Hey there, it's Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

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Ask an AI agent to draw how your software fits together and you get a diagram in seconds. It has boxes, arrows and labels, and it is roughly correct. You still won't put it in your blog post.

The model wrote that diagram as text and never saw the picture. Videos and app screens come out the same way. Hand a model a blank canvas and it fills it with something average.

This month's four projects take the blank canvas away. Each gives the model a form to fill in, in a text format it already writes well, and each has its own way of keeping the result honest: a rulebook, an inspector, a renderer, a list of approved parts.

The demand shows up in our data. Mermaid, the text format most AI tools use to draw diagrams today, gained 602 stars in the 30 days to September 27. Archify and Diagram Design, two diagram tools built for agents, gained 44,977 and 14,401. No project we track gained more than Archify.

The lineup

Keep an eye on the star column. The closing adds a second number that ranks these four differently.

Style it - Diagram Design

🔗 https://github.com/cathrynlavery/diagram-design

Diagram Design makes an agent's diagrams look like they belong on your site.

Think of it as a brand guide for diagrams. You give it your website address once. It reads your colors and fonts, and every diagram after that uses them. It also follows a designer's rules: few boxes, one highlight color, no shadows.

We tried it on star-history.com. The diagram above shows how a README chart gets made, in our white, charcoal and green. One thing didn't carry over: the hand-drawn font from our charts. The tool only accepts fonts from Google's free font library.

Cathryn Lavery, who runs BestSelf.co, built it because every diagram Claude drew for her was "a generic rounded-box thing that looked nothing like the rest of the site." She shipped it as a skill, a folder of instructions the agent reads before it draws (more on skills). The repo had 2,872 stars at the end of July and gained 26,241 in August.

• Tradeoffs

  • No program checks whether a diagram looks good. The agent grades itself against the author's taste

  • No version numbers on GitHub, so it is hard to stay on a version you like

• Best for: diagrams in blog posts and docs.

Check it - Archify

🔗 https://github.com/tt-a1i/archify

Archify checks an agent's diagram before you see it.

Here is how it goes. You tell your coding agent, "Draw how our payment service works: the website talks to a server, the server saves to a database and hands slow jobs to a background worker." Without Archify, the agent draws something, and you are the one who notices that two labels overlap or an arrow runs through a box. Then you ask again.

With Archify installed, the agent doesn't draw. It writes down the boxes and the arrows between them in Archify's format, then runs Archify. Archify draws the diagram as a web page you can click around in, and checks it: the layout, the routes the arrows take, the space between labels and lines. If a check fails, Archify says which rule failed and what may change, and the agent fixes it. It gets two tries. The diagram you open is one that passed. The screenshot above shows one, with "9/9 validation checks" printed on it.

Archify lists itself on GitHub as a Mermaid alternative. The chart below starts each project at its first day, and Mermaid is the long line.

Its growth is the steepest in this post: 8,189 stars at the end of July, 5,068 in a single day on August 27, and 44,977 in the 30 days to September 27. Most of the code comes from one person, tt-a1i. The day after DeepSeek Harness (last month's subject) launched, Archify had a plugin for it.

• Tradeoffs

  • "Checked" means the picture matches what the agent wrote down. If the agent misunderstood your system, the diagram is wrong and still passes

  • It won't import Mermaid files and has no drag-and-drop editor

• Best for: architecture diagrams you send to other people.

Render it - HyperFrames

🔗 https://github.com/heygen-com/hyperframes

HyperFrames lets an agent make a video the way it makes a web page.

The agent writes an ordinary web page and marks when each element should appear. HyperFrames plays the page frame by frame and records it as a video file. The same page always gives the same video, so you can fix a typo and make the video again, the way you would rebuild a website.

It comes from HeyGen, an AI video company, and its README says it is "inspired by Remotion," an older tool that does the same job from program code. HyperFrames bets that a plain web page is easier for agents to write.

Remotion has 61K stars after six years, and 25,873 of them arrived since early February. HyperFrames has 54K after six months.

• Tradeoffs

  • Making a video takes real computing power, because it runs a whole browser behind the scenes. Its own README says Remotion's cloud service is more mature

  • It changes fast: 442 releases in six months, about two a day

• Best for: product demos and new-feature videos you want to remake automatically.

Limit it - json-render

🔗 https://github.com/vercel-labs/json-render

json-render lets an AI answer with a screen instead of a paragraph, built only from parts your app already has.

Here is the problem it solves. A user of your app asks, "How did sales go this week?" A chatbot answers with text. A better answer is a small chart with a table under it. But you can't let the model write its own interface code. It would look different every time, and it might break.

json-render sits in between. You hand the model a list of parts your app already has, say a chart, a table and a button. The model replies with a list of instructions: a chart with these numbers, then a table with these rows. Your app draws that with its own parts. The result looks like your product because it is made of your product. The screenshot above is the demo on json-render.dev: you type what you want, the model's instructions appear on the left, and the finished card appears on the right.

It is also a reply to our March post, As AI Turns Prevalent, UI Becomes Irrelevant: the interface is still there, and the model now assembles it for each question. Most of the code is by one Vercel engineer, Chris Tate, and it is the quietest of the four on GitHub, with 2,324 stars in the 30 days to September 27 against Archify's 44,977.

• Tradeoffs

  • You have to build the list of parts first, so it takes developer time before anything appears

  • An experimental project maintained mostly by one person

• Best for: products where an AI answer should look like the product, with charts and tables instead of paragraphs.

Closing Thoughts

Back to the star column. A star tells you someone noticed a project. It doesn't tell you they used it, so it helps to have a second number.

The order changes. json-render has the fewest stars and by far the largest usage number. Archify and HyperFrames both have more installs than stars.

The install numbers are new. The command is npx skills add, and a site called skills.sh counts each time it runs. These numbers are rough. One person, or one automated build, can be counted many times.

Diagram Design has almost no second number. skills.sh shows 7.3K installs, but its README sends people to the plugin stores inside Claude Code, Codex and Copilot, and those stores don't publish counts. It gained 14,401 stars in 30 days, and nobody outside can say how many of those people drew a diagram with it.

So read this month's stars as attention. Agents that make diagrams, videos and screens got a lot of it. Whether people keep using them shows up in the second number, where there is one.

Hey there, it's Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

Follow @StarHistoryHQ for daily GitHub gems!

We spend a lot of time asking which AI model is best. GPT or Claude? DeepSeek or Gemini? But hand the same model to two different coding agents and it performs differently — as we'll see below, several percentage points apart on the same test.

The reason is everything around the model: what tools it can reach, what context it sees, what it remembers between steps, what rules it follows, how it checks its own work. That layer has a name now. It's the harness.

The model is the brain; the harness is the working environment. And it's the half most developers can actually change — retraining a model, altering its trained weights, is out of reach unless you work at one of a handful of labs. The harness is just code.

That asymmetry is why the term caught on. Stage five of six in Mitchell Hashimoto's February essay is Engineer the Harness: when an agent errs, you build the check or script "such that the agent never makes that mistake again." He wasn't sure the industry had a name for it. By summer it did, with an awesome-harness-engineering list at 3.5K stars.

Six months ago harness engineering was a practice. This month it showed up as four different things.

The lineup

Grouped not by what they share, but by the different question each asks about the same object.

Their popularity could hardly be more different: from about 550 stars to about 200,000. Hold on to that gap; it turns out to be backwards.

Own it - Pi

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

Pi is harness engineering as a personal practice, shipped: not a large configurable framework, but a small agent you are expected to rewrite yourself. Its homepage puts it as a slogan: "There are many agent harnesses but this one is yours."

In practice it's a coding agent you install with one command, its parts published separately: one API across 15+ model providers, the agent runtime, a terminal-UI library. Mario Zechner of libGDX started it in August 2025; Armin Ronacher of Flask is now its second-largest committer.

What explains its growth is what Pi leaves out. No built-in sub-agents, no built-in plan mode; the core stays small and the extension points sharp, so you write those or install someone else's — hence an ecosystem in the same org, including gondolin, a Linux micro-VM sandbox. The curve rewards it: 58K stars at the end of May to 98K now, roughly 600 a day across 256 releases since December.

• Tradeoffs

  • No permission system: Pi runs with its launching user's access, and sandboxing is your job

  • New contributors' issues and PRs are auto-closed by default, triaged later

• Best for: developers who would rather rewrite their agent than configure it.

Build it - DeepSeek Harness

🔗 https://github.com/deepseek-ai/deepseek-harness

Then a frontier lab industrialized the idea. Where Pi says own the code, DeepSeek says configure it — and treats an agent like Lego, where almost every piece swaps out without rebuilding the whole thing.

Taken literally: the model is a plugin. So are the tools, memory, storage, the sandbox (where the agent runs commands), the interface, and the agent loop itself — the cycle deciding what the model does next after each tool call. All of it mounts onto the Cordis kernel, a community framework from 2022 that DeepSeek adopted rather than wrote, so you reassemble an agent in configuration, never in source.

What makes it remarkable is the speed. First commit August 13; two weeks later, 200K stars — 111,089 of them in the first fourteen days, the largest two-week gain in our data, still adding 3,000 a day. For scale: Pi turned one year old on August 9 with 85,799 stars. DeepSeek matched that in a day — and Pi never slowed, holding its 600 a day straight through the frenzy. The two aren't fighting over the same developer.

• Tradeoffs

  • A self-described developer preview at v0.1.x, with breaking changes promised in capital letters

  • Issues are disabled. 200K stars, 28 contributors, and the only channels are Discussions and Discord

• Best for: anyone who wants every layer swappable, or curious how a frontier lab assembles an agent.

Optimize it - Meta-Harness

🔗 https://github.com/stanford-iris-lab/meta-harness

Now the stranger question: what if humans stop designing the harness at all?

Take one model and one hand-built harness. Run it against a hard benchmark. It scores 28.5%. Now hand a second AI agent the failed runs — the full logs, not a summary — and ask it to rewrite the harness. Test again. Repeat.

Seven rounds later: 46.5%. The model never changed. The harness did.

That's what Meta-Harness turns harness engineering into: an optimization problem. From Stanford's IRIS Lab, with Omar Khattab and Chelsea Finn among the authors, its key decision is refusing to compress the feedback — the rewriter gets every prior attempt's raw traces, up to 10M tokens a round, because harness bugs aren't diagnosable secondhand.

It spread because the harnesses generalize: one added 4.7 points across five models it was never tuned against, so the search found something real rather than memorizing a test. A March paper, 1.47K stars and a crowd of reimplementations followed, plus another group's Agentic Harness Engineering reaching 84.7% the same way. The benchmark both of them optimize against is the subject of the last section.

• Tradeoffs

  • Paper code; the maintainers say plainly it hasn't been tested beyond verifying that it runs

  • Every round is a full benchmark run, so searching is expensive and invites overfitting to that one benchmark

• Best for: teams with a fixed model, a real evaluation, and a harness already hand-tuned past the obvious wins.

Measure it - Terminal-Bench

🔗 https://github.com/harbor-framework/terminal-bench

If everyone claims their agent is better, how would you know?

Terminal-Bench is the exam. It hands agents hard, real terminal work — software engineering, machine learning, security, data science — and reports how many tasks they finish. It's a Stanford × Laude collaboration, and every frontier agent builder runs it.

Here's the part that matters. Every row on the leaderboard is not a model. It's an agent × model pair.

So you can read the same model twice. Fable 5 scores 83.8% inside Claude Code and 80.4% inside Terminus 2; GPT-5.5 scores 83.1% inside Codex and 78.0% inside Terminus 2. Identical weights both times. Only the harness changed, and it moved the result by about as much as a whole model generation would.

That is the whole argument of this post, published as a bar chart by the people whose job is to keep the scoring honest — and why "which model is best" is now half a question. The benchmark itself has grown from 80 tasks in January 2025 into a continuous one, merging new tasks year-round. It runs on Harbor, which has 4.7K stars to Terminal-Bench's 552.

• Tradeoffs

  • Running it seriously needs real sandbox infrastructure: Modal or Docker, hundreds of concurrent containers

  • Its own influence is a risk: once everyone optimizes against it, high scores measure the benchmark as much as the work

• Best for: anyone who needs an agent score they didn't report themselves, and anyone checking such claims.

Closing Thoughts

Now back to that star gap. DeepSeek Harness has more than 360 times Terminal-Bench's stars, yet Terminal-Bench is the one every lab in the top row must run before it can claim anything. Star counts are honest about attention and silent about influence, including ours.

