Ap

Appseed – Static binaries provided relative shared objects in Linux

Hacker News

Appseed – Static binaries provided relative shared objects in Linux

TLDR: A "semi" static binary which can run on any Linux created in a folder structure for better deduplication and something simple that I wish anybody can understand I also just wanted a tool from which people can probably understand and build upon to their freedom which I mention in the article , someone could use tar to convert it into a decent static object like thing and others could create deduplication of shared objects as well. Its written in MIT , so hack it all you want fellas ! I am a big fan of hackernews and recently many of its posts commented about creating blogs as well. So this is my first blog about how Claude 3.7 created what I believe to be a pretty cool tool in my opinion in just two prompts. I am not trying to jump on AI Hype but I am going to be honest , I was not expecting it to be this quick. I also just wrote everything in a more honest way and one of the reasons I also wrote this was probably because someone donated me a domain name and I had learned cloudflare tunnels around it (took me time on archlinux oof metric server , but that alone was worth it) and I just didn't want to waste the fact that I own a domain so I created this blog. Its Hosted on cloudflare pages , although my next blog will probably touch on something about these tunneling solutions which I believe to be a much better thing but I am trying to push that project to its most extreme but maybe perfection is the enemy of the good :)

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2points
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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
76%76% 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: claude, apps, new · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
42%42% 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 · Strong signals: apps, way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
36%36% 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
17%17% 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.

Correct prediction on native model

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