Agtrace – top and tail -f for AI coding agent sessions

Hacker News

Hey HN, I built agtrace because I kept losing track of what was happening in my Claude Code sessions – context pressure, tool calls, costs. It's basically `top` for AI coding agents: - Live dashboard showing context window usage and activity - Session history you can query and diff - Works with Claude Code, Codex, and Gemini CLI - 100% local, reads existing logs, no cloud Install: `npm i -g @lanegrid/agtrace` The core idea is pointer-based indexing (no log duplication) and schema-on-read (resilient to provider schema changes). Would love feedback, especially from heavy Claude Code / Codex users.

Visit product View on Hacker NewsDec 29, 2025

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

98%98% 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.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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

36%36% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

29%29% 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: exist, existing, ide · Missing: https docs, excited, just released

22%22% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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