The model race isn't over. But model choice is no longer the whole story, and this month the other half became a codebase you fork, a product a lab ships, a search space a machine explores, and the number behind every leaderboard.

Which is the small irony of the year. Harness engineering got its name because almost nobody can change the weights, making the code around them the only place left to do real work. That place turned out valuable enough that the people who can change the weights showed up to build there too.

Hey there, it’s Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

Follow @StarHistoryHQ for daily GitHub gems!

📉 Note: GitHub has restricted access to star data, so this issue ships without the usual star history charts. Here’s what changed and why →

Last month's terminal UIs were finished apps — you install lazygit, you use lazygit. This month we look at the opposite bet. In April, Mitchell Hashimoto published The Building Block Economy, arguing that the most effective way to build software and get massive adoption is no longer the polished mainline app but the building block: the embeddable component that other people's software is assembled from. His evidence is his own project — the Ghostty terminal took 18 months to reach a million daily update checks; libghostty, the library extracted from it, reached millions of daily users in two months by riding inside other people's apps.

His sharpest claim is about why this is happening now: AI agents are much better at gluing together proven, well-documented components than at writing everything from scratch — so the components themselves are becoming the growth story. Whether or not you buy the whole thesis, there's a set of star curves that look exactly like it. This month, four projects selling picks to the assembly economy: an embeddable terminal, a sync engine, a pair of code-review rendering primitives, and a JavaScript engine you've almost certainly run without knowing it.

The lineup

Four building blocks, each owning a different layer of the stack — and next to each, what gets built out of it.

Terminals - libghostty

🔗 https://github.com/ghostty-org/ghostty

The essay's own proof of concept. Ghostty the terminal emulator is a 58K-star app — but the interesting part is libghostty, the library being extracted from it, starting with libghostty-vt: a zero-dependency (not even libc) terminal-sequence parser and state machine, announced in September 2025 with the argument that terminal emulation is duplicated effort everywhere because it's nobody's core business. The Ghostty app itself is now just the first consumer of its own library.

The ecosystem that grew in ten months is the story. cmux, a macOS terminal for agentic coding workflows built on libghostty, went from zero to ~25K stars in under six months — the second line on the chart above. Around it: Ghostling (the official minimum-viable-terminal example), libghostty-rs Rust bindings, official Go bindings, coder/boo (a screen-style multiplexer by Coder), muxy, an Emacs terminal, and an awesome-libghostty list to keep track of it all. Ghostty's 1.3.0 release notes claim dozens of projects, free and commercial, already embed it — notably before libghostty has even cut a stable versioned release. The C API is still marked unstable; the adoption came anyway.

• Why it exists

- To make correct, fast terminal emulation an embeddable component instead of every app's side quest - To let the Ghostty app and other people's terminals share one production-grade core

• Tradeoffs

- No stable versioned release yet — the C API can still shift under early adopters - The full library story (rendering, input, widgets) is still being extracted piece by piece

• Best for: anyone building a product with a terminal in it — IDE, agent runner, multiplexer — who'd rather not write a VT parser from scratch.

Sync - Zero

🔗 https://github.com/rocicorp/mono

Zero is what happens when a team spends six years on one hard problem — client-side sync — and keeps re-shipping it as a better block. Rocicorp (Aaron Boodman of Greasemonkey and Google Gears fame, with Erik Arvidsson) built Replicache, then Reflect, then folded the lessons into Zero: a sync engine that pairs a client library with a server cache in front of your normal Postgres database. Queries run instantly against a local replica and sync in the background — the pitch on the repo is "99% of queries in zero milliseconds."

Zero hit 1.0 in March 2026 — its first stable release after more than fifty pre-releases — and has shipped a minor version roughly every two weeks since, reaching 1.8.0 in mid-July. The curve is the quiet-compounding kind: no viral launch spike, just steady accumulation as apps like Productlane and Ano rebuilt on it and the sync-engine debate (Zero, ElectricSQL, InstantDB, PowerSync, Convex) became one of 2026's liveliest infrastructure arguments. It's the smallest star count in this month's lineup — and probably the deepest technology.

• Why it exists

- To make instant, offline-tolerant UIs a property of the data layer, not hand-rolled caching code - To work against a normal Postgres schema instead of a proprietary backend

• Tradeoffs

- You're adopting an architecture, not adding a widget — the sync engine shapes the whole app - Young 1.x line in a crowded, fast-moving category

• Best for: teams building web apps that should feel local-first — dashboards, trackers, collaboration tools — on top of Postgres they already run.

Diffs - Pierre

🔗 https://github.com/pierrecomputer/pierre

Pierre is the building-block economy at company scale — a startup that pivoted from app to blocks and got its growth curve back. The Pierre Computer Company (YC W23, founded by Bootstrap co-creator Jacob Thornton and early Twitter engineer Ian Ownbey) spent roughly three years building an opinionated GitHub competitor, pierre.co: hosting, review, and CI rebuilt from the metal. It shut down in 2026. What survived is the parts, shipped as products: diffs.com is @pierre/diffs, an open-source diff and code rendering library built on Shiki with split and stacked layouts, theming, and an annotation framework made for accept/reject review UIs; trees.software is @pierre/trees, the matching file-tree component. Both live in the pierrecomputer/pierre repo — created September 2025, past 5.5K stars ten months later.

The timing is no accident. Every AI coding product — agents, review tools, cloud IDEs — suddenly needs to render diffs and file trees well, and almost none of them want to build that from scratch. The paid layer underneath is the same bet: Code Storage, Pierre's hosted git infrastructure for codegen platforms — Thornton describes it as "kinda like what Stripe does for payments" — funded by a $23M round the company announces, characteristically, in plain text on its own site. The app didn't win; the blocks it was made of became the business.

• Why it exists

- To make high-quality diff and file-tree rendering a drop-in dependency instead of every code tool's internal project - To give the wave of AI coding tools a shared, polished review UI layer — extracted from a real production git platform

• Tradeoffs

- Young libraries — trees is still in beta, and the API surface is evolving quickly - UI primitives only: the review workflow around them is still yours — and the git plumbing underneath is Pierre's paid product

• Best for: anyone shipping a tool that shows code to humans — review apps, agent UIs, internal platforms — who wants GitHub-quality diffs without building them.

JavaScript - quickjs-ng

🔗 https://github.com/quickjs-ng/quickjs

Proof that the pattern predates the essay. QuickJS — Fabrice Bellard's small, embeddable JavaScript engine — has been the block behind other people's products since 2019, when Figma rebuilt its plugin sandbox on QuickJS compiled to WebAssembly after its JavaScript-based sandbox kept springing escape vulnerabilities. You've "run" QuickJS every time you've used a Figma plugin; it just never had its name on the door.

quickjs-ng is the community fork keeping that block modern. Started in November 2023 by Ben Noordhuis and Saúl Ibarra Corretgé after upstream went quiet, it ships every couple of months, tests against the full ECMAScript conformance suite, and targets ES2023+ — and it's where new embedders land: Amazon's LLRT serverless runtime, the txiki.js runtime, radare2's scripting. Bellard's original has since woken back up, so the chart above is that rare thing: a fork and its upstream, both healthy, both shipping — the original with the stars, the fork with the release cadence.

• Why it exists

- To keep a small, embeddable, spec-current JS engine actively maintained in the open - To be the scripting layer for apps that need JavaScript without shipping V8

• Tradeoffs

- An interpreter, not a JIT — you trade peak speed for size, startup, and sandboxability - Two active QuickJS lineages means picking one (and tracking their drift)

• Best for: products that need to run untrusted or user-authored JavaScript — plugin systems, edge runtimes, embedded scripting — inside a tiny, controllable engine.

Closing Thoughts

Put the four curves side by side and the essay's argument mostly writes itself. The biggest star count in this lineup belongs to an app (Ghostty) — but its growth story now runs through the library inside it, and the fastest riser this month (cmux, zero to ~25K stars in six months) is an app built from that library. Zero and quickjs-ng never tried to be apps at all, and Pierre's story is the sharpest version: three years spent competing with GitHub as an app, then a second life selling the parts. Blocks are infrastructure that shows up in your life as someone else's product feeling faster, reviewing code better, running plugins safely.

The star counts here are smaller than last month's TUI giants, and that's part of the point: building blocks are undercounted by every popularity metric we have. Figma has run QuickJS since 2019 without most developers being able to name it; libghostty had commercial adopters before it had a version number. If Hashimoto is right that agent-written software is assembled from proven components, the projects on this page are early entries in a category that's about to matter a lot — and star charts like these are one of the few places you can watch it happen.

Hey there, it’s Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

Follow @StarHistoryHQ for daily GitHub gems!

Last month's standalone markdown editors made a brief stop in the terminal — Glow rendering markdown where developers already live. This month we stay there and ask a bigger question: what happens when the terminal stops being the place you run a command and becomes the place you do the work you used to open an app for?

The answer is a quiet defection. For several of the most common jobs a developer does every day, a keyboard-driven terminal UI has become the default tool — not a scrappy alternative to the graphical client, but the thing that replaced it. The clearest tell is the star counts: lazygit, a git client that runs in your terminal, out-stars the GUI git clients it competes with by a wide margin.

So this month we line up five TUIs, one per job, each of which displaced a graphical incumbent. They span Go, Rust, and C++, and every one shipped a release this spring — this is a category in active motion, not a novelty that peaked.

The lineup

One terminal app per job, laid out roughly in the order a developer's day flows — and next to each, the graphical tool it stands in for. The first five have already won their category; the last two are challengers just now making the jump.

Files - yazi

🔗 https://github.com/sxyazi/yazi

yazi is the youngest app in the lineup and the steepest curve — a file manager written in Rust, built on the Ratatui framework, that leans hard on async I/O for a "blazing fast" feel. Image and document previews, plugin support, and tight integration with tools like fzf and ripgrep make it a genuine alternative to a graphical file browser, not just a prettier ls.

Created in mid-2023, it's already passed 39K stars — the clearest sign that the GUI-defection pattern is still recruiting new categories, not just coasting on the tools that started it.

• Why it exists

- To make file management fast, previewable, and keyboard-driven in the terminal - To use async I/O and a plugin system for a responsive, extensible experience

• Tradeoffs

- A younger project with a faster-moving feature surface - Previews and integrations depend on external tools being installed

• Best for: developers who want a fast, modern file manager that never leaves the keyboard.

Git - lazygit

🔗 https://github.com/jesseduffield/lazygit

The defection's poster child. lazygit is a keyboard-driven interface for git — stage hunks, craft commits, branch, stash, and run interactive rebases without typing a single git subcommand — and at nearly 80,000 stars it out-stars almost every graphical git client on GitHub. That's the whole thesis of this roundup in one number: a job people used to reach for GitKraken or SourceTree to do, done faster in the terminal, and the audience voted.

Its curve is the steady-climb kind — no single viral spike, just relentless accumulation as it became the default recommendation in dotfiles and "set up your terminal" guides everywhere. It's the most-starred project in the lineup and the one that proved a TUI could win a job outright.

• Why it exists

- To make everyday git fast and visual without leaving the terminal - To turn intimidating operations like interactive rebase into a few keystrokes

• Tradeoffs

- You adopt its workflow and keybindings; the casual visit is punishing - Still a layer over git — power users sometimes drop back to the raw CLI

• Best for: anyone who does git all day and would rather never context-switch to a browser tab or desktop app to do it.

Containers - lazydocker

🔗 https://github.com/jesseduffield/lazydocker

Same author, same playbook, different domain. lazydocker (from Jesse Duffield, who also built lazygit) puts containers, images, volumes, and logs behind a single keyboard-driven dashboard — the things you'd otherwise click through Docker Desktop or stitch together from docker ps, docker logs, and docker stats. As Docker Desktop's licensing pushed some teams to look elsewhere, a free terminal dashboard found a ready audience.

At ~51K stars it's the second-most-starred app here, and its curve tracks lazygit's: a durable, GUI-replacing tool rather than a flash in the pan.

• Why it exists

- To manage Docker — containers, logs, stats — from one terminal view - To replace a sprawl of docker subcommands with a navigable UI

• Tradeoffs

- A monitoring-and-management view, not a full replacement for the Docker CLI - Tied to the Docker world rather than the broader container ecosystem

• Best for: developers who want an at-a-glance container dashboard without a desktop app.

