Ct

Ctxbin – A deterministic CLI for reliable AI agent handoffs

Hacker News

Ctxbin – A deterministic CLI for reliable AI agent handoffs

Hi HN, I built ctxbin, a minimal CLI that lets AI agents reliably save and load shared context using branch-scoped keys inferred from git. The problem I kept hitting was that handoffs between AI agents (or even between sessions) were fragile and non-repeatable. ctxbin solves this by storing structured context, agents, and skills in Upstash Redis with deterministic keys. Key ideas: - Branch-scoped context ({repo}/{branch} inferred automatically) - Explicit save/load semantics (no hidden state) - Reusable agents and skills (string, directory bundles, or GitHub refs) - Designed for agent-to-agent handoff, not humans It works well with tools like Claude Code, Codex CLI, or any AI agent workflow. Feedback very welcome. Docs: https://superlucky84.github.io/ctxbin/

Visit product View on Hacker NewsJan 31, 2026

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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

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

30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

28%28% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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