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Tclint – chip design inspired dev tools for Tcl

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Tclint – chip design inspired dev tools for Tcl

I used to work at a small semiconductor startup that wrote chip design automation infrastructure in Tcl. One of our engineers wanted modern dev tooling for Tcl but couldn't find any - so I decided to build it. Since then, I've been working on tclint in my spare time. It now includes: - A linter (with plugins to support non-standard commands) - A formatter - A language server + VS Code extension tclint is already being used at my former employer and elsewhere. I have lots of ideas for improvements, such as more linter checks, better extensibility, deeper support for chip design formats (e.g., SDC-specific linter checks), and expanding the language server. If you write Tcl, give tclint a try! I’d love to hear if you find it useful, and I’d especially love suggestions on what would make it even better.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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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