Kubernetes - k9s

🔗 https://github.com/derailed/k9s

k9s is the defection reaching the cluster. It gives you a live, navigable view of Kubernetes resources — pods, deployments, services, logs — that updates as the cluster changes, standing in for the web-based Kubernetes Dashboard or a desktop tool like Lens. For anyone who lives in kubectl, it turns a stream of typed queries into a browsable, real-time interface.

At ~34K stars with a release cadence that hasn't let up (v0.51.0 shipped on June 6), it's the proof that terminal UIs scale up to infrastructure work, not just local developer chores.

• Why it exists

- To navigate and manage Kubernetes clusters in real time from the terminal - To replace repetitive kubectl invocations with a live, browsable view

• Tradeoffs

- A read-and-operate cockpit; complex changes still go through manifests - A learning curve mapping its views onto Kubernetes concepts

• Best for: operators and developers who manage clusters and want a live cockpit instead of typing kubectl all day.

System Monitoring - btop

🔗 https://github.com/aristocratos/btop

btop closes out the winners where the GUI defection is oldest: the system monitor. Written in C++, it renders CPU, memory, disk, network, and process information as a dense, colorful, mouse-and-keyboard dashboard — the terminal answer to Activity Monitor, Task Manager, or GNOME System Monitor. It's the spiritual successor to a long line of *top tools, and the most polished of them.

At ~33K stars it caps a run of five winners that each cleared 32K — a reminder that "watch the machine" was one of the first jobs to move into the terminal and never moved back.

• Why it exists

- To present rich, real-time system metrics in a single terminal dashboard - To modernize the classic top/htop lineage with a denser, prettier UI

• Tradeoffs

- Monitoring only — it shows you the system, it doesn't manage it - A heavier visual footprint than minimalist monitors

• Best for: anyone who wants a good-looking, information-dense system monitor without opening a GUI.

Postgres - pgtui

The pattern isn't finished. The five above already won their categories; the last two are still climbing — jobs where the terminal is only now making its move, starting with the database.

🔗 https://github.com/pgplex/pgtui

pgtui is a terminal client for PostgreSQL — browse the schema tree, page through tables, filter rows, inspect JSONB, and run SQL from a built-in editor — aimed at the workflow people usually open pgAdmin, DBeaver, or TablePlus to do. It's written in Go on the Bubble Tea framework, and it wears its lineage on its sleeve: the README's pitch is "inspired by lazygit, built for developers who live in the terminal." That's the poster child of this roundup spawning a direct descendant into a new category.

At ~264 stars on a young v0.3 line (v0.3.0 shipped June 12) it's the smallest project here — far too early to call against incumbents as entrenched as pgAdmin. But it's the clearest evidence that lazygit didn't just win git; it became a template other developers now copy, job by job.

• Why it exists

- To browse and query PostgreSQL without leaving the terminal - To bring lazygit's keyboard-driven, single-screen feel to the database

• Tradeoffs

- Very early-stage; nowhere near the feature depth of pgAdmin or DBeaver - PostgreSQL-only, and focused on browsing and quick queries over heavy admin

• Best for: developers who live in the terminal and want a fast, Vim-friendly way to poke at their Postgres database.

API Clients - Slumber

The other frontier is the API client — the home turf of Postman and Insomnia, both graphical, both increasingly heavy, both nudging users toward accounts and cloud sync. It's the most GUI-anchored workflow on this list, which makes it the most interesting one to watch move.

🔗 https://github.com/LucasPickering/slumber

Slumber is the clearest example of what this defection looks like when it's about more than the surface. It's a terminal-based HTTP client built in Rust on the Ratatui framework — but the pitch isn't just "Postman in your terminal." It's source-first: your requests live in a slumber.yml collection of reusable "recipes" that you check into git like any other code, instead of inside a proprietary app's database. The data is local, the tool is free, and the README states the goal plainly — it will "never be enshittified." For developers who've watched API clients drift toward logins and paywalls, that's the whole appeal.

It backs the philosophy with range: usable as a TUI, a plain CLI, or a Python package; templating that builds requests from other requests, files, and shell commands; JSONPath response browsing; profile-based environments; and a one-step import from Insomnia. At ~1.2K stars it's tiny next to the giants above and too early to plot against them — but it's on a steady v5 line with near-monthly releases, and it's aimed squarely at the kind of GUI-heavy workflow this whole roundup is about.

• Why it exists

- To make HTTP requests source-first — version-controlled, local, and free - To offer one tool that works as a TUI, a CLI, and a Python package

• Tradeoffs

- Early-stage and small; nowhere near Postman's feature breadth or ecosystem - YAML-and-templates configuration is a different model than a point-and-click client

• Best for: developers who want their API requests to live in git alongside the code, with no account and no cloud.

A companion worth watching is Posting (darrenburns/posting, ~12K stars, built on the Textual framework) — the same bet from the Python side. Between them, the terminal is coming for one of the last big GUI-anchored workflows on the developer's desk. If the pattern holds, one of these ends up on a chart like the one at the top of this post in a year or two.

Closing Thoughts

These five tools, between them, replaced the default interface for five everyday jobs: browse files, commit code, manage containers, drive clusters, watch the machine. The star counts aren't measuring novelty — lazygit out-starring the GUI git clients, each of these five past 32K — they're measuring a real migration. The terminal didn't win on nostalgia; it won on speed, on staying inside one keyboard-driven environment, and on not asking you to leave for a separate app.

It helps that the foundations got good: this wave of apps rides on the modern framework ecosystem — yazi and Slumber on Ratatui, Posting on Textual, pgtui on Bubble Tea — that has made polished terminal UIs far easier to build than the ncurses era ever allowed. The apps are what you notice; the frameworks are why there are so many of them now.

If you're choosing, the question isn't really "GUI or terminal" anymore. For these jobs, the terminal version is the mature option. The only question left is which of your daily tools hasn't made the jump yet.

Hey there, it’s Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

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Back in January, we mapped the embedded web editors — the components developers slot into dashboards, documentation sites, and internal tools, sorted by rendering architecture. This month is the other half of the story: the markdown editors you open as a standalone app to actually write in. Same file format, a different job. They compete on the writing experience rather than on how cleanly they embed, and they spread across three surfaces — the desktop, the terminal, and a new wave of AI-native editors.

The chart holds a small surprise. The most-starred editor in this category had been silent for four years — its last release was March 2022 — and then, in this very month, it shipped again. It wasn't alone: across the lineup, May 2026 was a month of releases. Stars measure attention, not maintenance status, and for a long stretch those two lines told very different stories. This month, they converged.

The lineup

A clean ladder, one surface to the next. Five projects, zero overlap with January's embedded-editor roundup, and — by request — none repeated from it.

The Benchmark, Back from Dormancy

MarkText

🔗 https://github.com/marktext/marktext

MarkText is the most-starred project in this roundup, and for years it was also the cautionary tale. Its clean, Typora-like real-time preview made it the default recommendation for a distraction-free desktop markdown app — and then, after v0.17.1 in March 2022, it went quiet. Four years passed with no release. The star curve, meanwhile, never flinched: attention kept compounding well past 56,000 stars while the codebase sat still. It was the clearest example in the category of stars measuring reputation rather than activity.

That changed in May 2026. Four release candidates landed over a single week (v0.19.0-rc.1 through v0.19.0-rc.4, May 23–27), followed by the stable v0.19.0 on May 28 — the project's first release in over four years. The reorganization is visible in the repo: the old standalone muya editor engine and the website have been folded into the main monorepo as subtrees (the separate repos are now archived), and commits are landing daily again.

• Why it exists

- To offer a clean, real-time-preview desktop editor in the Typora mold, fully open source - To pair a polished writing surface with broad export and theming

• Tradeoffs

- Electron footprint - A four-year gap that left a large issue backlog to work through

• Best for: writers who want a familiar, no-friction desktop editor — and who are watching whether the revival sticks.

Into the Terminal

Glow

🔗 https://github.com/charmbracelet/glow

January's roundup lived entirely in the browser. Glow is the opposite surface: a terminal-native markdown experience, part of the Charm ecosystem that drove much of the recent TUI revival. One honest caveat up front — Glow is a renderer and interactive pager, not a text editor in the strict sense. It reads and browses markdown beautifully in the terminal; it doesn't author it. We include it because "where do people read and work with markdown" is a fair question, and the terminal is a real answer with a real, growing following.

Its curve is the steady-climb counterpart to MarkText's stop-and-restart: no dramatic kinks, just consistent accumulation riding the broader interest in terminal-first tooling.

• Why it exists

- To render and browse markdown without leaving the terminal - To fit cleanly into a keyboard-driven, CLI-centric workflow

• Tradeoffs

- Not an editor — pair it with your $EDITOR of choice - Terminal rendering has limits versus a GUI preview

• Best for: developers who live in the shell and want markdown to render where they already are.

The Vertical Specialist

Zettlr

🔗 https://github.com/Zettlr/Zettlr

If MarkText is the generalist, Zettlr is the specialist. It targets academic and long-form writing: Zotero/JabRef citation support, Pandoc-powered export to PDF and Word, and Zettelkasten-style linked notes. That focus shows up as a smaller but durable audience — roughly half MarkText's star count, but with a release cadence that never stopped. v4.5.0 shipped on May 8, 2026, continuing a steady four-point-x line of work.

It's the clean counterpoint to the anchor: fewer stars, but the maintainers are still here, shipping on schedule.

• Why it exists

- To make markdown a first-class tool for researchers and writers - To integrate citation managers and academic export pipelines

• Tradeoffs

- Heavier and more opinionated than a plain editor - Feature surface aimed at writers, not coders

• Best for: academics, students, and anyone managing citations and long documents in markdown.

The All-in-One Workbench

Yank Note

🔗 https://github.com/purocean/yn

Where Zettlr narrows, Yank Note expands. It's the "heavily armed" option — a hackable editor that pulls IDE and notebook ideas into markdown: built-in version control, document encryption, an integrated terminal, runnable code snippets, embedded mind maps and diagrams, Reveal.js presentations, plugins, and macro replacements. It also shipped an AI Copilot well before the current wave, and it runs local-first as a service you can reach from the desktop app or a browser. v3.90.0 landed on May 16, 2026.

It represents a clear direction in the category: the markdown editor growing toward a full personal workbench rather than a single writing pane.

• Why it exists

- To turn markdown into a programmable, all-in-one workspace - To keep data local while layering on power-user features

• Tradeoffs

- A steep feature surface; more to learn than a minimal editor - Local-service model is unusual for newcomers

• Best for: power users who want one extensible markdown environment for notes, docs, code, and slides.

AI-Native

MarkFlowy

🔗 https://github.com/drl990114/MarkFlowy

MarkFlowy is the newest shape in the lineup: an AI-native editor built on Tauri (so a sub-20 MB binary instead of a full Electron runtime) with a ProseMirror core and AI assistants — DeepSeek, ChatGPT — wired in from the start rather than bolted on. Its star count is the smallest here by design; it's the early-stage entrant, and v0.80.1 shipped on May 31, 2026, right at the close of the month.

One more to keep on the radar without putting it on the chart: Markra (murongg/markra) — local-first, AGPL, AI built in, still in the low hundreds of stars. Too early to plot a meaningful curve, but it's pointed at the same AI-native target.

• Why it exists

- To make AI assistance a default part of the writing surface - To stay lightweight via Tauri rather than Electron

• Tradeoffs

- Young project; smaller ecosystem and feature set - AI features depend on external model providers

• Best for: early adopters who want AI woven into a fast, native markdown editor.

Closing Thoughts

For most of the last four years, the headline number in this category was misleading. MarkText sat at the top of the star chart while shipping nothing — a reminder that stars measure attention, not momentum. The two lines can diverge for a long time.

May 2026 is interesting precisely because they converged. The dormant benchmark shipped again, the specialist and the workbench kept their cadence, and the AI-native newcomer closed the month with a release of its own. The energy that left "the standard markdown editor" didn't disappear — it spread across surfaces: into the terminal, into vertical workflows, into all-in-one workbenches, and into AI-native tools. And this month, some of it came back to where it started.

If you're choosing one, the surface matters more than the star count: the desktop generalist, the terminal reader, the academic specialist, the power-user workbench, or the AI-native upstart. The chart tells you where the attention is. It doesn't tell you which job is yours.

Hey there, it’s Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

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Last month we covered the skills ecosystem. This month: agents that don't just write code, but drive a screen.

"Computer use" is industry shorthand — coined by Anthropic in October 2024, adopted by OpenAI in January 2025 — for vision-based GUI control. A coding agent edits text inside a sandbox you built for it. A computer-use agent moves a cursor on the same desktop you use.

