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AutoBrief – Generate post-incident briefs from a structured form

Hacker News

AutoBrief – Generate post-incident briefs from a structured form

Hi HN, I built AutoBrief after noticing that resolving incidents wasn’t the longest part — writing about them was. After every incident we would write: • An engineering postmortem • An executive summary • A status page update • Runbook changes Same incident, multiple documents. AutoBrief lets you fill out one structured form (timeline, impact, root cause, mitigation, uncertainties) and generates tailored drafts for each audience. A few design decisions: • Sensitive fields encrypted at the application layer • Workspace isolation using Postgres RLS • Incident data is not used to train AI models • Meant as a draft accelerator, not a replacement for review Stack: Next.js 15, Supabase (Postgres + RLS), Claude API, deployed on Vercel. I’d especially appreciate feedback from engineers who run incident reviews. Would this reduce overhead in your workflow, or just add another tool?

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, models · Missing: mac, agents, macos
91%91% 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.
AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, 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
39%39% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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