DEV Community: Muhammad Tayyab

Filee: The Open-Source File Converter That Works by Dragging

Muhammad Tayyab — Fri, 30 Oct 2026 13:01:15 +0000

Filee skips the whole open-an-app, find-the-import-button ritual. Hold a key, drag your files, and a donut of formats pops up right at your cursor. Drop the files on a format and the conversion happens on your own computer, nothing uploaded, no Office required. It has 18,128 stars, 500,000-plus downloads, and one of the widest format lists of any free converter.

What Is Filee?

Filee is a free, open-source file converter built around one idea: converting a file should take about as long as dragging it. It is made by Myeonggu Kim, known on GitHub as KnifeLemon, written mostly in C#, and licensed under MIT. The repository had 18,128 stars and 1,119 forks when we checked on October 30, 2026, and the project reports more than 500,000 downloads.

The core interaction is the donut. Hold a key, Ctrl on Windows and Linux or Option on macOS, and drag your files. A ring of formats opens right at your cursor, showing only the formats those files can actually become. Drop the files on a format and the conversion runs. No app window to open, no import dialog, no upload progress bar. It works the same way for a picture dragged out of Chrome, Edge, Firefox or Safari: Filee saves it to your Downloads folder, or keeps only the converted file, and converts it. Links are downloaded without your browser's sign-in.

The format list is the other headline. The project counts more than 260 formats across images, PDF, Word, Excel, PowerPoint, HWP and HWPX, video, audio, e-books, archives, fonts, CAD files and 3D models. Most of them work out of the box with no Office, no Hancom Office and no LibreOffice installed. A smaller set needs an optional engine that Filee downloads only when you first need it: Ghostscript, LibreOffice, Pandoc, calibre or FFmpeg.

How the Donut Works

The donut is not just a menu, it is a small workspace. Each file type gets its own toolbar, arranged by dragging, with presets for quality, size and DPI on images, page ranges and compression on PDFs, video and audio settings, and output folder and file naming rules. Press Tab or turn the mouse wheel while the donut is open and it flips over to less common formats and tools, so the everyday choices stay on the front and the rare ones stay one flip away.

Two touches show the care that went into it. First, you can crop before converting: the Crop slice opens pictures one by one, you drag the frame and pick a ratio, and only that part is saved. Second, only formats that fit the files you dragged appear in the first place, so you never pick a conversion that cannot work. Multi-step routes are found automatically, so HWP to DOCX goes through HWPX on its own without you planning the chain.

Beyond the donut, Filee covers the boring-but-essential workflows. Many files convert at once, in parallel. Watch folders convert every file that lands in them. The Home screen keeps recent conversions with thumbnails, so you can open a result or open it with another app. And if you would rather not drag at all, there is a right-click menu, a keyboard shortcut, a drop zone, and a full command line tool.

What It Converts

The breadth is genuinely unusual. Images cover JPG, PNG, WEBP, AVIF, TIFF, BMP, GIF, HEIC, camera RAW formats like CR2, CR3, NEF and ARW, Photoshop PSD and PSB files, and even DICOM and OpenEXR. Output can be another image, a PDF, a traced SVG, a resized or grayscale version, or, for iPhone Live Photos and Android Motion Photos, the video itself. Video goes to MP4, WEBM, MOV, MKV, AVI, GIF, MP3 or M4A, with subtitle conversion between SRT, ASS, WebVTT and others. Audio covers MP3, FLAC, WAV, OGG, OPUS, WMA and more, including DSD and DTS formats most converters skip.

Documents are where Filee earns its keep for office work. PDF converts to DOCX, HWPX, TXT, images or a picture of each page as slides, and Filee can also merge PDFs in an order you choose, split them, take page ranges and compress them smaller. Word, Excel and PowerPoint files go to PDF, and spreadsheets go to JSON, CSV or HTML as well. Korean HWP and HWPX files, which most Western converters ignore entirely, convert to PDF, PNG, DOCX, TXT, Markdown and HTML. E-books convert between EPUB, MOBI, AZW3, FB2 and CBZ, archives extract or bundle into ZIP and 7Z, fonts convert between TTF, OTF, WOFF and WOFF2, CAD files go between DWG and DXF or out to PDF and SVG, and 3D models convert between GLB, glTF, STL, OBJ and 3MF with PNG previews.

Offline and Private by Design

Filee converts files on your own computer and never uploads them. It collects no usage data and sends no telemetry. That is not a marketing line buried in a privacy page, it is the architecture: the conversion engines run locally, and the project states plainly that it only connects to the internet for three things. It asks GitHub whether a newer release exists shortly after start and then every few hours, which you can turn off in Settings. It downloads the optional conversion engines you choose to install, from official locations pinned in the project. And it opens your browser when you click a link.

That matters more than it sounds. The usual way to convert a file is to upload it to a website, which means handing a stranger your document, your photos or your work files and trusting their retention policy. Filee removes that step entirely. It also removes the subscription: the project is free and says it stays free, funded by sponsors and supporters rather than paywalls. Windows releases are signed with a Certum open-source code signing certificate issued to the developer, and every release is signed and approved by hand through the project's release workflow.

The Command Line and Power Features

The filee command uses the same engines and presets as the app, and the installer can add it to your system. Converting a photo is filee convert photo.heic --to jpg. A batch with quality control is filee convert *.png --to webp --quality 80 -o converted. A watch folder that converts everything dropped into it and moves the originals is filee watch D:\Inbox --to pdf --move-originals. The full command reference lives at filee.sh/cli, and filee help lists every command and option.

For developers, the project builds with the .NET 10 SDK on Windows, macOS or Linux, and the documentation covers the architecture, the engine system, and how to add a converter or a new language. The interface itself ships in 20 languages, including Arabic with right-to-left layout, and themes come in light and dark with your own accent colour and donut opacity.

How to Try It

Download from the Releases page on GitHub or from the Microsoft Store. On Windows 10 or 11, run the Setup installer or unzip the portable package and run Filee.exe. Setup is the project's own installer, needs nothing else on the PC, and can add the Explorer menu, start at sign-in, the filee command and the optional engines. Silent installs take the usual Inno Setup switches.

On macOS 13 and newer, which is still a preview build, unpack the package and run install.sh, or open the .dmg and drag Filee to Applications. The macOS build is ad-hoc signed and not notarized yet, so the first start needs an explicit allow in System Settings under Privacy and Security. On Linux x64 or ARM64, Ubuntu 22.04 and newer, unpack the package and run install.sh with no root and Python 3 installed. One Linux caveat: drag gestures need X11, so on Wayland you use the drop zone and the file manager's right-click menu instead. Filee shows new releases in its menu and updates itself, keeping your settings and engines, with a short what's-new tour after an update.

What to Watch Out For

Filee is honest about its rough edges, and there are a few worth knowing. The macOS build is a preview: it works, but it is not notarized, so expect the extra security step on first launch. On Linux with Wayland, the signature drag gesture does not work, which takes away the project's best feature on that setup. On Windows, SmartScreen can still warn about a new release until it has been downloaded enough times, which is normal for independently signed open-source software, and the README documents a specific fix for older Windows 10 machines where a hardware security feature stops the app from starting at all.

The other caveat is the engines. Most formats work immediately, but the ones marked in the README need Ghostscript, LibreOffice, Pandoc, calibre or FFmpeg, downloaded when first needed. That is a one-time setup cost, not a paywall, but it means your first conversion of an unusual format takes a little longer while Filee fetches its engine. None of that changes the core deal: a free, offline converter with an unusually wide format list and an interaction that genuinely saves clicks. If you convert files more than a couple of times a week, it is worth the install.

Originally published at devpik.com/open-source/filee. Repo: KnifeLemon/Filee.

TradingAgents by Tauric Research: A Trading Firm of 12 LLM Agents, and What One Decision Costs

Muhammad Tayyab — Fri, 30 Oct 2026 07:42:37 +0000

TradingAgents is a free, open-source Python framework from Tauric Research that analyzes a stock the way a trading firm would, with a team of language-model agents instead of one prompt. Four analysts study the market, sentiment, news and fundamentals; a bull and a bear argue over their reports; a trader turns the result into a plan; three risk analysts argue over that plan; and a portfolio manager makes the call. You give it a ticker and a date, and it returns a rating and a folder of reports. It had 115,371 GitHub stars and more than 22,000 forks when we wrote this, and one sentence in the README matters more than the rest: the framework is designed for research purposes and is not intended as financial, investment or trading advice.

What Is TradingAgents?

The project began as a research paper, "TradingAgents: Multi-Agents LLM Financial Trading Framework" by Yijia Xiao, Edward Sun, Di Luo and Wei Wang, first posted to arXiv on 28 December 2024, the same day the repository was created. It is built on LangGraph, licensed under Apache 2.0, and the current release is version 0.7.0, from 16 October 2026.

Version 0.7.0 contains no broker integration and no code that places orders: a run ends with a rating and its reasoning, and what you do with them is up to you. That makes it a tool for studying how multi-agent LLM systems reason about markets, not a bot that trades for you, and this post treats it that way.

Twelve Agents, Modeled on a Trading Firm

Every role in TradingAgents is a separate agent with its own prompt, and most of them have their own data. The analysts run at the same time, and the debate starts once all four reports are in.

Role	What it does	Model tier
Market Analyst	Prices and up to 8 technical indicators (moving averages, MACD, RSI, Bollinger Bands, ATR, VWMA), checked against a verified price snapshot	Quick
Sentiment Analyst	Yahoo Finance headlines, StockTwits messages and Reddit posts, scored as one sentiment band	Quick
News Analyst	Company news, global macro headlines, FRED economic data and Polymarket prediction markets	Quick
Fundamentals Analyst	Balance sheet, cash flow, income statement, company overview and insider transactions	Quick
Bull and Bear Researchers	Argue for and against the trade from the four reports	Quick
Research Manager	Judges the debate and writes the investment plan	Deep
Trader	Turns the plan into a buy, hold or sell proposal	Quick
Aggressive, Conservative and Neutral Analysts	Argue over the trader's proposal from three risk appetites	Quick
Portfolio Manager	Makes the final decision on a five-step scale	Deep

The two tiers are the main cost control. You choose a quick model and a deep model, and each can run on its own provider, so the managers could run on Claude while everything else runs on a cheaper OpenAI model. The defaults are OpenAI's GPT-6 Luna for the quick tier and GPT-6 Sol for the deep one.

The final answer is one of five ratings: Buy, Overweight, Hold, Underweight or Sell. When the portfolio manager's output contains no readable rating, the code records REVIEW instead of quietly treating it as a Hold, because a decision nobody can read is not a decision to hold.

Fifteen Model Calls Is the Floor for One Decision

Nothing in the README says how many times a run calls a language model, and that number is what you pay for. So we counted it from the code: the graph's routing in conditional_logic.py, the analyst loop in analysts/turn.py, the default settings in default_config.py, and the project's own end-to-end test, which runs the whole graph offline with a scripted model. We read version 0.7.0 (commit e8f5503) and did not pay for a live run, so this is a count of calls, not a bill.

With the default settings and all four analysts, one decision makes at least 15 model calls:

Step	Calls at minimum	Tier
Market, news and fundamentals analysts	2 each: one round of tool calls, then the report	Quick
Sentiment analyst	1, because its data is fetched before the model is called	Quick
Bull and bear researchers	2, one debate round each	Quick
Research manager	1	Deep
Trader	1	Quick
Three risk analysts	3, one risk round each	Quick
Portfolio manager	1	Deep
Total	15	13 quick, 2 deep

The project's test agrees. It deliberately breaks the provider on the 12th call and describes that point as "past the analysts, before the decision", which is exactly where the count puts it: 7 analyst calls, then the bull, the bear, the research manager and the trader, so the 12th call is the first risk analyst.

Three things push a real run above 15:

Tool rounds. The market analyst is told to fetch prices first, then compute indicators, then check the verified snapshot, so a model that follows the instructions needs several rounds before it writes. Each analyst may take up to 20 rounds of tool calls, plus one final call without tools when it runs out.
Debate settings. The README's own Python example sets two rounds for the bull and bear, which adds 2 calls. Two rounds of both debates make it 20.
Past decisions. Each run settles earlier decisions whose holding period has passed and writes a one-paragraph reflection on each, one more quick call per decision.

Two conclusions follow. The quick model carries 13 of the 15 calls, so it decides most of the cost, and the deep model is called only twice. And backtests multiply: the README's example grid, two tickers every 7 days from 1 June to 1 August with only the market and news analysts, is 18 decisions and at least 216 model calls before the reflections.