Seven projects, three roles: the model that perceives the screen, the harness (Anthropic's term) that loops perception into action, and the infrastructure that gives the agent a desktop to drive. Which role you bet on says a lot about where you think the value lives.

You need all three roles, but not always three projects. Model and harness are non-negotiable; infrastructure can just be your real desktop. That's why this list looks lopsided — most teams bundle a sandbox into their harness (Bytebot) or skip it entirely.

The Model

Both projects ship open-weight vision-language models trained specifically for GUI control. They disagree on size.

UI-TARS

🔗 https://github.com/bytedance/UI-TARS

ByteDance's UI-TARS is a native GUI agent model — pixels in, click coordinates out. No accessibility tree, no DOM parsing. Trained end-to-end on screen recordings, it handles desktop, web, and mobile from one checkpoint.

The strategic move is the kicker: ByteDance also shipped the harness (see UI-TARS-desktop below). Most labs drop weights and let the community figure out the runtime. ByteDance closed the loop themselves.

• Best for: teams who want a model purpose-built for screens, not a general VLM coaxed into clicking.

Fara

🔗 https://github.com/microsoft/fara

Microsoft's Fara-7B takes the opposite bet: small. A 7B agentic model that runs on hardware you already have. The thesis — GUI control doesn't need a frontier model, it needs one trained on the right thing.

Once a category gets a "good enough at 7B" model, running it locally becomes realistic. That matters more than usual here: the agent is watching your screen, and shipping every frame to a cloud API is a privacy story nobody wants to tell.

• Best for: anyone who wants computer-use to run locally — privacy-sensitive workflows, or just laptops without a constant API bill.

The Harness

Four projects play the harness role — the loop that takes a screenshot, asks a model what to do, dispatches the action, checks the result. They disagree on what to perceive, where to run, and who to trust.

self-operating-computer

🔗 https://github.com/OthersideAI/self-operating-computer

The original. Released November 2023, it was the first widely-used framework that let a multimodal model drive a real computer. The architecture is embarrassingly simple — screenshot, prompt, click — and that simplicity is the point. It proved the loop works.

It's also model-agnostic: GPT-4o, Claude, Gemini, LLaVa, Qwen-VL all plug in. That stance — the model is a swappable component — became the default philosophy for everyone who came after.

• Best for: developers who want the canonical reference implementation to read, hack on, or fork.

UI-TARS-desktop

🔗 https://github.com/bytedance/UI-TARS-desktop

The vertical-integration play, and the largest project in the category at nearly 30,000 stars. Native desktop app, MCP support, browser-use integration — the full-fat stack ByteDance built around its own model.

The bet: when the same team trains the model and ships the runtime, the harness exposes exactly the action space the model was trained on. Apple, basically, for computer-use.

• Best for: teams who want the most complete out-of-box experience and don't mind picking a side in the model-vs-runtime debate.

Agent-S

🔗 https://github.com/simular-ai/Agent-S

Simular AI's Agent-S is the academic entry — "an open agentic framework that uses computers like a human." It pairs vision with retrieval-augmented planning, so the agent isn't just looking at the screen, it's looking up how to use the app it's looking at.

This is the hybrid perception camp. Pure pixel agents work great until they hit a UI element with no visual affordance. Agent-S falls back on docs, manuals, and prior task experience — a more honest picture of how humans actually drive an unfamiliar app.

• Best for: researchers and teams chasing benchmark performance on long-horizon tasks where pure vision falls down.

Bytebot

🔗 https://github.com/bytebot-ai/bytebot

Bytebot is the cleanest pitch among the harnesses: a self-hosted AI desktop agent that operates inside a containerized Linux desktop. Give it natural language, it does the work — in a sandbox, not on your machine.

The dirty secret of computer-use is that letting an agent loose on your real desktop is terrifying. One hallucinated keystroke and your in-progress work is gone. Bytebot's answer: have the agent, just don't give it your laptop.

• Best for: anyone who wants to run computer-use agents in production without the "what just happened to my files" problem.

The Infrastructure

One project here, but an important one: the unbundled alternative to ByteDance's vertical stack.

cua

🔗 https://github.com/trycua/cua

Trycua's cua is open-source infrastructure — sandboxes, SDKs, and benchmarks for agents that control full desktops across macOS, Linux, and Windows. Not the agent. The box the agent runs in.

The bet is unbundling. If ByteDance wins by owning model and harness together, cua wins by being the neutral substrate everyone else runs on. BYO model, BYO harness — cua provides the reproducible, multi-OS environment. The AWS of computer-use, if you want the analogy.

• Best for: teams building computer-use products who want production-grade sandboxing without writing it themselves.

Closing Thoughts

Three bets sit on the table. ByteDance bets vertical integration wins — train the model, ship the runtime, co-design both. Trycua bets the opposite — own the substrate and let the rest commoditize. Fara is the wildcard: if the model is small enough to ship locally, it can carry its own minimal runtime and the harness shrinks to a footnote.

The technology works. The category is up for grabs. The open question now isn't whether AI can drive a screen — it's which role wins the value: the model, the harness, or the box it all runs in?

Hey there, it’s Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

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Last month we covered the OpenClaw ecosystem. This month the spotlight shifts to skills — reusable instruction packs that turn a general-purpose agent into something opinionated.

A coding agent out of the box is a brilliant new hire on day one. It can do anything, but it doesn't know how you do things. Skills are the onboarding docs, except they're executable.

We picked seven projects. Two pairs of head-to-head competitors, and a trio of frontend-focused tools united by the conviction that AI-generated UIs shouldn't all look the same.

Ship Code vs. Think First

Both target founders. They disagree on what a founder's bottleneck actually is.

gstack

🔗 https://github.com/garrytan/gstack

Garry Tan (YC's CEO) built gstack to turn Claude Code into an entire engineering org. 23 slash commands, each a specialist role: /plan-ceo-review for product direction, /review for staff-level code review, /qa for real Playwright browser testing, /ship for PR creation, /cso for security audits. You run a structured sprint — think, plan, build, review, test, ship, reflect — and the agent handles each phase.

Think of it as a movie set. You're the producer. Each slash command is a crew member. Gstack can run 10–15 agents in parallel across isolated workspaces, like shooting on multiple sets at once. Tan claims 10,000–20,000 lines of production code per day. Works across Claude Code, Codex, Gemini CLI, Cursor, and Factory Droid. MIT-licensed, completely free.

• Best for: founders who want to ship fast and don't mind a thick playbook.

slavingia/skills

🔗 https://github.com/slavingia/skills

Sahil Lavingia (Gumroad CEO) took the opposite approach. 10 commands, none of them write code. Based on his book The Minimalist Entrepreneur, they walk you through building a business: /find-community, /validate-idea, /mvp, /pricing, /marketing-plan, /grow-sustainably.

The most interesting one is /processize — it tells you to deliver your product's value by hand before automating anything. Where gstack asks "how do I ship more code?", this asks "should I be writing this code at all?"

• Best for: first-time founders. Especially the ones who just spent three months building something nobody asked for.

Task-Level vs. Scenario-Level

The second pair competes over how agents should organize development work. Same goal, different unit of work.

superpowers

🔗 https://github.com/obra/superpowers

Jesse Vincent's superpowers is the most opinionated project here. Its skills fire automatically based on context — no manual invocation. Write code without a test? Superpowers deletes it and tells you to write the test first. It enforces strict TDD, dispatches subagents per task with two-stage code review, and runs everything in git worktrees.

If gstack is a movie set where you call each crew member by name, superpowers is a restaurant kitchen with a strict brigade system. You don't manage the brigade — you just order. It even has a meta-skill called writing-skills that teaches the agent to create new skills. Skills that write skills.

• Best for: teams who want their agent to enforce engineering discipline, not just follow orders.

starmap

🔗 https://github.com/rebelice/starmap

starmap exists because some problems have 500 scenarios, not 5. Think database migration compatibility matrices, cross-platform test suites, API coverage checklists.

/starmap init decomposes a goal into a SCENARIOS-<project>.md file — sometimes hundreds of checkboxes. It analyzes file-level dependencies to figure out which scenarios can run in parallel, spins up git worktrees, and guarantees that once a scenario passes, it stays passed.

The creators are explicit: "Superpowers handles individual tasks well... Starmap picks up where that leaves off: when the problem is too large for a single plan."

• Best for: large migrations, compatibility matrices, and any project where you'd otherwise maintain a Google Sheet with 200 rows and a prayer.

The Frontend Trio

Three projects, one shared gripe: AI-generated frontends all look the same.

Claude Code Frontend Design Skill

🔗 https://github.com/anthropics/claude-code (plugins/frontend-design)

Anthropic's first-party skill is a direct shot at "AI slop" — the generic, gradient-heavy interfaces every AI tool produces by default. It activates automatically when Claude detects frontend work and steers it toward bolder choices: distinctive typography, unexpected color palettes, higher-impact animations. A dashboard gets different treatment than a landing page.

It's a single skill, not a suite. But the real significance is strategic: Anthropic shipping a first-party skill signals that skills are a core part of Claude Code's future.

• Best for: solo developers shipping frontends who want something that looks like a human designed it.

frontend-slides

🔗 https://github.com/zarazhangrui/frontend-slides

A Claude Code skill for making animation-rich HTML presentations — zero dependencies, single file output. Start from scratch or feed it a PowerPoint.

The best part is style discovery. Instead of describing what you want in words, you get visual previews of 12 presets — Bold Signal, Neon Cyber, Terminal Green, Swiss Modern, Paper & Ink — and just pick one. Seeing beats describing every time. Deploys to Vercel with one command or exports to PDF via Playwright.

• Best for: developers who give conference talks and would rather write zero CSS to make slides look good.

remotion

🔗 https://github.com/remotion-dev/remotion

Remotion lets you make videos using React. Write components, use CSS and Canvas and WebGL, and it renders them to MP4. Fireship's "This video was made with code" and GitHub's "GitHub Unwrapped" were both built with it.

Why include it in a skills roundup? Because it's the missing piece. Agents can already generate landing pages and slide decks. Remotion is the infrastructure that lets them generate video. Agent writes React → Remotion renders it → you have a product demo that didn't exist 30 seconds ago. That workflow barely existed a year ago.

• Best for: developers who want to automate video production, especially if they're already generating React frontends.

Closing Thoughts

Gstack and slavingia/skills both serve founders but disagree on whether the bottleneck is shipping code or knowing what to build. Superpowers and starmap both automate dev workflows but disagree on whether you organize by task or by scenario. These aren't just tool choices — they're bets on where human judgment matters most.

The question used to be "can AI write code?" Now it's "whose playbook should the AI follow?" Increasingly, the answer is: yours — packaged as a skill.

Hey there, it’s Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

Follow @StarHistoryHQ for daily GitHub gems!

In this issue, we explore four layers of 🦞 OpenClaw's ecosystem:

OpenClaw, an open-source, locally-run AI agent, went from a weekend project to over 199,000 GitHub stars in just ten weeks — drawing two million website visitors in a single week. Peter, the project's creator, embedded the Star History chart in OpenClaw's README to show the speed of its rise, and the chart speaks for itself.

After a few chaotic name changes — from Clawdbot to Moltbot, and finally to OpenClaw — the project has found its footing, and now its creator is making his next move: Peter announced he is joining OpenAI to work on AI agents, and is transitioning OpenClaw into an independent, OpenAI-sponsored foundation — open-source, multi-model, and vendor-neutral.

Unlike monolithic AI assistants, OpenClaw is a privacy-first gateway that connects large language models to the chat apps users already have. This has given rise to a vibrant ecosystem of tools, extensions, and deployment strategies. By February 2026, this ecosystem has settled into four distinct layers.

Four Layers of the Claw

• The Skill Ecosystem curates the sprawling catalog of community-built extensions.

• The Core Toolkit provides the foundational agent logic — and a philosophy of self-extension.

• The Deployments offer three radically different ways to run your agent: cloud, bare-metal, and serverless.

• The Agent Browser gives agents a way to browse the real web without getting blocked.

The Skill Ecosystem

awesome-openclaw-skills

🔗 https://github.com/VoltAgent/awesome-openclaw-skills

The OpenClaw skill registry, ClawHub, now lists over 3,000 community-built skills. That's the good news. The bad news is that a significant number of them are spam, duplicates, or outright malicious. awesome-openclaw-skills is the community's answer: a curated, categorized, and security-audited list that filters the registry down to what's actually worth installing.

