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Feather – a fresh Tcl reimplementation (WASM, Go)

Hacker News

Feather – a fresh Tcl reimplementation (WASM, Go)

Hey HN! First time showing something here, but I've been furiously working over the holidays on Feather, a from scratch reimplementation of TCL designed for embedding in modern applications. It's starting out as a faithful reimplementation of TCL without I/O, OOP features, or coroutines. TCL has a special place in my heart because the syntax is so elegant for interactive use, and defining domain specific languages. My motiviation is twofold: faster feedback loops for AI, and moldable software for users. It turns out giving AI agents access to the runtime state of your program makes for really fast feedback loops, but embedding existing options in a world where shipping binaries for each platform is commonplace is tricky. Embedding the real TCL is tricky because it comes with its own event loop (in 2025 you alreay have one), a GUI framework (you have a web framework already, or develop on mobile), and has access to the filesystem (don't forget to delete all commands with file system access!). Feather just doesn't ship with those - expose only what you need from your application. A WASM build comes out of the box and clocks in at ~120kb plus 70kb for connecting it to the browser or node.js. And if embedding becomes easy, you can put a REPL everywhere: in mobile apps, in desktop software, as a control plane into web servers. I want to imagine a world where all software is scriptable just like Emacs and nvim, with agents doing the actual work.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, filesystem · Missing: https docs, excited, just released
72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: plus, platform, users · Missing: intuitive, reviews, host
69%69% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: mobile apps, apps, users · Missing: ios, personal, entrepreneurs
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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