Debates Are a Setting You Choose

The debates are what make TradingAgents different from asking one model for an opinion, and they are fully configurable. The bull and bear take turns until each has spoken max_debate_rounds times, then the research manager writes a plan from the transcript. The aggressive, conservative and neutral analysts do the same for max_risk_discuss_rounds rounds before the portfolio manager decides. Both default to 1.

More rounds give the agents more chances to answer each other, at a cost of 2 calls per extra debate round and 3 per extra risk round. The same settings are available as environment variables for the CLI: TRADINGAGENTS_MAX_DEBATE_ROUNDS and TRADINGAGENTS_MAX_RISK_ROUNDS.

You can also pass in what you already hold. Without a portfolio, the agents write guidance for a reader who applies it to their own position; with one, the trader, the risk analysts and the portfolio manager work against your actual cash and positions. The CLI takes it as a JSON file (tradingagents --portfolio my_book.json) and refuses to start when the file does not validate.

Point-in-Time Data, and the One Feed That Is Not

A backtest is only honest if each run sees what was public on its date. Most of TradingAgents' recent releases are about exactly that, and the README is specific about how each source behaves.

Fundamentals are read as filed. US company statements come from SEC EDGAR, which records the date every figure was filed, so a run dated in the past reads the statements as they stood that day. The README's example is Apple's total assets for fiscal 2008, filed as $39.6 billion and restated to $36.2 billion in 2010. We checked it against EDGAR's public API: the figure for the period ending 27 September 2008 appears as $39.572 billion in the 10-K filed on 27 October 2009, and as $36.171 billion in the amended 10-K filed on 25 January 2010. A run dated between those two filings reads the first number.

Other sources hold back what they cannot date. Yahoo Finance dates a statement by the period it covers rather than by when it was published, so a run dated in the past is told those statements are withheld. Insider trades are dated by when they happened, not when they were filed, so they are withheld for past dates too. News articles published after the analysis date are filtered out, and the project keeps regression tests for the ways that filter has leaked in the past.

Social sentiment is the exception. The sentiment analyst trims StockTwits and Reddit posts to the seven days before the analysis date, so nothing from after it leaks in. The problem is the other direction. Its code says these feeds "serve recent items and are not archived, so a historical run's sentiment inputs are not point-in-time." A run dated months back finds little or nothing in its window, and the feed tells the model the window was never observed rather than reporting that nobody posted. That is presumably why the README's backtest example selects only the market and news analysts, and it is the setting to copy: leave the sentiment analyst out of anything you backtest.

Memory and Backtests Score Decisions Against the Market

TradingAgents keeps a memory log of its own decisions at ~/.tradingagents/memory/trading_memory.md. While the analysts of a later run work, it settles every logged decision whose holding period has passed: it fetches the realized return, and the alpha against the market's benchmark (SPY for US tickers, regional indexes such as the Nikkei 225 or Nifty 50 elsewhere), then writes a short reflection. The portfolio manager reads recent decisions on the same ticker, plus recent lessons from others, before it decides.

The holding period is 5 trading days, counted from the first close after the analysis date. Version 0.7.0 moved it from the close of the analysis date itself, because a run reads news published after that day's close and could not have traded on the earlier price.

To judge whether the system decides well rather than reading one run, run_backtest sweeps a grid of tickers and dates into a memory log of its own and scores each settled decision on realized alpha, grouped by rating. An interrupted sweep resumes with the same run_id, skipping the cells that already ran. Each run also writes its full state to full_states_log_<date>.json under the ticker's results folder, with the date, the final rating and the model settings.

The paper reports that TradingAgents improved cumulative returns, Sharpe ratio and maximum drawdown over baseline strategies. The README is careful in the other direction: backtest results "are not guaranteed to match any published figure", and the framework is "a research scaffold for studying multi-agent analysis, not a strategy with a fixed, replicable return."

Two Runs of the Same Ticker Will Not Match

The README says it directly: two runs of the same ticker and date can differ, and that is expected, not a bug. Language-model sampling varies even at a fixed temperature, reasoning models vary most because their reasoning is itself sampled, and the live news and social feeds change from day to day.

Some things no longer vary. The company behind a ticker is resolved before any agent runs, and the market analyst grounds every exact price and indicator claim in a verified snapshot. The README says these two changes address earlier reports of runs analyzing the wrong company or inventing price levels.

To see the variation yourself, run the same ticker and date twice and save both reports. Each saved report is a folder of Markdown files, one per agent (market.md, bull.md, trader.md, decision.md and so on), plus complete_report.html as one page. Comparing the two decision.md files shows exactly where the reasoning changed.

For tighter repeatability, set temperature to 0 and choose models that honor it. The current default models are reasoning-first and largely ignore temperature, so the README suggests naming a non-reasoning model for both tiers when reproducibility matters more than depth.

Installing TradingAgents and Running Your First Analysis

TradingAgents needs Python 3.11 or newer and an API key for at least one model provider. The CLI asks for the ticker, date, provider, models and research depth, and remembers your answers for next time. For scripts and scheduled jobs it runs without questions from flags. Docker works too (docker compose run --rm tradingagents), with an Ollama profile for local models.

The provider list is long: OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen, GLM, MiniMax, OpenRouter, Mistral, Kimi, Groq, NVIDIA, Azure OpenAI, AWS Bedrock, Ollama, and any OpenAI-compatible server such as vLLM or LM Studio. Since version 0.7.0 it can also run on a ChatGPT Plus or Pro plan instead of an API key, after a one-time tradingagents login chatgpt; those runs count toward the plan's usage limits.

Any market Yahoo Finance covers works, using the exchange suffix: 0700.HK for Hong Kong, 7203.T for Tokyo, RELIANCE.NS for India, 600519.SS for Shanghai, and crypto pairs like BTC-USD. SEC EDGAR covers companies that file with the SEC, including foreign companies listed in the US; other listings fall back to the next data vendor.

Two defaults are worth knowing. Checkpoint resume is off unless you pass --checkpoint, which lets a crashed run continue from its last completed step. And the interactive CLI checks for a newer release at most once a day in the background; it sends nothing about your runs, and TRADINGAGENTS_NO_UPDATE_CHECK=1 turns it off.

Who Should Use TradingAgents, and Who Should Not

TradingAgents is a good fit if you want to study how LLM agents reason about markets: researchers and students working on multi-agent systems, developers who want a well-tested LangGraph project to learn from, and quantitative tinkerers who want to see whether structured debate changes a model's calls. Its strengths are the ones that matter for research. The roles are explicit, the debates are configurable, the data is point-in-time where it can be, and it has a real backtest harness that scores decisions against a benchmark.

It is the wrong tool if you are looking for something to trade your money. It places no orders, the README says it is not financial advice, two runs can disagree, and every decision costs at least 15 model calls. Treat its ratings as a research output to study, and keep the sentiment analyst out of anything you backtest.

FAQs

What is TradingAgents?

A free, open-source Python framework from Tauric Research that analyzes a stock with a team of LLM agents modeled on a trading firm: four analysts, a bull and a bear researcher, a research manager, a trader, three risk analysts and a portfolio manager. Given a ticker and a date, it returns one of five ratings (Buy, Overweight, Hold, Underweight or Sell) and a report from every agent. It is built on LangGraph and licensed under Apache 2.0.

Who made TradingAgents?

Tauric Research. The framework accompanies the paper "TradingAgents: Multi-Agents LLM Financial Trading Framework" by Yijia Xiao, Edward Sun, Di Luo and Wei Wang, first posted to arXiv on 28 December 2024. The GitHub repository was created the same day and had more than 115,000 stars in October 2026.

Is TradingAgents free?

The code is free under the Apache 2.0 license. Running it is not, unless you use local models: every decision makes at least 15 calls to the language models you configure, billed by your provider. You can run it on local models through Ollama or an OpenAI-compatible server, or on a ChatGPT Plus or Pro plan, where runs count toward the plan's usage limits.

Can TradingAgents trade with real money?

No. Version 0.7.0 has no broker integration and no code that places orders. A run ends with a rating and its reasoning. The README describes the framework as designed for research purposes and says it is not intended as financial, investment or trading advice.

How many API calls does a TradingAgents run make?

At least 15 with the default settings and all four analysts: 2 for each of the three tool-using analysts, 1 for the sentiment analyst, 2 for the bull and bear, 1 each for the research manager, trader and portfolio manager, and 3 for the risk analysts. Thirteen of those go to the quick model and two to the deep model. More tool rounds, more debate rounds and reflections on past decisions add to it; two rounds of each debate make it 20.

Which models and markets does TradingAgents support?

Most major providers, including OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen, GLM, MiniMax, OpenRouter, Mistral, Groq, Azure OpenAI and AWS Bedrock, plus local models through Ollama and any OpenAI-compatible server. It works with any market Yahoo Finance covers, using exchange suffixes such as .HK, .T, .NS or .SS, and with crypto pairs like BTC-USD.

DeepTutor by HKUDS: The Open-Source AI Tutor That Remembers You in Plain Markdown

Muhammad Tayyab — Fri, 30 Oct 2026 07:37:43 +0000

DeepTutor is an open-source AI tutor from HKUDS, the Data Intelligence Lab at the University of Hong Kong. Its README calls it an agent-native learning workspace: one app where you chat with a tutor, have it quiz you, research a topic into a cited report, work through problems step by step, turn your notes into an interactive book and follow a mastery path, all over your own documents and with one shared memory of how you learn. It had 47,024 stars and 5,858 forks when we checked on October 28, 2026, and the part that makes it genuinely different is not the ten modes. It is the memory: 11 Markdown files you can open, read and correct yourself.

What Is DeepTutor?

DeepTutor combines ten learning modes in one runtime with one shared session context, instead of ten separate apps. The full list is Chat, Ask Questions, Quiz, Research, Visualize, Solve, Course Study, Mastery Path, Immersive Reading and Audio Overview. Because they share context, a quiz can build on what you asked in chat an hour earlier.

The project is led by Bingxi Zhao within HKUDS, the group directed by Chao Huang that also publishes LightRAG and nanobot. It is licensed under Apache-2.0, with a Python 3.11+ backend and a Next.js 16 front end. The repository was created on December 28, 2025 and released the next day, and by its own news feed it reached 10,000 stars in 39 days and 20,000 in 111. It moves fast: we counted 87 GitHub releases between v0.2.0 on January 2 and v1.6.15 on October 18, a median of about two days apart. The research behind it is on arXiv as "DeepTutor: Towards Agentic Personalized Tutoring" (2604.26962). The paper reports a 10.8% average gain on personalization metrics, evaluated with TutorBench, a benchmark the same team introduced, and a 29.4% gain in agentic reasoning across five backbone models. Both are the authors' own results, so read them as claims rather than independent findings.

Its Memory of You Is 11 Markdown Files

Most AI tutors that "remember" you keep that memory where you cannot see it, usually as embeddings in a vector store. DeepTutor's README says the opposite on purpose: memory is a file-backed, three-layer system you can read, curate and audit, "deliberately not a hidden vector store". Reading the memory code shows how literal that is.

L1 is the raw record. Every surface that learns about you (chat, notebook, quiz, knowledge base, book, partner and Co-Writer) appends events to a daily file, trace/<surface>/<date>.jsonl. Nothing in it is rewritten. Each line is one JSON event.
L2 is one Markdown file per surface, such as L2/chat.md and L2/quiz.md: short curated facts about you, each pointing back at the L1 events it came from.
L3 is four Markdown files that synthesize across surfaces: recent.md, profile.md, scope.md and preferences.md.

That is seven L2 files and four L3 files, so DeepTutor's curated memory of a learner fits in 11 Markdown documents you can open in any editor. In the app, the Memory Graph draws the same pyramid, with L3 in the center, L2 around it and the L1 traces on the outside, and its edges are the actual citations between them.

A background consolidator keeps the files current in four passes. Update reads what is new since the last run, oldest first, cuts it into chunks at paragraph boundaries and makes one model call per chunk, at most 20 chunks per L2 run and 10 per L3 run by default. Audit re-checks existing lines against the raw evidence. Dedup merges repeated facts, three iterations by default, and runs automatically after every update. Merge is the only pass with no model call: it collapses duplicate citations into single footnotes. Those budgets live under memory: in data/user/settings/main.yaml and on the Settings, Memory page.

The Rules It Must Follow When It Writes About You

The more interesting part is what the consolidator is allowed to write. The prompts that turn your activity into memory carry hard rules, and one of them is enforced in code as well.

