writing directly in a specialized video framework, HTML and CSS are easier to author, inspect, and modify. Titles, cards, charts, gradients, animations, and layouts can all be represented with common web technologies.

The current default engine is Hyperframes. It loads animated HTML with Headless Chromium, records the frames, and hands them to ffmpeg for MP4 encoding.

4. Built-in Template Library

html-video includes 21 templates covering data visualization, product promos, title cards, outros, kinetic typography, decision trees, cinematic frames, and other common video scenes.

A data chart template like this is useful when a video needs to explain how a metric changes over time. Instead of designing every frame from scratch, the agent can fill the template with a headline, data points, annotations, and a source line.

A title template like this works well for openers, topic reveals, or technical videos that need a stronger “system online” visual style.

5. Local Agent Workflow

The project supports multiple coding agents, including Open Design, Trae CLI, Claude Code, Cursor Agent, Codex CLI, Gemini CLI, Grok, Qwen Code, OpenCode, Copilot CLI, Aider, Hermes, and the Anthropic API.

That means it is not locked to a single model or vendor. It behaves more like a local video generation workstation that can be driven by whichever coding agent you already use.

6. Optional Soundtrack and Narration

If a MiniMax API key is configured, html-video can also generate background music and TTS narration. During export, ffmpeg mixes the audio into the final MP4. Without an audio key, the rest of the video generation workflow still works normally.

Who It Is For

html-video is especially useful for:

Developers who want to turn technical articles, open-source projects, or product updates into short videos;
Content teams that want to generate project introduction videos with agents;
Engineers exploring “Video as Code” workflows;
Users who prefer local rendering and do not want per-render cloud fees;
People already using coding agents such as Claude Code, Codex, Cursor, or Hermes.

It is particularly well suited to content that already has a clear structure but needs to be turned into video more quickly: open-source project introductions, product updates, data explainers, technical tutorial intros, and social video assets.

Quick Start

The project is a pnpm-managed monorepo. The official quick start is:

pnpm install
pnpm -r build
node packages/cli/dist/bin.js studio

Then open the local Studio:

http://127.0.0.1:3071

Useful CLI commands:

node packages/cli/dist/bin.js doctor
node packages/cli/dist/bin.js search-templates --intent "github stars race" --top 3

doctor checks available local agents and render engines. search-templates helps find suitable templates based on a video intent.

Conclusion

The value of html-video is not simply “turning HTML screenshots into video.” Its stronger idea is connecting source input, agent understanding, storyboard generation, animated HTML, and MP4 rendering into one workflow.

For developers, it lowers the entry barrier for programmatic video. You do not need to commit to one video framework first, nor do you need to design every scene from a blank canvas. You can start with an article, a repository, or a prompt, then let an agent generate the video from templates.

Today, the truly runnable core is the Hyperframes engine, while other render engines are still on the roadmap. But if you care about AI agents, content automation, and technical video generation, html-video is an open-source project worth following.

html-ppt-skill: Let AI Generate Professional HTML Presentations

Tue, 02 Jun 2026 15:20:00 +0800

One-line Positioning

html-ppt-skill is an AgentSkill for AI agents to generate professional HTML presentations with themes, layouts, animations, and presenter mode using pure static HTML/CSS/JS.

Basic Information

Item	Information
Project	html-ppt-skill / HTML PPT Studio
GitHub	https://github.com/lewislulu/html-ppt-skill
Author	lewis
License	MIT
Main technologies	HTML, CSS, JavaScript, Shell
Stars	About 5.3k
Forks	About 505
Positioning	AgentSkill / HTML presentation generation toolkit
Core highlights	36 themes, 15 full-deck templates, 31 page layouts, 47 animations, presenter mode
Build model	Pure static HTML/CSS/JS, no build step required

What Problem It Solves

Many AI tools can generate a presentation outline, but turning that outline into slides that are presentable, well-designed, and ready for a real talk still usually requires a lot of manual layout work.

html-ppt-skill addresses this gap: it lets an agent move beyond writing content and directly generate an HTML presentation that can be opened, presented, previewed, and exported.

It packages common presentation authoring capabilities into an AgentSkill, including:

visual themes;
page layouts;
full-deck templates;
CSS and Canvas animations;
presenter mode;
export scripts;
example decks and verification screenshots.

For users, the interaction can be very direct. You can ask an AgentSkill-compatible agent something like:

“Create an 8-page technical sharing deck with a cyberpunk theme.”
“Turn this outline into an investor pitch deck.”
“Create a 9-card Xiaohongshu-style visual post with a soft white style.”

The agent can then use this skill to generate the corresponding HTML presentation.

Core Features

1. 36 Visual Themes

The project includes 36 themes covering minimal, editorial, dark, cyberpunk, blueprint, Xiaohongshu, investor pitch, academic, and other styles.

Themes are driven by CSS tokens. Swapping one <link> can reskin the entire deck. This makes it easy for an agent to choose a style based on the content type:

technical sharing: tokyo-night, cyberpunk-neon, engineering-whiteprint;
business reporting: corporate-clean, pitch-deck-vc;
Xiaohongshu-style posts: xiaohongshu-white, soft-pastel;
academic content: academic-paper;
architecture proposals: blueprint.

2. 15 Full-deck Templates

This is not just a collection of single-page components. It also provides complete multi-slide deck templates.

The templates fall into two groups.

Extracted visual styles

Xiaohongshu white editorial style;
dark knowledge graph style;
blueprint / architecture diagram style;
terminal cyberpunk style;
purple gradient card style;
warning / safety testing style;
soft pastel card style;
minimal keyboard navigation style.

Scenario templates

investor pitch deck;
product launch;
technical sharing;
weekly report;
9-page Xiaohongshu post;
course module;
presenter-mode template with full speaker scripts.

These templates are especially useful for agents because the agent does not need to design every page from scratch. It can start from a scenario-specific structure and adapt the content.

3. 31 Single-page Layouts

The project provides 31 common page layouts that cover most presentation needs.

The layout set includes:

cover;
table of contents;
section divider;
bullet list;
two-column and three-column pages;
quote pages;
statistic highlights;
KPI grids;
tables;
code pages;
diffs;
terminal pages;
flow diagrams;
timelines;
roadmaps;
mind maps;
comparison pages;
Gantt charts;
image hero pages;
image grids;
bar, line, pie, and radar charts;
architecture diagrams;
CTA pages;
thanks pages.

The README also shows a live cycling preview of real layout files:

4. 47 Animations

html-ppt-skill includes 27 CSS animations and 20 Canvas FX animations.

CSS animations include:

fade-in;
rise-in;
zoom-pop;
blur-in;
typewriter;
neon glow;
gradient flow;
staggered list entrance;
counter-up;
path drawing;
3D card flip;
page turn;
spotlight effects.

Canvas FX modules include:

particle burst;
confetti cannon;
fireworks;
starfield;
Matrix rain;
knowledge graph;
neural-network pulses;
constellation lines;
data stream;
magnetic field;
shockwave effects.

This means the result is not just a static web page pretending to be a slide deck. It can behave like a real presentation with motion and visual emphasis.

5. Presenter Mode

Presenter mode is one of the most interesting parts of the project.

Pressing S in any deck opens a separate presenter window with four draggable and resizable magnetic cards:

current slide preview;
next slide preview;
speaker script;
timer.

The implementation is thoughtful. The previews are not screenshots. Each preview is an iframe loading the same deck HTML with a ?preview=N parameter, so it renders only the target slide. The presenter preview and the audience view use the same CSS, fonts, theme, and viewport, which keeps the visual result consistent.

Slide changes are synchronized through BroadcastChannel and postMessage, so the preview does not need to reload and avoids flicker.

Who It Is For

This project is useful for:

users who want AI to directly generate presentation decks;
people using Claude Code, Codex, Cursor, OpenClaw, or similar agent tools;
anyone who often creates technical talks, product launches, weekly reports, or course materials;
developers who prefer HTML/CSS/JS over traditional slide tools;
users who need to generate Xiaohongshu-style visual posts, course slides, or project introductions in batches;
teams that want presentation assets to be versioned, scripted, and exported like code.

It is especially suitable when the content is generated by AI but the final output still needs a deliberate visual design.

Quick Start

Installation is simple:

npx skills add https://github.com/lewislulu/html-ppt-skill

After installation, an AgentSkill-compatible agent can use the skill directly.

If you clone the project manually, you can scaffold a new deck with:

./scripts/new-deck.sh my-talk

Browse the built-in resources:

open templates/theme-showcase.html
open templates/layout-showcase.html
open templates/animation-showcase.html
open templates/full-decks-index.html

Export PNG images:

./scripts/render.sh templates/theme-showcase.html
./scripts/render.sh examples/my-talk/index.html 12

Common keyboard shortcuts:

← → Space PgUp PgDn Home End Navigate
F Fullscreen
S Open presenter window
N Open notes drawer
R Reset timer
O Slide overview
T Cycle themes
A Demo animation on current slide
#/N Jump to slide N
?preview=N Single-slide preview mode

Conclusion

The value of html-ppt-skill is not simply “writing presentations in HTML.” It packages themes, layouts, templates, animations, presenter mode, and export scripts into a reusable capability that agents can call.

If you already use AI agents to write documents, prepare proposals, or generate code, this project makes the next step feel natural: let the agent turn the content directly into a presentable HTML slide deck.

Agents Need More Than Models: Context Infrastructure Is Taking Shape

Sun, 31 May 2026 22:55:00 +0800

Looking at recent GitHub projects, one pattern is becoming harder to ignore:

The central problem for agents is moving from “can I call a stronger model?” to “can I manage context reliably, continuously, and at a reasonable cost?”

This is not a single news event. Several projects are pointing in the same direction.

OpenViking describes itself as a context database for AI agents, using a filesystem-like model to manage memory, resources, and skills. Mirage tries to mount external services such as S3, Google Drive, Slack, Gmail, and Redis into one virtual file tree, so agents can use familiar Unix-like operations across different backends. SkillOpt goes one layer further and treats natural-language skills as external state that can be trained, validated, and improved instead of being a one-off prompt.

These projects solve different problems, but they are all answering a larger question:

When an agent starts running long tasks, calling tools, and accumulating experience, where should its working context live? How should it be read, edited, debugged, and reused?

My current view is simple: context infrastructure will become one of the most important layers in agent applications.

Meet The Three Projects

This article is not trying to blend three projects into one vague trend. They sit at different parts of the agent infrastructure stack: OpenViking is closer to a context database, Mirage is closer to a virtual filesystem for tools and data sources, and SkillOpt is closer to a skill optimizer.

In one table:

Project	One-line Positioning	Main Problem Layer
OpenViking	A context database for AI agents	How to manage memory, resources, and skills together
Mirage	A unified virtual filesystem for AI agents	How agents access different tools and data sources through one interface
SkillOpt	A natural-language optimizer for agent skills	How skills move from handwritten experience to evaluated, iterative assets

These projects are not just “another agent framework.” They are closer to missing infrastructure for agents that need to work over longer horizons.

OpenViking: A Context Database For Agents

OpenViking positions itself as The Context Database for AI Agents.

The problem it targets is fragmentation. An agent’s useful context may include project documentation, user preferences, task logs, skill descriptions, external resources, and historical experience. If these materials are scattered across prompts, chat history, vector databases, and temporary files, the agent has a hard time reusing them consistently.

OpenViking puts memory, resources, and skills into one context database and organizes them with a filesystem paradigm. It is not just about similarity search. Its README emphasizes hierarchy, recursive directory retrieval, layered context loading, and visualized retrieval traces.

You can think of it as a context workspace for agents:

memory stores preferences, experience, and historical state;
resources mount documents, code, references, and external knowledge;
skills describe reusable ways to perform tasks;
layered loading controls which context enters the current task;
retrieval traces help developers understand why the agent read certain information.

This is useful for agent applications that care about long-term memory, project-level context, and multi-skill collaboration. Coding assistants, enterprise knowledge assistants, and personal workflow agents can all run into this problem.

The boundary is also clear. OpenViking helps organize and retrieve context, but it does not replace permissions, security, task planning, or business approvals. If a context database touches sensitive information, access control and audit logging still need to be designed separately.

Mirage: Mounting The Outside World As A File Tree

Mirage positions itself as A Unified Virtual Filesystem For AI Agents.

It focuses on a different practical issue: agents need to access more tools. A single agent may need to read GitHub, inspect S3, search Slack, open Gmail, query Redis, and process local files. Each system has its own API, permission model, pagination behavior, error codes, and data shape. The more tools an agent uses, the more likely it is to fail at tool selection or parameter construction.

Mirage’s approach is to mount external data sources into one virtual filesystem. The repository examples show resources like /s3, /gdrive, /slack, /gmail, and /redis under one tree, so agents can explore, read, and combine data through file-like operations.

That design has two practical benefits.

First, it turns tool use into environment exploration. ls, cat, grep, directories, files, and paths are more stable abstractions than a large set of heterogeneous API schemas.

Second, it gives complex tasks an intermediate workspace. An agent can read data from different sources, organize it into files, produce intermediate outputs, and pass those outputs to the next step.

Mirage is a good fit for agent scenarios where there are many data sources, but most operations are reading, searching, organizing, and combining information. Research assistants, data cleanup agents, operations assistants, and internal knowledge tools all fit this pattern.

But Mirage should not be treated as a universal replacement for APIs. Operations with side effects, such as transfers, deletions, approvals, or sending messages, still need stricter permissions, confirmations, and audit trails. A filesystem abstraction can reduce reading and organization complexity, but it should not blur the risk boundary of business actions.

SkillOpt: Making Skills Trainable And Testable

SkillOpt comes from Microsoft Research and describes itself as Executive Strategy for Self-Evolving Agent Skills.

It targets the problem of reusable agent experience. Many agent systems now have skills, prompts, playbooks, or SOPs. Most of them are still written by humans and tuned through manual trial and error. Whether a skill got better or worse is often judged by a few ad hoc tests.

SkillOpt takes a different approach: it does not update model weights, but instead optimizes external natural-language skills. It uses task trajectories, feedback, and validation gates to repeatedly edit skills, accepting changes only when validation improves. The output is a deployable artifact such as best_skill.md.

This is interesting because it upgrades a skill from “prompt experience” into a trainable asset.

For development teams, this suggests that skills may be maintained more like code:

prepare fixed evaluation sets for common task types;
record the impact of each skill change;
feed failure cases back into the optimization loop;
require human review for high-risk skills;
release stable skills into real agent workflows.

SkillOpt is closer to research and methodology than a drop-in product for every agent platform. But the direction matters: as agents depend more on skills, skills themselves need versioning, evaluation, optimization, and release processes.

Why This Matters Now

Early AI applications were mostly chat.

A user provided input, the model returned output. Context management mostly meant putting conversation history into the prompt, truncating it when it got too long, summarizing it, or adding a simple RAG layer.

Agents are different.

A real working agent constantly produces new state:

which files it read;
which tools it called;
which commands failed;
which user preferences were confirmed;
which lessons should be reused next time;
which external resources should be loaded only when needed;
which skills worked for this project and which did not.

If this information is scattered across chat history, vector stores, temporary files, and tool logs, the agent becomes difficult to control.

The issue is not only that it forgets. The deeper issue is that even when it remembers, it may not know how to use what it remembered.

Traditional RAG helps with part of the retrieval problem, but it often flattens information into chunks and similarity scores. For long-running agents, that is not enough. Agents need hierarchy, provenance, freshness, permissions, and task relationships. When something goes wrong, developers also need to understand why the agent retrieved the context it used.

That is why projects like OpenViking, Mirage, and SkillOpt are worth watching. They are not simply expanding the context window. They are rethinking how agents organize external state.

Direction One: Context Becomes More Like A Filesystem

OpenViking’s core idea is straightforward: organize the memory, resources, and skills that agents need through a filesystem-like structure.

It emphasizes directories, files, layered loading, recursive retrieval, visualized retrieval traces, and automatic session management. The key point is not to vectorize everything. The key point is to give developers a structured and debuggable way to manage context.

The engineering judgment behind this is important:

An agent’s context should not be a black-box knowledge store. It should be an organized, observable, and debuggable workspace.

If an agent makes a wrong decision because of bad context, developers should not only see “the model answered incorrectly.” They should be able to inspect:

which directory it read;
which memory it used;
which resources it ignored;
why it selected a certain skill;
in which task loop the wrong assumption appeared.

This moves context management from retrieval augmentation toward context engineering.

Direction Two: External Tools Become A Virtual File Tree

Mirage is more about the tool access layer.

It mounts multiple services and data sources into one virtual filesystem. Instead of learning a separate API interface for every backend, an agent can use familiar files, directories, paths, pipes, and commands to explore its environment.

The value is reducing the cognitive overhead of tool use.

Many agent tool integrations look powerful, but they are costly in practice. Every MCP server, SaaS API, and database has its own parameters, permissions, pagination behavior, error codes, and data shape. The more tools an agent has, the more likely it is to fail while choosing tools or constructing arguments.

If backend capabilities are exposed through one filesystem abstraction, the agent can at least rely on a stable mental model:

ls to see available resources;
cat to read content;
grep to search;
cp or mv to organize intermediate results;
snapshots or versions to manage a task workspace.

This does not mean filesystems can solve every problem. Complex business actions still require clear APIs, permissions, and transaction boundaries. But for the high-frequency agent actions of reading, searching, organizing, and combining, a virtual filesystem is a natural abstraction.

Direction Three: Skills Become Optimizable Assets

SkillOpt focuses on another layer: how agents reuse experience.

Many agent platforms now have skills, instructions, playbooks, or prompt libraries. The problem is that most of them are hand-written and manually tuned. Whether a skill works is often judged by trying it a few times.

SkillOpt treats natural-language skills as trainable state outside the frozen model. It does not change model weights. It iterates skill documents through task trajectories, feedback, and validation gates, producing a deployable artifact such as best_skill.md.

That is interesting because it turns skills from prompt snippets into engineering assets.

A mature team may not only maintain code, tests, and documentation. It may also maintain:

skills for specific task types;
evaluation sets for each skill;
before-and-after success rates for skill changes;
rules for which tasks can be optimized automatically;
review gates before high-risk skills enter production.

This resembles testing, CI, and staged rollout in software engineering. The difference is that the optimization target is a natural-language strategy rather than code.

Why This Is Bigger Than Any Single Project

Looking at these projects together, the agent application stack is starting to separate into layers:

Layer	Previous Approach	What Is Emerging
Model layer	Pick GPT, Claude, Gemini, or DeepSeek	Model routing, cost control, edge models
Tool layer	Write API wrappers or MCP servers	Tool gateways, permissions, sandboxes, virtual filesystems
Context layer	Chat history, RAG, handwritten memory	Context databases, layered loading, retrieval traces
Experience layer	Prompts, system instructions, human SOPs	Skills, evaluation, automatic optimization
Operations layer	Watch tokens and logs	Cost, permissions, audit, replay, failure analysis

This suggests that agent competition will become increasingly engineering-heavy.

In the future, the quality of an agent product will not only depend on which model it uses. It will also depend on whether it has a stable context system: whether it remembers the right things, forgets noisy information, loads context on demand, explains what context it used, and turns successful workflows into reusable skills.

Practical Takeaways For Developers

If you are building an agent application, you do not necessarily need to adopt these projects immediately. But you should start treating context as a first-class design problem.

I would check the following questions first:

Is context structured?
Do not dump everything into the prompt. At least separate user preferences, project documentation, task logs, tool outputs, long-term memory, and temporary drafts.
Does context have provenance?
Every memory, resource, and skill should trace back to its source. Otherwise, when an agent acts on bad information, debugging becomes painful.
Does context have a lifecycle?
Some information is only useful for the current task. Some should be retained long term. Some must expire. Long-term memory is not better just because it is larger.
Is context observable?
At minimum, record what the agent read, what it skipped, which skill it used, and where it failed.
Are skills evaluated?
If a skill affects real task outcomes, do not change it purely by feel. Prepare fixed tasks and judge changes by success rate, latency, cost, and error patterns.

Boundaries

This kind of infrastructure should not be over-romanticized.

First, a context system does not replace a permission system. What an agent can see, call, and write still needs independent control.

Second, a virtual filesystem should not turn every business operation into a file operation. Actions such as transactions, approvals, deletions, and external messages need explicit confirmation and auditability.

Third, automatic skill optimization is not magic. If the evaluation set is weak, the optimized skill may overfit a few tasks and become more fragile in real use.

Fourth, these open-source projects are moving quickly. OpenViking, Mirage, and SkillOpt are worth watching, but before production use, teams still need to evaluate licenses, data boundaries, dependency complexity, and maintenance pace.

Conclusion

The next stage of agents will not be driven by stronger models alone.

Models still matter, but they only solve part of the reasoning problem. What makes agents work over longer horizons is another layer: how context is organized, how tools are exposed, how experience is captured, how failures are replayed, and how permissions are narrowed.

The shared signal from OpenViking, Mirage, and SkillOpt is this: agents need their own infrastructure layer.

If the last year was about making models use tools, the next question is more operational:

When tools multiply, tasks get longer, and context becomes more complex, why should an agent still work reliably?

The answer is probably not a longer prompt. It is a manageable, observable, and iterative context system.

References

The Free Era of AI Tools Is Ending: From Copilot Token Billing to Agent Budget Governance

Sun, 31 May 2026 01:20:00 +0800

Several recent signals point in the same direction: AI tooling costs are becoming harder to hide.

GitHub Copilot is moving to usage-based billing on June 1, 2026. Instead of relying only on a more abstract premium request model, GitHub is introducing GitHub AI Credits and calculating usage based on model token consumption.

