Share your coding agent sessions

Hacker News

Knowing it might soon be obsolete, I still went ahead and built aisessions.dev - a place to share sessions from Claude Code, Codex, and Gemini CLI. It was a fun zero-to-one project and an excuse to think through: - Safely accepting user generated data (security, PII scrubbing, etc) - Building a privacy conscious service - Designing for long-term low-maintenance operation - Developer experience — CLI tools, onboarding, and good defaults - UI, design, and simplicity - Keeping everything open source Kudos to AMP and opencode for the inspiration. If OpenAI, Anthropic, or Google launch native session sharing tomorrow, that’s fine. The experience is well worth the effort (which wasn't that big considering I wrote very little of the code).

Visit product View on Hacker NewsOct 24, 2025

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Launch Intel predictions

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

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.

Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

70%70% 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

45%45% 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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% 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

24%24% 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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