hydra-f

As per OpenRouter:

Input Price $10/M tokens

Output Price $50/M tokens

Cache Read $1/M tokens

Cache Write $12.50/M tokens

2x Claude Opus 4.8, same as Claude Opus 4.8 (Fast)

Frankly, not even Opus 4.8 would be enough of an incentive to use at that price range (enterprise-wise; would not even bat an eye as a consumer)

Yes, and the price reflects that

How much and what kind of data do you need to throw at these models to get a good design interface?

Extensibility has a cost which affects all my earlier points. Pi is fine for testing things you might include in your harness, but that's where I would draw the line

What makes pi so awesome? It feels as though the whole thing is held together with tape. Poor performance, poor UX. Security is an afterthought. Not that versatile (as of yet). You certainly are better off writing your own personal harness.

Completely different philosophies. I will share a TL/DR below:

MY KANBAN CLI IS:

A project management system to plan, estimate, track sprints, measure velocity, assign roles, and give agents structured tasks with acceptance criteria.

CLINE KANBAN IS:

An agent execution platform to run multiple AI agents in parallel on isolated worktrees, review diffs in a web UI, and auto-commit/PR their output.

SIMILARITIES:

- Local

- Git integration with worktree support

- Agent integration with parallel execution

- Task and dependency tracking

DIFFERENCES:

- Language: Rust 2021 vs TypeScript/Node.js

- Interface: Terminal CLI (JSON output) vs Browser web app (React SPA)

- Task Creation: Human-focused vs Agent-focused

- Agent Control: Agent runs the tool as a CLI utility vs Platform spawns and manages the agent as a child process

- Core Engine: Sprints, story points, velocity, and role-personas vs MCP server management, web diff viewers, and auto-PR toggles

- Lifecycle: 8-state validation engine vs 4-column drag-and-drop layout

- Concurrency: File locking (fd-lock) for safe multi-agent CLI access vs Git worktree isolation for massive parallelism (hundreds of agents)

- Output Target: JSON by default (agents consume it) vs Web UI by default (humans consume it)

- Self-Management: Recalibration engine (auto-heal data, reorder tasks, rebalance sprints, archive) vs Not present

- People Registry: Humans tracked separately from agents (people.json) vs Not present

- Storage: JSON files in .kanban/ (atomic writes, two-tier locking) vs Runtime state streams + config files (no persistent task database)

- Statistics: Cycle time, lead time, revisions, blocks, effort ratio vs None

- Coordination: Shared with teammates via git vs Independent instance

There are many more differences between the two, which brings me to my first point. All in all, I see my Kanban as being a much more structured, human-centric, and controlled environment, since it's a complete management system rather than just an execution layer.

You can practically plan the whole project using my tool, before any implementation even starts. That means complete project structure (via files_to_modify), acceptance criterias (+ context), commit messages, branch names, version bump (handled automatically by the tool as tasks are completed) and so on. It's a full project trace, which uses the full potential of a human + agent development workflow.

Hello! Completely local, nothing leaves your machine

You're either thinking of a service, an electron app or a platform. Which would be just a big of a gap as GitHub is to Git

Even a local browser interface would bloat the core tool with unnecessary maintenance and complexity. I love a good GUI, but that's just how I see it

Yes, any agent can authenticate using its model name. The tool automatically tracks the who, when, and what for all actions.

For monitoring, it comes with local analytics and high-level statistics across the entire board. It logs exactly how the agent ran the "kb" command and whether any failures occured (including the lefthook, for which it automatically scans and runs upon task transition).

I've also built in various quality-of-life features, such as the files_to_modify field, which automatically keeps track of the work done by the agent:

IF the agent edits only the specified files, the process proceeds exactly as instructed.

ELSE, if the agent deviates, the tool generates a full trace of what was and wasn't edited, and automatically appends any newly modified files directly to the JSON.

It's model-agnostic, so anything works. As long as the model is good at instruction following, it won't have issues with using the tool (and the AGENTS.md workflow).

Thank you! Looking forward to it!

Yes, since the former part relates to me as the individual (as I get the benefits of an open source project)

The latter is about the contributors that can no longer reap the benefits of OSS, since the amount of noise (e.g. low quality contributions, false-positives etc) leads to wasted time and effort to keep up with the flood

Were you to decide to one day file a genuine bug report or make a pull request, big chances that you will never get a response (1.5k issues, 329 pull requests open as of now). It's a Frankenstein('s monster) of a project from all perspectives, which is a shame, since I'm rather fond of the interface