Kanban CLI (A local-first, agent-first task manager for the terminal)
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…
In plain words
Kanban CLI is a terminal-based task manager built in Rust for managing AI agent workflows. It provides a kanban board interface with git integration, allowing users to define and control project tasks with strict validation schemas and isolated git branches. The tool is designed to be customizable for different workflows and works locally on users' machines, offering guardrails to ensure only beneficial changes proceed through the development pipeline.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com


Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com


Launched alongside, May 2026
the whole month →

Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai


- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com