• Why it exists

  • An ecosystem is only as good as its discoverability. With 29+ categories spanning coding agents, DevOps, search, AI integrations, and more, this list is how most developers will find their first OpenClaw skills — and avoid the ones they shouldn't touch.

• Tradeoffs

  • Curation is inherently opinionated. The maintainers have excluded entire categories (crypto-related skills, for instance) and the list explicitly warns that inclusion does not guarantee safety. It's a starting point, not a seal of approval.

• Best for: anyone setting up OpenClaw for the first time and wondering "what can this thing actually do?" Start here before you start browsing ClawHub directly.

The Core Toolkit

pi-mono

🔗 https://github.com/badlogic/pi-mono

pi-mono is the heart of the OpenClaw ecosystem. Created by Mario Zechner, it provides the foundational components upon which OpenClaw itself is built. Its core, an agent named "Pi" is a masterclass in minimalism, equipped with just four tools: Read, Write, Edit, and Bash.

In January, Armin Ronacher published a post that reframed the project — not as a toolkit, but as a philosophy. The core argument: rather than accumulating features through a plugin marketplace, Pi encourages the agent to write its own tools. Need a code review workflow? Don't download a skill — ask the agent to build one. Ronacher showcases custom extensions he built this way: /answer for reformatting responses, /review for branched code reviews, /todos for task management.

• Why it exists

  • To offer a robust, minimal, and highly extensible foundation for building AI agents. It is a direct philosophical challenge to the skill-ecosystem model — where awesome-openclaw-skills says "curate and discover," Pi says "build it yourself."

• Tradeoffs

  • The minimalist design means it lacks many features out of the box, such as MCP support. Self-extending agents are powerful but demand more from the developer — you need to trust the agent to write correct code, and there's no community review process for tools that exist only in your session.

• Best for: developers who read Ronacher's post and felt something click. If you believe the future of software is software that builds more software, pi-mono is where that future lives.

Three Ways to Deploy

By early 2026, the deployment landscape has split into three camps, each representing a clear philosophical tradeoff: the power of the cloud, the privacy of bare metal, and the elegance of serverless.

moltworker

🔗 https://github.com/cloudflare/moltworker

Cloudflare's official OpenClaw deployment runs the agent in a Sandbox container on their global network. It comes with a web-based control UI, multi-platform chat support (Telegram, Discord, Slack), device pairing authentication, and persistent conversation history. It is, for all practical purposes, your own always-on AI assistant running at the edge.

(And yes — I'm disappointed you don't call it Clawflare.)

• Why it exists

  • To provide a production-grade, hassle-free deployment path for OpenClaw. Cloudflare Access handles authentication, R2 handles persistence, and the global network handles availability. It even includes browser automation via Chrome DevTools Protocol.

• Tradeoffs

  • Production-ready means production-priced. At roughly $34.50/month for 24/7 operation on a standard container, plus the $5/month Workers paid plan, this is the most expensive option in the Clawflare. The setup requires familiarity with Cloudflare's ecosystem — Access, R2, Wrangler, API secrets. And it's explicitly a proof-of-concept that "may break without notice."

• Best for: teams and power users who need a reliable, high-availability OpenClaw deployment without the burden of managing their own infrastructure.

mimiclaw

🔗 https://github.com/memovai/mimiclaw

At the opposite end of the spectrum from moltworker lies mimiclaw. This project is a remarkable feat of engineering: a complete reimplementation of the OpenClaw agent architecture in pure C, designed to run on a tiny, $5 ESP32-S3 microcontroller.

• Why it exists

  • To prove that agentic AI is not the exclusive domain of powerful servers. It champions a privacy-first, local-first model where all data and processing happen on a cheap, low-power device that the user physically owns.

• Tradeoffs

  • The hardware constraints are significant. It is less powerful and has fewer features than its cloud-hosted siblings. The setup process requires knowledge of embedded systems development, including flashing firmware via a serial connection.

• Best for: tinkerers, privacy advocates, and embedded developers. mimiclaw is a compelling glimpse into a future where powerful AI can be truly decentralized and owned by the individual.

gitclaw

🔗 https://github.com/SawyerHood/gitclaw

gitclaw runs an OpenClaw assistant entirely within GitHub Actions. You interact with it by opening Issues and leaving comments. The agent's memory, conversation history, and state are all committed to the Git repository itself — giving it persistent, searchable, version-controlled memory across sessions.

• Why it exists

  • To create a truly serverless, zero-infrastructure AI assistant. By storing state in Git, gitclaw gives the agent something most deployments lack: long-term memory that the user can inspect, search, and even edit. The agent can read and write files, set up GitHub Pages, and iterate on projects over time — all through the Issues interface.

• Tradeoffs

  • Every interaction pays the latency tax of a GitHub Actions run. On public repositories, your conversations with the agent are visible to everyone. And you're ultimately bound by GitHub's usage limits and terms of service.

• Best for: developers who want a zero-infrastructure AI assistant that lives where their code already lives. Fork the repo, add an API key, open an Issue, and you're talking to your agent.

The Agent's Browser

camofox-browser

🔗 https://github.com/jo-inc/camofox-browser

Here's the problem: you give your agent tools to browse the web, and it immediately gets blocked by Cloudflare, Google, and every other bot-detection system. Stealth plugins? They become detectable fingerprints themselves. camofox-browser solves this at the deepest level — it's a headless browser server built on Camoufox (a Firefox fork) that implements anti-detection at the C++ level, not through JavaScript shims.

• Why it exists

  • An agent that can't browse the web is an agent with a blindfold. camofox-browser provides a REST API that gives agents stable element references, token-efficient accessibility snapshots (~90% smaller than raw HTML), session isolation, cookie import for authenticated browsing, and built-in search macros for Google, YouTube, Amazon, and Reddit. It turns web browsing from an agent's weakest capability into a reliable one.

• Tradeoffs

  • Anti-detection browsing exists in a gray area. The tool is legitimate for agent development and testing, but the same capabilities that let your agent read a recipe blog without getting blocked could be used for scraping at scale. The project is MIT-licensed and makes no judgments — that's left to the user.

• Best for: anyone building OpenClaw skills or agents that need to interact with the real web. If your agent has ever returned "Access Denied" when you asked it to check a website, this is the fix.

Closing Thoughts

The rise of OpenClaw in January 2026 was more than just another viral repository. It signaled a shift toward a more decentralized, privacy-focused, and agentic model of AI. The ecosystem that has sprung up around it is not defined by a single product, but by a diversity of approaches — from a curated catalog of 3,000 skills to a philosophy that says your agent should write its own.

Choosing a project from the "Clawflare" is about more than a feature list; it is about choosing a philosophy. Whether you believe in the power of the cloud, the privacy of bare metal, the elegance of serverless, or the autonomy of a self-extending agent, there is a lobster 🦞 in this flare for you.

Hey there, it’s Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

Follow @StarHistoryHQ for daily GitHub gems!

In this issue, we spotlight three distinct architectural models of Embedded Web Editors:

Modern web products increasingly rely on embedded editors — lightweight, composable components embedded in dashboards, admin consoles, documentation systems, and internal tools. Unlike full IDEs, these editors must prioritize correctness, embeddability, and maintainability over sheer feature count.

By January 2026, the ecosystem has stabilized around three distinct architectural models.

Three Architectures

By 2026, embedded web editors fall into three camps:

• Native input editors rely directly on browser text input (<textarea>), delegating selection, IME, and accessibility to the platform.

• Text-engine editors build on programmable engines such as CodeMirror, managing text and syntax internally.

• Document-model editors rely on contenteditable and frameworks like ProseMirror, representing content as a structured document tree.

Native Input Editors

OverType

🔗 https://github.com/panphora/overtype

OverType takes a deliberately anti-framework approach to editing. Rather than introducing an editor engine, it uses a native, invisible <textarea> as the single source of truth for text, cursor position, selection, and IME behavior. A lightweight rendering layer then mirrors the content visually.

This design avoids re-implementing some of the hardest problems in text editing — particularly mobile keyboards, non-Latin IMEs, and accessibility — by trusting the browser instead.

• Why it exists

  • To provide a drop-in editor primitive, not a document framework

  • To behave like native input everywhere, especially on mobile

• Tradeoffs

  • No rich document structure

  • Limited formatting features

• Best for: mobile-first apps, internal tools, and deeply embedded text editing where correctness matters more than richness.

Text-Engine Editors (CodeMirror-Based)

Text-engine editors treat content as text with syntax, not DOM nodes. They sit between native input and full document models.

HyperMD

🔗 https://github.com/laobubu/HyperMD

HyperMD pushes CodeMirror toward a WYSIWYG-like Markdown experience. It renders Markdown elements inline, hides syntax markers, and allows interactive manipulation of content — while still preserving a text-based foundation.

It represents one of the most ambitious attempts to stretch CodeMirror toward document-style editing.

• Why it exists

  • To make Markdown feel visual without abandoning text

  • To retain compatibility with Markdown tooling

• Tradeoffs

  • Increased complexity

  • Mobile and IME edge cases

Best for: Markdown-heavy products that want rich interactions without adopting ProseMirror.

EasyMDE

🔗 https://github.com/Ionaru/easy-markdown-editor

EasyMDE is the actively maintained successor to SimpleMDE. It embraces the classic Markdown editor pattern: toolbar actions, preview mode, and predictable behavior.

Unlike newer editors, EasyMDE intentionally avoids deep abstractions.

• Why it exists

  • To provide a stable, familiar Markdown editor

  • To minimize surprises and maintenance cost

• Tradeoffs

  • Limited extensibility

  • Minimal innovation

• Best for: products needing a reliable, no-nonsense Markdown editor.

SimpleMDE

🔗 https://github.com/sparksuite/simplemde-markdown-editor

SimpleMDE helped define the embeddable Markdown editor era. Many later projects — including EasyMDE — trace their lineage to it.

Today, it serves mainly as historical context.

• Why it exists

  • Early standardization of Markdown editor UX

• Tradeoffs

  • Largely unmaintained

  • Superseded by forks

• Best for: legacy systems where migration cost is prohibitive.

StackEdit

🔗 https://github.com/benweet/stackedit

StackEdit is closer to a full Markdown application than an editor component. Built on CodeMirror, it includes preview, offline support, and synchronization with external storage.

Its scope extends far beyond embedding.

• Why it exists

  • To provide a complete, end-user Markdown experience

• Tradeoffs

  • Heavy footprint

  • Not optimized for reuse as a small component

• Best for: standalone Markdown workflows or reference implementations.

Document-Model Editors

Document-model editors treat content as a structured tree, enabling rich layouts, embeds, and non-linear editing.

Milkdown

🔗 https://github.com/Milkdown/milkdown

Milkdown builds on ProseMirror to offer a framework-friendly Markdown editor. Markdown is treated as a serialized form of a structured document, rather than the editing primitive itself.

• Why it exists

  • To bring modern abstractions to Markdown editing

  • To integrate cleanly with React and Vue

• Tradeoffs

  • Higher conceptual overhead

  • Build-step and configuration complexity

• Best for: apps treating Markdown as a document format, not plain text.

TUI Editor

🔗 https://github.com/nhn/tui.editor

TUI Editor targets enterprise-grade editing needs, supporting both Markdown and WYSIWYG modes, plugins, and integrations.

It offers one of the richest feature sets in this space.

• Why it exists

  • To support complex, non-technical editing workflows

• Tradeoffs

  • Large bundle size

  • Complex setup

  • Mobile issues are common

• Best for: enterprise products requiring rich document editing.

Closing Thoughts

By January 2026, embedded web editors no longer converge toward a single solution. Instead, the ecosystem reflects clear architectural tradeoffs:

• Native input editors optimize for correctness and simplicity

• Text-engine editors balance flexibility and familiarity

• Document-model editors unlock rich structure at the cost of complexity

Choosing an embedded editor is ultimately about choosing the right editing model, not the longest feature list.

2025 has been a more eventful year for star-history.com. Our website visitors grew 45% compared to 2024.

This is our biggest YoY growth ever—as a comparison, we grew a modest 10% in 2024.

The accelerated growth is likely driven by the expanding open source ecosystem on GitHub. We're also seeing more hockey stick charts.

This year, more repos are embedding our live star history chart in their READMEs. As traffic grew, our API serving backend encountered some scaling challenges. Around the end of March, users began experiencing more failures when rendering charts.

This was caused by GitHub API rate limiting, as rendering the chart requires calling the GitHub API—and we make a lot of API calls.