Every fact needs evidence. An L2 fact is one line of at most 240 characters, verb-led ("uses X", "stuck on Y"), and it must cite at least one L1 event from the chunk the model was reading. The code checks this, not just the prompt: a fact with no citation is rejected, a citation to anything outside the chunk is dropped, and a fact left with no valid citation is rejected too.
Cross-surface claims must be hedged. L3 facts have to follow a template, Across N <surface> interactions, the user X or <surface> entries show the user X, so the profile says how much evidence a claim rests on instead of stating it as a trait.
Absolutes are banned. The prompts forbid deeply, truly, mastered, expert, passionate, loves, hates, always, never and fully understands, unless the word sits inside quotation marks, as when quoting the learner. The tutor may write "across 6 quiz interactions, the user missed recursion base cases". It may not write "has mastered recursion" or "always forgets base cases".

That banned list reads like the words a careful editor strips from generated prose, and for a similar reason: they sound confident while saying little. For a tutor the stakes are higher, because a profile that says you have "mastered" a topic is a profile that stops teaching it.

One detail from reading the code: the no-absolutes rule is enforced by the prompt alone. The repository has a code-level filter for those words, in English and Chinese, in consolidator/guards.py, with tests, but we found no call to it in the update, audit or dedup passes, on the main branch at v1.6.15 or on the dev branch as of October 28. The citation rule is code; the no-absolutes rule depends on the model following instructions. That is one more reason the plain files matter. You can open L3/profile.md, read what the tutor believes about you, and correct it.

Everything Else DeepTutor Does

Chat is the default mode and where most work starts. The model works in rounds, calls tools when they help, and finishes with a plain answer, and an ask_user tool lets it stop and ask a clarifying question instead of guessing. Tools you can switch on include web search, paper search, Zotero search, GeoGebra analysis, and image or video generation once you configure a generation model.

Knowledge Center lets you choose the retrieval engine per library: LlamaIndex by default (hybrid vector and BM25 search), PageIndex, GraphRAG, LightRAG or a remote LightRAG server, a self-hosted WeKnora knowledge base, Tencent IMA, MarginNote 4, a Kiwix ZIM archive or a linked Obsidian vault. A library can also follow a GitHub repository or a documentation site and re-sync it, every 24 hours by default for documentation sites. deeptutor kb eval scores a library against a JSONL file of questions and gold passages and reports Recall@k, Precision@k, nDCG@k, MRR and MAP.

Book turns your sources into a "living book" of editable blocks, including quizzes, flash cards, timelines, code, figures and concept graphs, and proposes a chapter outline for you to review before it writes anything. Co-Writer is a split-view Markdown editor where you select a passage and ask DeepTutor to rewrite, expand or shorten just that span. Immersive Reading brings documents, YouTube or Bilibili videos and audio into one reader with timestamp-grounded tutoring and saved notes.

Partners are persistent companions with their own SOUL.md, memory and channels, reachable over Telegram, Slack, Discord, WhatsApp, Microsoft Teams, Feishu and other messaging platforms. My Agents works the other way round: DeepTutor can run Claude Code, Codex, Grok CLI, Kimi, opencode and other agent CLIs on your machine and stream their work into a chat turn, and it can import your ChatGPT, Claude Code and Codex history as searchable conversations. Everything is also reachable from one deeptutor command, with an interactive REPL for people and JSON output for other agents.

How to Run DeepTutor

The quickest trial is one Docker command: run the ghcr.io/hkuds/deeptutor:latest image on port 3782, open http://127.0.0.1:3782, add a model provider under Settings, Providers, and the named volume keeps your settings and data for the next run. For a local install, run pip install -U deeptutor, then deeptutor init (ports, LLM provider and key, and optional embedding and search providers) and deeptutor start. That path needs Python 3.11 to 3.14 and Node.js 20 or newer, because it launches the packaged Next.js server. The README also has a prompt you can paste into Claude Code or Codex to have the agent install and configure everything, reading the repository's own setup guide first.

Two things to know before you start. DeepTutor is free, but the models are not: you bring API keys for the providers you use, or point it at local models served by Ollama, LM Studio or vLLM, and both the learning modes and the memory consolidator make model calls. An experimental OpenAI Codex sign-in can use your ChatGPT plan instead of an API key. And it runs single-user by default. Turn authentication on and one install hosts an admin plus isolated per-user workspaces, with learner and guardian roles for supervised learners. The first account to register becomes the admin, so register yours before you share the address.

Three Projects Called DeepTutor

Search for DeepTutor and you will hit three unrelated things. This page is about the open-source HKU project, documented at deeptutor.info. DeepTutor for Zotero is a separate product: a commercial AI reading add-on with paid plans, built on the Zotero reference manager by a different company. The HKU team states in its README that it has no paid online products of any form, so a price tag means you are looking at the other one. And DeepTutor was also the name of an earlier research system: a 2015 AAAI paper by Vasile Rus and colleagues described a conversational intelligent tutoring system of that name, a decade before large language models.

If a guide mentions a Zotero plugin, a subscription or a 2015 study, it is not about the project on this page.

Who Should Use It, and What to Watch Out For

DeepTutor fits three kinds of user best. Self-learners who study from their own PDFs, notes and lecture videos and want a tutor that remembers what they struggled with last week. Teachers and tutors who can run one shared install with learner accounts. And developers who want an AI tutor they can extend with their own tools, MCP servers and community skills from its EduHub catalog.

Four things to watch. First, scope: DeepTutor does a lot, and the README's own screenshots still show v1.4.6 while a refresh is under way, so expect to explore. Second, pace: a release every two days on median is great for fixes and hard on anyone who needs stability, and v1.6.3 was a breaking front and back end refactor, so pin a version before you roll it out to a class. Third, cost: the software is free, the model calls behind every mode and the background memory passes are not. Fourth, the memory is written by a model. The rules above make it cautious and the files make it checkable, but read profile.md now and then rather than trusting it blindly.

FAQs

What is DeepTutor?

An open-source AI tutor from HKUDS, the Data Intelligence Lab at the University of Hong Kong. It combines chat, quizzes, research reports, worked solutions, interactive books, reading and mastery paths over your own documents, with one memory of how you learn shared across all of them.

Is DeepTutor free?

Yes. It is open source under Apache-2.0, and the team states it has no paid online products. You pay only for the AI models you connect, or nothing extra if you run local models through Ollama, LM Studio or vLLM.

How do I install DeepTutor?

The quickest way is Docker: run the ghcr.io/hkuds/deeptutor:latest image on port 3782 and open http://127.0.0.1:3782. For a local install, run pip install -U deeptutor, then deeptutor init and deeptutor start, with Python 3.11 to 3.14 and Node.js 20 or newer.

How does DeepTutor remember what I have learned?

With a three-layer, file-based memory. L1 is an append-only daily log of events per surface, L2 is one Markdown file of cited facts per surface, and L3 is four Markdown files (recent, profile, scope and preferences) that synthesize across surfaces. A background consolidator updates, audits and deduplicates them.

Can I see or edit what DeepTutor remembers about me?

Yes. The memory is plain JSONL and Markdown files rather than a hidden vector store, and the app's Memory Graph shows how each fact links to the evidence behind it. You can read the files, edit them, and tune the consolidator under Settings, Memory.

Is DeepTutor the same as DeepTutor for Zotero?

No. DeepTutor for Zotero is a separate commercial add-on with paid plans, from a different company. The HKU DeepTutor is free and open source, and its team states it has no paid products. A 2015 tutoring research system by Vasile Rus and colleagues also used the name.

Who made DeepTutor?

HKUDS, the Data Intelligence Lab at the University of Hong Kong directed by Chao Huang, with the project led by Bingxi Zhao. The repository was created in December 2025 and had 47,024 stars in October 2026.

RuView: The Open-Source Project That Sees Through Walls With WiFi

Muhammad Tayyab — Thu, 29 Oct 2026 10:16:17 +0000

RuView reads the way your body disturbs the WiFi signals already flooding your rooms, and turns that into presence, movement, breathing and heart rate, through walls and in the dark, with no camera and no wearable. It has 101,423 stars for a reason. It also has honest limits the README does not hide: the pose model is early, the vitals are unvalidated, and the whole thing is beta.

What Is RuView?

RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring and presence detection, in the project's own words, without a single pixel of video. Your router already floods every room with radio waves. When a person moves, or even breathes, those waves scatter differently. RuView reads that scattering pattern, called Channel State Information or CSI, and reconstructs what happened: someone is in the room, how many people there are, whether they are moving, how fast they are breathing, and what their heart rate is doing.

The numbers explain the attention. The repository, from the ruvnet organization, was created in June 2025 and had 101,423 stars and 13,182 forks when we checked on October 29, 2026. It is written mostly in Rust, licensed under MIT, and built to run on edge hardware rather than in the cloud. A small pretrained model published on Hugging Face does the sensing, and the whole stack is designed so your rooms can be aware of you without a camera ever watching you.

How It Works: From Radio Waves to a Room Model

The pipeline starts with the WiFi you already have. A mesh of 4 to 6 ESP32 nodes captures CSI on channels 1, 6 and 11, and multi-band fusion combines 3 channels with 56 subcarriers each into 168 virtual subcarriers per link. Fusing the viewpoints of every node pair gives an attention-weighted picture of the room, a coherence gate throws out unstable measurements, and classic signal processing (Hampel filtering, SpotFi, Fresnel-zone geometry, breathing and heart-rate extraction, spectrograms) cleans up what is left. Then the AI backbone, called RuVector, turns those features into output: real-time pose, breathing, heart rate, a fingerprint of the room, and drift alerts when the room changes.

Two details make it more than a demo. First, the system learns each room on its own. Its self-learning pipeline builds compact fingerprints from raw CSI and adapts to a new environment, with spiking neural networks that the README says adapt in under 30 seconds. Second, the sensing model is tiny. The pretrained encoder fits in 8 KB when quantized to 4 bits and runs in microseconds on a Raspberry Pi, with a held-out temporal-triplet accuracy of 82.3 percent that the project re-benchmarked honestly after retracting an earlier, weaker 100 percent presence claim measured on a single recording. That kind of self-correction is rare in a README, and it is a good sign.

What You Can Actually Do With It

The most relatable use is the smart home. Room-level presence that works through walls means lights, heating and music that follow real occupancy instead of motion-sensor timeouts, with no dead zones. Elderly care is the most human one: fall detection in under 2 seconds and nighttime breathing monitoring with no wearable the person has to remember to charge or wear. Hospitals get continuous breathing and heart rate for non-critical beds without wired sensors, retail gets foot traffic and dwell time by zone, offices get true desk and meeting-room utilization, and hotels get occupancy-driven energy savings the project puts at 15 to 30 percent on HVAC.

Then it gets more extreme, because WiFi goes where cameras cannot. The disaster module, called WiFi-Mat, is built to detect survivors through rubble by their breathing signature, through 30 cm of concrete. Firefighters can locate occupants through smoke and walls before entry. Warehouses get forklift proximity alerts through shelving, and the project ships a catalog of 105 edge modules, small programs that run directly on the ESP32 sensor with no internet, covering health, security, building, retail, industrial and research jobs. Each access point distinguishes roughly 3 to 5 people, and a 4-AP mesh covers about 15 to 20 occupants, so the practical ceiling is signal physics, not software limits.

Smart Home Integrations

RuView works natively with the four major smart-home ecosystems, which is unusual for a project this deep in signal processing. Home Assistant connects through an MQTT publisher, Apple Home sees it as a discoverable HAP-1.1 bridge, and Google Home and Amazon Alexa come along through the same Home Assistant bridge or a Matter endpoint. Siri, Google Assistant and Alexa can then voice presence and vitals by room with no custom skills to build.

The practical effect is that a room can report itself. A bedroom that knows someone is asleep and breathing steadily, a living room that knows it is empty and can let the heating drop, a hallway that knows someone fell and has not moved. All of that arrives over the WiFi and sensor mesh you already run, with the heavier features, persistent memory and a cryptographic witness chain, available through the optional Cognitum Seed device the project sells for around 140 dollars total hardware.

The Privacy Pitch, and Where It Ends

The privacy argument is the project's strongest card, and it is real as far as it goes. There is no video, so deployments sidestep the consent, signage and retention rules that come with cameras, and the README notes that avoiding video keeps GDPR video and HIPAA imaging rules out of the picture by design. Everything runs on edge hardware, an ESP32 mesh paired with local processing, with no cloud and no internet required, and every measurement can be cryptographically attested through an Ed25519 witness chain.

But honest framing cuts both ways. A sensor that counts people through walls and reads breathing is still a sensor that watches people, and presence plus vitals is sensitive data even when no face is attached. The right question is not whether RuView is private in the abstract, it is who controls the sensor, who sees its output, and whether the people being sensed know it is there. In your own home, with your own hardware and your own data, the answer is good. Pointed at someone else's room, the same technology is surveillance. The tool does not decide that. You do.

