Kanban CLI (A local-first, agent-first task manager for the terminal)

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Hello HN, Ever since agents have become increasingly common in development, I've been scratching my head as to how to control their randomness. Recently, I decided to emulate an issue-tracking and project-management tool for agent-driven workflows. Kanban is a Rust-based coordination layer designed to provide a feature-rich terminal interface and enforce rigorous workflows. It aims to be versatile and extendable, made to be tailored to any preferred flow. It comes with full git integration and guardrails such that only what truly benefits a project can go through. The workflow boils down to 4 steps: 1. The model reads the skill to contextualize the requirements 2. It authenticates and receives a strict, schema-validated JSON payload outlining exact files, context, and acceptance criteria 3. Implementation is performed within an automatically isolated Git worktree and branch. The tool tracks progress (e.g., verifying all files were edited) before the task is submitted for review 4. A reviewer (preferably a human) evaluates the submission and manually transitions the task to "Done," which triggers the final merge and cleans up the task-specific environment. The tool significantly decreases the agent development time, while increasing the human planning phase. There is more to it than I can cover here, so I'd be happy to answer any questions about the architecture, the workflow, or the insights I gained while using it. For more information, I recommend skimming the README, which acts as an index to all documentation files. Repo: https://codeberg.org/hydrafog/kanban

Visit product View on Hacker NewsMay 24, 2026

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

96%96% 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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% 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

29%29% 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

11%11% 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.

Incorrect prediction on native model

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