At the same time, OpenRouter has launched Guardrails, a workspace-level governance layer for budgets, model and provider restrictions, zero data retention, prompt injection defense, and data loss prevention. On the open source side, projects like AgentBudget are appearing with a very direct goal: put an ulimit-style hard budget around AI agent sessions so one agent run cannot burn through an entire AI budget.

Taken together, the signal is clear:

AI tools are moving from “buy a subscription and use it freely” to “every long-running task, model choice, and agent loop needs to be measured and governed.”

This is not just a price increase story. It is the cost model catching up with the new shape of AI tools.

Why This Is Happening Now

Early AI coding tools were closer to enhanced editor plugins.

You asked for a code completion, an explanation, or a small test. Behind the scenes, the model still had a token cost, but the product could wrap that complexity inside a subscription plan or request quota.

That is no longer the full picture.

Modern AI coding tools now do things like:

read large parts of a repository;
plan changes across multiple files;
call terminals, browsers, MCP servers, and external APIs;
run tests and retry fixes;
create pull requests and review code;
keep long-running sessions alive while iterating on a task.

In GitHub’s announcement, Copilot is described as having evolved from an editor assistant into an agentic platform that can run long, multi-step coding sessions and iterate across repositories. That kind of agentic usage has a very different compute and inference profile.

That is the core issue: a short chat and a long autonomous coding session may have looked similar under the old billing model, but their real costs are not similar at all.

From a platform perspective, usage-based billing was likely inevitable.

What Copilot’s Change Really Means

There are several important details in GitHub’s change.

First, base Copilot subscription prices are not directly changing, but each plan now includes a certain amount of GitHub AI Credits. Usage beyond that is calculated from token consumption, including input, output, and cached tokens.

Second, the old premium request unit is being replaced. Users used to think in terms of “how many requests do I have left?” They now need to think in terms of “how many tokens or credits did this task consume?”

Third, code completions and Next Edit Suggestions remain included in Copilot plans and do not consume AI Credits. Heavier capabilities such as chat, agents, and code review are the ones moving toward finer-grained metering.

Fourth, fallback behavior is going away. In the past, when certain quotas were exhausted, some interactions could fall back to a lower-cost model. Under the new model, whether work continues depends more directly on remaining credits and administrator budget settings.

Fifth, Copilot code review consumes not only AI Credits but also GitHub Actions minutes. This matters because once AI features begin executing real workflows, they consume more than just model tokens. They consume runtime resources too.

So developers and teams can no longer ask only:

How much does this AI tool cost per month?

They also need to ask:

What does our usage pattern look like? Which tasks consume the most tokens? Which models are expensive? Which agent runs can spiral? When the budget is exceeded, should the system stop, downgrade, or keep billing?

Why Agents Need Budget Governance

The cost of a normal chat tool is usually easier to reason about. A user asks something, the model replies, and the main risk is that the context grows too long.

Agents are different. They have several natural failure modes that can burn through budget.

1. Agents Loop

A coding agent may go through a loop like this:

read code -> write patch -> run tests -> fail -> read more code -> patch again -> run tests again

Without a stopping condition, it can spend many rounds on the same error. Each round costs model calls, tool calls, and context transfer.

2. Agents Expand Context

To understand a task, an agent may read files, issues, pull requests, web pages, logs, and documentation. The larger the context, the more input tokens it consumes. The longer the response, the more output tokens it consumes.

3. Agents Call Tools

Search, browser automation, databases, vector retrieval, code execution, sandboxes, and CI all have costs. A real agent budget should include more than LLM tokens.

4. Agents Can Work Hard in the Wrong Direction

If the user’s goal is unclear or the context is noisy, an agent can choose the wrong direction. When the direction is wrong, more effort just means more cost.

That is why agent-era cost governance is not just about saving money. It is part of engineering safety.

From Quotas to Guardrails

OpenRouter’s Guardrails is a useful example of where this is heading.

It is not just a billing page. It puts governance into the request path: daily, weekly, and monthly budgets can be enforced; requests can fail once limits are reached; model and provider access can be restricted; zero-data-retention providers can be required; prompt injection and data loss protection can be added.

The value is that it turns “look at the bill later” into “set boundaries before execution.”

For individual developers, that may prevent a script from running wild. For teams, the questions are more operational:

Should every developer be allowed to use the most expensive model?
Should automated jobs be allowed to retry indefinitely?
Can each API key have its own budget?
Should test and production environments use different model pools?
When customer data is involved, should the system force zero-data-retention providers?

Once AI calls enter real business workflows, these questions become more important than simply asking which model is smartest.

Why AgentBudget Is Worth Watching

AgentBudget has a simple premise: put a hard budget around AI agent sessions.

It can wrap LLM calls, tool calls, and external API requests; track cost in real time; and trigger a circuit breaker when a limit is reached. It also addresses a practical issue: do not let an agent get cut off right before the final answer, so it can reserve part of the budget for the final response.

This shows that cost governance is moving from the platform layer into the application layer.

If you are building your own agent, you cannot rely only on the model provider’s invoice. A provider bill usually tells you how much you spent after the fact. Your application needs to know during execution:

How much has this task already consumed?
Is the remaining budget enough for the next step?
Is the agent stuck in a repeated-call loop?
Should each subtask have its own budget?
When the budget is almost exhausted, should the agent stop, downgrade, or summarize current progress?

This is similar to timeout, rate limit, and memory limit in traditional systems. We used to limit process resources. Now we need to limit agent resources.

Token Bills Need to Be Auditable Too

Once tokens become the core billing unit, another issue appears: can the token count itself be trusted?

A recent arXiv paper discusses the risk of token inflation. Its core point is that commercial model providers often hide model implementation details, tokenizers, and execution internals, which makes it hard for users to independently verify reported token counts.

This does not mean every provider will misreport usage. But it does mean that when token-based billing becomes mainstream, cost observability and auditability become infrastructure concerns.

A team should not store only a single final cost number. A more useful record looks like this:

Layer	What to record
Task	User goal, task ID, entry point
Model	Model, provider, pricing tier
Usage	Input, output, cached tokens, streaming mode
Tools	Search, browser, CI, external APIs, and other costs
Controls	Budget, rate limit, downgrade, blocking reason
Result	Completion status, retries, human handoff

This connects directly to agent observability. The more autonomous an AI system becomes, the more important it is to connect cost, permissions, context, and outcome.

Advice for Individual Developers

If you use AI coding tools every day, start with a few habits.

First, do not use the strongest model by default. Simple completions, error explanations, and small scripts can often use cheaper models. Save stronger models for architecture, complex debugging, and cross-file changes.

Second, ask for a plan before letting AI execute a large task. Planning exposes context scope, risks, and verification strategy before the costly loop begins.

Third, set stopping conditions for long tasks. Limit the number of test runs, directories to inspect, or retry rounds. When an agent fails repeatedly, have it summarize instead of guessing forever.

Fourth, look at the shape of your bill, not just the total. The useful question is which tasks, models, tools, and workflows consume the most.

Advice for Small Teams

Small teams should treat AI cost as engineering governance, not only as a finance issue.

Start with these steps:

Separate budgets by scenario
- daily Q&A
- code generation
- automated code review
- batch refactoring
- AI checks inside CI
Separate permissions by role
- regular developers default to cheaper models;
- senior developers can manually switch to stronger models;
- automated tasks need hard budgets;
- production-related tasks need approval.
Set hard boundaries for agents
- maximum spend;
- maximum iteration rounds;
- maximum context scope;
- maximum tool calls;
- downgrade strategy after failure.
Include cost in retrospectives
- How much human time did AI save?
- Did the cost come from models or tools?
- Which steps could be replaced by rules, caching, or scripts?
- Should the workflow become a skill to reduce repeated cost next time?

This Is Not Entirely Bad

When people see token-based billing, the first reaction is often that AI tools are getting more expensive.

That is real. But it is also a sign that AI tools are maturing.

Subscription pricing hides complexity and works well for early adoption. Usage-based billing exposes real resource consumption and forces both platforms and users to face the cost of execution.

Once agents start doing real work on your behalf, cost governance is no longer optional.

A more mature AI engineering system needs:

model selection;
context management;
permission control;
tool auditing;
budget limits;
task retrospectives;
downgrade and stopping strategies.

In short:

The next stage of AI tooling is not only about being smarter. It is about being more controllable.

The teams that manage cost, permissions, and execution boundaries well will be the ones best positioned to put AI agents into real workflows.

References

FreeDomain: A Free Domain Entry Point for Personal and Open Source Projects

Fri, 29 May 2026 20:00:00 +0800

One-line Positioning

DigitalPlat FreeDomain is a platform for free domain registration and DNS integration. Its goal is to help individuals, developers, open source projects, and organizations get an online identity with lower cost.

Basic Information

Item	Information
GitHub repository	https://github.com/DigitalPlatDev/FreeDomain
Website	https://domain.digitalplat.org
Registration dashboard	https://dash.domain.digitalplat.org/
Main language	HTML, with some JavaScript / Python
License	AGPL-3.0
GitHub popularity	About 171k stars and 3.3k forks
Available extensions	`.DPDNS.ORG`, `.US.KG`, `.QZZ.IO`, `.XX.KG`, `.QD.JE`

What Problem It Solves

A domain name is one of the most basic pieces of getting a website online. But for students, individual developers, small teams, open source projects, and nonprofits, buying and renewing a domain is still a cost to consider.

FreeDomain tries to lower that barrier. Users can claim a free domain and connect it to a DNS provider they already know, then use it for a personal homepage, project documentation, demo site, open source service, or organization website.

It is not trying to replace traditional commercial domain registrars. Its value is giving budget-constrained or early-stage projects an easier way to get started.

Core Features

Free Domain Registration

Users can apply for free domains through the DigitalPlat FreeDomain dashboard. The extensions listed in the README currently include .DPDNS.ORG, .US.KG, .QZZ.IO, .XX.KG, and .QD.JE.

For many personal projects, this is already enough for a public demo, test service, or shareable project page.

Freedom to Choose a DNS Provider

FreeDomain does not require users to stay with one fixed DNS platform. The README mentions Cloudflare, FreeDNS by Afraid.org, Hostry, and self-hosted DNS infrastructure as possible options.

That means after you get a domain, DNS records, CDN, SSL, access rules, and related setup can still follow the services and workflow you already prefer.

Beginner-friendly Documentation

The repository includes tutorial pages covering account creation, DNS hosting setup, and domain activation. The docs are written for first-time domain users, individual developers, open source projects, and nonprofits.

This is useful for people without prior DNS experience. You do not need to understand the whole domain system before following the basic setup steps.

Abuse Handling and Security Notices

Free domain platforms are easy targets for abuse, so the project provides an abuse report email for handling domain abuse reports.

The README also includes an important security notice: its previous Telegram account and group were compromised and are no longer official communication channels. The project currently points users to Discord. When using this kind of free service, it is safer to rely on GitHub, the official website, and the official Discord server instead of unofficial links or announcements.

Who It Is For

Individual developers who want a domain for a homepage, demo, or documentation site
Students and creators who want an independent domain but have limited budget
Open source projects that need a quick public entry point
Users who want to pair a free domain with Cloudflare or other DNS/CDN services
Small organizations, nonprofit projects, and early product prototypes

Quick Start

Open the registration dashboard: https://dash.domain.digitalplat.org/
Register or sign in to a DigitalPlat account.
Choose an available free domain extension, such as .us.kg or .dpdns.org.
Submit the domain request.
Configure DNS:
- use Cloudflare;
- use FreeDNS or Hostry;
- or connect your own DNS service.
Follow the official tutorial to complete activation and DNS setup: https://github.com/DigitalPlatDev/FreeDomain/blob/main/documents/tutorial/index.md

One thing to note: the FAQ says the current default limit is 1 domain per user account. This policy is meant to reduce abuse and keep usage fair.

Conclusion

FreeDomain’s value is straightforward: it helps more people get a usable domain first, then build their website or project on top of it.

If you are building a personal project, open source project, student portfolio, nonprofit page, or early product demo, it can be a low-cost domain entry point. You can still combine it with mature DNS services such as Cloudflare, so the overall management and migration cost remains low.

That said, free domains usually come with usage rules, quota limits, and abuse review. For long-term production use, read the FAQ and platform policies carefully. For learning, showcasing, validation, and lightweight projects, it is worth knowing about.

Pi: A Coding Agent Harness You Can Reshape Around Your Workflow

Wed, 27 May 2026 23:45:00 +0800

AI coding tools are increasingly becoming full products: built-in plan mode, permission prompts, sub-agents, MCP, task lists, background jobs, and graphical interfaces.

earendil-works/pi takes a different path: keep the core small, and make the rest open to user extension.

Project: https://github.com/earendil-works/pi

Documentation: https://pi.dev/

One-line positioning

Pi is a customizable terminal AI coding agent harness: it provides a coding agent, tool-calling runtime, unified model API, TUI components, and package system so you can adapt the agent to your workflow instead of adapting yourself to the tool.

If many coding agents feel like ready-made products, Pi feels more like a set of building blocks for your own agent setup.

Basic information

Field	Details
Project	Pi
Repository	https://github.com/earendil-works/pi
Website	https://pi.dev/
License	MIT
Main stack	Mostly TypeScript
Core packages	`pi-coding-agent`, `pi-agent-core`, `pi-ai`, `pi-tui`
Runtime modes	Interactive TUI, print/JSON, RPC, SDK
Main use cases	Terminal coding agent, custom agent workflows, model/tool/UI extension

Note: stars, forks, versions, and language percentages change over time. Check the GitHub repository for live numbers.

What problem does it solve?

Many agent tools are getting more powerful, but that also creates a problem: the default workflow keeps getting heavier.

Some users want built-in plan mode, permission confirmation, MCP, sub-agents, and a graphical interface. Others want a lightweight, scriptable agent that fits naturally into their terminal workflow.

Pi’s bet is:

Do not put every feature into the core.
Keep the agent harness small.
Expose extension points so users can compose their own workflow.

That is the most interesting part of the project. Pi is not just another replacement for Claude Code or Codex. It is asking a different question: what should the agent foundation look like if individuals and teams want to keep reshaping it over time?

Core features

1. A terminal-first coding agent

Pi’s main entry point is a terminal TUI. It supports the usual AI coding agent workflow: reading project context, calling tools, editing files, running commands, and iterating within a session.

The official positioning is clear: Pi is a minimal terminal coding harness. The goal is not to turn every feature into a fixed button, but to let the tool fit into your terminal habits.

2. Multiple models and providers

Pi uses @earendil-works/pi-ai as a unified multi-provider LLM API.

The website lists providers such as Anthropic, OpenAI, Google, Azure, Bedrock, Mistral, Groq, Cerebras, xAI, Hugging Face, OpenRouter, and Ollama. What you can actually use depends on your configuration, account, and API keys.

This is useful for teams: you can separate the agent workflow from the model vendor instead of replacing the whole tool whenever you switch models.

3. An extensible agent runtime

@earendil-works/pi-agent-core provides agent runtime capabilities such as tool calling and state management.

That means Pi is not only a CLI. It can also be reused as a lower-level agent component. You can embed it in a Node.js application, or call it from another system through RPC or JSON event streams.

4. Skills, prompt templates, extensions, and packages

Pi’s extension model is close to productized context engineering:

Skills: on-demand capability instructions and tool constraints;
Prompt templates: reusable prompts;
Extensions: TypeScript modules that can extend tools, commands, keybindings, events, and the TUI;
Pi packages: bundled extensions, skills, prompts, and themes that can be shared.

The value is that you do not need to control the agent with one long prompt every time. Stable rules, tools, and workflows can become reusable assets.

5. Session trees and shareable history

Pi stores sessions as trees. You can return to an earlier point, branch from there, export the session as HTML, or share it.

That matters when debugging agent behavior. Real workflows often involve attempts, failures, corrections, and rollbacks. A tree-shaped history is better than a purely linear chat for preserving those decision paths.

An important tradeoff: not everything is built in

Pi’s website has a dedicated “What we didn’t build” section: no built-in MCP, no built-in sub-agents, no built-in permission popups, no built-in plan mode, no built-in todos, and no built-in background bash.

This is not simply a missing feature list. It is a product philosophy:

The core provides primitives. More complex features can be built through extensions, skills, scripts, tmux, or packages.

That tradeoff filters the audience.

If you want a complete, conservative, ready-made agent product, Pi may feel too open-ended. If you want control over the system prompt, context, commands, UI, and runtime behavior, Pi becomes more interesting.

Who is it for?

Good fit

Developers who spend a lot of time in the terminal;
People who want to study how AI coding agents are built;
Teams that want to connect an agent to their own tools, scripts, or products;
Teams that want to accumulate reusable skills, prompt templates, and extensions;
Users who need multi-model and multi-provider switching without binding themselves to one platform.

Poor fit

Users who want a zero-config graphical product;
Users who do not want to understand terminals, config files, or extension mechanisms;
Teams that need enterprise-grade permission review, centralized control, audit logs, and SLA out of the box;
Users who only want the agent to write code immediately and do not care about workflow malleability.

Quick start

The official documentation shows this npm install path:

npm install -g --ignore-scripts @earendil-works/pi-coding-agent

On macOS and Linux, you can also use the installer:

curl -fsSL https://pi.dev/install.sh | sh

Then run it inside a project directory:

pi

Authentication can be done with /login, or by setting provider API keys such as ANTHROPIC_API_KEY. Follow the official docs for the current provider setup details.

How I would try it

If you are only evaluating Pi, do not start by studying every extension mechanism.

A better path:

Pick a non-critical repository;
Run pi and ask it to read the project and make a small change;
Try pi -p "..." in print mode and see whether it fits scripting use cases;
Check whether session trees, skills, and prompt templates can capture your repeatable workflows;
Only then decide whether to study extensions and packages.

The key question is not whether its first output is impressive. The question is whether you can put your own rules into it and reuse them over time.

Pros and limitations

Pros

Small core, good fit for terminal-heavy developers;
Clear extension points for context engineering and custom workflows;
Broad multi-provider support, reducing model lock-in;
Session trees, export, and sharing are useful for reviewing agent behavior;
MIT license, friendly to secondary development and commercial experiments.

Possible limitations

More freedom also means more learning cost;
Many advanced capabilities need to be assembled through extensions or external tools;
It is not as friendly to non-terminal users;
Team-level permissions, security, and auditing need additional work;
The maturity of the ecosystem depends on future packages, extensions, and community contributions.

Monetization directions

Custom team agent harnesses: build standardized coding-agent workflows for engineering teams on top of Pi.
Vertical skills and packages: create installable packages for frontend, data analysis, DevOps, security review, and other workflows.
Private enterprise agent foundation: connect model routing, audit logs, permissions, and internal tools into the Pi runtime.
Agent workflow consulting: help teams turn prompts, AGENTS.md, skills, templates, and CI verification into durable workflow assets.

Conclusion

Pi’s value is not just that it can write code. Its value is that it breaks a coding agent into layers that can be reshaped: CLI, runtime, model API, TUI, skills, extensions, and packages.

If you want a mature all-in-one product, tools like Codex, Claude Code, or Cursor may be more direct.

But if you care about this question: can my agent grow around my workflow? Pi is worth studying.

It points to an interesting direction: an agent is not only a tool. It can also be an evolving developer workbench foundation.

What GitHub Trending Says About AI Agents: Skills Are Becoming Engineering Assets

Wed, 27 May 2026 23:40:00 +0800

On May 27, 2026, I opened GitHub Trending and the interesting part was not one isolated breakout project.

The stronger signal was that several projects around AI agent skills, plugins, workflows, knowledge graphs, and domain playbooks were trending at the same time.

Individually, they solve different problems:

some help agents understand a codebase;
some give coding agents a software development methodology;
some package writing quality, design taste, and security analysis into skills;
some turn knowledge-worker roles into plugins;
some bundle memory, hooks, MCP configuration, rules, and security scanning into a broader agent harness.

Taken together, the direction is clear: AI agent competition is moving from “can the model answer?” to “can the work method be installed, reused, verified, and shared by a team?”

Projects Worth Watching

This is not a full Trending recap. It is the slice of today’s GitHub Trending list that feels most relevant to agent workflows.

Project	What it does	The signal
Lum1104/Understand-Anything	Turns codebases, knowledge bases, and docs into interactive knowledge graphs you can explore, search, and ask questions about	Agents need a system map before they can safely change systems
obra/superpowers	Provides a skill-based software development methodology for coding agents	Development process is becoming an installable skill chain
affaan-m/ECC	Bundles skills, memory optimization, hooks, security scanning, and research-first development across harnesses	The agent harness is starting to look like an operating layer
anthropics/knowledge-work-plugins	Provides plugins for sales, support, product, marketing, legal, finance, data, and other knowledge-work roles	Plugins are moving beyond programmers into role-specific workflows
hardikpandya/stop-slop	A skill for removing obvious AI writing patterns from prose	Quality standards can be packaged as reusable editors
Leonxlnx/taste-skill	A skill set for frontend taste, motion, density, and anti-generic output	Design judgment is being turned into a workflow surface
mukul975/Anthropic-Cybersecurity-Skills	A large library of structured cybersecurity skills mapped to security frameworks	Expert domain practice is becoming agent-executable playbooks

I think of these as agent capability packs.

They are not single prompts. They are not chat templates. They are work methods that can be versioned, installed, updated, combined, and audited.

Why This Matters More Than a Better Prompt

Early AI agent usage tends to focus on prompting:

write a longer system prompt;
add an AGENTS.md file;
tell the agent to behave like a senior engineer;
require a specific output format.