How to Try It

The fastest trial needs no hardware at all. Pull the Docker image ruvnet/wifi-densepose, run it on port 3000 and open localhost:3000, but know that Docker runs on simulated data, so it shows you the system, not your rooms. For live sensing you need CSI-capable hardware. The recommended path is an ESP32-S3 mesh, about 9 dollars per node: flash the CSI node firmware, provision your WiFi credentials, and watch live data with the project's npm toolkit, which also ships an MCP server and a Claude Code sensing pane.

A cheaper research option is the ESP32-C6 board at 6 to 10 dollars, and a Python package is on PyPI under both ruview and wifi-densepose, the same compiled wheel under two names. One warning from the README matters here: a normal laptop WiFi card only gives RSSI, coarse signal strength, which is enough for rough presence and motion but not for breathing, heart rate or pose. Those need real CSI from an ESP32-S3 or a research network card.

What to Watch Out For

RuView is beta software, and the README says so plainly. APIs and firmware may change. The ESP32-C3 and the original ESP32 are not supported, because a single core cannot handle the CSI signal processing, and a single ESP32 node gives limited spatial resolution, so plan on two or more nodes.

The bigger caveats are about what the models can actually do today. The on-device pose model is a first cut: its PCK@20 score is around 3 percent, far below the 35 percent target, and its runtime path is still a stub. The widely quoted 82.69 percent figure is a separate benchmark result on the MM-Fi dataset, not the live on-device model, and the project is explicit about that distinction. Vital-sign outputs are labelled unvalidated, and the README draws a safety boundary in bold: these are research and prototype applications, not medical devices, emergency systems or safety-certified controls, and any vital-sign or pose output needs independent validation on your exact hardware, room, subjects and failure conditions before operational use. Cross-room pose without retraining is still a proposed research direction, not a solved problem. None of that makes RuView uninteresting. It makes it a project to adopt with eyes open, which is exactly how its own documentation treats it.

Fuller write-up on DevPik: RuView: The Open-Source Project That Sees Through Walls With WiFi

Postiz: The Open-Source Social Media Scheduler That Lets AI Agents Post for You

Muhammad Tayyab — Tue, 27 Oct 2026 13:55:51 +0000

Postiz is the open-source answer to the monthly scheduler subscription: plan posts on a calendar, publish to 14 platforms at once, check analytics, and invite your team, all from one dashboard. The twist is the agentic part. ChatGPT, Claude, Codex and other AI agents can create and schedule posts for you through an MCP server and a CLI. Self-host it free under AGPL-3.0, or use Postiz Cloud if you would rather someone else run it.

What Is Postiz?

Postiz is a social media management platform for scheduling, automating and analyzing your content, and its open-source edition does the whole job for free. You connect your channels, plan posts on a visual calendar, publish everywhere at once, check how each post performed, and invite team members to collaborate, comment and schedule alongside you. The project calls itself the ultimate agentic social media scheduling tool, and the agentic part is what separates it from the dozen other schedulers on GitHub.

The numbers explain why it caught on. The repository has 38,946 stars, it reports over 7 million downloads, and it is still being updated, with commits landing days ago. Under the hood it is a TypeScript monorepo: Next.js on the front end, NestJS on the back end, Prisma over PostgreSQL, Temporal for scheduled work and Redis for queues. That stack matters less than what it buys you. The same core product ships two ways, as Postiz Cloud, the fully managed service, and as the open-source edition you deploy on your own server. The team is explicit that they do not gate features or limit the license. The difference between the two is who runs the infrastructure, not what the software can do.

The Agentic Part: Tell an AI Agent to Post for You

Here is the feature that makes Postiz a 2026 tool rather than a 2019 one. You can tell ChatGPT, Claude, Codex, OpenClaw, Cursor, Perplexity Computer, nanoclaw or Paperclip to create and schedule posts for you, and they do it through Postiz directly. The project ships an MCP server, a CLI and dedicated connectors for the major agents, plus a Smart Agent of its own. In practice that means a plain-language instruction like "schedule a post about our launch tomorrow at 9am on X, LinkedIn and Instagram" becomes a real scheduled post, not a draft you still have to copy somewhere.

If you would rather wire it yourself, the same idea holds through the API. Postiz has a public API and webhooks, a Node.js SDK, an n8n custom node and a Make.com integration, so it slots into automations alongside Zapier-style tools. RSS auto-post is built in too: a new blog post or video can turn into scheduled social posts without anyone touching the dashboard. The point is not that AI writes your content for you. It is that the boring middle step, opening five apps and pasting the same post five times, stops being your job.

Every Channel in One Calendar

Postiz schedules to 14 channels: Instagram, YouTube, TikTok, Facebook, X, LinkedIn, Threads, Bluesky, Mastodon, Reddit, Pinterest, Dribbble, Slack and Discord. One composer, one calendar, and the post goes out everywhere you picked. Around that core sit the features teams actually live in: cross-posting, repeated posts for evergreen content, delays between posts, post sets, signatures, and comments on posts so a teammate can approve or fix copy before it goes out.

Analytics covers the other half of the job. You can measure each post and each channel from the same dashboard instead of opening 14 analytics tabs. Customer groups and internal and global plugs let agencies and larger teams organize clients and reuse content blocks. None of this is exotic on its own. The appeal is that all of it, every channel and every feature, is included in the free self-hosted edition with no per-seat or per-post counting.

Self-Hosted vs Cloud: The Honest Trade-Off

Self-hosted Postiz is free forever under AGPL-3.0, with unlimited channels, unlimited posts per month and unlimited team members, and your data never leaves your own server. That is the whole pitch, and it is real. But free software is not free effort, and the README is unusually honest about the trade. You deploy it yourself with Docker, Coolify, Railway or any VPS, and you configure PostgreSQL, Redis, storage and environment variables. You create your own developer apps on each social platform and go through their approval process, which for Meta, YouTube and TikTok can take weeks. AI features like the copilot, AI images, AI videos and the Smart Agent work self-hosted, but you bring your own provider API keys and pay the provider. Updates, backups and security patches are your job too.

Postiz Cloud flips all of that. You sign up, the platform apps are pre-approved for every channel, and you are posting in minutes, with a 7-day free trial. What you pay for is the managed part: a subscription per plan, with limits on channels, posts and team members by tier, and monthly quotas on AI features. If you want to try the product before committing to a server, Cloud is the fast path. If you want the unlimited version and you can run a Docker container, self-hosting is the better deal, and the docs point you at a quick-start guide to get there.

Who Should Use It, and What to Watch Out For

Postiz fits three people best. Creators who post daily and are tired of paying a scheduler that charges per channel. Small teams that need one calendar, approvals and analytics without an enterprise contract. And agencies managing several clients, where customer groups and unlimited seats make the self-hosted math obvious. The agentic features are a bonus for anyone already living inside ChatGPT or Claude all day.

Three things to watch before you commit. First, self-hosting is a real deployment, not a one-click install, and the platform approval wait is the longest part of the setup. Second, AGPL-3.0 has teeth: if you modify Postiz and offer it as a service to others, you owe those users the source of your modified version. Using it for your own posting is unaffected. Third, the AI features are only as cheap as your provider keys. The software does not charge you, but the model calls behind the copilot and the Smart Agent still show up on your OpenAI bill. None of these are reasons to skip it. They are the difference between adopting it with eyes open and bouncing off it in week two.

FAQs

What is Postiz?

A free, open-source social media management platform for scheduling, automating and analyzing content. You plan posts on a calendar, publish to 14 channels at once, check analytics, and collaborate with your team from one dashboard.

Is Postiz really free?

The open-source edition is free forever under AGPL-3.0, with unlimited channels, posts and team members. You pay only for your own server. Postiz Cloud, the managed service, is a paid subscription with a 7-day free trial.

Which social media platforms does Postiz support?

Instagram, YouTube, TikTok, Facebook, X, LinkedIn, Threads, Bluesky, Mastodon, Reddit, Pinterest, Dribbble, Slack and Discord.

Can ChatGPT or Claude really schedule posts for me?

Yes. Postiz ships an MCP server, a CLI and connectors for ChatGPT, Claude, Codex, OpenClaw, Cursor and other agents, so an agent can create and schedule posts on your connected channels. Self-hosted, you point the MCP server or CLI at your own instance and bring your own LLM key.

What do I need to self-host Postiz?

A server with Docker, Coolify, Railway or any VPS, plus PostgreSQL, Redis, storage and environment variables, following the quick-start guide in the docs. You also create your own developer apps on each social platform, and approvals from Meta, YouTube or TikTok can take weeks.

What is the difference between Postiz Cloud and the open-source version?

The same core product and features in both, with no gated features. Cloud is fully managed with pre-approved platform apps and plan limits on channels, posts and team members. Self-hosted is free and unlimited, but you own the infrastructure, the approvals, the updates and the backups.

Who made Postiz?

The gitroomhq organization on GitHub. The repository was created in July 2023 and had 38,946 stars in October 2026.

Impeccable: The Open-Source Design Skill That Stops AI Websites Looking AI-Generated

Muhammad Tayyab — Sun, 25 Oct 2026 08:26:31 +0000

Every AI-built website looks the same: Inter everywhere, purple gradients, cards inside cards. Impeccable is the open-source design skill that teaches your AI coding agent real design: 24 commands like polish, bolder and delight, 62 deterministic checks that catch AI-looking design, and live iteration in the browser. It works inside Claude Code, Cursor, Codex and more, and it is free under Apache 2.0.

What Is Impeccable?

Impeccable is design guidance for AI coding agents, in the form of a skill your agent can actually use. One skill, 24 commands, live browser iteration, and 62 deterministic detector rules, all aimed at one problem: AI-generated frontend design looks the same everywhere. If you have opened a few AI-built sites lately, you know the look. Inter for every font. Purple-to-blue gradients. Cards nested inside cards. Gray text sitting on colored backgrounds. The little rounded-square icon tile above every heading. Every model trained on the same SaaS templates, so without guidance they all converge on the same handful of tells.

Impeccable started from Anthropic's frontend-design skill, the first widely used design skill for Claude, and pushed it further. Instead of a document your agent reads once and forgets, it gives the agent a shared design vocabulary: commands it can run, checks it can execute, and a live mode where it iterates on real elements in the browser. The goal is not to make the agent a designer. It is to stop the agent from shipping the default look every single time.

The 62 Rules That Catch AI Slop

The part that makes Impeccable different from a prompt full of taste is the detector rules. Sixty-two of them are deterministic, which means the CLI and the browser extension can run them with no language model and no API key. They check for the tells directly: overused fonts like Arial, Inter and system defaults, gray text on colored backgrounds, pure black or gray that should have been tinted, everything wrapped in cards or cards nested in cards, and bounce or elastic easing that feels dated. On top of those, there are LLM-only critique checks for the things a rule cannot judge, like hierarchy and emotional resonance.

That split matters. Deterministic checks are fast, repeatable and free to run, so they can sit in the loop every time the agent touches a surface. The agent does not have to remember the rules or feel inspired by them. The rules just run, and the design either passes or it does not.

24 Commands, One Shared Vocabulary

The skill itself is one install, and everything hangs off a single slash command. Run npx impeccable install from your project root, then run /impeccable init inside your AI coding tool. Init inspects the project, asks only about the material gaps, and writes PRODUCT.md: durable product truth like the audience, purpose, operating context, constraints, voice and evidence. Visual direction lives separately, in DESIGN.md, so the agent stops confusing what the product is with what it should look like.

From there the vocabulary opens up. Shape plans the UX and UI before code exists. Craft runs the full shape-then-build flow with visual iteration. Critique reviews hierarchy, clarity and emotional resonance. Audit runs the technical checks for accessibility, performance and responsiveness. Polish is the final pass before shipping. Bolder amplifies a boring design, quieter tones down an over-the-top one, distill strips a design to its essence. Animate adds purposeful motion, colorize introduces strategic color, typeset fixes font choices and hierarchy, layout fixes spacing and rhythm, delight adds moments of joy, overdrive adds technically extraordinary effects. Harden covers error handling, internationalization, text overflow and edge cases. Onboard handles first-run flows and empty states. Clarify improves unclear copy, adapt adjusts for different devices, optimize improves performance. Live and generate iterate on elements directly in the browser, with generate picking variants without manual selection. And pin turns any command into a standalone shortcut, so pin audit gives you /audit on its own.

