I wrote a from-scratch OS to serve my blog

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Hey HN! This is a fun/educational project I built to learn OS programming. I started working on it right after graduating high school last year and have been working on it on and off during my first year of university. It features a TCP/IP stack, an HTTP server, a RAM file system, a BIOS bootloader, paging and memory management, and concurrent tasks based on cooperative scheduling, along with a custom library. It's written in a C programming style focused on safety (based on a custom library of core abstractions) that's inspired by the writing of Chris Wellons (nullprogram.com). There is a link to a test deployment in the README. The TCP/IP implementation is nowhere near perfect, of course, so there may be issues loading the page. I'm curious how the system holds up if this post gets any attention ;-)

Visit product View on Hacker NewsSep 16, 2025

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

87%87% 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.

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

77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% 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.

Correct prediction on native model

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