Those things help, but prompts hit three limits quickly.

First, they are hard to maintain. A long instruction block keeps growing until nobody knows which rules are still active and which ones are old baggage.

Second, they are hard to compose. Coding, UI design, security investigation, copy editing, and data work should not all live inside one giant prompt.

Third, they are hard to verify. If one output gets better, was it the model, the context, or the rule?

Skills and plugins matter because they split “how to do this kind of work” into smaller capability units.

A good skill should answer:

what task it applies to;
what task it should not handle;
what context it needs;
what it produces;
how it verifies that it stayed on track;
whether it can move across Claude Code, Codex, Cursor, Gemini CLI, and other harnesses.

That is the difference between a prompt and an engineering asset.

Four Directions Behind This Wave

1. Code Understanding Becomes Infrastructure

Projects like Understand-Anything show that large codebases cannot rely only on an agent running rg, opening a few files, and guessing the architecture.

The more useful layer is a structured map:

flowchart LR
A[Codebase] --> B[Static structure parsing]
A --> C[LLM semantic summaries]
B --> D[Knowledge graph]
C --> D
D --> E[Search / Q&A / impact analysis / onboarding]

This is different from traditional documentation. Docs often drift. If a knowledge graph can update with commits, it becomes long-lived context for agents.

For many teams, the first step will not be “let the agent change code.” It will be “let the agent build a queryable system map.”

2. Work Methods Become Installable

Projects like superpowers and ECC are not just adding commands. They are giving agents working discipline.

Clarify the goal. Write a design. Break down the plan. Verify the result. Review the diff. Use worktrees to isolate risk.

Those used to be team habits. Now they are becoming skills and hooks.

I do not think every task needs a full methodology. Small jobs should not be slowed down by process. But when agents touch real repositories, real permissions, and real deployment paths, method matters more than one-off intelligence.

3. Domain Expertise Becomes Playbooks

Anthropic-Cybersecurity-Skills turns security work into many structured skills and maps them to MITRE ATT&CK, NIST CSF, MITRE ATLAS, D3FEND, and NIST AI RMF.

The interesting point is not only that security has skills.

The bigger signal is that expert judgment paths are being transformed into agent-callable playbooks.

Security will not be the only field. Legal, finance, sales, support, product, growth, and analytics can all move toward this shape:

task type -> matching skill -> required context -> steps -> risks -> checks -> deliverable

That moves AI agents from generic assistants toward role-specific operators with SOPs.

4. Quality Standards Become Skills

stop-slop and taste-skill are small but important examples.

They do not teach the agent a new API. They package fuzzy quality expectations into triggerable skills: avoid AI-sounding prose, do not ship generic UI, tune rhythm, density, motion, hierarchy, and design language.

Most AI output does not fail because it is completely wrong. It often fails because it is:

correct but generic;
functional but awkward;
structured but lifeless;
visually complete but product-blind;
readable but obviously machine-written.

Once quality standards are packaged, teams no longer need to re-explain their bar every time. That is also knowledge reuse.

What Developers Should Do

If you already use Codex, Claude Code, Cursor, or another coding agent, do not only track which model is stronger.

Start collecting three kinds of assets.

The first is project-level rules. Put them in AGENTS.md, CLAUDE.md, README, or docs. Tell the agent how this repository builds, tests, publishes, and which directories are risky.

The second is task-level skills. Examples: writing a bilingual Hugo article, debugging slow startup in a Go service, generating a GitHub issue cover image, or running a frontend visual audit. If a task repeats, it probably deserves a skill.

The third is verification scripts. Without verification, a skill is just attractive advice. Common tasks should have matching checks such as hugo --renderToMemory --minify, go test, pnpm test, SEO checks, screenshots, or diff checks.

My filter is simple:

Question	If yes, consider a skill
Does this task repeat?	Yes
Do you explain the same context every time?	Yes
Do failures usually come from the same traps?	Yes
Is there a clear acceptance check?	Yes
Can the skill limit what it reads or edits?	Yes

If three or more are true, stop relying only on ad hoc prompting.

What Comes Next

I expect three things to become more visible.

First, skill registries will matter. People need to discover, install, update, rate, and combine skills instead of copying Markdown files across machines.

Second, private team skills will become productivity assets. Public skills solve general problems. The most valuable skills will encode a team’s own workflows, code standards, deployment rules, and incident history.

Third, skills, memory, and observability will merge. Agents need to know which method to use, remember previous lessons, and leave an auditable chain of execution.

So today’s GitHub Trending projects are more than a curiosity.

They point to a larger shift: AI agents are moving from smart chat windows to engineering systems built from installable methods, reusable capability packs, and verifiable execution paths.

That is one of the developer-tool trends I would keep watching in 2026.

References

GitHub Trending Today: https://github.com/trending?since=daily
Understand-Anything: https://github.com/Lum1104/Understand-Anything
Superpowers: https://github.com/obra/superpowers
ECC: https://github.com/affaan-m/ECC
Knowledge Work Plugins: https://github.com/anthropics/knowledge-work-plugins
Stop Slop: https://github.com/hardikpandya/stop-slop
Taste Skill: https://github.com/Leonxlnx/taste-skill
Anthropic Cybersecurity Skills: https://github.com/mukul975/Anthropic-Cybersecurity-Skills

AI Agents Need Observability: Why Security Logs Must Capture Intent to Action

Sat, 23 May 2026 21:05:00 +0800

When teams adopt AI agents, the first security instinct is usually to add permissions, approvals, and sandboxes.

Those are necessary, but they are not enough. Agents also need observability.

Traditional security logs usually answer three questions:

who called the interface?
when did it happen?
did it succeed or fail?

AI agents are different. They do not simply click a button or execute a fixed API call. They interpret a goal, make a plan, read context, choose tools, generate arguments, wait for approval, and then perform an action.

So the next generation of security logs should not only record “what happened.” They also need to record why the agent believed the action was appropriate.

The short version

Agent security logs should connect “user intent → agent plan → tool call → policy decision → actual action → execution result” into one traceable chain.

If the only log you see is:

DELETE /api/users/123 200 OK

you still do not know whether:

the user explicitly asked for deletion;
the agent confused “deactivate account” with “delete account”;
malicious page content persuaded the agent to act;
an automation flow called the tool without enough context;
the approval logic allowed the wrong action.

All of these can look identical in a traditional API audit log.

Why Traditional Logs Are Not Enough

In a normal application, the user clicks a button and the backend executes an endpoint. Logging the interface, identity, IP address, and status code is often enough to reconstruct the responsibility chain.

With an agent, there are more layers in the middle:

flowchart LR
A[User goal] --> B[Agent interpretation]
B --> C[Task plan]
C --> D[Tool selection]
D --> E[Argument generation]
E --> F[Permission / policy check]
F --> G[Human approval]
G --> H[Execution]
H --> I[Result feedback]

The dangerous part is often not the final API call.

An agent may call a valid API with valid credentials and sufficient permission, while the real problem happened earlier:

the user meant “archive,” but the agent interpreted it as “delete”;
retrieved web content contained hidden or misleading instructions;
the agent selected the wrong tool;
tool arguments came from untrusted context;
the policy engine checked permission but not intent;
the approval UI showed the action but not the reason.

If you only inspect the API audit log, everything can look normal.

What Agent Logs Should Capture

I would split agent security logs into six layers.

Layer	What to record	Why it matters
Intent	Original user goal, summarized task intent, task source	Shows whether the action drifted from the user’s goal
Reasoning	Agent plan, key decision points, reason for choosing a tool	Reconstructs why the workflow moved in this direction
Context	Files, web pages, retrieval snippets, memory sources	Helps detect influence from untrusted context
Tool	Tool name, action type, argument summary, resource scope	Shows what the agent intended to do
Control	Policy decision, permission result, human approval, block reason	Proves whether safety controls worked
Result	Execution result, external system response, rollback information	Completes the audit trail

This does not mean saving every prompt, web page, or raw argument.

Security logs should preserve audit evidence, not create a new copy of all sensitive context.

A Practical Log Shape

For a first version, I would start with an event like this:

{
 "event.name": "agent.tool.execution",
 "trace_id": "01HT...",
 "agent.session_id": "sess_123",
 "agent.intent_id": "intent_456",
 "agent.task_id": "task_789",
 "user.id": "u_001",
 "user.goal_summary": "Deactivate a test account without deleting account data",
 "agent.plan_step": "Call the account status update tool",
 "agent.decision": "Selected update_user_status because the goal is deactivation",
 "tool.name": "user_admin",
 "tool.action": "update_status",
 "tool.args_hash": "sha256:...",
 "target.resource": "user:123",
 "risk.level": "medium",
 "policy.decision": "allow",
 "approval.required": true,
 "approval.actor": "human",
 "context.refs": [
 "issue:29",
 "file:docs/account-lifecycle.md"
 ],
 "result.status": "success"
}

The important parts:

trace_id connects multiple events inside one agent task;
intent_id separates the user goal from individual tool calls;
tool.args_hash avoids storing sensitive raw arguments;
policy.decision records whether the policy layer participated;
approval.* records whether a human approved the action;
context.refs keeps references instead of copying full context into logs.

When something goes wrong, you can ask:

What was the original intent?
At which step did the agent choose this tool?
Where did the arguments come from?
Why did policy allow it?
Was there human approval?
Did the execution result match the plan?

That is a very different level of evidence from “who called which API.”

Pre-Action Logs Matter Most

Many systems only log after an action finishes.

For agents, that is not enough.

At minimum, agents need two event types.

1. Pre-action events

Record what the agent is about to do:

current intent;
plan step;
reason for choosing the tool;
argument summary;
risk level;
permission decision;
whether human approval is required.

If the risk is visible here, the action can still be blocked before execution.

2. Post-action events

Record what actually happened:

which external system was called;
what result came back;
whether the call failed or retried;
whether a compensating action was triggered;
whether data state changed.

Pre-action logs explain “why this action was attempted.” Post-action logs explain “what happened in the end.”

Without the first part, many agent incidents only show the result, not the intent drift.

Do Not Turn Logs Into a New Leak Surface

The more detailed agent logs become, the easier it is to create another security problem: storing sensitive data in logs.

I would explicitly avoid logging:

API keys, tokens, cookies, SSH private keys;
full prompts and full tool arguments;
private fields from user input;
sensitive business data from web pages;
raw orders, accounts, students, customers, or financial records;
long memory excerpts.

Better patterns:

keep raw text only in short-lived secure storage with expiration;
keep long-term logs to summaries, hashes, resource IDs, and references;
label high-risk fields separately and restrict queries;
audit access to the logging system itself;
exclude raw sensitive logs from training, analytics, and dashboards by default.

Observability is not “save everything.” It is “preserve enough evidence to reconstruct the important path safely.”

How to Implement It

The first version does not need to be complex. I would implement it in this order.

1. Connect the trace first

A user request, an agent run, and a tool call must be linkable.

At minimum:

trace_id
agent.session_id
agent.intent_id
tool.call_id

OpenTelemetry already has a log data model with TraceId, SpanId, attributes, and event names. It is a good fit for structured agent events. Do not reduce agent behavior to an unstructured log string.

2. Log at the tool gateway

Do not let every tool invent its own logging format.

A better boundary is a tool gateway:

Agent → Tool Gateway → Policy Check → Human Approval → Tool Execution

At that boundary, you can consistently record:

intent, plan, and argument summary before the call;
policy decision;
approval status;
execution result;
failure and retry behavior.

Putting safety control and logging at the same boundary makes incidents easier to investigate.

3. Promote high-risk actions

Not every action needs the same log detail.

These actions deserve higher log severity:

deleting data;
exporting large amounts of data;
changing permissions;
changing billing or payment state;
touching production systems;
accessing sensitive systems;
committing code or triggering deployments automatically.

For high-risk actions, log intent, policy, approver, pre-action argument summary, and post-action result.

4. Start With Useful Alerts

Do not start with a wall of dashboards.

Start with alerts that expose real agent risk:

high-risk tool call without human approval;
read-only plan followed by a write action;
unusual number of tool retries under one intent;
tool arguments derived from untrusted context;
action allowed while the policy engine is unavailable;
one agent accessing too many resources in a short window;
execution result that does not match the plan step.

These are difficult to detect with traditional API logs alone.

A Simple Test

When designing agent security logs, ask:

If an agent accidentally deletes data tomorrow, can I answer within 30 minutes: what did the user want, how did the agent interpret it, why was this tool selected, who approved it, and what was the impact?

If not, the logs are not good enough yet.

Conclusion

Agent risk does not only come from “does it have permission?”

It also comes from “it had valid permission and still did the wrong thing.”

That means security logs need to evolve.

The next generation of agent security logs should capture:

user intent;
agent plan;
context sources;
tool selection;
policy decision;
human approval;
actual action;
execution result.

This is not about assigning blame after the fact. It is about making agent systems explainable, auditable, blockable, and recoverable.

A production-ready AI agent should not only complete work. It should also leave enough evidence to explain why every important step happened.

References

OWASP: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/
OpenTelemetry Events: https://opentelemetry.io/docs/specs/semconv/general/events/
OpenTelemetry Logs Data Model: https://opentelemetry.io/docs/specs/otel/logs/data-model/
NIST AI RMF: https://www.nist.gov/itl/ai-risk-management-framework

Codex Is No Longer Just a Coding Tool. It Is Becoming a Long-Running Task Workbench

Sat, 23 May 2026 15:40:00 +0800

My current view of Codex is simple: it is no longer just a tool that helps me write code.

More precisely, Codex is shifting from a code generator into a long-running task workbench.

The important change is not one extra button. It is the way threads, tools, permissions, verification, automations, memory, and project rules can work together, so a task can keep moving while you step in to steer, approve, or review the result.

The short version

The old Codex felt like “help me edit this code.” The newer workflow feels more like “help me move this goal toward a verified result.”

That matters because useful agent work is not only about a strong first draft. It is about whether the work can:

stay continuous: keep context and decisions inside the same thread;
stay steerable: let you correct direction or approve the next step;
stay verifiable: run tests, inspect diffs, capture screenshots, and check pages;
stay reusable: move repeated rules into AGENTS.md, skills, or automations.

How I would use it

1. Give every important task its own long-running thread

Do not treat Codex as a disposable Q&A window.

A bug, an article, a refactor, or a release flow should each have its own thread. That thread can preserve what Codex has already read, what it has tried, which commands it ran, and what feedback you gave.

My prompt usually looks like this:

Goal: move this issue from diagnosis to a verified fix.
Context: this is a production report, and the relevant module may be xxx.
Constraints: do not touch unrelated files, do not refactor public interfaces, do not modify public build output.
Definition of done: root cause explained, code changed, tests or local verification passing.
Working style: read code and logs first, explain the finding, then make the change.

The key is to define what “done” means. Codex can handle long tasks better when the target is concrete.

2. Make it verify with tools, not just produce an answer

The risky part of long-running agent work is that the agent may believe it is done before you have evidence.

So I prefer asking Codex to leave checkable proof at each step:

code work: git diff, test command, failure log, fixed output;
frontend work: local screenshot, browser console, key interaction check;
content work: source links, citation boundary, final Markdown file;
release work: dry run, build output, deployment log, rollback path.

This is why Codex starts to feel like a workbench: it can connect terminal output, files, browser checks, and diffs inside one task flow.

3. Move repeated requirements into project rules

If you repeatedly say:

Do not modify public
Use Chinese by default
Run hugo --renderToMemory --minify first
Put article images under static/img/YYYYMMDD

then those rules should not live only in your prompt history.

A better place is AGENTS.md, or a reusable skill. When Codex enters the repository, it can immediately understand the local boundaries, language preference, verification command, and files it should avoid.

This looks small, but it matters for long-running tasks. Long tasks are not kept on track by a single perfect prompt. They stay on track through stable rules.

4. Turn repeated work into automations

OpenAI is also pushing Codex Automations: let Codex come back on a schedule, do repeatable work, and hand the result to you for review.

I would start with low-risk, repetitive, easy-to-check work:

summarize new article drafts every week;
scan TODOs or failing tests every day;
summarize GitHub issues and PR status;
check site build and SEO output on a fixed schedule;
generate a weekly progress note for a long-running project.

A practical automation prompt could be:

Every Friday afternoon, return to this thread and review this week's new articles and drafts.
Output three sections:
1. published articles;
2. titles or images worth improving;
3. the 3 most promising topics for next week.
Only summarize and suggest. Do not edit files automatically.

Let it organize and report first. Only later should you consider giving it execution authority.

5. Use mobile handoff to keep work from stalling

OpenAI has also connected Codex to the ChatGPT mobile app. For long-running tasks, the useful part is that you do not need to stay at the computer the whole time.

Codex may stop and ask:

should I choose plan A or plan B?
may I run this command?
should this diff be expanded?
do you want me to update the test too?

If you can answer those small decisions from your phone, a long task does not get stuck on a minor approval.

But I would not treat mobile access as “let AI change production remotely.” A better use is: let Codex continue investigation, organization, verification, and drafting, while keeping high-impact actions under human review.

A reusable long-task prompt

For a complete Codex task, I would start from this template:

Goal:
Complete [specific task] until it reaches [verifiable end state].

Context:
- Business context:
- Relevant paths:
- Known problem:

Constraints:
- Do not modify:
- Do not do:
- Must follow:

Working style:
1. Read the relevant files and logs first.
2. Explain the root cause or execution plan.
3. Wait for confirmation before changing high-risk areas.
4. Run verification commands after editing.
5. Final response must include changed files, commands, and results.

Definition of done:
- [ ] Code or document updated.
- [ ] Verification command passed.
- [ ] Key risks explained.
- [ ] No unrelated files touched.

For especially long tasks, add this:

If the context grows too long, summarize current state, decisions already made,
and remaining work before continuing.

Where I would not fully let go

Codex is getting stronger, but that does not mean everything should be fully automatic.

I would keep human confirmation for:

deleting production data;
changing account, security, payment, or secret configuration;
large refactors of public interfaces;
deployments with no rollback path;
browser actions involving private data.

The value of a long-running task workbench is moving from “manually operate every step” to “make decisions at the important points.” It should not remove judgment from the process.

Conclusion

The most interesting change in Codex is not that it can write a few more lines of code. It is that it can increasingly work around a goal over time.

For me, the practical workflow is:

one important task, one thread;
define done before starting;
make Codex verify with tools;
move repeated rules into AGENTS.md or skills;
automate only low-risk repeated work first.

If you still use Codex as “autocomplete plus chat,” try changing the prompt shape. Do not only ask how to write something. Give it a goal, constraints, and a definition of done, then use it as a workbench you can inspect and take over at any point.

References

OpenAI: https://openai.com/index/introducing-the-codex-app/
OpenAI: https://openai.com/index/work-with-codex-from-anywhere/
OpenAI Academy: https://openai.com/academy/codex-automations/
OpenAI Developers: https://developers.openai.com/codex/use-cases/follow-goals

HTML Anything: Let Local AI Agents Generate Publish-Ready HTML

Sat, 23 May 2026 13:01:30 +0800

HTML Anything: Let Local AI Agents Generate Publish-Ready HTML

One-line positioning

HTML Anything is a local-first agentic HTML editor: it takes Markdown, tables, drafts, and data, then asks the AI coding agent already logged in on your machine to generate publish-ready single-file HTML or PNG.

Project: https://github.com/nexu-io/html-anything
Live demo: https://open-design.ai/html-anything/

Basic information

Item	Details
Repository	`nexu-io/html-anything`
Positioning	An HTML editor for the agent era
Main stack	Next.js, React, Tailwind, SSE, local CLI agent execution
License	Apache-2.0
Current traction	About 4.6k stars / 476 forks
Supported agent CLIs	Claude Code, Cursor Agent, Codex, Gemini CLI, GitHub Copilot CLI, OpenCode, Qwen Coder, Aider
Typical outputs	HTML, PNG, WeChat / Zhihu-ready rich content, social images, data reports, decks, posters, video frames

What problem does it solve?

Markdown is great for drafting, but it is not always the final format readers should see.

When content is meant for WeChat, Zhihu, X, Xiaohongshu, data reports, long images, decks, or posters, Markdown is usually only the first step. You still need layout, typography, spacing, screenshots, and platform-specific paste compatibility.

HTML Anything makes a simple bet: if the final reader-facing format is HTML or an image, the AI agent should generate that final artifact directly instead of asking the user to keep tweaking styles by hand.

It changes the workflow from:

Write Markdown → manually format → adjust CSS → screenshot / paste into platforms → fix formatting again

to:

Enter content → choose a template → local agent generates HTML → preview → copy or export

Core features

1. Automatically detects local AI agents

HTML Anything does not ask you to configure another API key. It scans your local PATH and detects coding-agent CLIs that are already installed and logged in.

Supported tools include Claude Code, Cursor Agent, OpenAI Codex, Gemini CLI, GitHub Copilot CLI, OpenCode, Qwen Coder, and Aider.

As long as you have already logged in through the terminal, HTML Anything can reuse the existing session and treat the local agent as the engine for generating HTML.

2. Uses Skill templates to control output quality

The project ships with 75 Skill templates covering documents, decks, posters, social cards, web prototypes, data reports, and Hyperframes video frames.

These Skills are not just visual themes. They are templates with explicit design constraints: font stacks, an 8px baseline grid, color contrast, radius, shadows, and the requirement to use real user-provided data. The goal is to reduce rough, generic AI-looking output.