How It Fits Your AI Coding Tool

Impeccable is built for the harness you already use. The installer detects the folders and CLIs on your machine, from Claude Code and Cursor to Codex, GitHub Copilot, Gemini CLI, Grok Build, Hermes Agent and Veto, and installs into the project or globally, whichever you choose. The skill itself needs no runtime of its own. Every copy ships a small launcher that runs a self-contained engine binary, downloaded once on first run. Node only enters the picture if you use the npx installer, which is a shim around the same binary. Manual and Git installs work without it.

The project is also honest about what it is for. The docs point to a real before-and-after case study, the Neo Mirai project, transformed with Impeccable commands. That is the pitch in one image: same project, same agent, different design judgment. And it is a big part of why the numbers look the way they do.

Why It Blew Up

Impeccable launched in November 2025 and, less than a year later, passed 86,000 GitHub stars with over 5,000 forks, and it is still being updated, with commits landing days ago. The timing explains a lot of it. AI coding agents got good at writing working code faster than they got good at making it look considered, and everyone shipping with them hit the same wall: the product worked, but it looked generated. Impeccable names that gap and gives the agent something concrete to do about it, in the tools developers already live in, for free under Apache 2.0. If your agent's output looks like every other agent's output, this is the skill that breaks the pattern.

Microduck: The Tiny Open-Source Robot Duck That Taught Itself to Walk

Muhammad Tayyab — Sat, 24 Oct 2026 13:06:28 +0000

What Is Microduck?

Microduck is a tiny biped robot duck, about 25 cm tall and 800 g, built by Pollen Robotics, the robotics team at Hugging Face. The repository is, in the project's own words, the duck's brain: everything needed to run a Microduck, from the control loop to the update machinery that gets new software onto the robot without bricking it.

What it does reads like a toy list and a robotics demo at the same time. Pick up a gamepad and drive it around. Put wheels on and it rolls in roller mode. It picks things up with its beak, kicks a ball, sits down, and quacks in a voice that is its own. Knock it over and it stands itself back up. That last trick is the one that tells you this is not a remote-controlled toy with a duck shell on it. Getting back up is a learned skill, not a scripted animation.

The numbers explain the attention. The repo passed 10,000 GitHub stars within months of its July 2026 debut, with over 1,300 forks, and it is Apache 2.0 licensed, so anyone can use, modify and build on it. If you want the physical duck, the project points you to its own page. If you want the brain, it is all here.

It Taught Itself to Walk

Microduck did not learn to walk by being programmed step by step. It learned in simulation, using reinforcement learning, and the training lives in a sibling repository called microduck_rl. There, policies are trained with MuJoCo and PPO, with domain randomization to close the gap between simulation and the real world, and then exported to ONNX, the format this repo loads and runs.

That division of labor is the whole design. One repo trains the policies. This repo runs them. On the robot, a 50 Hz control loop drives fifteen servos from those neural policies, alongside the radios, the camera and the update system. Roller mode works the same way: ask for it and the duck loads the other brain, the one trained for wheels instead of feet.

Training in simulation first is also why a duck this small can be this capable. A real robot that falls over thousands of times while learning would break. A simulated one just resets. By the time a policy reaches the hardware, it has already fallen over, gotten up and tried again more times than any physical duck ever could.

Under the Hood: Rust Daemons on a Rockchip

The robot runs on a Rockchip RK3566, and the software is Rust with no framework, one workspace, a handful of daemons that each own one job. robotd owns the control loop and the motor bus. updaterd installs signed releases and rolls them back when a robot comes up unhealthy. configd owns wifi and identity. btd is the Bluetooth path a phone uses. padd reads the gamepad. mediad streams the camera over WebRTC. tofd serves the depth sensor.

They all talk over one JSON-RPC contract on Unix sockets, and every client sends exactly the same calls: the app, the console, the gamepad, your script. That uniformity is a deliberate choice. It means there is no special privileged path into the robot. If you can drive it from the app, you can drive it from a script with the same calls.

Updates get the same care as movement. Every update is verified, health-gated and reversible: install, roll back, pin. A bad release does not brick a duck, it gets rolled back. The project writes its interesting decisions down in its design docs, and its project docs record what has gone wrong and what would close it, which is the kind of honesty that makes an open-source hardware project worth building on.

What It Can Do on Your Desk

Start with the obvious: walking. Pair a gamepad, drive the duck around, and it walks. Then the party tricks, which are really capability demos. Roller mode swaps the walking brain for the rolling one. The beak picks things up off the floor on one button press. It kicks a ball. It sits. And when you knock it over, it gets back up by itself, which never stops being satisfying to watch.

The owner's cheat sheet covers every robotctl command: drive, configure, voice, chorale, theremin, wifi, updates and logs. Yes, chorale and theremin are on that list. A robot duck that can sing in a choir and play a theremin is exactly the kind of detail that tells you the team building this is having fun, and that the platform is flexible enough to allow it.

Control is not limited to the gamepad. duckctl drives the robot from a laptop over Bluetooth, with no network and no ssh. And if you do not have a duck yet, the simulation runs the real daemons against a body in MuJoCo, one duck in a window or four as machines you log into, so you can develop against the same software the hardware runs.

Get One, or Build on It

If you want the physical robot, the project page is the place to go. If you already have one, the docs are organized by what you are trying to do: the cheat sheet for everyday commands, the gamepad guide for pairing a pad and its full button mapping, duckctl for laptop control, and the updates guide for installing, rolling back and pinning releases.

If you want to build on it, the doors are all open. The training repo has the MuJoCo and PPO setup, the sim2real recipe and the ONNX export. The architecture doc puts the whole system on one page, the daemons, the bus, how an update reaches a robot, and then goes a page per part. The dev board guide takes you from a blank board to a robot running branch builds, and the dev cheat sheet covers branch builds, release candidates, driving from a laptop and the restart traps after an update. Push your branch over ssh and it installs in about a minute.

The honest summary is the README's own: no duck was harmed in the making of this robot. Several were consulted. What you get is a small, real, open-source biped that learned to move the modern way, with software you can read, run and change.

Originally published at devpik.com/open-source/microduck. Repo: github.com/pollen-robotics/microduck.

HyperFrames: HTML to Video, a Free Remotion Alternative (70K Stars)

Muhammad Tayyab — Fri, 23 Oct 2026 12:39:29 +0000

What Is HyperFrames?

HyperFrames is a free, open-source framework from HeyGen that turns HTML, CSS, media and seekable animations into MP4 video. You describe a video as an ordinary HTML page with a few timing attributes, and the renderer opens it in headless Chrome, captures it one frame at a time and encodes the result with FFmpeg. The project's own tagline is three short sentences: "Write HTML. Render video. Built for agents."

The last sentence explains the attention. HeyGen published the repository in March 2026 under the Apache 2.0 license, and it passed 70,000 GitHub stars in under eight months. Coding agents such as Claude Code, Codex and Cursor already write HTML well, so a video format that is just HTML is one they can produce without learning an editor. HyperFrames adds agent skills that teach them its rules, a CLI that never stops to ask questions, and a catalog of about 450 ready-made blocks.

It works without an agent too. You need Node.js 22 or newer and FFmpeg. HeyGen says it uses HyperFrames in production, and the README lists tldraw and TanStack among the teams with community examples.

A Video Is an HTML File With Timing Attributes

A HyperFrames composition is a normal HTML file. The root element names the composition and fixes its size, and every element that appears on screen gets a start time, a duration and a track. That says when things are on screen. How they move is left to an animation library.

GSAP is the default, but the runtime also seeks CSS animations, the Web Animations API, Lottie, Three.js, Anime.js and TypeGPU, and an experimental FrameAdapter interface lets you plug in anything else that can jump to a given frame. There is no build step: index.html plays as it is, so you can open a composition in a browser and inspect it with the same devtools you use for any web page.

The same file can target any shape. hyperframes init takes resolution presets for landscape (1920x1080), portrait (1080x1920), square and their 4K versions.

Seek, Never Play: Why the Same Input Gives the Same Frames

The design decision that matters most is stated in the project's determinism docs: rendering never plays your video. A screen recorder captures whatever happens on screen as time passes, so a slow frame becomes a dropped frame. HyperFrames asks for one frame at a time instead. For frame 90 at 30 fps it works out the time with integer math (floor(frame) / fps, never a clock), seeks every registered animation to exactly that moment, grabs the pixels in one atomic operation through Chrome's HeadlessExperimental.beginFrame, and passes the frame to FFmpeg.

Because the only thing that changes between frames is the frame number, the same composition produces the same video on every run. That is what makes it usable in CI, in regression tests and in pipelines where an agent edits a file and renders again. It also means an expensive scene never stutters in the output. The docs put it plainly: a composition that stutters in preview still renders perfectly, it just takes longer.

The guarantee comes with rules, and the docs list them:

No wall clock. No Date.now(), no requestAnimationFrame, no system timers.
No unseeded randomness. Math.random() without a seed gives a different frame every run.
No fetching mid-render. Every asset loads before the first frame.
Fixed output size. Frame rate, width and height are locked before frame 0.
A finite length. Every composition has a known end.

One caveat is about machines rather than code. Fonts and Chrome builds differ between computers, so a local render can shift by a pixel from one machine to the next. npx hyperframes render --docker pins the Chromium version, the font set and the FFmpeg encoder when you need identical output everywhere.

We Rendered a Title Card: 3.7 Seconds, Byte-Identical Twice

To test the determinism claim, the devpik.com article rendered the three-second title card from HyperFrames' own Remotion comparison guide: a white "HELLO" on near-black that fades in over half a second, holds, and fades out, at 1280x720 and 30 fps.

hyperframes init scaffolded the project in 0.5 seconds.
The first render took 87 seconds of wall time, mostly one-time setup. The CLI reported 6.4 seconds for the render itself.
The second render took 3.7 seconds (4.6 seconds of wall time): 1.0 second of setup and 2.4 seconds capturing 90 frames, with encoding running alongside the capture.
The output was a 16.2 KB H.264 MP4: 1280x720, 30 fps, 90 frames.
The two files were byte-identical. The whole MP4 had the same MD5, and so did every one of the 90 frames.

The renderer also labels what it writes. ffprobe shows two container metadata keys, hyperframes_renderer and hyperframes_version, and nothing about the project itself. They are not a watermark, and ffmpeg -i out.mp4 -map_metadata -1 -c copy clean.mp4 strips them.

A title card is the easy case. A composition with video clips, audio, shaders and dozens of layers takes far longer per frame, and the project's performance guide shows one CSS declaration moving a 300-frame 1080p render from 25.0 seconds to 9.8. The property that matters, the same input giving the same output, held exactly.

The One Line That Turns a Page Into a Video

The comparison guide warns that HyperFrames "breaks quietly" if you skip its rules, so the article tested the easiest rule to forget: registering the timeline on window.__timelines. Deleting those two lines from the title card and rendering it again gave a three-second video in which all 90 frames were the same image: an empty dark background, because the title starts at zero opacity and nothing ever animated it. The process exited with code 0, so a script or CI job would have counted it as a success.

hyperframes lint caught the mistake straight away, with two errors (missing_timeline_registry and gsap_timeline_not_registered) and the fix printed underneath. hyperframes render reported the same errors and carried on anyway. It waited 45 seconds for a timeline that was never coming, then finished in 48.8 seconds.

Two habits prevent this:

Render with --strict. On the same broken file it stopped before rendering, exited with code 1 and wrote no video.
Run npx hyperframes check before a long render. It runs lint, a headless runtime check and a layout inspection as one gate.

A composition that is deliberately driven by CSS animations rather than a GSAP timeline can mark its host element with data-no-timeline, which the renderer's own message suggests, so it skips the wait.

Seven Skills for Claude Code, Codex and Cursor

The quickest way into HyperFrames is to let a coding agent drive it. Other agents, including Codex, Cursor, Gemini CLI and OpenCode, install the skills with npx skills add heygen-com/hyperframes, or with npx hyperframes skills update, which pulls the current core set straight from the main branch. Then you ask for a video in plain language: a ten-second product intro with a fade-in title, a background video and quiet music, for example.

HyperFrames publishes seven skills. Four make up the core set:

/hyperframes: the render contract and the work loop. Read first for any video request.
/hyperframes-cli: the CLI by job, from scaffolding and checking to inspecting frames, editing the timeline, previewing, rendering and publishing.
/hyperframes-registry: about 450 ready-made blocks and components, from transitions and kinetic type to captions, charts, maps and 3D.
/media-use: music, sound effects, images, voiceover, avatars, transcripts and colour grades, using HeyGen or free local tools.