We ultimately resolved it by employing several techniques. While the issue hasn't completely disappeared, it's much better than before.

Generating and serving images in real-time also consumes considerable bandwidth. To avoid breaking the bank, we migrated our backend from a PaaS vendor to GKE. This not only saves money, but also improves our service reliability.

Besides those hidden improvements under the hood, this year we also implemented log scale—a feature that was requested almost 8 years ago.

A growing number of repos have outgrown linear scale, and we wanted to support them.

Later, we enhanced the feature to allow users to adjust the legend placement.

Beyond the technical improvements, we also kept creating content. Throughout the year, we issued 12 monthly picks, each covering a different topic.

• Jan - Knowledge Management

• Feb - DeepSeek

• Mar - MCP Server

• Apr - AI Verse

• May - Agent Protocol

• Jun - Agent Browser

• Jul - IDE for Vibe Coding

• Aug - Vertical LLM

• Sep - Proprietary AI Alternatives

• Oct - Declarative

• Nov - Agent SDK/Framework

• Dec - React

This year, we also decided to pause the Starlet program, which we launched in 2023 to promote less well-known open source projects. After publishing 30 issues, we found that coordinating content creation with external contributors posed significant challenges. Moving forward, we'll focus more on our own original content while staying true to our mission of highlighting noteworthy projects in the open source ecosystem.

Finally, we'd like to highlight our annual sponsor Dify and our very own editorial team from Bytebase. Maintaining star-history.com gives us a platform to stay connected with the open source community, even when that means firefighting scaling issues through the end of 2025.

Happy coding in 2026. Here's to more hockey stick charts—just hopefully not for our API rate limits.

Hey there, it’s Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

Follow @StarHistoryHQ for daily GitHub gems!

In this issue, we spotlight four standout React ⚛ projects shaping how teams build interfaces, data flows, dashboards, and developer tooling:

React entering the Linux Foundation this year marks a new chapter for one of the world’s most influential open-source projects. Governance is opening, the ecosystem is expanding, and developers are rallying around tools that push React toward a faster, more modular future.

This month, we highlight four standout React projects shaping how teams build interfaces, data flows, dashboards, and developer tooling:

React Scan

→ Real-Time React Performance Insights

React Scan is an innovative runtime profiler that overlays component rendering behavior directly in your UI. With a single import, developers can see precisely which components re-render — and why.

• Strengths: Instant feedback, zero-config setup, powerful for production debugging.

• Limitations: Early-stage project; API and features evolve quickly.

👉 React Scan represents the new wave of DX-first React tooling, helping teams catch performance regressions before they ship.

xyflow

→ Building Node-Based Editors & Graph Interfaces

Xyflow is the engine behind some of today’s most polished workflow editors — including Cloudflare Workers’ visual editor. From node-based diagrams to no-code builders, it provides a flexible canvas for interactive graph-based UIs.

• Strengths: Production-ready, highly customizable, large ecosystem.

• Limitations: Complex use cases may require learning its layout/custom node APIs.

👉 xyflow shows how far React can stretch — powering the next generation of low-code tools, editors, and data visualizations.

TanStack Query

→ Server-State Management Done Right

TanStack Query (formerly React Query) remains one of the most essential libraries in modern React apps. It simplifies fetching, caching, mutations, retries, pagination — everything related to "server state".

• Strengths: Predictable async flows, strong TypeScript model, works across frameworks.

• Limitations: Not intended for client state; pairs best with Zustand/Jotai/Redux.

👉 A stable, battle-tested cornerstone of enterprise React, TanStack Query continues to grow steadily as apps become more data-heavy.

refine

→ Full-Stack React Framework for Internal Tools

Refine is a full-stack React framework designed for building dashboards, admin panels, and CRUD-heavy apps. It integrates cleanly with UI kits (Ant Design, Mui, Chakra), authentication providers, and backend services from Supabase to NestJS.

• Strengths: Strong conventions, extensible providers, accelerates CRUD development.

• Limitations: Best suited for internal tools; not a general-purpose app framework.

👉 Refine brings a Rails-like productivity layer to React — ideal for teams that need fast, maintainable tools.

📈 Star History Overview

To compare growth and momentum, here’s the combined star-history chart:

✨ That’s December - React’s move into the Linux Foundation signals a more open and community-driven future. These projects — from performance profilers to visual editors and full-stack frameworks — showcase the breadth and momentum of today’s React ecosystem.

Hey there, it’s Adela 👋 Welcome to Star History!

Each month, we bring you the standout highlights from the open-source world ✨

Follow @StarHistoryHQ for daily GitHub gems!

In this issue, we spotlight four projects reshaping intelligent app development. 🤖

AI agents are no longer just prototypes - they’re becoming the backbone of production systems. Developers are building structured, autonomous software that reasons, uses tools, and collaborates with users - powered by a fast-growing ecosystem of SDKs and frameworks.

This month, we highlight four standout projects shaping how intelligent applications are built and deployed 🤖

Assistant UI

→ Building Agent Interfaces

Assistant UI is an open-source toolkit for creating chat-style interfaces. It provides React components and APIs to embed conversational agents directly into your product - with modern design, beautiful defaults, and full TypeScript support.

• Strengths: Polished UI components, flexible integration, and built on shadcn/ui + Tailwind.

• Limitations: Primarily web-focused; native and mobile support are evolving.

👉 Assistant UI gives your agents a front-end identity - transforming LLM logic into elegant, human-centered experiences.

Mastra

→ Full-Stack TypeScript Agent Framework

Mastra is a rapidly maturing, full-stack framework for building AI agents with TypeScript. It unifies model orchestration, retrieval workflows, and observability - enabling developers to ship scalable, production-grade AI systems.

• Strengths: Type-safe, modular, integrates with 40+ model providers; strong tooling and documentation.

• Limitations: APIs evolve quickly as the framework expands.

👉 Mastra brings engineering rigor to AI development - making agents testable, composable, and ready for real-world use.

Claude Agent SDKs

→ Building Agents with Claude

Anthropic’s Claude Agent SDKs, available for multiple languages, including Python and TypeScript, provide structured abstractions for building Claude-powered agents. They include components for tool use, Model Context Protocol (MCP) integration, and persistent multi-step workflows - enabling developers to design reliable, autonomous systems.

• Strengths: First-party SDKs from Anthropic, unified across languages, aligned with Claude’s safety and reasoning architecture.

• Limitations: Memory persistence and orchestration layers are still maturing.

👉 The Claude Agent SDKs elevate agent development to a first-class discipline - offering developers a safe, composable foundation for building intelligent systems.

VitePlus

→ Next-Generation JavaScript Toolchain

(Note: VitePlus is in active development by VoidZero, the team behind Vite, with a public preview expected in early 2026.)

VitePlus isn’t an AI framework - it’s the evolution of Vite into a unified JavaScript toolchain. It adds commands for building, testing, linting, formatting, and managing monorepos, all powered by Rust-based tools like Rolldown and Oxlint for near-instant feedback.

• Strengths: Unified developer experience, high-speed incremental builds, strong TypeScript support.

• Limitations: Still in private development; not yet generally available.

👉 As AI-powered agents merge into everyday web apps, VitePlus provides the performance foundation and developer experience to ship them efficiently.

✨ That’s November - four projects leading the Agent SDK / Framework Frontier. From SDKs and full-stack frameworks to next-generation developer toolchains, these projects are shaping how developers turn large language models into production-ready, intelligent applications.

Hey there, it’s Adela 👋 Welcome to Star History!

Each month, we gather open-source highlights — and occasionally feature a Starlet project that shines a little brighter. ✨

Follow @StarHistoryHQ for daily GitHub gems!

In this issue, we feature Starlet #30: NPC Shell - Intelligent Bash Environment with AI Agents.

NPC Shell transforms your terminal into an intelligent workspace that extends the traditional bash environment with AI capabilities. Inspired by biological intelligence, it provides a unified command-line interface for seamless interaction with multiple LLM providers and specialized agents -- enabling multi-modal collaboration, code execution, and automation of complex tasks.

Core Agent Tools

npcsh - ReAct-style Agentic Shell

npcsh provides a ReAct-style agentic shell with tool capabilities through Jinxs. It processes natural language commands and routes to appropriate tools and NPCs using reasoning and action loops.

corca - MCP Development Agent

corca is an MCP tool-calling agent specialized for software development workflows. It uses Model Context Protocol for extended capabilities and code analysis.

guac - Interactive Coding Agent

guac is a coding agent that directly executes code in an active Python session. You can inspect outputs and it builds on code you input, creating an interactive development environment.

Key Features

Multi-Provider Support - OpenAI, Anthropic, Gemini, Deepseek, Ollama, LMStudio, vLLM, MLX integration through LiteLLM.

Data Management - Database support for PostgreSQL, SQLite, and other databases for command history and conversations. Knowledge graph for structured information stored within the same database with knowledge derived with context (path, npc, team, etc) in mind, ChromaDB for vector embeddings and RAG, persistent state between sessions.

Extensible Framework - Jinxs are Jinja execution templates that provide function-like capabilities, allowing users to build reusable tools that reference each other. NPC files (.npc) for agent definitions, Context files (.ctx) for team preferences to make context management easier, Assembly lines for workflow pipelines.

SQL Models with NPC Integration - Create SQL models that call NPCs directly within queries using nql. functions. Execute complex data processing workflows where NPCs analyze, summarize, or transform data as part of SQL operations. Support coming soon for PostgreSQL, SQLite, Snowflake, Databricks, and other databases.

Powerful Command Macros

npcsh comes with an extensive set of command macros:

• /alicanto - Deep research with multiple perspectives, identifying gold insights and cliff warnings

• /corca - Enter MCP-powered agentic shell for development

• /guac - Enter interactive coding shell with pomodoro workflow

• /plonk - Computer use and GUI automation with vision models

• /vixynt - Image generation and editing

• /wander - Temperature-based problem solving for thinking outside the box

• /yap - Voice chat mode with speech-to-text and text-to-speech

• /rag - Semantic search with ChromaDB embeddings and command history

• /ots - Take screenshot and analyze with vision models

• And many more for search, configuration, knowledge graphs, and workflow automation

Learn More

npcsh is MIT-licensed and actively maintained. Here are some links to get started:

• Documentation

• GitHub Repository

• NPC Studio GUI

• Community Newsletter

• Support the Project

Hey there, it’s Adela 👋 Welcome to Star History #35!

Each month, we gather open-source highlights — and occasionally feature a Starlet project that shines a little brighter. ✨

Follow @StarHistoryHQ for daily GitHub gems!

In this issue, we explored 5 projects leading the declarative wave. 🌊

The declarative programming paradigm, which focuses on describing what you want to achieve rather than how to achieve it, is rapidly becoming the standard for managing complex systems. From infrastructure and databases to even diagramming, the declarative approach offers a more predictable, maintainable, and automated way to work. This shift is empowering developers to manage complexity with greater confidence and ease.

This month, we highlight five projects that exemplify the power and versatility of the declarative approach:

D2
→ Declarative Diagramming

D2 is a modern diagram scripting language that turns text into diagrams. It allows you to create complex, high-quality diagrams using a simple, declarative syntax. Instead of manually drawing boxes and lines, you describe the components of your system and their relationships, and D2 handles the layout and rendering.

• Strengths: Clean and modern output, extensible with plugins, and supports native rendering on GitHub.

• Limitations: As a newer tool, it has a smaller community and fewer integrations than established tools like Mermaid or Graphviz.

👉 D2 is making "diagrams as code" a practical reality, enabling developers to version, review, and share diagrams alongside their code.

pgschema
→ Declarative Schema Migration

pgschema brings the popular Terraform-style declarative workflow to PostgreSQL schema management. Instead of writing imperative migration scripts (e.g., ALTER TABLE ...), you declare the desired state of your schema in a file. pgschema then generates and applies the necessary changes to get your database to that state.

• Strengths: State-based approach eliminates the need for a migration history table, works without a shadow database, and supports a wide range of Postgres objects.

• Limitations: It's a new project, so the community is still growing, and it currently only supports PostgreSQL.

👉 pgschema simplifies database schema management by making it as easy and reliable as managing infrastructure with Terraform.

Terraform
→ The Original Declarative Infra

Terraform is the tool that popularized the concept of declarative infrastructure as code (IaC). It allows you to define and provision infrastructure across a wide range of cloud providers using a high-level configuration language called HCL. For years, it has been the de facto standard for IaC.

• Strengths: Mature, extensive ecosystem of providers, and a large, experienced community.

