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Was tired of drowning in HN comments, so I built an AI Chief of Staff

Hacker News

Was tired of drowning in HN comments, so I built an AI Chief of Staff

I've been lurking on HN for years. You know the drill: interesting headline, 200+ comments, you dive in thinking "I'll just skim for 5 minutes"... and an hour later you're 36 chambers deep in a thread about memory allocation patterns in Postgres and you've completely forgotten what the original article was about. I don't just want a "summary" (which usually just shortens the noise). I want the meta-consensus: "What is the actual trade-off being debated? Who is winning the argument? Why does this matter?" So I built HNSignals. Think of it less like a "summarizer" and more like a Chief of Staff who reads the entire thread for you and hands you a one-page executive brief. How it works: 1. Filters: Shows trending stories (50+ comments) where the discussion has heated up. 2. Extracts: An AI (Qwen 3 via Venice.ai) reads the top comments. 3. Structures: Instead of a wall of text, you get 4 specific signals: - The Hook: Why you should care. - The Gist: The core technical facts. - The Debate: The actual friction point (e.g., "Rust vs. C++ safety"). - The Verdict: The community consensus. Why I'm showing this (Beta): Most AI tools just shorten the text. I'm trying to extract the signal. It's a work in progress - the AI sometimes gets too clever, and I'm still tuning the cache strategy. I'd love feedback on whether this structured approach is actually better than a standard "TL;DR." Bonus meta-game: If this Show HN gets 50+ comments and makes it onto HNSignals itself, I'll read the AI's analysis of people analyzing my analyzer. (P.S. Please go easy on the intentional stress testing - my Lambda inference budget is finite!) Try it: https://hnsignals.com Example output The Heartbleed Bug (2014): https://hnsignals.com/signal/7548991 Ask HN: What is the most unethical thing you've done as a programmer? (2018): https://hnsignals.com/signal/17692005 Comparing the Same Project in Rust, Haskell, C++, Python, Scala and OCaml (2019): https://hnsignals.com/signal/20192645 OpenAI's GPT-3 may be the biggest thing since Bitcoin (2020): https://hnsignals.com/signal/23885684 Apple, What Have You Done? (2026): https://hnsignals.com/signal/46763592

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Indie HackersFits the IH revenue-focused audience · 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, openai, open · Missing: mac, agents, macos
69%69% 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: 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 · Missing: mobile apps, ios, personal
48%48% 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
45%45% 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
23%23% 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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