CLI for agentic activity tracking in Codex

Hacker News

Hey hackers, I made a CLI to sync your Codex prompts + agent reasoning + file diffs to a shared memory called Codaph ( https://codaph.com ). The goal was to sync agent activity across my team for a much richer understanding of the codebase. Under the hood, it uses Mubit ( https://mubit.ai ) - memory engine we built based on associative retrieval. Mubit is built on the concepts of hypervectors and clustering (with time based decay for now) Currently, Codaph works with Codex with plans to add other agentic tools soon. Codaph is open source (vibe coded lol). Would love to hear some feedback and if you want to try it Mubit is free (api key is available on https://console.mubit.ai )

Visit product View on Hacker NewsFeb 26, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, agentic, codex · 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

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source · Missing: https docs, excited, just released

36%36% 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 · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, 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 · 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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