3. Supports multiple content surfaces

Beyond regular articles, HTML Anything is closer to a “content artifact generator.” The same input can become different deliverables:

Magazine-style articles
Keynote-style decks
Resumes
Posters
Xiaohongshu cards
X / Tweet cards
Web prototypes
Data reports
Hyperframes / Remotion video frames

The value is that a piece of content is no longer limited to plain Markdown. It can be shaped into different visual artifacts depending on where it will be published.

4. Streams generation into a sandboxed preview

The generation flow uses SSE. HTML produced by the agent is appended into a frontend iframe in real time, so the user can watch the page appear as it is generated.

The overall structure looks like this:

flowchart TD
A[User input: Markdown / CSV / Excel / JSON / draft] --> B[Choose local agent]
B --> C[Choose Skill template]
C --> D[POST /api/convert]
D --> E[Local CLI agent generates HTML]
E --> F[SSE streaming response]
F --> G[Sandbox iframe live preview]
G --> H[Copy to platform or export HTML / PNG]

The preview runs inside a sandboxed iframe, which isolates generated content while still letting the user confirm the final result directly in the browser.

5. Exports directly to publishing platforms

After generation, the result can be copied or exported for different destinations:

WeChat Official Account: inline CSS for fewer style losses when pasting
Zhihu: rich text and formula-friendly handling
X / Weibo / Xiaohongshu: high-DPI PNG export from the preview area
Local files: download single-file .html or .png

This makes it more than a page generator. It is closer to a publishing-oriented workbench.

Who is it for?

HTML Anything is a good fit for:

People who often publish Markdown articles to WeChat, Zhihu, or social platforms
Content creators who want to quickly produce long images, posters, decks, or data reports
Developers already using local AI coding agents such as Claude Code, Cursor, or Codex
Teams that want to move from “write first, format later” to “generate the deliverable directly”
Developers interested in agentic editors, AI design workflows, and Skill-template systems

Quick start

git clone https://github.com/nexu-io/html-anything
cd html-anything
pnpm install
pnpm -F @html-anything/next dev

Then open:

http://localhost:3000

Basic workflow:

Choose a locally detected AI agent from the top bar
Choose a Skill template
Paste Markdown, tables, JSON, SQL, or a draft
Press ⌘ + Enter
Preview the generated result in the iframe
Copy it to a platform, or export .html / .png

Conclusion

HTML Anything is not just another HTML editor. Its real value is connecting local AI agents, design templates, and platform exports into one content-delivery workflow.

It targets a very concrete problem: you already have the content, but you do not want to spend more time formatting, screenshotting, and adapting it for each platform.

If you already use tools like Claude Code, Cursor, or Codex, HTML Anything is a natural extension: let the agent generate not only code, but also the final artifact readers will actually see.

OpenAI's Image Verification Tool: Adding Source Context to AI-Generated Images

Thu, 21 May 2026 17:30:38 +0800

AI-generated images are becoming more realistic, easier to share, and easier to edit. That creates a practical problem: when an image appears on a social platform, in a news context, or in a group chat, it is often hard to tell whether it came from a camera, design software, or an AI tool.

OpenAI’s “Verify OpenAI-generated images” tool is built for this problem. You upload an image, and the system checks whether it contains provenance signals associated with OpenAI-generated images. In other words, it helps determine whether the image may have been created by tools such as ChatGPT, the OpenAI API, or Codex.

This is not a tool for detecting fake news, and it does not decide whether the content of an image is true. It is closer to a source checker: it looks for technical traces that indicate whether the image came from OpenAI’s image generation systems.

What does it do?

The workflow is simple: upload a PNG, JPG, or WEBP image, and the tool analyzes it and returns a detection result.

It mainly looks for two kinds of signals:

C2PA content credentials
SynthID digital watermarks

If these signals are present, the tool can indicate that the image was likely generated by an OpenAI tool.

The key idea is provenance, not truth verification. For example, if an AI-generated image claims to show a flood in a certain city, a successful detection result can only tell you that the image came from an OpenAI tool. It cannot tell you whether the event actually happened, whether the location is correct, or whether the image is being used in a misleading context.

So the tool answers this question:

Was this image generated by an OpenAI tool?

It does not answer this one:

Is the claim made by this image true?

What are C2PA and SynthID?

OpenAI’s verification tool relies on two different technical approaches.

The first is C2PA content credentials.

C2PA can be understood as source information embedded into digital media metadata. For supported OpenAI-generated images, the credential can show that the image was created by tools such as ChatGPT or the API. As long as the credential remains intact, it is a strong provenance signal.

But it has a limitation: it depends on metadata. After an image is shared, compressed, screenshotted, or edited, that metadata may be removed. Once it is stripped away, the tool may no longer detect it.

The second is SynthID digital watermarking.

SynthID is a more invisible watermarking technique. Instead of placing a visible mark in a corner of the image, it embeds signals directly into the image content. This makes the signal more resilient to operations such as cropping, filters, and lossy compression.

In simple terms:

C2PA is more like an ID card inside the image file
SynthID is more like an invisible mark embedded in the image itself

Together, they improve the reliability of AI image provenance checks.

What does a positive result mean?

If the tool detects relevant signals, the image was very likely generated by an OpenAI tool.

OpenAI also notes that these detections are highly reliable and have very few false positives. So when the tool clearly finds OpenAI-related provenance signals, it should be treated as a strong indication of origin.

But there is still an important boundary: the result does not tell you what happened to the image after it was created.

For example, it does not prove whether the image was:

cropped
recompressed
annotated with text
used in the wrong context
combined with other images
otherwise edited after generation

So a positive result supports the image’s likely origin, but it does not verify the surrounding context.

What if nothing is detected?

This is the part most likely to be misunderstood.

If the tool does not detect any supported signals, it does not mean the image was definitely not generated by OpenAI.

Possible reasons include:

the image metadata was removed
the digital watermark was damaged or degraded
the image was screenshotted, compressed, or edited
the image was generated by an older OpenAI image model
the image was created before the relevant provenance features were introduced
the image may have been generated by another company’s AI model

So “not detected” only means that the uploaded image does not contain the OpenAI provenance signals currently supported by this tool.

It does not prove that the image is not AI-generated. It also does not prove that the image is real.

That distinction matters.

Why does this tool matter?

AI images can spread quickly across the internet, especially around news events, public safety topics, social controversies, and marketing campaigns. A single image can easily be separated from its original context.

Content provenance gives people one more signal to work with.

When you see an image, knowing whether it was generated by an AI tool can help you decide more carefully whether to:

trust it
share it
treat it as evidence
look for additional context

For media organizations, platforms, researchers, creators, and everyday users, tools like this can reduce the chance of misreading online media.

It does not solve every problem, but it makes the question “where did this image come from?” more transparent.

What should you pay attention to when using it?

OpenAI recommends uploading a single image for analysis. If you are using a screenshot, crop it closely around the image and avoid uploading a file that contains multiple images.

That helps reduce noise and gives the system a better chance of detecting C2PA metadata or SynthID watermarks.

OpenAI also states that uploaded images are processed to detect provenance signals associated with OpenAI-generated content. Unless required by law, uploaded images are not stored and are not used to train OpenAI’s models.

What does this really change?

In the past, people often relied on visual inspection to judge whether an image might be AI-generated: lighting, fingers, text, texture, or small visual artifacts. But as image generation improves, visual inspection becomes less reliable.

OpenAI’s verification tool points to a different direction: instead of only asking whether an image “looks real,” we can ask whether it carries trustworthy technical source information.

That is more robust than pure visual guessing, and it fits better with the future of online media.

Of course, it is not a universal answer. It only covers supported OpenAI-generated images, it cannot identify every image generated by every AI model, and it cannot judge whether the message conveyed by an image is true.

But it offers a clear direction: AI content should not only be generated; it should also be traceable, explainable, and verifiable.

As AI-generated content becomes more common, transparency itself becomes part of the infrastructure. OpenAI’s image verification tool is one piece of that infrastructure.

Marketing Skills for AI Agents: A Marketing Workflow Pack for AI Agents

Wed, 20 May 2026 09:06:51 +0800

Project: https://github.com/coreyhaines31/marketingskills

One-sentence positioning

marketingskills is a marketing skill pack for AI coding agents. It gives Claude Code, OpenAI Codex, Cursor, Windsurf, and other agents more professional workflows and evaluation criteria when handling SEO, conversion optimization, copywriting, ads, analytics, and growth tasks.

Basic information

Item	Details
Project	Marketing Skills for AI Agents
GitHub	`coreyhaines31/marketingskills`
Author	Corey Haines
Website	https://marketing-skills.com
License	MIT
Main languages	JavaScript / Shell
Latest version	v2.0.1
Repository popularity	About 29.5k Stars and 4.8k Forks
Target agents	Claude Code, OpenAI Codex, Cursor, Windsurf, and other AI agents that support the Agent Skills spec

What problem does it solve?

Many AI coding agents are already good at writing code. But once the task moves into marketing, the quality often becomes inconsistent.

For example, you might ask an agent to:

optimize the conversion rate of a landing page;
write a cold email sequence;
run an SEO audit;
plan an A/B test;
design a product launch plan;
analyze churn and retention problems.

The agent can usually produce suggestions, but those suggestions often stay at a generic level: they look reasonable, yet lack concrete workflows, checklists, and marketing judgment.

That is the problem marketingskills tries to solve. It turns common marketing tasks into reusable Agent Skills, so an AI agent does not have to improvise every time it meets a marketing scenario. Instead, it can follow a more professional workflow.

Core capabilities

1. Covers a full marketing workflow

This project is not a single prompt. It is a skill library built around marketing work. The skills listed in the README cover multiple stages from acquisition to conversion and retention.

Common areas include:

SEO and content: seo-audit, ai-seo, schema, content-strategy, programmatic-seo
Conversion optimization: cro, signup, onboarding, popups, paywalls
Copywriting and content production: copywriting, copy-editing, cold-email, emails, social, video, image
Ads and paid acquisition: ads, ad-creative
Growth and retention: referrals, free-tools, churn-prevention, community-marketing
Sales and GTM: revops, sales-enablement, launch, pricing, competitors
Strategy and research: customer-research, marketing-ideas, marketing-psychology

That means it is not only helping you “write a piece of marketing copy.” It can support a much broader marketing task chain.

2. Uses product context as the foundation

One of the most important design choices in the project is the product-marketing skill.

According to the README, other skills read product-marketing first to understand the product, users, positioning, and market context before entering a specific task. This is important.

Marketing tasks are highly dependent on context. Different products have different users, use cases, pricing models, and competitors. Without a shared product context, AI can easily produce content that is “correct but useless.”

product-marketing works like a base profile for all later marketing tasks. It helps the agent understand what the product is, who it serves, what problem it solves, and why people should care before writing pages, emails, ads, or SEO content.

3. Skills can work together

Another important characteristic of marketingskills is that its skills are connected.

For example:

copywriting can work with cro and ab-testing for landing page optimization and experiment design;
revops can work with sales-enablement and cold-email for sales lead and outreach workflows;
seo-audit can work with schema and ai-seo for search and AI-search optimization;
customer-research can provide inputs for copywriting, cro, and competitors.

This design is closer to real marketing work. Tasks are not isolated. Research, positioning, copy, channels, data, and experiments all influence each other.

4. Supports mainstream AI coding agents

The project explicitly mentions support for:

Claude Code
OpenAI Codex
Cursor
Windsurf
other agents that support the Agent Skills spec

This makes it feel more like a portable marketing capability pack than a prompt collection tied to a single AI product.

If a team already uses AI coding agents for engineering tasks, it can add this skill pack to the project and let the same agent assist with parts of growth, content, and conversion optimization work.

Who is it for?

This project is especially suitable for these groups:

Technical founders

They have a product and engineering ability, but may lack systematic marketing experience. The skill pack can help with positioning, landing pages, cold-start content, SEO, cold emails, and growth experiments.

Indie hackers

Many indie developers do not struggle with building products as much as they struggle with acquiring users consistently. marketingskills can turn common marketing actions into repeatable workflows and lower the startup cost.

SaaS and AI tool teams

These products usually need continuous work on content, conversion, trial activation, retention, and sales enablement materials. Skills such as CRO, SEO, onboarding, pricing, and sales enablement are especially relevant.

Technical marketing teams using AI agents

If a team already uses Claude Code, Codex, or Cursor for engineering work, it can also hand repetitive marketing workflows to agents: generating ad ideas, improving page copy, preparing competitor comparison pages, or designing experiment plans.

Quick start

The recommended way is to install all skills with npx skills:

npx skills add coreyhaines31/marketingskills

If you only want to try a few common skills first, install selected skills:

npx skills add coreyhaines31/marketingskills --skill product-marketing cro copywriting seo-audit

You can also list the available skills first:

npx skills add coreyhaines31/marketingskills --list

After installation, you can ask an agent to handle tasks such as:

Rewrite the hero copy of our homepage based on the current product positioning.

Audit this landing page for conversion issues and prioritize the fixes.

Design an A/B test plan for the signup flow of this SaaS product.

Write a B2B cold email sequence and follow-up sequence based on our product information.

Plan an AI SEO content optimization workflow for this product.

Conclusion

The value of marketingskills is not that it collects another batch of prompts. Its value is that it breaks marketing work into reusable, composable skill modules that AI agents can call automatically.

For developers and technical teams, it adds a marketing workflow layer to AI coding agents. It cannot replace real market judgment, but it can significantly lower the starting cost of marketing execution: from writing copy and doing SEO to planning conversion experiments and product launches, the agent has clearer processes to follow.

If you already use Claude Code, Codex, Cursor, or a similar agent for development work, and you also want AI to help with some growth and marketing tasks, marketingskills is worth adding to your toolbox.

Persistent Memory Is Not About Remembering Everything: What Should Agents Actually Keep?

Sun, 10 May 2026 11:20:00 +0800

Many AI agent products now talk about memory: long-term memory, project memory, user preferences, chat history, knowledge bases, vector retrieval, and automatic summaries.

That makes it easy to believe one simple idea: the more an agent remembers, the smarter it becomes.

For engineering agents, I think the opposite is closer to the truth.

Persistent memory should not be a dumping ground for chat history. It should not mean putting every conversation, file, and assumption into a vector database. It should act more like a filter: only stable, reusable context that can improve future behavior deserves to survive.

Otherwise, memory becomes noise. The agent may treat stale facts as current, turn one-off instructions into permanent rules, preserve unverified assumptions, or carry sensitive context into future tasks.

The Short Version

An agent should remember information that:

will likely be useful again;
will not expire quickly;
reduces repeated explanation;
changes the agent’s default behavior;
has a source, a scope, and a way to be verified.

It should not turn these into long-term memory:

secrets, passwords, tokens, cookies, recovery codes;
temporary branch or workspace state;
prices, schedules, versions, policies, and other volatile facts;
guesses without evidence;
raw private transcripts;
one-off emotions, preferences, or temporary instructions.

In short: memory is an index, not an archive. It is working policy, not raw logs.

Why Remembering More Can Make Agents Worse

For a chat assistant, remembering your name, tone, or writing preference is usually harmless. Engineering agents are different. They read code, edit files, run commands, call MCP servers, control browsers, access internal tools, and sometimes participate in release workflows.

When memory quality is poor, the blast radius becomes much larger.

For example:

the agent remembers that a project uses pnpm, but the project later migrates to bun;
it remembers that the release branch can deploy directly, but the rule later changes to require parity with origin/release;
it remembers that an API returns groupName, while the real source of truth changed to displayName;
it remembers that the user skipped tests once, then applies that shortcut to a risky backend change;
it stores a temporary SQL query from a debugging session and treats it as a general diagnostic path.

These are not model intelligence problems. They are memory lifecycle problems.

OpenAI’s memory documentation separates saved memories from reference chat history and emphasizes user controls such as managing, deleting, and disabling memory. Claude Code also separates memory by scope through files such as CLAUDE.md. The underlying lesson is important: useful memory needs boundaries.

Engineering agents should follow the same principle.

Three Kinds of Context

I like to separate agent context into three layers.

1. Current Task Context

This is short-lived.

Examples:

the user’s latest request;
the current git diff;
terminal output from this turn;
the current browser page state;
logs found during this debugging session;
the active plan and checklist.

This context is important while the task is active, but most of it should not become long-term memory. After the task is done, it should either be captured in code, documentation, issues, commits, or simply disappear.

If every task scene becomes memory, future tasks will be polluted by old state.

2. Project Source of Truth

This is the context agents should trust first.

Examples:

AGENTS.md / CLAUDE.md;
README.md;
Makefile;
CI configuration;
schema and migrations;
package catalogs;
API type definitions;
tests;
project design documents.

These files may not be called memory, but for an agent they are the most important verifiable memory.

If a memory conflicts with the repository, the repository should win. Memory may point the agent toward the right file, but it should not replace the file.

3. Long-Term Working Memory

This is what persistent memory should store.

It captures cross-task knowledge that is stable enough to reuse but may not yet belong in project files.

Examples:

the user prefers Chinese by default;
this repository publishes paired .en.md articles for new Chinese articles;
the public directory is often dirty, so article validation should avoid writing into it;
for certain UI changes, the user explicitly does not want unrelated screens touched;
a deployment script must preserve linux/amd64 because the local machine is macOS on arm64;
for debugging tasks, inspect logs first instead of guessing fixes.

The value of this layer is to reduce repeated setup and keep future work inside known boundaries.

What Should Agents Remember?

This is the table I use.

Keep	Example	Why
Stable preferences	Default language, concise answers, conclusion first	Changes collaboration style
Project conventions	Articles live in `content/article/`; English uses the same slug plus `.en.md`	Reduces repeated discovery
Hard constraints	Do not change backend contracts; do not touch unrelated UI; avoid writing to `public`	Prevents real regressions
Repeated failure patterns	A known install issue came from cache permissions; a slow deploy required checking workflow logs	Speeds up debugging
Confirmed business rules	Collaborators can add or subtract points but cannot edit student data	Prevents product rule drift
Approved direction	A specific UI direction was accepted and future work should iterate from it	Preserves continuity
Reusable workflows	Validate articles with `hugo --renderToMemory --minify`	Improves delivery consistency

These are not historical details. They are future defaults.

A good memory should answer this question:

Will this make the agent behave more correctly next time?

If not, do not store it.

What Should Be Verified Again?

Some information is useful as a clue but unsafe as a fact.

Examples:

latest API behavior;
current prices and plans;
GitHub Actions status;
whether a remote branch has changed;
whether a model is still available;
laws, policies, and platform rules;
release dates and schedules;
production service status.

These facts can expire even if they were correct last time.

So memory should say:

Last time, the release slowdown was traced to the image build step.
For similar deploy issues, first inspect the current workflow and recent Actions logs.

It should not say:

Deploys are slow because of image builds.

The first version is a reusable diagnostic clue. The second is a stale conclusion waiting to happen.

What Should Never Be Stored?

The biggest danger is not that memory is incomplete. It is that memory contains material it should not contain.

1. Secrets and Credentials

This includes:

API keys;
tokens;
cookies;
SSH private keys;
database passwords;
payment system secrets;
recovery codes;
one-time verification codes.

These should not enter memory, articles, logs, screenshots, or issues.

2. Raw Private Content

Do not store large chunks of email, customer data, order records, student information, medical information, financial records, or private conversation transcripts.

If context is needed, compress it into a non-sensitive rule:

For education SaaS work, the user cares strongly about teacher collaboration permissions and parent-facing visibility boundaries.

Do not preserve the raw personal data behind that rule.

3. Unverified Guesses

Agents form hypotheses during debugging. Hypotheses belong in the current task, not long-term memory.

Only conclusions backed by code, logs, tests, or production verification deserve to survive.

4. One-Off State

Examples:

the current temporary branch;
a port that happened to be busy today;
a file generated during this task;
the fact that this specific task skipped tests;
the fact that a page was open in a specific Chrome tab.

These should remain local to the active session.

A Simple Test Before Saving Memory

Ask five questions:

Will this be useful next time?
Will it probably remain true a month from now?
Will it change the agent’s default behavior?
Can it be traced or re-verified?
If it is wrong, will it create risk?

Only save the memory if the first four answers are mostly yes and the fifth risk is acceptable.

If something is useful but likely to drift, write it as a place to check next time, not as a permanent fact.

If something is high risk, do not solve it with memory. Put it in permissions, CI guards, tests, docs, or human approval workflows.

How Should Memory Be Written?

Good memory is short, stable, and operational.

Bad:

The user asked us to update an article, public was dirty, then we ran Hugo and it seemed okay.

Better:

For silenceper.com article work, source articles live in content/article/; English articles use the same slug plus .en.md; validate with hugo --renderToMemory --minify to avoid writing into public.

The better version:

removes process noise;
keeps future behavior;
names the repository scope;
includes concrete paths and commands;
avoids treating temporary state as truth.

Where Should Different Information Live?

Not every durable fact belongs in the same memory system.

Information type	Better home
Project rules	`AGENTS.md` / `CLAUDE.md`
Build and deploy commands	`README.md` / `Makefile` / CI
Business rules	Code, tests, product docs
Personal preferences	User-level memory
Repository conventions	Project-level memory
Reusable procedures	Skill / runbook
Active task state	Current session / issue / PR description
Volatile facts	Re-check the source every time

A mature agent workflow should not rely on memory alone.

The better structure is:

memory points the agent in the right direction
repo files define the rules
tests prevent regressions
logs reconstruct the scene
human approvals guard risky actions

That way, even if memory is incomplete, the agent can recover by reading the source of truth.

The Real Value of Persistent Memory

Persistent memory is not valuable because it makes an agent remember everything.

It is valuable because it helps the agent avoid repeated mistakes.

