Self-contained Linux apps in Lisp

Hacker News

Not too long ago, a project of mine was shared here on HN. https://news.ycombinator.com/item?id=38126052 In that thread I wrote: > I have this vision in my mind: embedding lone modules into sections of the lone ELF and shipping it out. Zero dependencies, self-contained. I've been working on that since that day. Proud to say I've gotten it to work and thought I'd make it the subject of my first Show HN. Some free software projects gained features along the way too. The link is to an article with a proper demonstration, technical details and what happened in the past few weeks. The complete repository itself can be found here: https://github.com/lone-lang/lone I've completely reorganized it since the last thread. Would be very happy if you guys tried it out.

Visit product View on Hacker NewsNov 25, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

89%89% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps, new · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, 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.

Incorrect prediction on native model

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