lmcli - A sleek, sandboxed alternative to Claude Code

Hacker News

Hi HN, I started `lmcli` in 2023 when I wanted my own tool to play with the shiny new LLM technology. If you look through the history, you'll see I had implemented tool calling with a vision for the "agentic" coding loop back around early 2024. Back then, models weren't smart enough to make good use of the tools and I settled in with using `lmcli` for simpler copy-paste based interactions. But now, `lmcli` can do the coding loop just as well as Claude Code! I invite HN over to take a look. There are still rough edges, some incomplete or confusing features, and yes, LLMs were used in the making. I still review all new code and make the architectural decisions. Enjoy.

Visit product View on Hacker NewsMar 30, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, model · Missing: mac, agents, macos

95%95% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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