For example:

it does not ask every time whether to use Chinese or English;
it does not reverse a business rule that was already settled;
it does not repeat the same deploy mistake;
it does not bring back a UI direction the user rejected;
it does not modify unrelated files in a dirty worktree;
it does not answer plan or SKU questions from memory when the code should be checked.

These are not dramatic AGI abilities. They are exactly the things that make engineering collaboration smoother.

The productivity gain comes from remembering fewer things more accurately, not from storing more fragments.

My Recommendation

If you use Codex, Claude Code, Cursor, or similar coding agents heavily, manage memory with these rules:

Treat memory as an index, not the source of truth.
Put stable rules in project files.
Re-check volatile facts from live sources or the repository.
Keep sensitive material out of memory.
Write each memory as future behavior, not historical narration.
Regularly prune stale, duplicate, and vague memories.

The goal is not to make the agent remember everything. The goal is to make it interrupt you less, drift less, and repeat fewer mistakes.

When memory becomes a maintainable engineering asset instead of accumulated chat residue, an agent starts to feel like a reliable long-term collaborator.

References

Codex Chrome Extension: When Should AI Use Your Browser Login State?

Sat, 09 May 2026 22:15:00 +0800

When people first hear about the Codex Chrome extension, it is easy to describe it as “letting AI open web pages.” That is too narrow.

A more accurate way to think about it is this: when a task depends on your Chrome login state, browser context, or real web-page interactions, the extension lets Codex enter that workflow within the permissions you grant.

For example:

you need Codex to read a CRM, mailbox, admin dashboard, or internal system that only works after login;
you want to reproduce a bug in the real business page;
you want Codex to inspect page state, return to the repository, fix the code, and verify the page again;
you need to upload a local file to a web form and check the result;
you have an internal system with no stable API, only a web UI.

Without browser access, AI can usually only tell you what to click next. The Chrome extension fills the gap around the real browser environment.

The short version

The Codex Chrome extension is not a generic web-page summarizer or a standalone chatbot. It is a browser capability inside the Codex App workflow.

My rule of thumb is simple:

for local previews, public pages, and pages that do not require login, use Codex’s in-app browser first;
when a system has a dedicated plugin or structured integration, use that first;
use the Chrome extension only when the task truly depends on Chrome login state, browser profile, cookies, or real web interactions;
for payments, transfers, production deletes, security settings, or sensitive exports, let Codex prepare and verify, but keep the final action in human hands.

Its value is not “opening a browser more conveniently.” Its value is letting Codex participate in the full verification loop: inspect the page, operate an admin flow, reproduce the issue, return to code, fix it, and verify again.

Basic information

Item	Details
Name	Codex Chrome extension
Official docs	https://developers.openai.com/codex/app/chrome-extension
Product	Codex App
Positioning	Let Codex use Chrome for browser tasks that need signed-in state
Typical scenarios	Gmail, Salesforce, LinkedIn, CRM, internal tools, logged-in web flows
Works with	Codex App, Plugins, In-app Browser, Computer Use
Permission model	Ask before new websites by default; supports allowlist and blocklist
Security focus	Treat page content as untrusted context; keep sensitive actions under human review

The real boundary it solves

Many AI coding tools can read code, edit files, and run commands. But once the task enters a real web environment, new boundaries appear:

the page requires login;
data exists only inside a browser session;
an internal system has no stable API;
reproducing the issue requires clicking through the real page;
local development pages and production dashboards must be checked together;
the workflow involves copying, uploading, downloading, screenshots, or checking visual state.

With the Chrome extension, a task can look like this:

@Chrome open the order detail page in the admin dashboard and check this user's subscription status.
If the page looks wrong, return to the repository, inspect the related API and frontend rendering logic,
fix the issue, then verify the page again in the browser.

The important part is not browser automation by itself. The important part is that Codex can connect “evidence from the page” with “changes in the codebase” inside one working thread.

How it differs from the in-app browser

OpenAI’s docs draw a clear line: local development servers, file-backed previews, public pages, and pages that do not require login should use the in-app browser first. That keeps preview and verification inside Codex without touching your Chrome profile.

The Chrome extension is better for cases where:

the page needs your Chrome login state;
the task must access data under your real account;
the workflow depends on browser profile, cookies, or extensions;
the in-app browser cannot complete login, permission checks, or business-state verification;
the target system has no usable API and only exposes a web UI.

My recommendation: do not use real login state when you do not need it. When you must use it, narrow the permission to the current task.

Core capabilities

1. Connect Chrome and use real signed-in state

After setup, the Codex extension in the Chrome toolbar shows a connected state. In a new Codex thread, you can invoke Chrome directly:

@Chrome open Salesforce and update the account from these call notes.

The key point is that Codex is using your Chrome environment, not a temporary browser with no login state. That makes it useful for Gmail, Salesforce, LinkedIn, CRM systems, and internal dashboards that only work after sign-in.

2. New websites require approval by default

The extension does not automatically give Codex access to every website. By default, Codex asks before interacting with each new website, usually at the host level such as example.com.

You can choose to:

allow the website for the current chat;
always allow that host;
decline the website.

This matters because a signed-in browser may contain email, admin systems, finance tools, internal tools, and personal accounts. “Can access” should not automatically mean “should access.”

3. Allowlist and blocklist support

The docs mention domain allowlists and blocklists in Computer Use settings:

allowlist: Codex can use those domains again without asking every time;
blocklist: Codex should not use those domains;
removing a domain from the allowlist makes Codex ask again;
removing a domain from the blocklist lets Codex ask again instead of treating it as blocked.

For frequent, low-risk, well-scoped sites, an allowlist can be convenient. For mail, finance, account security, or production dashboards, I would prefer temporary approval or continued human confirmation.

4. File uploads require file URL access

If a Chrome task needs to upload local files, OpenAI’s docs say you need to enable Allow access to file URLs in the Chrome extension details page:

Chrome toolbar → Extensions → Manage Extensions → Codex → Details → Allow access to file URLs

This should be enabled only when needed. Once local files are involved, the boundary expands from “web operation” to “local file + web upload.” Turn it on for explicit upload tasks, then reassess whether it should remain enabled.

What to watch out for

Treat web pages as untrusted context

The official docs explicitly say page content should be treated as untrusted context.

That is important because web pages can include misleading text, hidden instructions, or malicious content. If Codex treats page content as user instructions, it may take actions outside the intended scope or leak data.

Practical habits:

do not let Codex visit sensitive pages unrelated to the task;
read the approval prompt carefully when a new site is requested;
keep human review for mail, finance, account, and security settings;
do not put passwords, secrets, recovery codes, or keys into browser tasks;
split complex tasks into small steps that are easy to inspect.

Be careful with “Always allow browser content”

OpenAI marks “Always allow browser content” as Elevated Risk. When enabled, Codex no longer asks before using websites.

That may be convenient for frequent low-risk sites, but it raises the risk in real account environments. I would keep it off by default unless the site is clearly non-sensitive and the task boundary is stable.

Browser history is not ordinary context

Browser history may contain internal URLs, search terms, admin paths, account activity, and business signals. The docs say Codex asks separately when it wants to use browser history, and history does not have an always-allow option.

That is the right design. Browser history is not just “helpful context”; it can reveal how you work and what internal systems exist. Do not authorize it unless the task truly requires it.

Good fits

The Chrome extension is useful for:

updating data in a CRM or admin dashboard according to clear instructions;
reading logged-in business pages and combining that with code changes;
reproducing bugs in real web pages;
verifying flows that require account state;
working with internal tools, low-code systems, or ticketing systems without stable APIs;
using page results to continue debugging in the codebase;
uploading files, checking page state, downloading results, and continuing the workflow.

It is not a good fit for giving AI full control over payments, transfers, production deletes, security settings, or large personal-data exports. Even if the tool can do it, the final action should stay with a human.

Quick start

The basic setup from the official docs is:

1. Open the Codex App
2. Go to Plugins
3. Add the Chrome plugin
4. Follow the setup flow to install or connect the Chrome extension
5. Accept Chrome permission prompts
6. Confirm the Codex extension shows Connected in Chrome
7. Start a new Codex thread
8. Use @Chrome in your prompt for browser tasks

If the connection fails, check:

whether the Chrome extension shows Connected;
whether the Chrome plugin is enabled in Codex;
whether you are using the Chrome profile where the extension is installed;
whether the target website is in the blocklist;
whether Chrome or Codex needs to be restarted;
whether file uploads require Allow access to file URLs.

My recommendation

If you treat Codex as an engineering collaborator rather than just a chatbot, the Chrome extension is useful. It lets Codex move beyond the repository and participate in the real verification loop.

But the closer a task gets to real accounts and production systems, the tighter the permission boundary should be.

I would use it in this order:

public or local pages: use the in-app browser first;
structured systems: prefer dedicated plugins or APIs;
signed-in browser state: use the Chrome extension only when needed;
high-risk actions: let Codex prepare, inspect, and explain, but keep final confirmation human.

Used this way, the Chrome extension is not “handing your browser to AI.” It is turning a manual verification workflow into a controlled, reviewable collaboration loop that can return to the codebase.

Warp: An AI Agent Terminal with Built-in Code Review Workflow

Sat, 09 May 2026 21:47:00 +0800

One-sentence positioning

Warp is an AI-native development environment born from the terminal. It brings the command line, coding agents, Git diff review, code review, and commit workflows into one workspace.

Basic information

Item	Details
Project	Warp
GitHub	https://github.com/warpdotdev/warp
Website	https://warp.dev
Positioning	An AI-native development environment evolved from the terminal
Main language	Rust
Stars / Forks	About 57k Stars / 4.3k Forks
License	Mainly AGPL-3.0; `warpui_core` and `warpui` are MIT licensed
Core capabilities	AI Coding Agent, terminal workspace, Git Diff review, agentic open-source workflows
Getting started	Download from the official website; developers can build locally with `./script/bootstrap` and `./script/run`

What problem does it solve?

The terminal is powerful, but the daily development workflow is often scattered across many tools: commands in a terminal, code changes in an editor, diffs in a Git client, review in a PR page, and AI coding assistance in yet another window.

Warp’s approach is to pull these actions back into an AI-native workspace. You can ask an agent to work on a task, generate code changes, run checks, inspect the Git diff, leave comments, and prepare commits in one place.

Core highlights

1. A built-in AI Coding Agent

Warp is not just a terminal with a chat box. It includes a coding agent that can work through development tasks: implementing features, changing files, running checks, and producing diffs.

The README also notes that Warp supports its built-in coding agent and can work with external CLI agents such as Claude Code, Codex, Gemini CLI, and others.

2. Terminal + Agent + Git Diff in one workspace

The screenshot shows a typical development flow:

the left side displays the agent’s task execution process;
the right side shows a Git Diff review interface;
uncommitted changes are visible;
code can be reviewed line by line;
comments can be added directly in the diff;
the workflow can move toward committing changes without leaving the workspace.

That makes Warp feel more like a development workbench than a traditional terminal window.

3. Agentic workflows for open-source collaboration

Warp also provides build.warp.dev, where you can watch Oz agents participate in open-source project workflows, including:

issue triage;
spec writing;
implementation;
PR review;
active agent sessions.

This means Warp is not only a personal productivity tool. It is also exploring how agents can participate in open-source maintenance and collaboration.

4. Open-source client code

Warp’s client code is open source, and the repository includes contribution documentation.

The local build flow is roughly:

./script/bootstrap
./script/run
./script/presubmit

It is worth reading if you are interested in modern terminals, AI-agent development workflows, or Rust desktop application architecture.

Who is it for?

Warp is a good fit for:

developers who spend a lot of time in the terminal;
people who want to integrate AI coding assistants into real development workflows;
users who want commands, code changes, diff review, and commits in one interface;
teams exploring how AI agents can help with open-source maintenance, issue handling, and PR workflows;
developers studying Rust, desktop apps, terminals, or developer tools.

Quick start

Regular users can download Warp from:

https://www.warp.dev/download

Developers can start from the GitHub repository:

https://github.com/warpdotdev/warp

Local build reference:

./script/bootstrap
./script/run
./script/presubmit

Conclusion

Warp’s most interesting point is that it does not simply add an AI chat panel to a terminal. It puts the AI agent directly into the development workflow.

From task execution and code modification to diff review and committing, Warp tries to combine the tools developers switch between every day into one AI-native workspace. If you already work heavily in the terminal and want to use AI coding tools more systematically, Warp is a project worth watching.

DeepSeek TUI: Turning DeepSeek V4 into a Local Terminal Coding Agent

Fri, 08 May 2026 17:20:00 +0800

One-sentence positioning

DeepSeek TUI is a terminal-native coding agent built for DeepSeek V4: it brings model reasoning, file editing, shell execution, Git, MCP, sub-agents, LSP diagnostics, and cost tracking into a keyboard-driven TUI workflow.

If many AI coding tools feel like “code completion inside an editor”, DeepSeek TUI feels more like starting a local terminal workspace that can read a project, edit files, run commands, inspect diagnostics, and still keep permissions and cost visible.

Basic information

Item	Details
Project	DeepSeek TUI
GitHub	https://github.com/Hmbown/DeepSeek-TUI
Positioning	Terminal coding agent for DeepSeek models
Main language	Rust
License	MIT
Target models	DeepSeek V4, including `deepseek-v4-pro` and `deepseek-v4-flash`
Install options	npm, Cargo, Homebrew, release binaries, Docker
Platforms	Linux x64/ARM64, macOS x64/ARM64, Windows x64

What problem does it solve?

There are many AI coding tools now, but a lot of them share a similar limitation: they are either too chat-oriented, too editor-completion-oriented, or they hide automation behind layers of UI.

DeepSeek TUI targets a different workflow:

you like working in the terminal;
you want the agent to understand the current workspace;
you want it to read files, edit files, run commands, and manage Git;
you do not want it to freely mutate the project without boundaries;
you also want to see context usage, token cost, and prefix-cache behavior.

So it is not just a “DeepSeek chat client in the terminal”. It is a TUI agent designed around real coding workflows.

Core highlights

1. Native fit for DeepSeek V4

The first notable feature is that DeepSeek TUI is not merely a generic OpenAI-compatible wrapper. It is designed around DeepSeek V4’s capabilities.

It supports:

deepseek-v4-pro / deepseek-v4-flash;
1M-token context windows;
streaming reasoning blocks;
prefix-cache-aware cost reporting;
Auto mode that chooses both model and thinking level for each turn.

This makes it suitable for long-context tasks: reading large repositories, debugging across files, summarizing architecture, and running batched code analysis, not just answering questions about one function.

2. Auto mode chooses both model and thinking level

One of the most distinctive ideas in the project is Auto mode.

Use:

deepseek --model auto

Or inside the TUI:

/model auto

Before the real turn runs, Auto mode performs a lightweight routing step and selects:

deepseek-v4-flash or deepseek-v4-pro;
thinking level: off, high, or max.

Simple turns can stay on Flash with thinking off. More complex debugging, architecture, security review, or release tasks can move to a stronger model or higher reasoning level.

This is practical because real development work does not need the highest-cost setting on every turn. Letting the tool select a tier per task is more natural than asking the user to manually switch models all the time.

3. A full coding-agent workflow inside the terminal

DeepSeek TUI includes a broad tool suite:

file operations;
shell execution;
Git;
web search and browsing;
apply-patch;
sub-agents;
MCP servers;
RLM batched analysis;
HTTP/SSE runtime API.

Its architecture roughly follows this path:

deepseek dispatcher CLI
→ deepseek-tui companion binary
→ ratatui terminal interface
→ async Agent Engine
→ OpenAI-compatible streaming client
→ typed tool registry and streamed tool results

In other words, it is not just rendering model output in a terminal. It has a real loop for tool calls, session state, task queues, and diagnostic feedback.

4. Plan, Agent, and YOLO make permissions explicit

Many agent tools leave users unsure: will it edit files, run commands, or do risky operations?

DeepSeek TUI exposes three modes:

Mode	Good for
Plan	read-only exploration and planning
Agent	default interactive mode with approval gates
YOLO	auto-approved mode for trusted or isolated workspaces

This separation matters. For a new project or unfamiliar repository, start with Plan. After the direction is clear, move to Agent. YOLO should be reserved for trusted, reversible, or isolated environments.

5. side-git rollback without touching your project Git

DeepSeek TUI also has a useful safety mechanism: workspace rollback.

It uses side-git snapshots around turns and supports:

/restore

as well as turn-level rollback such as revert_turn.

The key point is that this does not depend on or pollute your project’s own .git. That is useful for AI coding because an agent may perform many consecutive edits. If one turn goes in the wrong direction, a turn-level rollback is often easier than manually reconstructing the state with Git.

6. LSP diagnostics feed back into the next reasoning turn

Another strong feature is LSP diagnostics.

After edits, the project can collect errors and warnings through language servers such as:

rust-analyzer;
pyright;
typescript-language-server;
gopls;
clangd.

These diagnostics are not only shown to the user. They are fed back into the model context before the next reasoning turn. That means the agent can quickly notice type errors, lint issues, and broken references after editing.

This makes it closer to a real development environment than a pure text-editing agent.

7. Cost tracking and prefix-cache visibility

DeepSeek TUI tracks more than model responses. It shows:

per-turn token usage;
session-level token usage;
estimated cost;
cache hit / miss information;
prefix-cache-related telemetry.

For long-context agents, this is important. Real project work costs tokens not only for the latest question, but also for history, file content, tool results, and repeated reasoning. Seeing that cost structure gives users more control.

8. Rust binary distribution, not a Node/Python runtime dependency

DeepSeek TUI is written in Rust and distributed as self-contained binaries.

You can install it through npm:

npm install -g deepseek-tui

But the npm package acts mainly as an installer that downloads the correct prebuilt Rust binaries. The project also supports:

cargo install deepseek-tui-cli --locked
cargo install deepseek-tui --locked

plus Homebrew, direct release binaries, and Docker.

For terminal tools, this is a good distribution model: many installation paths, but a simple runtime.

9. Practical attention to China and ARM64 environments

The README includes mirror-friendly installation notes for mainland China and documents Linux ARM64 prebuilt support.

For npm, for example:

npm install -g deepseek-tui --registry=https://registry.npmmirror.com

Cargo can also be configured with a Tsinghua mirror.

This may not look like a headline feature, but it directly affects whether users can actually install the tool. For mainland China networks, Raspberry Pi, Graviton, domestic ARM devices, and HarmonyOS PCs, installation quality often decides whether people keep trying.

Who is it for?

DeepSeek TUI is a good fit if you:

already use the DeepSeek API and want a terminal coding agent;
prefer TUI, keyboard-driven, command-line workflows;
often work with large repositories and multi-file tasks;
want an AI agent that can run commands and edit files, but still has approvals and rollback;
want MCP, sub-agents, LSP diagnostics, and task queues in a local development workflow;
care about token usage and cost visibility.

If you already know tools like Codex CLI, Claude Code, or Gemini CLI, DeepSeek TUI’s value is that it turns DeepSeek V4’s context, reasoning, cache, and cost model into a terminal-native development experience.

Quick start

Install:

npm install -g deepseek-tui

Check version:

deepseek --version

Start:

deepseek

Configure API key ahead of time:

deepseek auth set --provider deepseek

Check auth status:

deepseek auth status

Verify setup:

deepseek doctor

For the first run, avoid jumping straight into YOLO mode. Start with automatic model routing:

deepseek --model auto

Then ask it to read the project and plan first:

Read this project structure first. Explain the main modules and startup flow. Do not modify files.

After the understanding looks correct, move into implementation or debugging.

Conclusion

DeepSeek TUI is interesting not because it is “another terminal chat UI”, but because it packages DeepSeek V4 into a fairly complete local agent workflow.

Its identity is clear:

deeply aligned with DeepSeek V4;
terminal-native TUI;
Auto mode for model and thinking-level routing;
Plan / Agent / YOLO permission modes;
side-git rollback;
LSP diagnostic feedback;
MCP, sub-agents, RLM, and HTTP/SSE API;
visible cost and prefix-cache telemetry.

If you want a coding agent that is more DeepSeek-native, terminal-first, and control-oriented, DeepSeek TUI is worth trying.

Codex Command Cheat Sheet: From Basic Usage to a Reliable Workflow

Fri, 08 May 2026 14:20:00 +0800

This article is for people who have installed Codex CLI but still use it mainly as “ChatGPT inside a terminal”. Codex becomes much more useful when you stop treating it as one long chat and start using its commands to separate one-off questions, persistent sessions, automation, code review, context management, and permission control.

Many people start with Codex by opening a terminal, typing a request, and waiting for an answer. That works, but it quickly creates two problems:

You are not sure when Codex should edit code directly and when it should only analyze.
You have to explain the project background, permission boundary, and task goal again and again.

A better way is to understand Codex commands in a few groups:

startup and login
interactive sessions
non-interactive execution
review and applying changes
session resume and fork
permissions, sandboxing, and configuration
MCP, plugins, and extensions

Below is a practical command guide ordered by how often you are likely to use them.

1. Install and log in: make sure Codex can run first

You can install Codex CLI with npm or Homebrew:

npm install -g @openai/codex
# or
brew install --cask codex

Then run:

codex

On first use, Codex usually asks you to log in. You can sign in with your ChatGPT account or use an API key:

codex login

If you want to use an API key, pass it through stdin:

printenv OPENAI_API_KEY | codex login --with-api-key

Check login status:

codex login status

Log out:

codex logout

These commands answer the basic question: can Codex run correctly? Once authentication is ready, the rest of the workflow becomes useful.

2. `codex`: the main interactive entry point

The simplest entry point is:

codex

It opens an interactive session. You can describe a task inside it, for example:

Review this project startup flow and point out the three most fragile areas.

