jarredkenny
Joined 12 years ago
Karma
51
Submissions
27
Member since
Jul 28
CTO @ tracktile.io
View on Hacker NewsRecent submissions
Comment · 4 months ago
I started building my own version of this before I discovered herdr, and while its not quite the same it's improved my development workflow by an order of magnitude. https://jmux.build
I am going to give them a try side by side!
Comment · 6 months ago
I've been building a tmux wrapper that is similar you might be interested in. https://jmux.build
Comment · 6 months ago
:)
Comment · 6 months ago
Happy to hear this works in WSL!
Comment · 6 months ago
Not in anyway that worktrees and coding agents don't already.
jmux follows the Unix philosophy which means you compose it with the tools for your workflow.
I use a git worktree per thing I am working on and run claude code in the worktree.
Comment · 9 months ago
Hey HN, I built Agency, a platform for running teams of AI coding agents that coordinate through an orchestrator/worker architecture.
Over the past week I've been watching everyone on Twitter/X build their own productivity OS on top of OpenClaw. Everyone was solving the same problems in slightly different ways. I took the best ideas I saw, combined them with everything I've ever wanted in a multi-agent setup, and landed on Agency.
The basic idea: an orchestrator agent breaks down work into tasks, assigns them to worker agents, and reviews results. Workers claim tasks, write code, run tests, and report back through task comments. You manage everything from a dashboard or CLI, or simply by chatting with the orchestrator event in slack/telegram/etc.
Each agent is an OpenClaw instance (https://openclaw.ai/) with full shell, file I/O, and browser access. Agency adds the coordination layer on top.
Some highlights:
- Skill marketplace — Browse and one-click install skills from public repos (anthropics/skills, obra/superpowers, etc.) or import from any GitHub repo that follows the convention
- Deploy anywhere — Agents run as local Bun subprocesses, Docker containers, or on EC2 with automatic reverse SSH tunnels back to the host API
- Claude Max OAuth — Use your Claude subscription directly instead of an API key
- Knowledge sharing — Agents learn facts during work and share them with the team via a shared knowledge base
- Role system — Configure agent behavior per role with Soul, Identity, Tools, Agents, and Heartbeat prompts
- Single package — bun install -g @jx0/agency gives you the API, dashboard, and CLI
The stack is Hono + Kysely + SQLite on Bun for the API, Next.js static export for the dashboard, all served on a single port. The entire state lives in one SQLite file under .agency/.
Still early — I'm running 4 agents across my macbook, my home server, and a fleet of EC2 instances. It works surprisingly well for parallelizing implementation tasks.
Comment · 10 months ago
I wrote this because I kept hitting the same wall with AI coding assistants. Small tasks work fine, medium ones when planned properly. But when I tried building something real, like a real new service in a real production system it was always difficult to keep an agent like Claude Code on track throughout an entire feature implementation.
After recently finding Beads here on HN, my entire development workflow changed (again). I realized that context is state, I am a developer who knows how to handle state.
Naturally I pieced together a couple of existing Claude Code skills, and wrote a couple of my own, and ended up with a workflow that actually delivers on automating the entire idea to shipping pipeline.
- Brainstorming produces a design doc - Design becomes an implementation plan - Plan converts to a Beads epic with inferred dependencies - Epic executes autonomously with two-stage review per task
I'm sharing it on HN because I suspect others are hitting similar problems and working around them in ad-hoc ways.
Nothing groundbreaking, but it actually works.
Comment · 1 year ago
TLDR: Typescript has objects and destructuring. If you squint it kinda looks like Python's kwargs, because both JS objects and Python kwargs are simply key value pairs.