Record and share your coding sessions with CodeMic

Hacker News

You can record and share coding sessions directly inside your editor. Think Asciinema, but for full coding sessions with audio, video, and images. While replaying a session, you can pause at any point, explore the code in your own editor, modify it, and even run it. This makes following tutorials and understanding real codebases much more practical than watching a video. Local first, and open source. p.s. I’ve been working on this for a little over two years* and would appreciate any feedback. * Previously: CodeMic: A new way to talk about code - https://news.ycombinator.com/item?id=42485088 - Dec 2024 (58 comments)

Visit product View on Hacker NewsJan 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, coding, code · Missing: mac, agents, macos

90%90% 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 · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

63%63% 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: video, way · Missing: mobile apps, ios, personal

40%40% 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

32%32% 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

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

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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