You can also pass the first prompt directly:

codex "Read this repository and summarize its module structure"

Use -C to specify the working directory:

codex -C /path/to/project "Analyze this project's test structure"

Common options include:

codex -m gpt-5.5 "Refactor this function"

codex --search "Check the latest docs and see whether this SDK usage is outdated"

codex -s workspace-write "Implement this feature, but only modify files in the current workspace"

My rule of thumb:

Use codex when you want to work interactively.
Use codex exec when you want a task to run once and finish.
If the task scope is unclear, ask Codex to analyze and plan before giving it write access.

3. Slash commands inside an interactive session

After entering codex, type / to see available slash commands. These commands are more like a session control panel than normal chat messages.

`/model`: switch model

/model

Use it to inspect or change the model for the current session.

If a task requires stronger reasoning, switch to a stronger model. If you only need a small copy edit or light analysis, a faster model may be enough.

`/fast`: toggle fast mode

/fast on
/fast off
/fast status

This is useful for small edits, quick questions, and simple code navigation. For complex refactors, cross-module analysis, or production debugging, do not blindly optimize for speed.

`/plan`: ask for a plan before editing

/plan

Or:

/plan Design a migration plan for this service while keeping the old API compatible

This is one of the most useful commands. Very often you do not want Codex to act immediately; you want it to explain the risk, steps, and impact first.

Use /plan for:

core module refactoring
database schema changes
API behavior changes
large frontend changes
deployment workflow changes

A good habit: ask for a plan first, confirm the direction, then let Codex implement.

`/review`: ask Codex to review code

/review

Use it before committing. Codex will focus on possible bugs, edge cases, and regression risks.

If you want Codex not only to write code but also to catch problems, use this command often.

`/diff`: inspect what changed

/diff

When Codex modifies many files, do not commit immediately. Use /diff to see the changed scope and make sure it did not touch unrelated files.

Pay extra attention when it changes:

configuration files
test snapshots
lock files
frontend, backend, and scripts at the same time

`/status`: inspect session state

/status

Use this to check the current model, permissions, workspace, and configuration. It is helpful when you wonder why Codex cannot write files, why web search is unavailable, or why the current directory is wrong.

`/permissions`: adjust permissions

/permissions

Codex’s capability depends not only on the model, but also on the permissions you give it.

Permission controls answer questions like:

Can it write to the current workspace?
Can it run commands?
Does it need confirmation every time?
Are there additional readable directories?

For important repositories, keep permissions conservative: read first, plan next, then allow writing.

`/sandbox-add-read-dir`: add a read-only context directory

/sandbox-add-read-dir /path/to/another/repo

Sometimes a task needs to reference another repository, but you do not want Codex to modify it. Add that path as read-only context.

Typical use cases:

a new project needs to reference an old implementation
backend work needs frontend type definitions
a new service needs to match an old service API
documentation work needs another reference directory

`/mcp`: inspect MCP tools

/mcp

If you configured MCP servers for databases, browsers, internal tools, GitHub, Linear, or other systems, this command shows the tools currently available to Codex.

MCP lets Codex reach beyond the codebase, which also means permission boundaries matter more. Decide in advance what it can read, write, and execute.

`/init`: generate project rules

/init

This is commonly used to initialize project rules such as AGENTS.md. That file can record conventions like:

code style
test commands
branch rules
directory explanations
paths that should not be changed
pre-release checks

If you use Codex on a long-running project, AGENTS.md is very important. It reduces the amount of project context you have to repeat in every session.

`/compact`: compress context

/compact

Long sessions accumulate a lot of context. /compact summarizes earlier conversation into a shorter form while preserving the main task state.

Use it when:

a task has been running for a long time
you have looked up many references
the first half is done and you are moving into implementation
Codex starts forgetting earlier constraints

`/clear`, `/new`, `/quit`

/clear
/new
/quit

These commands clean up or end sessions:

/clear: clear the current context
/new: start a new session
/quit or /exit: leave Codex

Do not keep unrelated work in one long session. One session should usually have one clear goal.

4. `codex exec`: use Codex as a scriptable tool

codex exec runs in non-interactive mode. It is useful for automation, batch processing, CI assistance, and log analysis.

Basic usage:

codex exec "Summarize this repository structure and list five risk areas"

Pipe command output into it:

npm test 2>&1 \
 | codex exec "Summarize the failing tests and suggest the smallest likely fix"

Analyze logs:

tail -n 200 app.log \
 | codex exec "Identify the likely root cause and suggest the next debugging steps"

Generate release notes:

codex exec "Generate release notes from the last 10 commits" \
 | tee release-notes.md

Write the final message to a file:

codex exec "Extract project metadata" -o project-summary.md

If you want Codex inside scripts or automation, exec is usually better than the interactive TUI.

5. `codex review`: dedicated code review

Besides /review inside a session, Codex also has a standalone review command:

codex review

Review uncommitted changes:

codex review --uncommitted

Review against a base branch:

codex review --base main

Review a specific commit:

codex review --commit <SHA>

Add custom review instructions:

codex review --uncommitted "Focus on concurrency safety, permission checks, and database migration risk"

This is useful before committing, especially after Codex has written code. Let it look at the change again from a reviewer’s perspective.

6. `resume` and `fork`: reuse the context you already built

The most valuable part of a Codex session is not a single answer. It is the context Codex has already learned.

Resume the latest interactive session:

codex resume --last

Resume a specific session:

codex resume <SESSION_ID>

Non-interactive mode can also resume:

codex exec resume --last "Continue fixing the issues you found"

If you want to branch from an existing session without polluting the original context:

codex fork --last

Use resume to continue the same task. Use fork to explore another direction.

7. `apply`: apply a Codex-generated change

If you use Codex Cloud or a remote task flow, you may receive a task ID. Apply its diff locally with:

codex apply <TASK_ID>

After applying, immediately check three things:

git diff

npm test
# or your project's test command

git status -sb

Do not treat apply as an auto-merge button. It only brings the change into your workspace. You still decide whether to accept it.

8. Sandbox and approvals: control what Codex can do

Codex permissions revolve around two ideas:

sandbox: what Codex can access and modify
approval: when Codex must ask you for confirmation

Common sandbox modes:

codex -s read-only
codex -s workspace-write
codex -s danger-full-access

A practical interpretation:

Mode	Good for
`read-only`	analysis only, no edits
`workspace-write`	normal project edits
`danger-full-access`	high risk; only in externally isolated environments

Common approval policies:

codex -a on-request
codex -a never
codex -a untrusted

Daily recommendations:

Reading and asking questions: read-only
Normal feature work: workspace-write + on-request
Automation: use never carefully
Do not casually use danger-full-access on an important machine

9. Configuration: keep repeated preferences in files

Codex configuration usually lives at:

~/.codex/config.toml

A project can also have:

.codex/config.toml

Use -c to override config for one run:

codex -c model="gpt-5.5" "Analyze this module"

Use profiles when you have multiple working modes:

codex --profile work

A simple example:

model = "gpt-5.5"
approval_policy = "on-request"
sandbox_mode = "workspace-write"

[features]
web_search = true

Put long-term preferences in config files. Put task-specific choices in command-line flags.

10. MCP and plugins: connect Codex to external tools

Manage MCP servers:

codex mcp list
codex mcp get <name>
codex mcp add <name> <command>
codex mcp remove <name>

If an MCP server requires login:

codex mcp login <name>
codex mcp logout <name>

Plugin entry point:

codex plugin marketplace

MCP and plugins are not required on day one. A better learning order is:

Learn codex and slash commands.
Learn codex exec for automation.
Add MCP and plugins when you need external systems.

Otherwise, the toolchain and permissions become complicated before your basic workflow is stable.

11. A daily workflow that works

If you are new to Codex, try this rhythm.

Step 1: ask it to read the project

codex -C /path/to/project "Read the project structure, explain the main modules and startup flow, and do not modify files"

Step 2: ask for a plan

/plan Design an implementation plan for this feature. List files to change, risks, and tests.

Step 3: confirm, then implement

Implement the plan with the smallest reasonable change and add necessary tests.

Step 4: inspect the diff

/diff

Step 5: review

/review

Or from the terminal:

codex review --uncommitted "Focus on regression risk and edge cases"

Step 6: run tests and pipe failures back

npm test 2>&1 | codex exec "Summarize the failure and suggest the smallest fix"

This workflow is better than simply telling Codex to keep editing until everything passes. It separates planning, implementation, review, and verification.

12. Common mistakes

Mistake 1: giving broad permissions too early

danger-full-access feels convenient, but it is the easiest way to create damage. Unless you are in a container, temporary machine, or otherwise isolated environment, do not make it the default.

Mistake 2: putting every task into one long session

Writing a feature, editing copy, debugging production, and refactoring config in the same session will eventually confuse the context. When the task boundary changes, use /new.

Mistake 3: not reading the diff

AI coding tools often make “extra” changes. They may reformat files, update dependencies, touch snapshots, or edit configuration. Always inspect the diff after implementation.

Mistake 4: no project rules

Long-running projects need AGENTS.md or project-level rules. Otherwise you repeat test commands, directory conventions, commit habits, and forbidden paths every time.

Mistake 5: treating `exec` as autopilot

codex exec is excellent for automation, but it does not remove responsibility. Any task that writes files, sends requests, deploys, or deletes data needs clear permissions and verification.

Conclusion

Codex is not just another AI chat window. It is more useful when you turn coding work into a controllable workflow:

use codex for interactive collaboration
use slash commands to manage model, plan, permissions, and context
use codex exec for automated analysis and batch tasks
use codex review before committing
use resume and fork to reuse or branch existing context
use sandbox, approval, and config files to control risk

If you only remember one suggestion: do not let Codex edit immediately. First let it read the project, write a plan, and explain risks. Then open the right permissions for implementation. It feels a little slower, but the result is usually much safer.

Codex Beginner Guide: From Zero to Having AI Read, Edit, and Deliver Code with You

Thu, 07 May 2026 23:39:08 +0800

In this article, Codex refers to OpenAI’s current Codex coding agent. It can help you read code, understand unfamiliar repositories, review code, debug problems, and work through Codex App, IDE extensions, CLI, and Web / Cloud entry points.

The screenshots in this article come from official OpenAI pages or OpenAI’s official GitHub repository. The actual UI may change over time.

Who This Is For

This guide is for people who have never used Codex before, including readers who are not yet comfortable writing code.

You do not need to understand every AI agent concept first. Start with one simple idea:

Codex is not just an AI chat window. It can enter a code project, read files, edit files, run commands, and show you the resulting diff.

Because Codex can touch project files, the most important beginner habit is not asking it to “build a complete project” right away. It is learning how to:

ask it to read the project first;
ask it to propose a plan;
make one small change at a time;
review the diff;
run tests;
keep a Git checkpoint.

What Is Codex?
What Does Codex Look Like?
Five Keywords Beginners Should Know
Choosing an Entry Point: App, IDE, CLI, or Web
First Use: Start with Codex App
First Prompt: Ask Codex to Read Before Editing
First Code Change: Change One Small Thing
Prompt Templates You Can Copy
Safe Usage: Permissions, Sandboxing, Approvals, and Privacy
A 7-day Beginner Practice Plan
Advanced: Use AGENTS.md to Teach Codex Your Project
FAQ

1. What Is Codex?

Codex is an AI coding assistant that can work inside a code project.

You can describe tasks in natural language, for example:

Please explain what this project does.

Please find the cause of this bug and make the smallest necessary fix.

Please add a unit test for this function.

Codex can read project files, analyze code, edit files when needed, run commands or tests, and then show you the result and the diff.

Put simply: ChatGPT feels like asking questions in a chat window. Codex feels more like having someone sit inside your project directory and work with you.

The Basic Codex Workflow

flowchart LR
A[You describe a task] --> B[Codex reads project files]
B --> C[Codex analyzes code and context]
C --> D[Codex proposes a plan or edits directly]
D --> E[Codex runs commands / tests / checks]
E --> F[Codex shows a summary and diff]
F --> G[You review, ask follow-ups, accept, or roll back]

The most important part is the last step: you still review the result.

Codex is powerful, but it is not an automatic production deployer. For beginners, the safest mindset is to treat it as a capable assistant whose work still needs review.

2. What Does Codex Look Like?

Figure 1: Codex Welcome / Login Screen

When you open Codex App for the first time, you usually see a welcome screen where you can continue with a ChatGPT account or enter an API key.

Figure 2: Choose a Project Folder

Codex is not only a chat tool. It usually needs to know which project it should work in. You can choose a project folder or Git repository.

Figure 3: First Task Input

After choosing a project, you can describe what you want Codex to do. For the first task, ask it to explain the project instead of editing code.

Figure 4: Codex App Main Interface

Codex App is a desktop workspace. It can run multiple threads, work with worktrees, use automations, and integrate with Git workflows.

Figure 5: Local / Worktree / Cloud Modes

In Codex App, a thread can use different modes: Local, Worktree, or Cloud.

Local: work directly in the current project directory.
Worktree: isolate changes in a new Git worktree.
Cloud: run in a configured remote cloud environment.

Figure 6: Git Commit Panel

Codex App includes Git tools. It can show diffs and help with staging, committing, pushing, and creating pull requests.

Figure 7: Integrated Terminal

Each thread can use an integrated terminal. Common commands include:

git status
npm test
pnpm run lint

Figure 8: In-app Browser

For frontend projects, Codex can use the in-app browser to preview local pages, inspect page elements, or verify UI fixes.

Figure 9: Codex Extension in VS Code / Cursor / Windsurf

If you already use VS Code, Cursor, Windsurf, or another VS Code-compatible editor, you can use the Codex IDE extension.

Figure 10: Permission Modes in the IDE

The Codex IDE extension supports modes such as Chat, Agent, and Agent Full Access. Beginners should start with Chat or normal Agent mode instead of granting Full Access immediately.

Figure 11: CLI Interface

Codex CLI runs the coding agent inside your terminal. It is better suited for people who are already comfortable with command-line workflows.

Figure 12: Requesting Codex Review on GitHub

After Codex Cloud / GitHub integration is configured, you can request a review in a GitHub PR comment with @codex review.

Figure 13: Codex Review Comment in a GitHub PR

Codex can leave review comments on a PR and point to specific code locations and issues.

3. Five Keywords Beginners Should Know

Keyword	Beginner-friendly meaning	Why it matters
Project folder	The folder that contains your code	Codex needs to know where it should work
Git	A version control tool for code	If something breaks, you can roll back
diff	The before-and-after change	After Codex edits, you review the diff before accepting
PR / Pull Request	A set of changes submitted for review	Common in Codex Cloud and GitHub workflows
Sandbox / Approval	Limits where Codex can work and when it must ask for permission	Helps beginners avoid accidental deletion or risky commands

If you only remember one word for now, remember diff.

After Codex edits files, do not only read its summary. Review the diff, because the diff is what it actually changed.

4. Choosing an Entry Point: App, IDE, CLI, or Web

Codex has several entry points. Beginners often get stuck because they do not know where to start.

flowchart TD
A[I want to start using Codex] --> B{Do I already use a code editor?}
B -- Not really --> C[Start with Codex App]
B -- Yes, VS Code / Cursor / Windsurf --> D[Use the Codex IDE extension]
B -- I am comfortable with terminals --> E[Use Codex CLI]
A --> F{Is the project on GitHub?}
F -- Yes, and I want background tasks or PRs --> G[Use Codex Web / Cloud]
F -- No, mainly local folders --> C

Entry point	Best for	Main use
Codex App	Beginners	Desktop app, choose a project, then send tasks
IDE extension	People already using VS Code, Cursor, Windsurf, or JetBrains	Use Codex from the editor sidebar
CLI	People comfortable with terminals	Let Codex read, edit, and run a project from the terminal
Web / Cloud	GitHub projects that need background work	Run tasks in the cloud, review diffs, create PRs

My recommendation is simple:

Beginners should start with Codex App. Developers who already live in an editor can use the IDE extension. Use CLI after you are comfortable with terminals. Use Web / Cloud when a GitHub project needs background tasks.

5. First Use: Start with Codex App

5.1 Account and Plan

Codex visibility, quotas, and available features can depend on your ChatGPT plan, workspace permissions, and login method.

If you use Codex App, you can usually log in with a ChatGPT account or an OpenAI API key. When using an API key, some Cloud-related features may be unavailable.

5.2 Download, Open, and Log In

For beginners, use this order:

Install Codex App.
Log in with your ChatGPT account.
Choose a practice project folder.
Use Local mode for the first task.
For the first task, ask Codex to explain the project instead of editing code.

5.3 Recommended First Prompt

Please do not modify any files yet. Explain this project in English:

1. What does this project roughly do?
2. What are the main folders for?
3. Where might the entry points be?
4. If I want to run it, what commands would I usually execute?
5. What are you unsure about? Say "not sure" explicitly instead of guessing.

The key idea is: read first, do not edit yet.

6. First Prompt: Ask Codex to Read Before Editing

The most common beginner mistake is starting with prompts like:

Refactor the whole project for me.

Build a complete website for me.

Fix all bugs.

These tasks are too broad. It will be hard for you to judge whether Codex did the right thing.

A safer sequence looks like this:

flowchart TD
A[Step 1: Explain the project without editing files] --> B[Step 2: Ask Codex for a plan]
B --> C[Step 3: Change one small thing]
C --> D[Step 4: Run checks or explain why checks cannot run]
D --> E[Step 5: Review the diff]
E --> F[Step 6: Accept, ask follow-ups, or roll back]

For the first task, you can ask:

Please do not modify files yet. Explain the structure of this project and tell me which files a beginner should read first.

After you confirm that Codex understands the project well enough, move to the next step.

7. First Code Change: Change One Small Thing

After you are comfortable with Codex’s project understanding, ask it to make a tiny change.

A good first target is the README:

Please only modify README.md and add a section named "How beginners can start this project".

Requirements:
- Do not modify any other file.
- Do not install new dependencies.
- Do not change code.
- After editing, tell me what you changed.
- If the project does not clearly document startup commands, say "not sure" instead of inventing commands.

After Codex finishes, check two things:

git status
git diff

If you use Codex App, you can also review the changes in the review pane or diff pane.

What Should You Look for in the Diff?

Did Codex change files you did not allow it to change?
Did it add dependencies you do not recognize?
Did it delete important configuration?
Does the text or code match your request?
Does the diff actually support what Codex claimed in its summary?

As a beginner, you do not need to understand every line of code immediately. Start by checking which files were changed. That alone prevents many problems.

8. Prompt Templates You Can Copy

8.1 Universal Structure

Goal:
What do I want you to complete?

Context:
What files, errors, reproduction steps, or screenshots are relevant?

Scope:
Which files may you edit? Which files must you not edit?

Constraints:
What must you avoid? For example: do not add dependencies, do not change the database schema, do not change API fields.

Verification:
What commands should you run after finishing? If you cannot run them, explain why.

Output:
List the changed files, verification result, risks, and suggested next steps.

This structure is simple, but useful. It reduces guessing.

8.2 Project Reading Template

Please do not modify files yet. Help me understand this project:

1. What does this project do?
2. What is the tech stack?
3. Where are the entry points?
4. What are the main directories responsible for?
5. In what order should a beginner read the code?
6. What are you unsure about?

8.3 Bug Fix Template

I am running into a bug. Please help me find and fix it.

Symptom:
[Paste the error or describe the behavior]

Reproduction steps:
1. [Step one]
2. [Step two]
3. [Step three]

Requirements:
- First explain what you think is causing it.
- Make the smallest necessary change.
- Do not add dependencies unless you ask me first.
- After fixing, run relevant tests or explain why you cannot run them.
- Finally, list changed files and risks.

8.4 Feature Template

Please help me add a small feature.

Feature goal:
[Describe the feature]

Scope:
- You may modify: [files/directories]
- Do not modify: [files/directories]

Requirements:
- Start with a short plan.
- Keep each step small.
- Follow the existing code style.
- If you need a new dependency, stop and ask me first.
- Run relevant checks after finishing.

Output:
- What files changed
- How to verify
- What I still need to manually check

8.5 Code Review Template

Please review the current uncommitted changes.

Focus on:
1. Obvious bugs
2. Missing edge cases
3. Possible regressions
4. Security or performance risks
5. Whether tests should be added

Requirements:
- List high-risk issues first.
- Then list medium- and low-risk suggestions.
- Do not modify files unless I explicitly ask you to.

8.6 UI Work with Screenshots Template

Codex supports image input. For UI tasks, you can attach screenshots, mockups, or error screenshots as context.

Please implement a page based on the attached screenshot.

Requirements:
- Use the current project's existing tech stack.
- Match the layout, spacing, and type hierarchy as closely as possible.
- Do not add a new UI library unless you ask me first.
- Add only the necessary components.
- After finishing, tell me which command to run and which URL to open to view it.

9. Safe Usage: Permissions, Sandboxing, Approvals, and Privacy

Codex can read files, edit files, and run commands, so safety boundaries matter.

9.1 Recommended Beginner Permissions

flowchart LR
A[Chat / Read-only<br/>Read, explain, plan] --> B[Agent / Auto<br/>Read, write, and run commands in workspace]
B --> C[Full Access<br/>Broader permissions and network access]
A -. Best for beginners .-> A
B -. Good for small tasks .-> B
C -. Use carefully .-> C

Mode	Good for	Beginner recommendation
Chat / Read-only	Explaining projects, asking questions, writing plans	Use this first
Agent / Auto	Small file edits and tests	Review the diff before accepting
Full Access	Cross-directory, networked, or complex tasks	Do not use as the default when starting

9.2 How to Handle Approval Prompts

When Codex asks for approval, first read what it wants to do.