Three more install on demand: /figma for bringing in Figma designs and brand tokens, /slideshow for navigable decks rather than MP4s, and /remotion-to-hyperframes for porting existing video code.

hyperframes init also writes an AGENTS.md and a CLAUDE.md into every new project, so an agent that opens the folder finds the rules without being told. Two defaults are worth knowing before you script the CLI: it sends anonymous usage telemetry (turn it off with npx hyperframes telemetry disable, HYPERFRAMES_NO_TELEMETRY=1 or DO_NOT_TRACK=1), and init keeps the installed agent skills up to date from GitHub unless HYPERFRAMES_SKIP_SKILLS=1 is set.

Catalog, frame.md and Every Output Format

Most videos reuse the same pieces, and HyperFrames ships them as source you install into your project rather than as a locked library. frame.md takes the same idea to branding: every brand has a design.md and none of them were written for a camera. frame.md inverts a web design spec for the frame: the same tokens, rewritten for a fixed canvas so an agent can compose a promo without guessing at scale.

Rendering goes well beyond MP4:

Format	Use it for
MP4	Normal sharing and delivery
MOV	A ProRes 4444 editing intermediate that keeps transparency
WebM	Web delivery, including transparent overlays
GIF	Short previews in issues, pull requests and docs
PNG sequence	Frame-by-frame handoff to compositing software
HLS	Streaming, with fixed-length segments

Renders run locally by default, in Docker for a pinned environment, and on HeyGen's hosted cloud, AWS Lambda or Google Cloud Run for distributed work. Around the renderer sit Studio, a browser editor that changes the same DOM the renderer captures, a native desktop app for Mac and Linux, and the hyperframes.dev playground.

HyperFrames vs Remotion

HyperFrames says openly that it is inspired by Remotion, the established React framework for programmatic video. Both open a real browser, draw each frame and encode it with FFmpeg. The difference is what you write and how time works.

In Remotion, a component is a pure function of the frame number: you call useCurrentFrame() and compute each value yourself, usually with interpolate. In HyperFrames, you write HTML and an ordinary animation, and the renderer pauses that animation and seeks it to each frame.

| | HyperFrames | Remotion |
| --- | --- |
| You write | HTML, CSS and JavaScript | React components |
| Timing | The renderer seeks your paused animation | Your code reads the frame number |
| Build step | None | Bundler required |
| Existing web work | GSAP, Lottie and CSS animations drop in | Rewritten as React |
| License | Apache 2.0 | Remotion License: free for individuals and companies of up to three people, paid above that |

The guide is unusually fair to its rival. It says Remotion is older, with more templates, tutorials and production history; that Remotion Lambda is the more mature cloud renderer; and that a team already writing React gets its components and design system for free. It also admits that the HyperFrames model asks you to follow rules and breaks quietly when you don't. HyperFrames' side of the case is no React, no build step and no rewrite, HTML that agents already write well, and an Apache 2.0 license with no seat count.

For teams moving across, the /remotion-to-hyperframes skill maps useCurrentFrame and interpolate onto timeline tweens and Sequence onto clips. The docs estimate that about 80% of a typical composition translates mechanically.

Installing HyperFrames and Rendering Your First Video

By hand, the loop is four commands: scaffold, preview, check and render. Before a final render, the docs recommend npx hyperframes lint and npx hyperframes check, and npx hyperframes doctor when something in the environment looks wrong. Render flags worth knowing:

--output final.mp4 to name the file;
--format webm, mov, gif, png-sequence or hls;
--quality draft for quick reviews, or high for a master;
--fps 60 when the destination needs it;
--docker for a pinned environment;
--strict to stop on lint errors.

The requirements are Node.js 22 or newer and FFmpeg; the CLI manages the Chrome it renders with. Cloning the repository is only needed to contribute, and it needs Git LFS for about 240 MB of golden test videos.

Who Should Use HyperFrames, and Who Can Skip It

HyperFrames fits best when video has to be made repeatedly or by software: product announcements built from a template, PR walkthroughs with animated code diffs, data videos and chart races, captioned social clips, or a pipeline where an agent writes the composition and a CI job renders it. Web developers get the most from it, because what they already know about HTML, CSS and animation carries over, and determinism means a render can be tested like code.

It is a weaker fit if your team is already productive in Remotion and React, where switching buys little, or if you cut footage by hand and want a timeline editor first. Studio and the desktop app exist, but the project's center of gravity is the HTML file. It also asks for discipline: no clocks, no unseeded randomness, a registered timeline. Render with --strict and those rules stop being traps.

Frequently Asked Questions

Q: What is HyperFrames?

A: HyperFrames is a free, open-source framework from HeyGen that renders HTML, CSS, media and seekable animations to video. You write a composition as an HTML file with timing attributes, and its CLI captures it frame by frame in headless Chrome and encodes it with FFmpeg into MP4, WebM, MOV, GIF, a PNG sequence or HLS. It ships agent skills so Claude Code, Codex, Cursor and other coding agents can write the videos for you.

Q: Is HyperFrames free?

A: Yes. HyperFrames is open source under the Apache 2.0 license, with no per-render fees, seat limits or commercial-use thresholds, and it renders on your own machine. The optional extras run on other infrastructure: HeyGen's hosted cloud renderer, your own AWS Lambda or Google Cloud Run account, and HeyGen's media services, which the media skill can use alongside free local tools.

Q: Is HyperFrames better than Remotion?

A: It depends on what you write. Remotion is older and more established, with more templates and a more mature cloud renderer, and a React team gets its existing components for free. HyperFrames needs no React or build step, takes plain HTML with GSAP, Lottie or CSS animation, suits AI agents, and is Apache 2.0 rather than Remotion's license, which is paid for companies above three people.

Q: Why is my HyperFrames render a still or black video?

A: The most common cause is a timeline that was never registered on window.__timelines under the composition's id. In the test, removing that registration produced a three-second video of 90 identical dark frames after a 45-second wait, while lint had already reported two errors. Run npx hyperframes lint or check first, render with --strict so lint errors stop the render, and mark compositions driven only by CSS animation with data-no-timeline.

Q: Is HyperFrames rendering really deterministic?

A: On one machine, yes in the test: two renders of the same title card produced byte-identical MP4 files with matching hashes for all 90 frames. HyperFrames seeks each frame by number instead of playing in real time, so the output depends only on the composition. Fonts and Chrome builds differ between computers, so render with --docker when the result must match across machines.

Q: What do I need to run HyperFrames?

A: Node.js 22 or newer and FFmpeg. The CLI manages the Chrome it renders with, Docker is optional for pinned environments, and npx hyperframes doctor checks the setup. You do not need an AI agent: init, preview and render work by hand, and the agent skills are an optional layer on top.

Originally published at devpik.com. GitHub: heygen-com/hyperframes

ComfyUI: The Open-Source Node-Graph Engine for Local AI Images, Video and 3D (141K Stars)

Muhammad Tayyab — Fri, 23 Oct 2026 12:35:24 +0000

What Is ComfyUI?

ComfyUI is a free, open-source engine for generating images, video, audio, 3D models and text with AI models that run on your own hardware. Instead of a form with a prompt box and a Generate button, it gives you a canvas of nodes: one loads a model, one encodes your prompt, one samples, one decodes the result into pixels, one saves the file. You wire them together, and the wiring is the program.

The project describes itself as "the most powerful and modular AI engine for content creation", and the adoption behind that line is real: more than 141,000 GitHub stars, nearly 17,000 forks, a GPL-3.0 license and a release roughly every week. It runs on Windows, Linux and macOS as a desktop app, a portable build or a manual Python install, and there is a paid Comfy Cloud for people without a capable GPU.

It began in early 2023 as an interface for Stable Diffusion, and the list of what it runs now shows how far it has moved past that: Flux, SD3.5 and Qwen Image for pictures, Wan 2.2 and HunyuanVideo for video, ACE-Step and Stable Audio for sound, Hunyuan3D for meshes, and Gemma and Qwen3 for text.

The Graph Is the Program, and It Only Re-Runs What Changed

Most image generators hide the pipeline behind settings. ComfyUI puts it on screen. A basic text-to-image graph is a handful of nodes: a loader for the checkpoint (or for a separate diffusion model, VAE and text encoder, which ComfyUI can load individually), a text-encode node for the positive and negative prompts, a sampler that does the denoising, a VAE decode that turns the result into pixels, and a save node. LoRAs, ControlNets, adapters and upscalers are more nodes dropped in between.

For a single picture that is more work than a prompt box. The payoff comes from three rules in how the graph runs:

Only complete paths run. A part of the graph executes only if it ends in an output and every input along the way is connected. A half-built branch just sits there.
Only what changed runs again. Submit the same graph twice and the second run does nothing. Change the last node, and only that node and whatever depends on it are recomputed. Swap the upscaler and you keep the sampled image you already waited for.
Bypass and mute are built in. Ctrl+B bypasses a node, as if it were removed and the wires reconnected through it, and Ctrl+M mutes it, so you can compare a pipeline with and without a step without rewiring anything.

The second rule is the one that changes how you work. In a form-based tool, adjusting the final step means generating again from scratch. In ComfyUI an expensive video sample is computed once while you iterate on everything downstream of it.

Every Image Carries Its Own Recipe

ComfyUI saves workflows as JSON, and it also writes the workflow into the media it generates. Drag a PNG that ComfyUI made back onto the canvas and you get the whole graph that produced it, seeds included. This is why ComfyUI workflows travel so well: someone posts a striking image and the image itself is the tutorial.

For handing a finished pipeline to people who should not touch the wiring, App Mode exposes even an elaborate workflow through a simple interface, and reusable subgraphs fold a cluster of nodes into one. For software, the same graphs are reachable through a local API, which is how ComfyUI ends up inside production pipelines and not only on artists' desktops.

Big Models on Small GPUs

The claim that gets ComfyUI recommended to people with modest hardware sits in the first lines of its README: it "can run even the biggest open source models on as low as 4GB vram + 8GB ram relatively quickly", using asynchronous weight streaming, alongside smart VRAM and RAM management, model offloading and support for quantized models. In other words, the whole model does not have to fit on the graphics card at once.

Take the word "relatively" seriously. A model streaming through 4GB of VRAM will be slower than the same model sitting on a 24GB card. What ComfyUI promises is that it runs and keeps the GPU busy, not that a budget card matches a flagship.

Hardware support is unusually broad. NVIDIA works out of the box. AMD works through ROCm on Linux and, on Windows 11, through AMD's ROCm 10 PyTorch packages. Intel Arc runs on PyTorch's xpu backend, Apple Silicon on PyTorch nightly, and there are instructions for Ascend NPUs, Cambricon MLUs and Iluvatar Corex as well. One trap: the default Windows portable build targets NVIDIA 20 series cards and newer, and 10 series and older cards need the separate CUDA 12.6 build.

What It Generates in 2026

Images: SD 1.5, SDXL, SD3.5, Flux.1 and Flux.2, Qwen Image, HiDream, Chroma and more.
Image editing: Flux Kontext, Qwen Image Edit, OmniGen2 and others.
Video: Wan 2.1 and 2.2, LTX-Video 2, HunyuanVideo 1.5, CogVideoX and Mochi.
Audio: ACE-Step 1.5 and Stable Audio 3.
3D and vision: Hunyuan3D 2.1, SAM 3, Depth Anything 3 and SUPIR upscaling.
Text: Gemma 3 and 4 and the Qwen3 family, including multimodal inputs.

Around those models sit built-in nodes for inpainting, outpainting, masks and compositing, model merging, upscaling, frame interpolation, segmentation and depth estimation. Output goes beyond the 8-bit PNG most generators stop at: 16-bit PNG, 32-bit EXR, 10-bit AVIF and HDR video.

Installing ComfyUI: Desktop, Portable or Manual

There are four routes, and the README is clear about which one most people should take.

Desktop app (Windows and macOS): the recommended way for new users, downloaded from comfy.org/download.
Windows portable: download, extract with 7-Zip (or Windows Explorer on recent versions) and run. It ships Python 3.13 and PyTorch with CUDA 13.0, with separate builds for AMD, Intel and older NVIDIA cards.
comfy-cli: pip install comfy-cli, then comfy install.
Manual install (any OS, any GPU): clone the repo, install PyTorch for your GPU, then the dependencies, then start it.

Checkpoints go in models/checkpoints and VAEs in models/vae. Python 3.13 is the well-supported version; 3.14 works, but some custom nodes may not.

If you already have another Stable Diffusion interface with a folder full of models, you do not need to copy gigabytes of checkpoints. Rename extra_model_paths.yaml.example to extra_model_paths.yaml and point it at your existing folders.

Custom Nodes and ComfyUI-Manager

Much of ComfyUI's reach comes from custom nodes: third-party packages that add support for new models, utilities and integrations. ComfyUI-Manager is the extension that installs, updates and manages them.

