SimpleNet – A modern BBS moderated by local LLMs

Hacker News

Hi HN, I’ve always missed the high-signal, community-centric feel of old-school Bulletin Board Systems, but the moderation overhead for small communities is usually what kills them. I built SimpleNet to see if we could bring back the BBS vibe using modern tech. The Tech Stack: The core of the platform is a modern BBS architecture, but the "secret sauce" is the moderation layer. Instead of relying on big-tech APIs or an army of volunteers, I’m using open source LLMs to assist with content moderation and community health. This allows for: Privacy: No user data is sent to OpenAI or Google for sentiment analysis. Speed: Instant feedback for users on community guideline adherence. Sovereignty: The community rules are baked into the local model weights, not a corporate TOS. Why .directory? I want this to be a jumping-off point for niche communities that want a "back-to-basics" text-heavy interaction model without the doom-scrolling algorithms of modern social media. I’m a software engineer by trade, but this is my first foray into a public-facing community platform of this scale. I’d love your feedback on the latency of the moderation and the general "feel" of the UI. Check it out: https://www.simplenet.directory

Visit product View on Hacker NewsJan 28, 2026

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

89%89% 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 · Missing: supports, reddit linkedin, podcasting

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

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

49%49% 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: google, users, way · Missing: mobile apps, ios, personal

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

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

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

16%16% 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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