Be careful with actions like:

rm -rf

sudo

curl ... | sh

npm install some-package-you-do-not-understand

Accessing system directories or directories outside the project

If you are unsure, choose the narrower approval option or reject it and ask Codex to explain why the command is needed.

9.3 Do Not Give Codex These Secrets

Do not paste the following directly into your project, prompt, logs, or screenshots:

API keys
Database passwords
Production tokens
Customer private data
Sensitive personal information
Internal company material you are not allowed to share
Production database connection strings

Be especially careful with error logs. They may contain tokens, cookies, URL parameters, or database connection information. Scan them before pasting.

9.4 Create a Git Checkpoint Before Editing

Inside the project directory, run:

git status
git add .
git commit -m "checkpoint before codex task"

After Codex edits files, run:

git status
git diff

If something goes wrong, Git gives you a way back.

If you do not know Git yet, start with this rule: do not let Codex make large changes in an important production project without a backup.

10. A 7-day Beginner Practice Plan

Day 1: Read the Project Only

Please do not modify files. Explain what this project does, its directory structure, entry points, and how to run it.

Goal: understand what the project roughly is.

Day 2: Ask Codex to Write a Project Overview

Based on your understanding of the project, draft docs/project-overview.md.

Requirements:
- Write for beginners.
- Explain the main directories and startup steps.
- Do not modify code files.

Goal: practice asking Codex to generate documentation.

Day 3: Modify Only the README

Please only modify README.md and add installation and startup instructions.
Do not modify any other file.

Goal: practice reviewing diffs.

Day 4: Fix a Small Bug

Here is the error: [paste error]

Reproduction steps:
1. [Step one]
2. [Step two]
3. [Step three]

Please find the cause and make the smallest fix. Run relevant checks after fixing.

Goal: learn to provide symptoms and reproduction steps.

Day 5: Add a Test

Please add a minimal test for the issue we just fixed.

Requirements:
- First explain what the test covers.
- Do not do a broad refactor.
- Run the test after finishing.

Goal: do not only ask Codex to write code. Ask it to verify code.

Day 6: Ask Codex to Review Your Changes

Please review the current uncommitted changes. Focus on bugs, edge cases, regression risk, and whether tests are needed.
Do not modify files yet. Only output review comments.

Goal: use Codex as a second pair of eyes.

Day 7: Try Worktree or Cloud

Please try implementing this small feature in an isolated branch or worktree: [feature description]

Requirements:
- Start with a plan.
- Keep the change small.
- After finishing, explain how to verify it.

Goal: learn how to try more complex changes in an isolated environment.

11. Advanced: Use AGENTS.md to Teach Codex Your Project

When you use Codex often, consider adding an AGENTS.md file at the project root.

Think of it as a project guide written for AI agents. Codex reads these instructions before starting work, which helps it follow your team’s conventions.

Example:

# AGENTS.md

## Project Conventions

- After modifying JavaScript files, run npm test.
- Do not add production dependencies unless the user confirms first.
- When modifying shared utility functions, update docs as well.
- In the final summary, include changed files, verification, and risks.

Good things to put in AGENTS.md include:

startup commands;
test, build, and lint commands;
code style;
directories that must not be touched;
release process;
PR requirements;
database and API compatibility rules.

A useful rule: when you remind Codex of the same thing twice, consider putting it in AGENTS.md.

12. FAQ

Q1: Can I use Codex if I do not know how to code?

Yes, but start with “explain the project”, “organize documentation”, “read this error”, and “write a learning path”. Do not begin with broad refactors.

You can use Codex as a learning assistant, but after it edits files, you still need to review the diff and verify the result.

Q2: Should I use Codex App, IDE, CLI, or Web?

Beginners should start with Codex App. If you already use VS Code, Cursor, or Windsurf every day, use the IDE extension. Use CLI after you are comfortable with terminals. Use Web / Cloud when a GitHub project needs background work or PR creation.

Q3: What if Codex makes the wrong change?

Review the diff first. If you do not like it, ask Codex to adjust the change. If the project becomes messy, roll back with Git.

If you have not learned Git yet, practice on a sample project before letting Codex make broad changes in an important project.

Q4: Codex asks me to approve a command. Should I allow it?

Only approve commands you understand.

Be especially careful with deleting files, installing dependencies, accessing directories outside the project, downloading from the network, or running scripts.

If you do not understand the command, ask:

Please explain what this command does, why it is necessary, and whether there is a safer alternative.

Q5: Do I need an API key?

Not always.

Codex App can use a ChatGPT account or an OpenAI API key. When using an API key, some features such as cloud threads may be unavailable.

Q6: Can Codex deploy directly for me?

Do not let Codex operate production without human review.

A safer flow is:

Small task
-> Review diff
-> Run tests
-> Review risks
-> Commit / PR
-> Human confirmation before release

Final Summary

The right way to start with Codex is not:

Build the whole project for me in one shot.

It is:

Explain the project first
-> Propose a plan
-> Make one small change
-> Review the diff
-> Run tests
-> Iterate

Keep tasks small, keep permissions tight, and make verification explicit. That is how you can safely start using Codex to read code, edit code, and review code from day one.

References

How to Apply for ChatGPT Pro for Free: OpenAI Codex for Open Source Guide

Thu, 07 May 2026 22:30:00 +0800

Many people search for “how to use ChatGPT for free.” A more accurate version of that question is: how can eligible open-source maintainers apply for official ChatGPT Pro benefits through an OpenAI program?

ChatGPT already has a free tier. If you are not maintaining an open-source project, this is not a general “free Pro giveaway” guide.

But if you are an open-source maintainer, OpenAI’s Codex for Open Source program gives you an official path to apply. According to OpenAI’s official page, maintainers can apply for API credits, six months of ChatGPT Pro with Codex, and conditional access to Codex Security.

This article is based on the OpenAI pages available on May 7, 2026. Program rules and forms may change, so check the official pages before applying.

One-line Summary

Codex for Open Source is not a free ChatGPT Pro campaign for everyone. It is an OpenAI support program for open-source maintainers.

If you maintain a public open-source project and can clearly explain the project’s value, your maintainer role, and how AI tools will support real maintenance work, it is worth applying.

What Is Codex for Open Source?

Codex for Open Source is an OpenAI program designed to support maintainers behind important open-source software.

OpenAI describes open-source maintainers as people who often carry significant responsibility behind the scenes: reviewing pull requests, triaging issues, maintaining releases, and preserving security and code quality. The program is meant to reduce coding and review load with tools built for real maintenance workflows.

The official page currently lists these possible benefits:

Benefit	Best suited for
Six months of ChatGPT Pro with Codex	Day-to-day coding, code understanding, issue analysis, PR review, documentation, and maintainer workflows
API credits	PR review automation, maintainer automation, release workflows, and open-source project bots
Codex Security	Deeper security-related analysis for eligible repositories

These benefits are not guaranteed. OpenAI’s program terms say approved applicants may receive one or more benefits, as determined by OpenAI. Availability, duration, scope, and timing may vary by applicant, repository, or use case.

Who Should Apply?

The program is mainly for the following groups.

1. Primary or Core Maintainers

If you are the primary maintainer or a core maintainer of an open-source project, and you regularly handle issues, pull requests, releases, security fixes, documentation, tests, or community maintenance, you are a strong fit.

The application form asks you to describe your role, so be specific. Do not just say you are a user or a fan of the project. Explain what maintenance responsibilities you actually hold.

2. Maintainers of Public Projects with Real Usage

The official page encourages core maintainers or people who run widely used public projects to apply.

That does not mean only projects with huge star counts are eligible. If your project plays an important role in a language ecosystem, framework ecosystem, plugin ecosystem, infrastructure layer, or developer tooling workflow, explain why it matters.

Useful proof points include:

GitHub stars, forks, and contributors
npm, PyPI, Maven, crates.io, Docker Hub, or other package download numbers
downstream dependencies, user stories, or production usage
PR, issue, and release activity over the last three to six months
your own work on review, triage, releases, security fixes, or documentation

3. Maintainers with a Clear AI Workflow

Your application will be more convincing if you can describe how Codex or API credits will be used in open-source maintenance.

Good examples include:

first-pass pull request review
change summaries
test suggestions
release note generation
maintainer scripts
issue classification and triage

A strong application should not simply say, “I want free ChatGPT Pro.” It should explain:

why the project deserves support
what maintenance role you hold
how ChatGPT Pro, Codex, or API credits will help with open-source work
how maintainers will review AI output before acting on it

What Should You Prepare Before Applying?

The official OpenAI application form currently asks for the following information:

Field	Notes
First name / Last name	Your name
Email	The email associated with your ChatGPT account
GitHub username	Your GitHub profile visibility should be public
GitHub repository URL	The repository should be public
Maintainer role	Whether you are a primary maintainer or core maintainer
Why does this repository qualify?	Include GitHub stars, monthly downloads, or ecosystem importance, within 500 characters
I’m interested in	Codex Security and/or API credits
OpenAI Organization ID	The form provides a link to find it
How will you use API credits for your project?	Explain how credits will support project maintenance, within 500 characters
Anything else we should know?	Additional project context or maintainer workload, within 500 characters

The form also says OpenAI reviews applications on a rolling basis and notifies selected applicants by email.

Step-by-step Application Guide

Step 1: Confirm That You Fit the Program

First, check whether you are truly maintaining an open-source project.

Do not create an empty repository just to apply. Do not exaggerate your project’s impact. OpenAI’s program terms require applicants to provide accurate and complete information about themselves, their repositories, and their maintainer or administrator role.

OpenAI may consider repository usage, ecosystem importance, evidence of active maintenance, role or permissions, and program capacity.

In short: submitting an application does not guarantee ChatGPT Pro, API credits, or Codex Security access.

Step 2: Prepare Evidence of Project Impact

Prepare a short explanation of why your repository qualifies. The form limits this field to 500 characters, so make every sentence concrete.

You can use this structure:

I am the core maintainer of [project], responsible for PR review, issue triage, releases, and security fixes. The project has [stars/downloads/users] and is used by [ecosystem/users/downstream projects]. It plays an important role in [specific ecosystem or scenario].

If your project has a smaller GitHub footprint but matters in a specific ecosystem, say that clearly. For example, it may be a framework plugin, deployment tool, SDK, MCP server, CLI tool, language ecosystem component, or a dependency used by real downstream projects.

Step 3: Open the Official Application Page

Go to the official Codex for Open Source page, then click Apply today to open the OpenAI application form.

The form currently says selected maintainers may receive:

6 months of ChatGPT Pro, which includes Codex
Conditional access to Codex Security
API credits for coding, maintainer automation, release workflows, and core open-source work

Step 4: Write the Two Most Important Answers

The most important parts of the form are the two short text fields.

The first is Why does this repository qualify?

This should explain why your repository deserves support.

Example:

I am the primary maintainer of [project], responsible for PR review, issue triage, releases, and documentation. The project has [stars] GitHub stars and about [downloads] monthly downloads. It is used by [downstream projects/users] and provides [specific value] for the [ecosystem] ecosystem.

The second is How will you use API credits for your project?

This should explain how credits will be used for open-source maintenance.

Example:

We plan to use API credits for pull request review, change summaries, test suggestions, release note generation, issue triage, and maintainer automation. All outputs will be reviewed by maintainers and used only for authorized repositories and core open-source maintenance workflows.

Keep the use case tied to open-source maintenance. Do not describe personal experiments, unrelated commercial projects, or unrestricted sharing with a team.

Step 5: Submit and Wait for Email

After submission, wait for OpenAI’s review.

The form says applications are reviewed on a rolling basis and selected applicants are notified by email. OpenAI’s terms also say it may approve or deny applications at its discretion, and may request additional information to verify identity, repository affiliation, maintainer status, or repository control.

How to Improve Your Application

Do Not Lead with “I Want Free ChatGPT”

The program is about open-source maintenance, not personal free access.

A stronger angle is: you want to use ChatGPT Pro with Codex to reduce maintainer workload and improve PR review, bug triage, release, documentation, and testing workflows.

Make Project Impact Specific

This is more convincing:

The project has 2,000 GitHub stars, 50,000 monthly npm downloads, and is used by several downstream developer tools.

If you do not have package download numbers, use activity, downstream usage, community examples, or ecosystem role.

Make Your Maintainer Role Clear

The form asks for your GitHub profile and repository to be public. Your text should also make your role obvious.

Examples of concrete responsibilities:

reviewing pull requests
triaging issues
maintaining releases
fixing security issues
maintaining documentation
managing CI or release workflows

Keep API Credit Usage Compliant

Avoid writing:

I want to use the credits for my personal AI experiments.

Write something closer to:

We will use API credits for PR review summaries, test suggestions, release note generation, and issue triage. Maintainers will review all AI outputs before taking action.

Important Caveats

1. Applying Does Not Guarantee Approval

OpenAI’s program terms are clear: submitting an application does not guarantee selection, funding, or access.

OpenAI may consider repository usage, ecosystem importance, evidence of active maintenance, maintainer role or permissions, and program capacity.

2. Benefits Are Not Transferable

Program benefits are personal, limited, non-transferable, and have no cash value. They may not be sold, assigned, sublicensed, exchanged, or shared.

If OpenAI provides a redemption code, invitation, or activation flow, the recipient must follow the applicable redemption instructions and any additional terms.

3. Codex Security and API Credits May Require Extra Review

Codex Security and API credits are optional additional benefits. They may require separate review, additional eligibility checks, or additional terms.

For Codex Security especially, OpenAI may limit access to repositories that the applicant owns, maintains, or is otherwise authorized to administer.

4. Do Not Review Unauthorized Repositories

OpenAI’s terms also restrict use of the program, including Codex Security, for repositories, systems, or codebases that you do not own or lack permission to review.

If authorization is unclear, OpenAI may require proof of control or authorization, and may limit or revoke access.

Copy-ready Application Templates

Repository Qualification Template

I am the core maintainer of [project name], responsible for PR review, issue triage, releases, documentation, and security fixes. The project has [stars/downloads/users] and is used by [ecosystem/users/downstream projects]. It plays an important role in [specific ecosystem or scenario], and ChatGPT Pro with Codex would help reduce review workload and maintain code quality.

API Credits Usage Template

We plan to use API credits for pull request review, change summaries, test suggestions, release note generation, issue triage, and maintainer automation. All outputs will be reviewed by maintainers before use, and credits will only be used for authorized repositories and core open-source maintenance workflows.

Conclusion

Applying for ChatGPT Pro through Codex for Open Source is not about exploiting a free offer. It is about OpenAI supporting real open-source maintenance work.

If you maintain a public open-source project and can clearly explain its value, your role, and how AI tools will improve your maintenance workflow, the program is worth a serious application.

For maintainers, the most valuable part is not only six months of ChatGPT Pro. It is the chance to connect Codex, API credits, and security tooling to real workflows: faster PR review, better issue triage, easier release notes, and more time for architecture, quality, and community decisions.

Official Links

open-slide: A Slide Framework Built for AI Agents

Thu, 07 May 2026 19:47:00 +0800

One-line positioning

open-slide is a React slide framework built for AI agents: you describe what the deck should be, the agent writes React pages, and open-slide handles the canvas, preview, presentation mode, export, and iteration loop.

Basic information

Item	Details
Project	open-slide
Author	1weiho
Positioning	Agent-native slide framework
Stack	TypeScript, React, Vite, pnpm, Turbo
License	MIT
Latest version	`@open-slide/core@1.0.6`
GitHub popularity	2.1k+ stars / 143 forks
Website	https://open-slide.dev
Init command	`npx @open-slide/cli init my-slide`

What problem does it solve?

The traditional slide-making workflow usually happens in Keynote, PowerPoint, Figma, or a browser-based editor. You drag elements around, adjust styles, and manually polish layout details.

But a lot of that work can now be handled by AI agents: structure, copy, visual hierarchy, colors, and even code implementation. The idea behind open-slide is simple: if slides are visual code, let agents write that code directly.

open-slide provides a stable slide runtime:

Each page is a React component.
Every slide is rendered into a fixed 1920 × 1080 canvas.
The framework supports preview, hot reload, presentation mode, and export.
Agents can follow built-in skills and rules to generate and edit decks.

This means you do not need to set up React, Vite, rendering, and export logic from scratch. You describe the deck you want, the agent writes the pages, and open-slide turns those pages into a presentable and iterable deck.

Core features

1. Agent-native authoring

The main selling point of open-slide is that it is designed around agents writing slides.

It can work with coding agents such as Claude Code, Codex, and Cursor. A scaffolded workspace ships with agent-facing skills, including:

/create-slide: drafts a deck end to end, starting from topic, aesthetic, page count, text density, and motion preference.
/slide-authoring: gives the agent technical rules for the 1920 × 1080 canvas, type scale, palette, and layout constraints.

In other words, open-slide is not just a slide component library. It also defines how an agent should author and revise a deck.

2. React-first: every slide is code

open-slide does not introduce a constrained slide DSL. Each slide page is just an arbitrary React component.

That means:

You can write normal TSX.
Layout, styling, animation, and component structure are fully controllable.
Slides can be versioned with Git.
Agents can edit the source directly.
The deck can be reviewed like a regular frontend codebase.

The website summarizes this well: A slide is a file. Just React, nothing else.

3. Fixed 1920 × 1080 canvas

Every slide renders into a fixed 1920 × 1080 canvas.

This matters for agent-generated slides because it gives the agent a clear visual boundary. Font sizes, layout, element positions, and proportions can all be designed around a stable canvas.

Compared with a responsive web page, this fixed canvas is closer to a real presentation deck and is easier to export and present.

4. In-browser inspector

open-slide also provides an interesting iteration flow. In the dev server, you can click an element and leave a comment on it.

For example, you can click a heading and write:

“make this red”
“shrink the headline”
“change the copy to Open Slide Rocks”

Those comments are persisted as @slide-comment markers in the source. Then /apply-comments lets the agent read pending comments, update the code, and clear the markers.

This creates a useful loop: let the agent generate the first version, click to leave human feedback, and let the agent apply the next round of changes.

5. Asset manager and logo search

open-slide includes an assets panel for managing images, videos, and fonts per deck.

It also integrates the svgl logo catalogue, so you can search for and insert brand logos without hunting for SVG files manually. This is especially useful for product decks, pitches, and technical talks.

6. Professional presentation mode

open-slide is not limited to dev preview. It includes a proper present mode with:

fullscreen playback
keyboard navigation
current and next slide preview
speaker notes
timer

The result is not just a browser page. It can be used as an actual presentation deck.

7. Export to static HTML and PDF

open-slide can export a deck as:

self-contained static HTML
print-ready PDF

The static HTML output can be deployed to Vercel, Cloudflare Pages, Netlify, Zeabur, or any static hosting service. It works for both local presenting and online sharing.

8. Slide manager

The project also includes a slide manager for organizing multiple decks with folders, custom emoji, and drag-and-drop reordering.

Once you have more than a few generated decks, that management layer becomes useful.

Who is it for?

open-slide is a good fit for:

People who want to generate decks quickly with Claude Code, Codex, or Cursor.
Developers who prefer “slides as code.”
Teams that need presentable decks for technical plans, product demos, or project updates.
Users who want slide decks to be versioned, reviewable, and automatable.
Anyone tired of manually dragging elements around in traditional slide tools.

It is especially suitable for technical content: architecture explanations, product demos, roadmaps, open-source project introductions, and engineering talks.

Quick start

Initialize a project:

npx @open-slide/cli init my-slide
cd my-slide
pnpm dev

The generated workspace includes the open-slide runtime and agent-related rules. You can ask an agent to generate a deck through /create-slide, or directly edit:

slides/<id>/index.tsx

If you want to work on open-slide itself, the repository is a pnpm + Turbo monorepo:

pnpm install
pnpm dev
pnpm build
pnpm check
pnpm lint

Main directories:

Path	Description
`packages/core`	Core runtime, including home page, slide viewer, present mode, inspector, Vite plugin, and dev/build/preview CLI
`packages/cli`	Scaffolder for `npx @open-slide/cli init`
`apps/demo`	Demo workspace used for local development and examples

Conclusion

open-slide points to a natural new workflow: instead of asking AI to generate a static PowerPoint file, let an agent write a runnable, iterable, and version-controlled slide codebase.

It combines React, a fixed canvas, presentation mode, inspector comments, asset management, and export into a stable runtime for agent-authored decks.

If you already use Claude Code, Codex, or Cursor for content and code generation, open-slide is a good project to try for agent-generated presentations.

OpenLess: An Open-Source AI Voice Input Tool That Turns Speech into Polished Text

Thu, 07 May 2026 11:26:00 +0800

One-line positioning

OpenLess is an open-source desktop voice input tool: hold a hotkey, speak, release it, and AI-polished text appears at your current cursor.

Basic information

Item	Details
Project	OpenLess
GitHub	https://github.com/appergb/openless
Website	https://openless.top
Positioning	Open-source voice input plus AI text polishing
Platforms	macOS 12+, Windows 10+
Stack	Tauri 2, Rust, React, TypeScript
License	MIT
Current version	v1.2.21-tauri
GitHub popularity	900+ stars

What problem does it solve?

Many voice input tools only produce a literal transcript. The result often still needs manual cleanup: removing filler words, fixing punctuation, and restructuring the text.

OpenLess focuses on a more practical goal: turning spoken language directly into usable written text.