Two cautions. Custom nodes are Python code that runs with your user's permissions, so install them from sources you trust, the same way you would treat any package. And mind the release train: core ships a stable version roughly every two weeks, and the README warns that commits between stable tags "may be very unstable and break many custom nodes". If your workflows lean on a lot of third-party nodes, stay on the stable releases.

Offline First, Paid Models Optional

ComfyUI's core "does not download anything unless you request it". That matters for anyone working with client material or unreleased designs: prompts, images and models stay on the machine.

The optional exception is partner nodes, which call paid closed-source models such as Nano Banana, Seedance and Hunyuan3D through Comfy's API. Two flags control them: --offline disables them and forces everything built in to stay local, and --disable-partner-nodes turns off only those nodes. Comfy Cloud, the paid hosted version, is the other way to spend money, aimed at people without suitable hardware.

Prompt Syntax Worth Knowing

Emphasis: wrap a phrase in parentheses with a weight, such as (good code:1.2) or (bad code:0.8). Plain parentheses mean 1.1.
Wildcards: {day|night} is replaced by one of the options at random each time you queue, which is a cheap way to generate variations.
Comments: dynamic prompts accept C-style // comments and /* block comments */.
Embeddings: put textual inversion files in models/embeddings and reference them in a prompt as embedding:filename.

And for previews while sampling, --preview-method auto turns on the fast built-in previews; for higher-quality TAESD previews, add their decoder files to models/vae_approx and start with --preview-method taesd.

Who Should Use It, and Who Can Skip It

ComfyUI is for people who want control and reproducibility: artists building repeatable pipelines, video and 3D creators chaining several models, developers who want a generation backend they can call from their own code, and anyone with a modest GPU who has been told their card is too small. If you generate the same kind of output repeatedly, the graph pays for its learning curve within days.

It is a harder sell if you want one picture from one prompt with zero setup. A hosted generator gets you there faster, and ComfyUI asks you to learn what a sampler, a VAE and a LoRA are, to manage models that run to gigabytes, and to keep custom nodes in step with core releases.

The verdict: ComfyUI has earned its place as the default open-source engine for generative media. The node graph that looks intimidating on day one is the reason it scales, because graphs re-run only what changed, travel inside the images they produce, and can be wrapped in App Mode or called through an API. Install the desktop app, load a template from the workflow library, and change one node at a time.

Frequently Asked Questions

Q: Is ComfyUI free?

A: Yes. ComfyUI is free and open source under the GPL-3.0 license, and it runs locally on your own hardware. The optional costs are Comfy Cloud, a paid hosted version for people without a suitable GPU, and partner nodes, which call paid closed-source models through Comfy's API.

Q: How much VRAM does ComfyUI need?

A: According to the README, ComfyUI can run even the biggest open-source models on as little as 4GB of VRAM and 8GB of system RAM, relatively quickly, by streaming weights to the GPU as they are needed and offloading models.

Q: Does ComfyUI work on Mac and AMD GPUs?

A: Yes. The desktop app runs on macOS, and Apple Silicon (M1 to M4) works with a PyTorch nightly build. AMD GPUs are supported through ROCm on Linux and through AMD's ROCm 10 PyTorch packages on Windows 11. Intel Arc GPUs work through PyTorch's xpu backend.

Q: How do I install custom nodes in ComfyUI?

A: Use ComfyUI-Manager. Install its dependencies, then start ComfyUI with the manager enabled, and install, update or remove custom nodes from its interface. Custom nodes are third-party Python code that runs on your machine, so only install ones from sources you trust.

Q: Can I get the workflow back from a ComfyUI image?

A: Yes. ComfyUI embeds the workflow in the media it generates. Drag a PNG that ComfyUI produced onto the canvas, or load it, and you get the full graph that created it, including the seeds. Workflows can also be saved and loaded as JSON files.

Originally published at devpik.com. GitHub: Comfy-Org/ComfyUI

Context Mode: Cut Claude Code Context Usage by 98% (Open Source)

Muhammad Tayyab — Fri, 23 Oct 2026 12:31:19 +0000

Here is a number that should worry anyone paying for Claude Code by the token. A single Playwright snapshot costs 56 KB of context. Twenty GitHub issues cost 59 KB. One access log, 45 KB. Every MCP tool call dumps raw data straight into the context window, and after about 30 minutes of real work, 40% of it is gone.

Then comes the second blow. When the conversation gets too long, the agent compacts it to free space, and the compaction wipes its working memory. It forgets which files it was editing, what tasks were in progress, and what you last asked for. You spend the next ten minutes re-explaining everything.

And there is a third leak most people never notice: the agent burns output tokens on filler, pleasantries and verbose explanations, eating context from both sides at once.

This is the problem Context Mode was built for. It is an open-source MCP server by mksglu that attacks context bloat from four directions simultaneously, and the numbers are startling: 315 KB of raw tool output becomes 5.4 KB. That is a 98% reduction, and it extends a typical session from about 30 minutes to around 3 hours.

How It Works: Sandboxes, Memory, and Thinking in Code

Context Mode solves what its README calls all four sides of the context problem.

1. Context saving. Sandbox tools intercept raw tool output and keep it out of the context window. Instead of the model reading the full output, it gets a compact reference. A Playwright snapshot goes from 56.2 KB to 299 bytes (99% saved). Twenty GitHub issues go from 58.9 KB to 1.1 KB (98%). An access log of 500 requests goes from 45.1 KB to 155 bytes, effectively 100%. The benchmark table in the repo lists 21 scenarios, and the pattern holds across all of them.

2. Session continuity. Every file edit, git operation, task, error and user decision is tracked in a local SQLite database. When the conversation compacts, Context Mode does not dump all of that back into context. It indexes events with FTS5 full-text search and retrieves only what is relevant via BM25 ranking. The model picks up exactly where it left off, and it only pays context for the memories it actually needs. There is a privacy-conscious default worth knowing: if you do not pass --continue, previous session data is deleted immediately. A fresh session means a clean slate.

3. Think in code. This is the most opinionated idea in the repo, and arguably the smartest. Instead of reading 50 files into context to count functions, the agent writes a script that does the counting and logs only the result. One ctx_execute call replaces 47 Read calls: 700 KB becomes 3.6 KB. The philosophy is blunt: stop treating the LLM as a data processor and start treating it as a code generator. This is enforced as a mandatory paradigm across all supported clients.

4. No prose policing. Context Mode keeps raw data out of context but deliberately never tells the model how to write its final answer. Brevity, completeness, formatting, that is your model's call (or yours, via your own CLAUDE.md). The README cites research showing aggressive brevity prompts degrade coding benchmarks. The routing stays focused on where data goes, not how the model talks.

The Benchmarks: 315 KB Becomes 5.4 KB

The repo ships a full benchmark table, and it is worth reading because the savings are consistent rather than cherry-picked. Playwright snapshot: 56.2 KB raw, 299 bytes in context, 99% saved. GitHub issues (20): 58.9 KB to 1.1 KB, 98%. Access log with 500 requests: 45.1 KB to 155 bytes, effectively 100%. Context7 React docs: 5.9 KB to 261 bytes, 96%. An analytics CSV with 500 rows: 85.5 KB to 222 bytes, 100%. Git log of 153 commits: 11.6 KB to 107 bytes, 99%. Test output across 30 suites: 6.0 KB to 337 bytes, 95%. Even a deep repo-research subagent run drops from 986 KB to 62 KB, 94% saved.

Over a full session, the headline number is 315 KB of raw output becoming 5.4 KB in context. Session time extends from roughly 30 minutes to around 3 hours. If you are paying per token, that is not a marginal improvement, it is the difference between an agent that taps out mid-task and one that finishes.

Installing It Takes Two Commands on Claude Code

On Claude Code, installation is a plugin marketplace add. You need Claude Code v1.0.33 or newer. Open Claude Code and run:

/plugin marketplace add mksglu/context-mode
/plugin install context-mode@context-mode

Restart Claude Code (or run /reload-plugins), then verify with /context-mode:ctx-doctor. Every check should show green. The plugin registers hooks for SessionStart, PreToolUse, PostToolUse, UserPromptSubmit, PreCompact and Stop, plus 11 MCP tools: six sandbox tools (ctx_batch_execute, ctx_execute, ctx_execute_file, ctx_index, ctx_search, ctx_fetch_and_index) and five meta-tools (ctx_stats, ctx_doctor, ctx_upgrade, ctx_purge, ctx_insight).

The hooks do the heavy lifting automatically. The SessionStart hook injects routing instructions at runtime, so no file gets written into your project. PreToolUse and PostToolUse hooks intercept tool calls and redirect raw output to the sandbox before it ever touches your context.

There is also a status line you can add with a one-time edit to ~/.claude/settings.json. It shows dollars saved this session, dollars saved across sessions, and your efficiency percentage, updating in real time as you work. Watching the savings accumulate is the fastest way to believe the benchmarks.

17 Platforms, but Hooks Are What Make It Work

Context Mode supports 17 platforms: Claude Code, Qwen Code, Gemini CLI, VS Code Copilot, JetBrains Copilot, GitHub Copilot CLI, Cursor, OpenCode, KiloCode, OpenClaw, Codex CLI, Kimi Code, Antigravity, Antigravity CLI, Kiro, Zed and Pi. That breadth matters because most developers do not live in one agent.

But the README is honest about a split that matters more than the platform count. On hook-capable platforms, hooks intercept tool calls programmatically: they can block dangerous commands and redirect output to the sandbox before execution. Measured savings sit around 98%. On platforms without hook support, Context Mode falls back to routing instruction files (a copied AGENTS.md or GEMINI.md), which guide the model through prompt instructions but cannot block anything. Measured savings there drop to around 60%, because one unrouted curl or Playwright snapshot can dump 56 KB into context and wipe out a whole session's savings.

Concretely: Claude Code, Gemini CLI, the Copilots, Cursor, OpenCode, Codex CLI, Kiro and Pi get the full ~98% treatment. Antigravity and Zed do not support hooks, so they rely on the manually copied routing file at ~60%. If you are choosing where to run it, pick a hook-capable host. The README's own advice is direct: always enable hooks where supported.

The Utility Commands You Will Actually Use

Inside any AI session, you just type the command and the model calls the MCP tool for you. ctx stats shows your context savings with a per-tool breakdown, tokens consumed and savings ratio. ctx doctor diagnoses runtimes, hooks, FTS5 and versions. ctx index indexes a local file or directory into the persistent knowledge base, and ctx search searches it later. ctx upgrade pulls the latest from GitHub, rebuilds and fixes hooks. ctx purge permanently deletes all indexed content. ctx insight opens the hosted Insight dashboard at context-mode.com/insight, which gives org-level analytics for AI-assisted engineering teams.

From the terminal, the same operations run without an AI session: context-mode doctor, context-mode index, context-mode search, context-mode upgrade. There is also a debug script (scripts/ctx-debug.sh) that collects OS info, runtime versions, hook validation and environment into a single pasteable markdown report for bug reports, which tells you something about how the maintainer thinks about support.

One design decision deserves credit: everything is stored locally in SQLite under your own machine. The privacy section of the README is explicit about the architecture, and combined with the delete-on-fresh-session default, it is clear the project was designed by someone who has thought about what it means to let a tool watch every file edit.

Who Should Install It (and Who Can Skip)

Install it if you use Claude Code (or another hook-capable agent) for sessions longer than 30 minutes, you pay per token and watch usage climb, your agent keeps forgetting context after compaction, or you run lots of MCP tools like Playwright, GitHub or database queries whose raw output is enormous relative to what you actually need from it.

Skip it if your sessions are short and simple, you work exclusively in Zed or Antigravity where only the ~60% instruction-file mode applies, or you are uncomfortable with a tool that hooks every tool call, even though the hooks run locally and the data stays in SQLite on your machine.

The social proof is hard to ignore: #1 on Hacker News with 570+ points, 30,000+ GitHub stars, and a README that lists usage across teams at Microsoft, Google, Meta, Amazon, IBM, NVIDIA, ByteDance, Stripe, Datadog and Salesforce. It is ELv2 licensed and free to use. If context bloat is the tax you pay on every agent session, this is the most concrete rebate anyone has shipped.

What is Context Mode?

Context Mode (mksglu/context-mode) is an open-source MCP server for AI coding agents. It reduces context window usage by up to 98% by sandboxing raw tool output, persists session memory across context compaction using SQLite and full-text search, and enforces efficient routing with hooks. It supports 17 platforms including Claude Code, Cursor, Gemini CLI and GitHub Copilot.