• Controversy: In August 2023, HashiCorp switched Terraform's license from the open-source MPL-2.0 to the source-available Business Source License (BSL), raising concerns about its open-source future.

👉 Terraform remains the established giant in the IaC space, but its license change has opened the door for new challengers.

OpenTofu
→ The Community's Terraform

OpenTofu is a community-driven, open-source fork of Terraform created in response to HashiCorp's license change. Hosted by the Linux Foundation, it aims to keep Terraform truly open source (under the MPL-2.0 license) and is managed by a community of developers and organizations. It serves as a drop-in replacement for Terraform.

• Strengths: Truly open source, community-governed, and maintains compatibility with Terraform, including its vast ecosystem of providers and modules.

• Challenges: As a fork, it needs to continue to innovate and grow its community to compete with the original.

👉 OpenTofu is a powerful testament to the open-source community's ability to rally and preserve access to critical tools.

Pulumi
→ Infrastructure as Real Code

Pulumi offers a unique take on declarative infrastructure by allowing you to use general-purpose programming languages like Python, TypeScript, Go, and C# to define your infrastructure. This means you can leverage familiar tools, concepts, and libraries—like loops, functions, and classes—to build and manage your cloud resources.

• Strengths: Extreme flexibility, the ability to use existing language ecosystems, and a more natural fit for developers who are already comfortable with programming.

• Limitations: Can have a steeper learning curve for those not familiar with the supported programming languages, and it requires managing both the Pulumi and language-specific dependencies.

👉 Pulumi bridges the gap between infrastructure and software development, empowering teams to manage infrastructure with the same tools and practices they use for their applications.

✨ That's October: five projects that showcase the power and breadth of the declarative revolution. By focusing on the "what" instead of the "how," these tools are making it easier than ever to build, manage, and scale complex systems with confidence.

Hi and welcome to Star History Weekly #34!

If you‘re new, it's Adela here. Each week, we curate some open-source news and take you to explore an open-source project, the Starlets. Meanwhile, we share fascinating GitHub repos daily over at @StarHistoryHQ, so make sure to follow us if you haven't already.

In Issue #34, we explored 6 projects shaping the "open vs proprietary" landscape.

As proprietary AI softwares like ChatGPT, Midjourney, Perplexity, Claude, Devin, Lovable, VEO continue to dominate the headlines, open-source counterparts are not far behind. Developers and hobbyists wish to take back control: running models locally, avoiding vendor lock-in, and trying out infinite possibilities of customization.

This month, we highlight six outstanding projects shaping the "open vs proprietary" landscape:

LocalAI

→ OpenAI GPT API / Anthropic Claude

LocalAI is not a model, but an inference layer that wraps open models (LLaMA, Mistral, Gemma, Mixtral, Qwen) behind an OpenAI-compatible API. Developers can swap out OpenAI’s endpoint for LocalAI with minimal code changes.

• Strengths: API-compatible, multi-modal (text, embeddings, TTS, images), privacy-friendly, cost-saving.

• Limitations: Models still trail GPT-4o and Claude 3.5 in reasoning and creativity. Speed depends on hardware.

👉 The closest open-source alternative to GPT APIs. Not raw parity, but all about control, cost, and freedom.

ComfyUI

→ Midjourney / Runway

ComfyUI is a node-based workflow editor for diffusion models. Artists can wire together samplers, ControlNet modules, LoRA models, and video nodes like Lego blocks.

• Ecosystem: Hundreds of community plugins and nodes, covering 3D, video, and advanced controls.

• Community & Marketing: comfy.org acts as both product site and gallery, showcasing user creations and workflows. Messaging is clear: ComfyUI is for artists of the future.

👉 Midjourney is easy but closed. ComfyUI is harder to learn but infinitely more flexible. Its rise shows creators want control and community, not just outputs.

Wan2.2

→ Google DeepMind VEO 3

Developed by Alibaba, Wan2.2 is an ambitious open video generation model. It supports text-to-video, image-to-video, and speech-to-video.

• Community: Available on Hugging Face and ModelScope, integrated into ComfyUI workflows, with tutorials and guides shared widely.

• Strengths: Motion consistency, cinematic feel, multi-modal inputs.

• Limitations: Heavy GPU requirements, occasional artifacts, not plug-and-play.

👉 Proof that open video generation is real — and people are already experimenting with it despite the cost.

OpenHands

→ Devin

Devin sparked huge buzz as the “AI software engineer,” but it’s closed. OpenHands is the community’s open alternative.

• Features: AI coding agents that can plan, code, test, debug, and collaborate. Extensible and self-hostable.

• Why it matters: Proprietary dev agents are black boxes. OpenHands is transparent and hackable — designed as a collaborative teammate.

• Challenges: Early days, far from Devin’s demos, needs ecosystem growth.

👉 The first step toward an open Devin developers can actually use and extend.

Perplexica

→ Perplexity AI

Perplexica is an AI-powered search engine that serves as an open-source alternative to Perplexity AI. It combines web search with AI reasoning to provide comprehensive answers with sources.

• Features: Real-time web search, multiple AI model support (OpenAI, Ollama, Groq, Anthropic), focus modes for different search types, and chat-based interface.

• Self-hosted: Complete control over your search data and AI interactions, with Docker deployment for easy setup.

• Limitations: Requires technical setup, dependent on external search engines (SearXNG), and needs API keys for AI models.

👉 The open alternative to Perplexity AI — giving users control over their AI-powered search experience without vendor lock-in.

Firecrawl

(with Open-Lovable demo) → Lovable

Firecrawl is an open-source crawler that turns websites into clean Markdown or JSON, perfect for LLMs. To demonstrate its power, the community built Open-Lovable, a clone of Lovable’s instant app builder.

• Why it matters: Proprietary app builders hide their stack. Firecrawl shows that the building blocks for Lovable-style apps already exist in open source.

👉 Less about cloning Lovable directly, more about proving you can rebuild it with open infrastructure.

✨ That's September: six open-source projects pursuing giants, each with its own story. The polish may be lacking, but the creativity, vibrancy, and freedom of open source are impossible to ignore.

Hi and welcome to Star History Weekly #33!

If you‘re new, it's Ayra here. Each week, we curate some open-source news and take you to explore an open-source project, the Starlets. Meanwhile, we share fascinating GitHub repos daily over at @StarHistoryHQ, so make sure to follow us if you haven't already.

In Issue #33, we explored 4 Vertical LLMs fine-tuned for specific industries or domains, enhancing their performance and relevance in those areas.

AI capabilities expand across industries, forcing the era of one-size-fits-all language models giving way to specialized solutions. Vertical LLMs represent a paradigm shift where domain-specific models deliver superior performance by focusing on particular industries, use cases, and data types through targeted training and optimization.

Today we'll explore four cutting-edge vertical LLMs that are pioneering domain-specific AI excellence:

Qwen3-Coder

Qwen3-Coder is a 480B-parameter Mixture-of-Experts coding AI model with 35B active parameters. The model achieves state-of-the-art agentic coding performance through advanced reinforcement learning. It supports 256K native context length and integrates seamlessly with popular developer tools like Claude Code and Cline.

Qwen3-Coder uses 7.5T tokens with 70% code data for training while maintaining general language abilities. The model employs reinforcement learning with 20,000 parallel environments to improve multi-turn coding tasks, achieving 55.4% on SWE-Bench Verified according to the latest SWE-bench leaderboard, which evaluates models' software engineering capabilities through real-world GitHub issue resolution.

The model offers an open-source CLI tool and integrates with existing developer workflows for practical coding assistance.

Meditron

Meditron is a suite of open-source medical Large Language Models developed by EPFL, offering both 7B and 70B parameter versions adapted from Llama-2 through continued pretraining on comprehensively curated medical corpora. The models are trained on selected PubMed papers, medical guidelines, and domain-specific datasets to deliver specialized healthcare AI capabilities.

Meditron-70B supports medical exam question answering, differential diagnosis assistance, and disease information queries. While designed to encode high-quality medical knowledge, Meditron includes important safety guidelines recommending extensive testing and clinical validation before any medical applications.

Neta Lumina

Neta Lumina is a high-quality anime-style image generation model built on the open-source Lumina-Image-2.0 foundation, fine-tuned with vast corpus of anime images and multilingual tag data to deliver exceptional creative capabilities.

Neta Lumina specializes in diverse creative scenarios including Furry, Guofeng traditional Chinese aesthetics, and character design with wide coverage from popular to niche concepts.

The model features accurate natural-language understanding with excellent adherence to complex prompts and native multilingual support for Chinese, English, and Japanese. Built on the Lumina2 Diffusion Transformer framework, it supports resolutions from 1024×1024 to custom aspect ratios optimized for creative workflows.

FinGPT

FinGPT delivers open-source financial large language models that democratize Internet-scale financial data processing, offering lightweight adaptation capabilities that significantly reduce the cost.

FinGPT's full-stack framework encompasses five layers: data source layer for comprehensive market coverage, data engineering layer for real-time NLP processing, LLMs layer with LoRA fine-tuning methodologies, task layer for fundamental financial operations, and application layer showcasing practical demos.

The platform includes specialized models for financial sentiment analysis, forecasting, and multi-task financial operations across different base models. It provides cost-effective alternatives to expensive proprietary models, enables monthly or weekly updates through automatic data curation pipelines and supports RLHF for personalized robo-advisory services.

Summary

These vertical LLMs demonstrate how specialized AI models outperform general-purpose alternatives through focused training and domain-specific optimization, each model delivers superior performance in its specialized field.

As industries adopt AI at scale, vertical LLMs are becoming the foundation for professional-grade applications that demand precision and reliability in specialized domains.

Hi and welcome to Star History Weekly #32!

If you‘re new, it's Ayra here. Each week, we curate some open-source news and take you to explore an open-source project, the Starlets. Meanwhile, we share fascinating GitHub repos daily over at @StarHistoryHQ, so make sure to follow us if you haven't already.

In Issue #32, we dived into IDE for vibe coding, the development environment optimized for AI-assisted programming, enabling real-time natural language-to-code generation and automated debugging.

As AI transforms software development, the traditional IDE is evolving into something far more powerful. Vibe Coding represents a paradigm shift where development environments become intelligent partners, enabling real-time natural language-to-code generation and automated debugging through seamless AI integration.

Today we'll explore four cutting-edge IDEs that are pioneering the Vibe Coding revolution:

Crystal

Crystal revolutionizes AI-assisted development by enabling parallel Claude Code sessions through git worktrees, allowing developers to experiment with multiple AI-generated solutions simultaneously while maintaining clean version control.

Crystal resumes AI conversations and maintains context across development sessions. With its built-in Diff Viewer, users can review and manually edit AI-generated changes before merging. It excels at exploratory development where you want AI to generate multiple solutions to the same problem, making it perfect for rapid prototyping and architectural experimentation.

Void

Void delivers the power of Cursor as an open-source alternative, providing AI agents that can work directly on your codebase with checkpoint visualization and support for any AI model or local hosting.

Void's AI agents understand your entire codebase context and visualize checkpoints with all AI-generated modifications. Messages are sent directly to AI providers without data retention. Void democratizes AI-assisted coding by removing vendor lock-in while maintaining enterprise-grade capabilities for teams that prioritize control and transparency.

IntelliJ IDEA

IntelliJ IDEA has evolved beyond traditional IDE capabilities to become an AI-enhanced development powerhouse, integrating intelligent code completion, automated refactoring, and context-aware suggestions into its robust platform foundation.

IntelliJ IDEA's AI integration delivers intelligent code completion that understands project context and coding patterns, automated refactoring with smart code transformations and AI-assisted impact analysis, and context-aware debugging that helps identify bug patterns while suggesting targeted fixes. This comprehensive AI assistance spans Java, Kotlin, Python, and dozens of other programming languages.

IntelliJ IDEA seamlessly bridges traditional enterprise development with cutting-edge AI capabilities, making it the ideal choice for teams transitioning to AI-assisted workflows while preserving their proven development practices and institutional knowledge.

CodeSandbox

CodeSandbox is an instantly ready, full-featured online IDE that eliminates the friction of local development setup. What sets CodeSandbox apart is its seamless real-time collaboration capabilities, allowing multiple developers to code together simultaneously, combined with an extensive template library that accelerates project initialization.

The platform supports popular frameworks including React, Angular, Vue, Preact, and Svelte, making it accessible for diverse web development projects. Built with a cloud-native architecture, it enables developers to create, experiment, and share web applications directly in the browser without any configuration overhead. The integrated deployment pipeline enables teams to iterate rapidly on ideas and ship web applications with minimal infrastructure complexity.