The workflow is straightforward. Put your cursor in ChatGPT, Claude, Cursor, Notion, an email draft, WeChat, Slack, or almost any other text field. Press a global hotkey and speak. OpenLess records your voice, transcribes it, polishes the text with AI, and inserts the result at the cursor. If direct insertion is blocked, it falls back to copying the output to the clipboard so your dictation is not lost.

Its most distinctive mode is the AI prompt mode. You can speak loosely, and OpenLess turns your words into a structured prompt with clearer context and constraints, ready to send to ChatGPT, Claude, or Cursor.

For example, you might say:

I want ChatGPT to write a SQL query from the orders table, get last month’s orders, group by customer, sort by amount descending, and return the top ten.

OpenLess can turn that into:

Please write a SQL query that:

- Pulls orders from last month from the `orders` table.
- Groups by customer.
- Sorts by total amount, descending.
- Returns the top 10 rows only.

In this workflow, OpenLess is not just a dictation tool. It turns spoken thoughts into text that is easier to send, ask, or submit.

Core features

1. Global-hotkey voice input

OpenLess lets you trigger recording with a global hotkey. You can place your cursor in any input field and start speaking. The project currently supports both push-to-talk and toggle recording modes, and Esc can cancel the current session.

2. Multiple output modes

OpenLess does not stop at raw transcription. It provides several output modes:

Raw: output the transcript directly.
Light polish: fix filler words, punctuation, and obvious wording issues while preserving meaning.
Structured: turn loose speech into a structured AI prompt.
Formal: rewrite spoken text into a more formal written style.

The structured mode is the key differentiator, especially for people who frequently write AI prompts.

3. Insert text at the current cursor

OpenLess is not trying to become another note-taking app. Its purpose is to reduce input friction. The polished text is inserted directly into the app you are already using, such as a browser, chat app, editor, or document tool.

If an app blocks direct insertion, OpenLess falls back to the clipboard, and you can paste the result manually.

4. Dictionary and hotwords

The project includes a dictionary feature for product names, people names, and technical terms. Enabled entries are used as hotwords during speech recognition and as semantic hints during polishing.

This is useful if you often dictate English product names, technical terms, or internal team vocabulary.

5. History and settings

OpenLess provides a main window for overview, history, dictionary, style, and settings. You can review the original transcript and polished output for previous recordings, and you can adjust language, hotkeys, and model credentials.

6. Local-first and bring-your-own credentials

Many similar tools are closed-source subscription products where your data, dictionary, and model calls stay inside the vendor’s account system.

OpenLess takes a different approach: the app is open source, data is kept primarily on your own machine, and you configure the speech recognition and text polishing services yourself. It currently supports Volcengine ASR, OpenAI Whisper-compatible batch ASR, and Ark / DeepSeek / OpenAI-compatible Chat Completions.

Who is it for?

OpenLess is a good fit for:

People who frequently write prompts for ChatGPT, Claude, Cursor, or Gemini.
Users who write emails, specs, and long messages but do not want to type everything manually.
Developers who want to dictate code comments, commit messages, or PR descriptions.
Users who prefer an open-source option over fully closed subscription voice input tools.
Heavy macOS or Windows desktop users.

If your daily work involves turning thoughts into written text, OpenLess can fit naturally into that workflow.

Quick start

End users can download installers from GitHub Releases:

macOS: OpenLess_<version>_aarch64.dmg
Windows: OpenLess_<version>_x64-setup.exe

On first launch, you need to grant the required permissions.

For macOS:

Grant microphone access.
Grant accessibility access.
Quit and reopen OpenLess.
Fill in ASR and LLM credentials in Settings.

For Windows:

Grant microphone access.
Check that the global hotkey listener is active in Settings.
Fill in ASR and LLM credentials.

Developers can run it from source with:

git submodule update --init --recursive

cd openless-all/app
npm ci
npm run tauri dev

The active app lives in openless-all/app/. The desktop client is built with Tauri 2, a Rust backend, and a React / TypeScript frontend.

Conclusion

OpenLess can be understood as an open-source AI voice input assistant.

It does not answer questions, execute tasks, or try to become another AI client. It focuses on one job: turning what you say into clearer text that is ready to send, ask, or paste into the current input field.

For people who frequently write prompts, long messages, emails, specs, or code-related descriptions, this is a practical direction. Open source, bring-your-own credentials, local-first storage, and AI-prompt-oriented restructuring are the main reasons OpenLess is worth watching.

Petdex: Find an Animated Companion for Codex

Wed, 06 May 2026 23:20:00 +0800

One-line Positioning

Petdex is a public gallery of animated companions for Codex. It lets you browse, preview, collect, and install pixel-style companions so your local Codex experience feels a little more personal and playful.

Basic Info

Item	Info
Website	https://petdex.crafter.run/
GitHub	https://github.com/crafter-station/petdex
Positioning	Codex-compatible animated pets public gallery
Current scale	870+ pets shown on the homepage, with 600+ GitHub stars
Quick install example	`npx petdex install boba`

What Problem Does It Solve?

Command-line tools like Codex are efficient, but they can also feel a bit cold: text, tasks, logs, and results fill the window, while the environment itself has very little personality.

Petdex does something simple and charming. It turns animated Codex companions into a public gallery, so users can browse it like an avatar library or sticker collection and pick a pet for their own Codex setup.

It solves three small but real problems:

Centralized discovery: instead of hunting for pet packs across scattered links, you can browse them in one place.
Preview before installing: each companion has visuals, states, descriptions, tags, and metadata.
Clear installation path: the site gives you a direct install command, making the first try low-friction.

This is not a tool for making the model more capable. It is a small layer of personality and ritual around a developer workflow.

Core Features

1. Browse the Pet Gallery

The homepage presents a large set of companion cards. Each card includes details such as number, name, category, description, tags, author, and install data.

For example, Boba is a tiny otter sipping bubble tea. Duo, Doraemon, Noir Webling, and other companions are presented in pixel-art or chibi-style forms.

That makes Petdex feel more like a living pet index than a plain file directory.

2. Install with One Command

The homepage shows a direct install example:

npx petdex install boba

The interaction is straightforward: find a companion you like, copy the command, and install it.

3. Featured Collections

Petdex also includes collections, with featured sets on the homepage. Examples include:

GRAYCRAFT: an original mech-style character set
Meme Lords: meme-inspired characters such as Pepe, Wojak, and Doodlebob
Anime Heroes: anime-inspired companion designs

Collections make browsing more thematic and give creators a way to present related designs together.

4. Community Submission

Petdex includes a Submit entry point. Its README also mentions browser-based validation and submission for community pet packages.

That means Petdex is not just a read-only catalog. It is designed to grow through community contributions.

5. Open-source Maintenance

The Petdex GitHub repository is public. The README describes it as a Codex-compatible animated pets gallery and lists several core capabilities:

Browse approved pet packs
Preview every animation state
Download individual ZIP packages
Download the full gallery pack
Validate and submit community pet packages in the browser

From the repository structure, pet packages live under public/pets, while downloadable archives are generated under public/packs.

Who Is It For?

Petdex is a good fit for:

people who use Codex often and want a more personal local environment;
developers who like pixel art, chibi characters, and desktop-pet-style details;
users who want their toolchain to feel a little less sterile;
creators who want to design and share Codex companions.

If you only care about model quality, task success rate, or engineering automation, Petdex is not essential. But if you spend a lot of time working with Codex in a terminal, it can make that daily experience feel lighter.

Quick Start

Open the website:

https://petdex.crafter.run/

Pick a companion, such as Boba, and run:

npx petdex install boba

You can also keep exploring more pets, collections, and creators on the site.

To learn more about the project itself, visit the GitHub repository:

https://github.com/crafter-station/petdex

Conclusion

Petdex is not a large or complicated tool. It is more like a playful entry point in the Codex ecosystem: a public index where companions can be browsed, previewed, installed, and submitted.

Its value is not in making Codex smarter. Its value is in making the experience of using Codex more personal, lighter, and more like a workspace that the community can decorate together.

Keep Codex Fast: A Safe Maintenance Skill for Keeping Local Codex State Lightweight

Mon, 04 May 2026 15:49:45 +0800

One-line Positioning

Keep Codex Fast is a local-state maintenance skill for heavy Codex users. It inspects first, creates handoff notes, backs up important state, and then safely archives old sessions, logs, and temporary workspaces that can make the local experience feel heavier over time.

Basic Info

Item	Info
GitHub	https://github.com/vibeforge1111/keep-codex-fast
Author	vibeforge1111
License	MIT
Main language	Python
Default branch	main
Positioning	Codex local-state inspection and safe maintenance skill
Current traction	About 559 stars and 29 forks, checked on 2026-05-04

What Problem Does It Solve?

After using Codex for a long time, many users run into a kind of “heaviness” that is hard to pin down:

Long conversations keep accumulating.
Old threads are frequently resumed.
Multiple repositories are active at the same time.
Local terminals, dev servers, logs, and workspaces keep piling up.
You want to clean things up, but worry about deleting useful context.

Keep Codex Fast is not about making the model answer faster. It is about organizing the local runtime state Codex leaves on your machine. Its core principle is intentionally conservative:

Make handoffs first. Archive, don’t delete. Apply changes only when you are ready.

In other words: write continuity notes first, archive instead of deleting, and only apply changes after you have confirmed that you are ready.

This is practical for people who use Codex for real development work. The part that makes cleanup hard is usually not the files themselves. It is the project decisions, debugging paths, failed commands, and next-step plans hidden inside older chats. Keep Codex Fast turns “cleanup” into a safer workflow: inspect first, preserve recoverable context, then archive.

Core Workflow

flowchart TD
A[Codex local state becomes heavy] --> B[Read-only inspection]
B --> C[Generate maintenance report]
C --> D{Any important old sessions?}
D -- Yes --> E[Create handoff notes]
E --> F[Back up important state]
D -- No --> F
F --> G[Archive old sessions and workspaces]
G --> H[Rotate large logs]
H --> I[Prune stale project references]
I --> J[Inspect again and confirm results]

The most important part of this flow is the first step: read-only inspection by default. Keep Codex Fast does not immediately move files or delete history. It first reports what local state exists and where the weight is coming from.

1. Read-only Report by Default

The project README makes the default mode clear: it only reports. It does not write files, create backups, move directories, or mutate local Codex state. It helps you see which areas are growing, including active sessions, archived sessions, logs, old workspaces, stale project references, and heavy Node or dev processes.

That makes the first run low-risk. You can treat it as a health check rather than a cleanup operation.

2. Handoffs First

Keep Codex Fast puts special emphasis on handoff notes. A handoff is a small continuity document that records:

what you are working on;
what has already changed;
which files matter;
which commands or checks have already been run;
what errors, warnings, or open problems remain;
what should happen next.

With that document in place, you can archive a heavy old chat and continue from a fresh Codex session without relying on a growing historical thread.

3. Back Up First, Archive Instead of Deleting

When it actually performs maintenance, Keep Codex Fast still takes the conservative path: back up important state first, then move old sessions, stale workspaces, and large logs into an archive location. It removes items from the hot path instead of permanently deleting them.

According to the project description, it can handle:

old non-pinned active sessions;
expired worktrees;
large logs_2.sqlite* log files;
stale or temporary project references in config.toml;
extended Windows path inconsistencies such as \\?\C:\...;
reports for heavy Node or dev processes.

4. Manual Application, Not Automatic Cleanup

The project also draws an important boundary: scheduled maintenance is better suited for reminders and reports, not automatic cleanup. The reason is simple. Automation cannot know which old sessions are still important, or whether a handoff is good enough.

A safer workflow is to inspect regularly, confirm manually, and only apply maintenance after Codex is closed or after explicitly waiting for Codex to exit.

Who Is It For?

Keep Codex Fast is most useful for Codex users who:

use Codex for development every day;
work across multiple repositories;
keep long chat histories;
often resume old threads for debugging or implementation;
run multiple local terminals, services, or worktrees;
want to clean local Codex state without losing useful context.

If you only open Codex occasionally for small questions, you may not feel the value yet. But once Codex becomes part of your daily development workflow, this kind of local-state maintenance becomes increasingly important.

Quick Start

Inside Codex, you can ask it to install the skill directly:

Install the keep-codex-fast skill from https://github.com/vibeforge1111/keep-codex-fast

After installation, start with a read-only inspection:

Use $keep-codex-fast to inspect my Codex local state and recommend a safe maintenance plan.

If the report shows old sessions worth archiving, create a handoff for any project that may continue:

Create a comprehensive handoff document for this repo/session before I archive Codex history.

Include:
- repo/path and branch
- current goal
- what we already completed
- files touched or investigated
- commands/tests already run
- known errors, warnings, or failing checks
- open decisions
- constraints, user preferences, and do-not-touch areas
- the next 3-7 concrete steps

Also include a reactivation prompt I can paste into a fresh Codex chat so it can continue from this handoff without relying on the old chat context.

After important sessions have handoff notes, you can consider safe maintenance:

Use $keep-codex-fast to apply safe Codex maintenance.

Before changing anything, confirm that important active repo chats have handoff docs or do not need them.

Then back up first, archive instead of deleting, move stale worktrees, rotate large logs, prune dead config references, and verify the result.

If Codex is currently running, do not mutate local state. Tell me to close Codex first.

If you prefer running the script directly, the project also provides a CLI path:

python scripts/keep_codex_fast.py

Beyond the read-only report, you can use options such as:

python scripts/keep_codex_fast.py --details
python scripts/keep_codex_fast.py --backup-only
python scripts/keep_codex_fast.py --apply --archive-older-than-days 10 --worktree-older-than-days 7
python scripts/keep_codex_fast.py --apply --wait-for-codex-exit

Conclusion

The value of Keep Codex Fast is not aggressive cleanup. Its value is turning Codex local maintenance into a controlled process: report first, hand off first, back up first, then archive.

For heavy Codex users, old sessions and local state are both a burden and an asset. The project’s approach is practical: extract truly valuable context into handoff notes, move state that does not need to stay on the hot path, and let new Codex sessions start light again.

If your local Codex experience already feels heavier, Keep Codex Fast is worth running in read-only mode first. Even if you do not apply cleanup immediately, it can show which local state is growing and how to handle it safely later.

HyperFrames: An Open-source Rendering Framework for Generating Video with HTML

Sat, 02 May 2026 10:39:18 +0800

One-line Positioning

HyperFrames is HeyGen’s open-source HTML video rendering framework. It lets you define video scenes, timelines, and animations the same way you write web pages, then preview them in a browser and render them to MP4.

Basic Info

Item	Info
GitHub	https://github.com/heygen-com/hyperframes
Docs	https://hyperframes.heygen.com/introduction
NPM package	`hyperframes`
Main language	TypeScript
License	Apache-2.0
Current version	0.4.41
Requirements	Node.js >= 22, FFmpeg
Positioning	HTML-native, AI-first, deterministic video rendering

What Problem Does It Solve?

There are usually two common paths for video automation. One is to use traditional editing tools, which are great for manual production but not ideal for batch generation. The other is to use code-based video frameworks, which are automation-friendly but often require developers to enter a specific component system or DSL.

HyperFrames takes a route that feels closer to normal web development: the video itself is HTML.

It puts clips, subtitles, images, audio, timing, and track relationships into HTML elements and data-* attributes. You can organize scenes like a web page, animate them with familiar frontend tools such as CSS, GSAP, Lottie, and Three.js, then render the result to MP4 from the command line.

This approach is also friendly to AI agents. Large language models are already good at generating HTML, CSS, and simple scripts. HyperFrames adds skills, plugins, and CLI workflows for agents, so an agent can go from “describe a video” to “create the project, preview it, inspect it, and render it.”

Core Features

1. Describe Video Structure with HTML

The core input to HyperFrames is an HTML document. Each element uses attributes such as data-start, data-duration, and data-track-index to describe when it appears, how long it lasts, and where it sits in the timeline.

A simple video can be composed from ordinary HTML elements such as video clips, titles, images, and background music:

<div id="stage" data-composition-id="my-video" data-start="0" data-width="1920" data-height="1080">
 <video
 id="clip-1"
 data-start="0"
 data-duration="5"
 data-track-index="0"
 src="intro.mp4"
 muted
 playsinline
 ></video>

 <h1
 class="clip"
 data-start="1"
 data-duration="4"
 data-track-index="1"
 >
 Welcome to HyperFrames
 </h1>

 <audio
 data-start="0"
 data-duration="5"
 data-track-index="2"
 data-volume="0.5"
 src="music.wav"
 ></audio>
</div>

The benefit is a low learning curve. You do not need to learn a new video-editing format first, and you do not have to rewrite everything as React components.

2. Browser Preview, Local MP4 Rendering

HyperFrames provides a CLI for project initialization, preview, and rendering:

npx hyperframes init my-video
cd my-video
npx hyperframes preview
npx hyperframes render

During preview, you inspect the composition in a browser. During rendering, headless Chrome captures frames and passes them to FFmpeg to produce the final video. The official docs emphasize deterministic rendering: the same input should produce the same output, which matters for automation pipelines.

3. AI-agent-oriented Workflow

HyperFrames is not just a CLI. It also provides supporting workflows for AI coding tools. The project README recommends installing skills first:

npx skills add heygen-com/hyperframes

These skills teach agents how to write valid compositions, GSAP timelines, Tailwind v4 browser-runtime styles, and adapters for different animation runtimes.

The project also provides instructions or plugin entry points for tools such as Claude Code, Cursor, Codex, and Gemini CLI. In other words, “AI support” is not just a marketing phrase. Agent-friendly initialization, linting, previewing, and rendering are part of the framework’s design.

4. Support for Multiple Animation and Asset Runtimes

HyperFrames uses a Frame Adapter pattern to integrate animation runtimes. You can build animations with GSAP, Lottie, CSS animation, Three.js, or the Web Animations API, as long as they can seek frame by frame and participate in deterministic rendering.

This matters for video automation. Video is not just screen recording. You need a deterministic frame at a specific timestamp. HyperFrames abstracts the relationship between “how the page plays” and “how the renderer captures frames”, allowing different animation technologies to enter the same video-generation pipeline.

5. Built-in Component Registry and Multiple Packages

The project provides 50+ ready-to-use blocks and components, such as social-media overlays, shader transitions, data visualization, and cinematic effects. They can be added through commands:

npx hyperframes add flash-through-white
npx hyperframes add instagram-follow
npx hyperframes add data-chart

From the repository structure, HyperFrames is a monorepo with several key packages:

Package	Purpose
`hyperframes` / `@hyperframes/cli`	CLI for init, preview, lint, and render
`@hyperframes/core`	Types, parser, generator, linter, runtime, and frame adapters
`@hyperframes/engine`	Puppeteer + FFmpeg based page-to-video rendering engine
`@hyperframes/producer`	Full rendering pipeline, including capture, encoding, and audio mixing
`@hyperframes/studio`	Browser-side composition editor UI
`@hyperframes/player`	Embeddable `<hyperframes-player>` Web Component
`@hyperframes/shader-transitions`	WebGL shader transitions for compositions

Official Video Examples

npx hyperframes init my-video --example warm-grain

Example	Type	Video link
Warm Grain	Brand / lifestyle	https://static.heygen.ai/hyperframes-oss/docs/images/templates/warm-grain.mp4
Play Mode	Social media / product launch	https://static.heygen.ai/hyperframes-oss/docs/images/templates/play-mode.mp4
Swiss Grid	Enterprise / technology / data	https://static.heygen.ai/hyperframes-oss/docs/images/templates/swiss-grid.mp4
Kinetic Type	Title card / promo	https://static.heygen.ai/hyperframes-oss/docs/images/templates/kinetic-type.mp4
Decision Tree	Explainer video / tutorial	https://static.heygen.ai/hyperframes-oss/docs/images/templates/decision-tree.mp4
Product Promo	Product showcase	https://static.heygen.ai/hyperframes-oss/docs/images/templates/product-promo.mp4
NYT Graph	Data story / chart video	https://static.heygen.ai/hyperframes-oss/docs/images/templates/nyt-graph.mp4
Vignelli	Vertical title / announcement	https://static.heygen.ai/hyperframes-oss/docs/images/templates/vignelli.mp4

Who Is It For?

HyperFrames is a good fit for several scenarios:

Frontend developers who want to generate video with code
If you already know HTML, CSS, and JavaScript, the mental model is easy to pick up.
Teams that need batch-generated marketing, tutorial, or data videos
For example, turning CSV files into animated charts, product docs into short videos, or templates into many personalized variants.
People building AI-agent content production workflows
Its CLI, skills, and plugin design all lean toward automation, making it suitable as an execution layer for agent-generated video.
Teams that need reproducible video generation pipelines
Compared with manual editing tools, HyperFrames is easier to run from scripts, CI, Docker, or backend jobs.

Quick Start

If you want an AI agent to participate in video generation, the official recommendation is to install the skills first:

npx skills add heygen-com/hyperframes

Then you can ask the agent to create a video with /hyperframes context, for example:

Using /hyperframes, create a 10-second product intro with a fade-in title, a background video, and background music.

If you want to create a project manually, use the CLI:

npx hyperframes init my-video
cd my-video
npx hyperframes preview
npx hyperframes render --output output.mp4

Prepare these locally:

Node.js >= 22
FFmpeg
an environment that can run Chromium / Puppeteer

Conclusion

The core value of HyperFrames is not “another video editor.” It brings video generation back into the web stack: HTML handles structure, CSS and animation libraries handle presentation, the browser handles rendering, and FFmpeg handles output.

For developers, that means videos can be generated, versioned, and automated like web pages. For AI agents, it provides a target format that is easier to understand and operate.

If you are working on content automation, batch video generation, data videos, or text-to-renderable-video workflows for AI agents, HyperFrames is an open-source project worth watching.