How does Context Mode reduce Claude Code context usage?

Four mechanisms: (1) sandbox tools keep raw tool output out of context (315 KB becomes 5.4 KB), (2) session events are stored in SQLite and retrieved via BM25 search only when relevant, (3) the agent is made to write analysis scripts instead of reading files into context, and (4) hooks enforce the routing automatically. Measured savings are ~98% on hook-capable platforms.

Is Context Mode free to use?

Yes. Context Mode is open source under the Elastic License v2 (ELv2) and free to use. You install it as a plugin or MCP server for your AI coding agent; there is no subscription and no paid tier for the core functionality.

Which AI coding agents does Context Mode support?

17 platforms: Claude Code, Qwen Code, Gemini CLI, VS Code Copilot, JetBrains Copilot, GitHub Copilot CLI, Cursor, OpenCode, KiloCode, OpenClaw, Codex CLI, Kimi Code, Antigravity, Antigravity CLI, Kiro, Zed and Pi. Hook-capable platforms get ~98% context savings; Antigravity and Zed rely on instruction files at ~60%.

How do I install Context Mode in Claude Code?

You need Claude Code v1.0.33+. Run /plugin marketplace add mksglu/context-mode then /plugin install context-mode@context-mode, restart Claude Code, and verify with /context-mode:ctx-doctor. All checks should pass. The SessionStart hook then injects routing automatically with no files written to your project.

Does Context Mode keep working after context compaction?

Yes, that is one of its core features. Every file edit, git operation, task, error and decision is tracked in SQLite and indexed with FTS5. When the conversation compacts, Context Mode retrieves only the relevant memories via BM25 search instead of dumping everything back into context, so the model picks up exactly where it left off.

What is the difference between hooks and instruction files in Context Mode?

Hooks intercept tool calls programmatically and can block or redirect raw output to the sandbox before it reaches context (~98% savings). Instruction files (AGENTS.md/GEMINI.md copies) only guide the model via prompt text and cannot block anything (~60% savings). Platforms like Zed and Antigravity lack hook support and use the instruction-file mode.

How is Context Mode different from just using a bigger context window?

A bigger window delays the problem but does not fix the economics: raw tool output still costs tokens on every call, and compaction still wipes working memory. Context Mode changes what enters the window at all (98% less per call) and makes memory survive compaction, so sessions run ~3 hours instead of ~30 minutes regardless of window size.

Originally published on DevPik.

AI Job Search: The Open-Source AI That Got Its Creator Hired (62K Stars)

Muhammad Tayyab — Wed, 21 Oct 2026 12:43:49 +0000

What AI Job Search Is

AI Job Search is a free, open-source job application framework built on Claude Code. You fork it, fill in your profile, and let Claude run your job search: it searches job portals, evaluates each posting against your actual background, drafts a tailored CV and cover letter for the good matches, and prepares you for interviews.

The pitch is simple and the repo states it plainly: the job search that runs on your machine. Nothing is sent to a third-party job platform. Your profile, your applications and your tracking live in your own fork, and the AI does the repetitive parts of applying while you stay in control of what actually gets sent.

It has passed 62,000 stars on GitHub under the MIT license, and it is currently the number one trending repository on the platform. That kind of attention is rare for a career tool, and it makes sense once you see the story behind it.

Why It Matters: It Actually Got Its Creator Hired

Most job search tools promise to save you time. This one has receipts. The author is a geophysicist by training whose position was cut in late 2025. He built this framework to run his own job search, using the same workflow in the repo week after week on his own career.

The numbers from his run: 69 tailored applications, 20 first interviews, and one signed contract. He started as an AI engineer in June 2026. He was upfront with every employer about using AI in his applications, and instead of counting against him, it usually sparked a genuine technical conversation.

That is the strongest possible endorsement for a tool like this. It was not built as a demo or a side project for stars. It was built by someone who needed a job, and it worked.

Key Features

Fit scoring before you apply: Every posting is evaluated against your profile and scored, so you spend effort on roles you can actually land instead of spraying applications everywhere.
Tailored CV and cover letter drafting: For each match, Claude drafts a CV and cover letter tailored to that specific posting, compiled to PDF with LaTeX for a clean, professional result.
Drafter-reviewer pipeline: A second agent reviews and critiques every draft before you see it, then revises. Nothing goes out without a second pair of eyes.
Interview preparation: The /interview command builds a stage-specific prep pack from the exact posting, the CV and cover letter the interviewer actually read, and your earlier rounds, then maps likely questions to your STAR examples and offers a mock interview.
Application tracking: The /outcome command records interviews, offers, rejections and silence, archives everything per application, and surfaces applications that have gone quiet so you can follow up.
Posting integrity checks: Every posting passes a deterministic gate first that looks for hidden text, invisible characters and instruction-shaped lines, since postings are treated as untrusted input.
Language- and country-agnostic core: The self-profiling, fit evaluation and drafter-reviewer pipeline work anywhere. The portal search skills are built for the Danish market, but the pattern is designed to be swapped for your local job boards.
Gmail and Notion sync: Optional commands read your Gmail for status signals on open applications and publish a read-only pipeline view into Notion, so the tracker stays current without manual data entry.

How It Works

You start by forking the repo and cloning it, then installing the job portal CLI tools. Inside Claude Code you run /setup, which builds your profile three ways: it can read a documents folder with your CV, LinkedIn export, diplomas and reference letters, import a single CV pasted in chat, or walk you through an interview.

Then /scrape searches multiple job portals for positions matching your profile, deduplicates the results and presents them sorted by fit. If a scrape returns more jobs than you want to eyeball, /rank batch-scores them all against the fit framework and hands you a ranked shortlist.

When you pick a match, /apply runs the full workflow on it: evaluate fit, draft the CV and cover letter, run the reviewer agent over both, revise, and present the final output. If a posting URL cannot be fetched, you can paste the job description directly instead.

From there, /interview preps you for each scheduled interview, and /outcome records what happened and keeps the tracker honest. It is a pipeline, not a one-click apply button, and that is the point.

Who It's For

AI Job Search is for anyone actively job hunting who is tired of the copy-paste application grind: developers, engineers, and career switchers who want every application tailored without spending a Sunday per cover letter. It is especially strong for people who already use Claude Code, since the whole workflow lives inside it.

It is also for skeptics. The author's own funnel is public, the workflow never auto-submits anything, and every draft passes a reviewer agent before you see it. You stay the human in the loop at every step that matters.

If you are casually browsing and not ready to set up a profile, it is probably more machinery than you need. But if you are sending real applications this month, it pays for itself in the first week.

Limitations

First, the cost of entry: Claude Code has no free tier, so you need a Claude Pro, Max or Team subscription, or Anthropic API credits. The framework itself is free, but the engine it runs on is not.

Second, the portal search skills are built for Denmark (Jobindex, Jobnet, Akademikernes Jobbank and similar). The core workflow is portable, but if you are outside Denmark you will need to adapt the search skills to your local boards, or paste postings in manually.

Third, there is a real privacy gotcha the README flags in bold: a fork of a public repo is always public on GitHub, and /setup writes your personal data into tracked files. If the copy is for your own job search rather than contributing back, use a private repository with this repo as upstream instead. Do not put your CV and salary expectations in a public fork.

Finally, the CV pipeline needs a LaTeX distribution with lualatex and xelatex, plus Python 3.10 and Bun. It is a developer tool with developer setup, not a consumer app.

Verdict

AI Job Search earns its trending spot the honest way: it solved a real problem for its creator, then he gave the solution away. The 69-application funnel that ended in a signed contract is the kind of proof most career tools never offer, and the workflow itself is thoughtful, with fit scoring, a reviewer agent, integrity checks and no auto-submit.

At 62,000 stars, free under MIT, and built on a tool many developers already pay for, it is an easy recommendation for anyone running a serious job search right now. Just set it up in a private repo, not a public fork.

Originally published at devpik.com. GitHub: MadsLorentzen/ai-job-search

Orca: Run a Fleet of AI Coding Agents in Parallel and Keep the Best Result (99K Stars)

Muhammad Tayyab — Tue, 20 Oct 2026 10:41:23 +0000

What Orca Is

Orca is a free, open source desktop app that orchestrates AI coding agents. Instead of betting everything on one agent and one attempt, you give Orca a prompt and it fans that prompt out across several agents at once: Claude Code, Codex, OpenCode, Grok, Cursor, Copilot, Pi and more. Each agent works in its own isolated git worktree, so five agents can attack the same task at the same time without overwriting each other's files.

When they finish, you compare the results side by side and merge the winner. The rest get discarded. Think of it as running a small competition for every task instead of hoping your first pick nailed it.

Orca runs on macOS, Windows and Linux, works with your existing agent subscriptions, and ships with a mobile companion app so you can monitor and steer your agents from your phone. It is built by Stably AI, is backed by Y Combinator, and has passed 98,900 stars on GitHub under the MIT license.

Why It Matters

Anyone who uses AI coding agents seriously runs into the same wall. One agent writes a clean solution, another overengineers it, a third quietly breaks something unrelated. You never know which attempt you are going to get, and rerunning the same prompt serially burns hours.

Orca's answer is parallelism with isolation. Git worktrees mean each agent gets a full, separate checkout of your repo, so parallel runs cannot collide. The bottleneck stops being the agent's typing speed and becomes your review speed, which is exactly where a developer adds the most value.

It also solves the subscription juggle. Most heavy users pay for more than one agent, and each has its own usage limits and reset windows. Orca shows your Claude and Codex usage in one place and lets you hot-swap accounts without logging in and out, so a rate limit on one agent no longer stops the whole workflow.

Key Features

Parallel worktrees: Fan one prompt across up to five agents, each in its own git worktree, then compare diffs and merge the winner.
Works with almost any CLI agent: Claude Code, Codex, Grok, OpenCode, Cursor, Copilot, Amp, Cline, Goose, Kiro, Qwen Code and dozens more. If it runs in a terminal, it runs in Orca.
Mobile companion: iOS and Android apps notify you when an agent finishes and let you send follow-ups from anywhere.
Terminal splits: Ghostty-class terminals with WebGL rendering, infinite splits, and scrollback that survives restarts.
Design Mode: Click any element in a built-in Chromium window to send its HTML, CSS and a cropped screenshot straight into your agent's prompt.
GitHub and Linear built in: Browse PRs, issues and boards inside the app, and open a worktree directly from a task.
SSH worktrees: Run agents on a powerful remote machine with file editing, git and terminals, plus auto-reconnect and port forwarding.
Annotate AI diffs: Drop comments on any diff line and send them back to the agent, then review, edit and commit without leaving Orca.
Usage tracking: Claude and Codex limits and reset times in one view, with an account switcher for when one runs dry.
Orca CLI: Agents can drive Orca too, scripting worktrees, snapshots and UI actions from the command line.

How It Works

You install Orca, open your repo, and write a prompt the way you would for any coding agent. Then you pick which agents to run it on. Orca creates a separate git worktree for each one and starts them all at once, each in its own terminal pane.

From there you watch progress in one window, or walk away and let the mobile app ping you when an agent finishes or gets stuck. When the runs are done, you review the diffs, annotate anything you would change, and merge the best result into your branch. The losing worktrees are deleted, so your repo stays clean.

Because Orca sits on top of the agents you already use, there is no new subscription and no API key juggling. You bring your Claude, Codex or other plan, and Orca multiplies what one prompt can do.

Who It's For

Orca is for developers who already use AI coding agents daily and feel the single-agent ceiling: founders shipping fast, senior engineers running several tasks at once, and anyone who has ever rerun the same prompt three times hoping for a better answer. If you pay for two or more agent subscriptions, the usage dashboard alone is worth it.

It is probably overkill if you use one agent occasionally for small edits. Parallel agents pay off when the tasks are big enough that attempt quality varies, and when your time is worth more than the agents' runtime.

Limitations

Running five agents at once multiplies your subscription usage just as fast, so heavy parallel runs can hit rate limits quickly on smaller plans. Comparing five diffs also takes real review effort; on trivial tasks the overhead is not worth it. Orca is a desktop app with a lot of surface area and it ships updates daily, so expect the occasional rough edge. And while it supports a huge list of agents, the experience is clearly tuned first for Claude Code and Codex.

Verdict

Orca is the strongest answer yet to the question every heavy agent user eventually asks: why am I only running one of these at a time? The worktree isolation makes parallel runs safe, the compare-and-merge flow makes them useful, and the mobile companion makes them practical. At 98,900 stars and climbing, with an MIT license and no extra subscription required, it is an easy recommendation for anyone serious about AI-assisted development.

Originally published at devpik.com. GitHub: stablyai/orca