I built an open-source analytics platform for Claude Code sessions

Hacker News

Claude Code is becoming central to how teams write software, but session data is ephemeral and costs are opaque. So I built Confabulous — a self-hosted platform that captures, archives, and analyzes every session across your team. Your sessions contain real code and architecture decisions — they never leave your network. Multiplayer with GitHub/Google/OIDC auth. What you get (so far): → archive with full-text search → per-session and aggregate, by model, with trend charts → — tool usage, files modified, agent invocations, Claude utilization % → - with actionable improvement suggestions → with automatic secret redaction → — sessions auto-link to PRs and commits Example from Christmas time: https://lnkd.in/guVb5Nx2 Things are moving fast — new features rolling out regularly. Open source: https://lnkd.in/gi5eqxh8 Or try it at https://confabulous.dev Generated with Claude Code

Visit product View on Hacker NewsFeb 26, 2026

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Launch Intel predictions

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

97%97% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

50%50% 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, io · Missing: https docs, excited, just released

47%47% 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 · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

29%29% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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