Comment by thomasahle
15 hours ago
> duping is a great way to show people that even the app layer can be commoditized
Everyone is already cloning the app layer. There are 30+ claude-code clone, and codex work already cloned cowork, grok and gemini is doing the same.
I highly recommend to anyone that uses them a lot - try to clone your own. I’ve got mine fully formed into how I want it, with features I haven’t seen anywhere else yet and with integration into everything I work with. We’ve lived in a world where every app had to fit enough people well enough - but now we live in a world where an app can be molded to be absolutely perfect for just you!
Most people don’t want to play product manager and just want to pay someone to make the decisions for them.
We used to have a time where every small business had its own custom accounting software, and discovered that these businesses aren’t actually special snowflakes and are better off just using the generic software and conforming to whatever it does
Out of curiosity, what are your favourite features that are unique to your implementation?
Not parent, but favorite feature of my own harness I haven't seen somewhere else, is the ability for the agent to execute code on different hosts in a transparent and easy way. Fairly simple and probably could prompt the agent + set things up with ssh, but was doing other stuff related to a server<>client model, so figured why not add it as a couple of tools. Now the agent can build cross-platform applications while I'm mostly on Linux and actually verify it works, without me manually switching between three OSes.
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This is the way. Guess what am I working right now? My own “Work” app that will be better than anything else available today!
Same here! https://github.com/rush86999/atom
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How did you build your own coding agent? What language/framework did you use?
Not the parent commenter, but most of it is surprisingly simple. You basically start with a "chat app" where you have a list of messages, send the whole conversation to an LLM and it replies back, which also gets added to the same list.
And you add a small twist, that instead of a 1-to-1 back and forth, you instead put it into a loop, where the LLM reply can itself "have a turn", e.g. a tool invocation, where your system is the one that replies (e.g. with the tool invocation's result). That's pretty much it, you have a 1 to potentially many "chat".
The harder part is getting all the "soft" parts right, like how to have well-behaving tool calls, timeouts, prevent huge cycles eating up tokens, but there are no one way to solve these, it's a fundamentally heuristic-heavy area.
This comment is a great example of how large and strange the skills gap in AI is right now.
Curious why your first impulse is not simply to point your favorite agent at a few examples and start brainstorming/planning from there?
Multiple times I’ve built a purpose specific bespoke tool starting this way. In fact, it’s a great way to learn how specialized tools are built.
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It's pretty simple nowadays if you know conceptually how they work. Running the LLM calls in a loop with tools is an agent. You only need 10 or so basic tools to accomplish nearly anything, and you can build a dynamic skill system from that. Look at https://github.com/patw/pengy, ignore the app look at the spec.md file, feed that to your current agent of choice and make your own version. Use whatever tech stack or UI you're comfortable with. Change some of the choices in how it works, so it fits what you want to work.
Implementing your own agent is very easy. Here is a minimal agent in 60 lines of Python without dependencies:
https://github.com/99991/MinimalAgent
You only need a single tool to start with. All recent LLMs know how to use bash for reading/writing/editing/executing.
Why would the language matter? Just use your favorite.
Look at pi coding agent.
It would have been much easier to clone six months ago but today Codex/Claude desktop apps are so feature packed it seems it wouldn’t be worth it.
For example, the mobile/remote control feature I find very useful but not sure it would be worth cloning. If you aren’t replicating every feature you aren’t exactly “cloning” it, you’ll just end up with a crappier version with a fraction of the features of the real thing.
But dont you have to be faithful to rl environment in which it model was trained in the harness?
only labs knows what the rl enviroment was so they will always be one step ahead.