The Evolution of Development Environments

These IDEs represent different philosophies in AI-assisted development, from Crystal's experimental multi-session approach to Void's open-source flexibility, IntelliJ's enterprise integration, and CodeSandbox's cloud-native collaboration.

They collectively demonstrate how Vibe Coding is transforming software development by making AI assistance natural and integrated rather than an afterthought, enabling developers to think in terms of outcomes rather than implementation details.

The future of development environments lies in this seamless integration of human creativity with AI capability, where the IDE becomes an intelligent collaborator that understands context, anticipates needs, and amplifies developer productivity through natural language interaction.

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Hi and welcome to Star History Weekly #31!

If you‘re new, it's Ayra here. Each week, we curate some open-source news and take you to explore an open-source project, the Starlets. Meanwhile, we share fascinating GitHub repos daily over at @StarHistoryHQ, so make sure to follow us if you haven't already.

In Issue #31, we explored how browser AI agents enable natural language automation of complex tasks that traditionally required manual intervention.

As AI agents become increasingly sophisticated, the browser has emerged as the primary interface for digital interaction. Browser AI agents represent a revolutionary shift in how we interact with the web, enabling natural language automation of complex tasks that traditionally required manual intervention.

Today we'll explore five browser AI agent frameworks that are transforming web automation:

Browser-Use

Browser-Use enables AI agents to autonomously control and interact with web browsers, allowing for automating complex web tasks by simply describing what you want done in natural language.

Browser-Use highlights:

• Playwright Integration: Built on Playwright's reliable foundation for cross-browser compatibility

• Memory Functionality: Advanced memory capabilities for handling multi-step processes

• Production Ready: Designed for scaling browser automation in production environments

Browser-Use excels at automating repetitive web tasks like data extraction, form filling, and e-commerce operations. Its Python-first approach makes it ideal for developers already working in data science and automation workflows.

Nanobrowser

Nanobrowser brings AI web automation directly to your browser through a Chrome extension, offering a free alternative to OpenAI Operator with a sophisticated multi-agent system, where specialized agents work together for complex task completion, and different models can be assigned to different agents for cost optimization. All processing happens locally in your browser.

Nanobrowser's multi-agent architecture consists of specialized components:

• Planner: Analyzes tasks and creates execution strategies

• Navigator: Handles page interactions and element identification

• Validator: Ensures actions are completed successfully and validates results

Nanobrowser democratizes web automation by eliminating the need for coding knowledge while providing enterprise-level capabilities through its intelligent agent system, featuring real-time interaction with a chat-based interface that includes live status updates and conversation history, making complex web tasks accessible to users without programming expertise.

Stagehand

Stagehand by Browserbase offers the flexibility to use Playwright for known tasks while leveraging AI for dynamic navigation and unfamiliar pages.

With advanced schema-based data extraction with Zod integration, the ability to preview AI actions before execution and cache repeatable actions, as well as native support for state-of-the-art computer use AI models, Stagehand bridges the gap between traditional automation tools and agents, offering developers precise control.

Notte

Notte delivers a full-stack web AI agent framework that transforms the internet into an agent-friendly environment through its revolutionary perception layer that converts complex web pages into structured, natural language descriptions AI models can easily understand and act upon.

Combining notte.Session, notte.Vault, and notte.Agent for comprehensive automation, Notte impresses users with ultra-fast performance, say 86.2% success rate with 47-second average completion time in benchmarks. It is available as both cloud service and local installation.

Notte's perception layer allows smaller, faster AI models to understand web pages better, reducing costs while maintaining accuracy.

Nxtscape

Nxtscape reimagines the browser itself as an AI-native platform, running AI agents directly within the browser locally rather than in the cloud, providing a privacy-first alternative where your data never leaves your computer.

Nxtscape is built on Chromium with full Chrome extension support and complete offline capability using local models. You can use your own API keys or run local models with Ollama through a familiar browser interface.

Nxtscape envisions seamlessly integrating AI assistance into every aspect of web browsing, from task automation to content filtering and productivity enhancement.

The Future of Web Interaction

These tools above represent different approaches to the same fundamental challenge: making the web more accessible and automatable through AI.

They are enabling a new paradigm where complex web workflows can be automated through natural language, making advanced automation accessible to non-technical users while providing developers with powerful tools for building sophisticated applications.

The convergence of AI and browser technology marks a crucial step toward a future where the web becomes a fully programmable, agent-friendly environment, transforming how we interact with digital services and information.

Hi and welcome to Star History Weekly #30!

If you‘re new, it's Ayra here. Each week, we curate some open-source news and take you to explore an open-source project, the Starlets. Meanwhile, we share fascinating GitHub repos daily over at @StarHistoryHQ, so make sure to follow us if you haven't already.

In Issue #30, we explored outstanding agent protocols that set the standards how AI agents communicate with tools, data sources, and other agents.

As AI agents become increasingly powerful and widely deployed, a critical challenge has emerged: how should these agents communicate with external tools, data sources, and other agents? Agent protocols provide standardized frameworks that enable seamless interaction between AI systems.

Today, we'll explore three groundbreaking open-source protocols that are setting the standards for AI agent communication:

X402

X402 by Coinbase is an innovative protocol named after HTTP status code 402 (Payment Required), establishing a standardized framework for enabling paid API access between AI agents and services. The HTTP 402 status code was originally reserved for future use in the HTTP specification, intended for scenarios where payment is required before a server fulfills a request - a concept that was ahead of its time until now.

X402 follows a straightforward flow:

1. A client (AI agent or application) requests access to an X402-enabled HTTP server

2. The server responds with a 402 Payment Required status, including payment details (price, acceptable tokens)

3. The client sends a signed payment payload using a supported token (like USDC)

4. After payment verification and settlement, the server fulfills the original request

Key Features include:

• Payment-Required Interactions: Seamlessly handles the economics of agent-to-service interactions, allowing agents to access paid APIs with proper authorization

• Cryptocurrency Integration: Native support for cryptocurrency payments facilitating trustless, cross-border micropayments between agents and services

• Open Standards: Built on open web standards to ensure broad compatibility across platforms and implementations

• Scalable Architecture: Designed to handle millions of micro-transactions between agents and services with minimal overhead

• Security-First Design: Incorporates robust authentication and authorization mechanisms to protect sensitive operations

X402 solves economic challenges in agent ecosystems by creating sustainable business models for AI services. By embedding payments directly into HTTP protocols, it eliminates specialized wallet interfaces and makes transactions between agents and services as seamless as loading a webpage.

Model Context Protocol

Model Context Protocol (MCP) developed by Anthropic provides a standardized way to connect AI models to data sources and tools, much like how USB-C connects your devices to peripherals and accessories.

MCP addresses the fundamental challenge of context management in LLMs by creating a unified approach for handling diverse data types and sources. It establishes clear standards for context representation, enabling seamless integration between AI models and external systems.

Key capabilities include:

• Universal Context Format: Standardizes how various data types (text, images, structured data) are represented and passed to AI models

• Tool Plugins Architecture: Allows agents to dynamically discover and interact with tools through a consistent interface

• Context Window Management: Sophisticated mechanisms for handling limited context windows through compression, summarization, and selective attention

• Semantic Versioning: Clear compatibility guidelines ensuring backward compatibility as the protocol evolves

• Cross-Model Compatibility: Works across different AI models from various providers, creating a foundation for model interoperability

By providing this standardized interface, MCP reduces integration complexity and enables developers to focus on building valuable agent applications rather than solving context representation problems repeatedly.

A2A

A2A (Agent-to-Agent) by Google establishes a new paradigm for enabling AI agents to communicate directly with each other, facilitating collaboration on complex tasks that require multiple specialized agents.

Key Features include:

• Standardized Message Format: Well-defined structure for inter-agent communication, including requests, responses, and notifications

• Discovery Mechanism: Allows agents to discover each other's capabilities and establish communication channels

• Task Delegation Framework: Enables agents to break down complex tasks and delegate subtasks to specialized agents

• Capability Advertisement: Agents can advertise their specialized capabilities, creating a marketplace of agent services

• Security and Access Control: Comprehensive security model for authenticating and authorizing agent-to-agent interactions

A2A addresses the critical challenge of agent interoperability, moving beyond isolated AI systems to create collaborative networks of specialized agents. This approach mirrors how human experts collaborate, with each contributor focusing on their area of expertise while working toward a common goal.

Lastly

These three protocols represent different but complementary approaches to solving the critical challenges in AI agent communication. X402 addresses the economic layer, Model Context Protocol standardizes how agents interact with data and tools, and A2A enables direct agent-to-agent collaboration.

As highlighted in the recent survey of AI Agent Protocols, these standardized communication frameworks are essential for enabling agents to work together effectively, scale across applications, and tackle complex real-world tasks. The development of these protocols marks a crucial step toward a future where AI systems can form collective intelligence through structured collaboration and resource sharing.

Hi and welcome to Star History Weekly #29!

If you are new, it's Ayra here. Each week, we curate some open-source news and take you to explore an open-source project, the Starlets. Meanwhile, we share fascinating GitHub repos daily over at @StarHistoryHQ, so make sure to follow us if you haven't already.

In Issue #29, we explored most advanced AI verse projects in terms of digital twins, geographical visualization, and robotic simulation.

Recently, the concept of an "AI Verse" (AI-powered virtual spaces and ecosystems) is gaining momentum as developers create increasingly sophisticated tools for building intelligent virtual entities and environments.

Today, we'll explore three cutting-edge open-source projects that are pushing the boundaries of what's possible in this exciting space:

Second-Me

Second-Me is an innovative project that allows users to create personalized AI versions of themselves. This digital twin technology aims to bridge the gap between human identity and AI representation.

Key Features include:

• Hierarchical Memory Modeling (HMM): Three-layer memory architecture that ranges from short-term interaction memory to long-term personalized cognitive memory, enabling pattern recognition and adaptation

• Me-alignment Architecture: Personalized alignment system that transforms scattered personal data into deep understanding, outperforming standard retrieval methods by 37%

• Complete Privacy Control: Runs locally with user-controlled data sharing and peer-to-peer communication in a decentralized network

• Contextual Adaptation: Roleplay framework allowing your AI to present different versions of itself across various situations while maintaining your authentic core identity

• Autonomous Representation: Your Second-Me can navigate digital environments, make decisions aligned with your values, and connect with other Second-Mes while you maintain control

Second-Me represents a shift from centralized AI systems to a network where each AI authentically embodies an individual's values and interests, illustrating a future where AI amplifies human uniqueness and agency rather than diminishing our distinctive qualities and autonomy.

Arnis

Arnis creates complex and accurate Minecraft Java Edition worlds that reflect real-world geography and architecture using OpenStreetMap.

Users generate accurate geographical representations by providing coordinates via the command line or GUI. Then Arnis fetches OpenStreetMap data, processes elements (buildings, roads, terrain), and constructs them in Minecraft with proper scaling and positioning.

The tool supports various customization options including elevation data integration, scale adjustment, and themed generation modes. Its lightweight design, cross-platform compatibility, and open-source nature make it an excellent foundation for AI-powered applications, educational projects, and geographical visualizations without requiring complex infrastructure setup.

RoboVerse

RoboVerse creates a virtual environment where AI-powered robots can learn, interact, and evolve. It helps researchers and developers create, test, and refine AI-powered robotics applications while addressing the traditional challenges of data collection and standardized evaluation in robotics research.

At its core is MetaSim, an infrastructure that creates a universal interface across diverse simulation environments, allowing seamless transitions between different simulators and robotic embodiments.

With this advanced architecture, RoboVerse distinguishes itself through:

• Versatile Simulation Environment: Supports multiple simulators and robot types with standardized interfaces for consistent development

• High-Quality Synthetic Dataset: Features photorealistic rendering and accurate physics, created through diverse approaches including public dataset migration and policy rollouts

• Standardized Benchmarks: Unified evaluation protocols for imitation learning and reinforcement learning that enable consistent assessment across different generalization levels

• Improved Real-World Transfer: Enhances sim-to-real transfer capabilities, making virtual training more applicable to physical robots

Lastly

These three projects represent different facets of the emerging AI Verse ecosystem. From personal digital twins to geographical visualizations and robotic simulations, we're seeing the building blocks of more immersive, intelligent digital environments taking shape. As these technologies mature, the boundaries between digital and physical reality will continue to blur, creating new opportunities for work, play, and connection.