I built a similar idea as a hobby project to see what was possible with harnesses. My idea was give a model a chat input, but outputs can be data models (sqlite), and panels that are custom react components that can be generated and mounted on the fly by the model.
The basic idea in todo list form was:
"Let's build a todo model to manage my daily tasks, then build a task list panel where i see today's todos."
It got more interesting from there as I kept building ontop of it. For example, I added data hooks (trigger something when data changes, e.g. custom code or even webhooks) and it felt like an AI native Airtable.
Everything persists and becomes the UI of the app itself.
rush86999 [3 hidden]5 mins ago
I took it a step further with AI accessibility and recordings for teaching AI agents. Canvas context forms the who, what, when, where, and why, along with the agent conversation for training & agent maturity --- https://github.com/rush86999/atom/tree/main/docs/canvas
AdityaK_9999 [3 hidden]5 mins ago
The project seems to be a great initiative however does it have checkpoints? I find that feature a must with agentic workflow for very obvious reasons.
Gabry848 [3 hidden]5 mins ago
In my opinion it can be more usefol if I can add it with an mcp server, so I can use with my agent and test it in real workflows to see if it can add power and some prons to my agent
quietraster [3 hidden]5 mins ago
letting the agent build its own panes is a neat idea. do people keep the layouts it creates?
janket [3 hidden]5 mins ago
Sounds cool, can you explain a bit more how it works?
greentfrapp [3 hidden]5 mins ago
Hi HN, I built Panel because research work means switching all the time between a chat with an agent, the papers I'm reading, the notebook I'm running and the data files I'm looking at.
Panel is a local web app with dockable panes made with dockview. You get chat, files, PDFs, markdown and Jupyter notebooks.
The agent can also create custom panes on request, like a protein structure viewer or an SQLite browser.
There's also an early "Module" system. Modules are like Skills, but with typed inputs, outputs and intermediates, so a long job shows its progress and its results can feed the next step. Right now the working one is a literature review, with an early version for hypothesis generation.
How it's built: a Vue + dockview frontend and a Python (FastAPI) sidecar that drives the agent backends. Everything runs on your machine.
Honest limits for now:
- Full support is Claude Code only, so you need it installed and signed in. Codex and other harnesses are not supported yet.
- Still very rough around the edges, I've been adding to it as I use it myself. Feedback welcome!
HN.zip
The basic idea in todo list form was:
"Let's build a todo model to manage my daily tasks, then build a task list panel where i see today's todos."
It got more interesting from there as I kept building ontop of it. For example, I added data hooks (trigger something when data changes, e.g. custom code or even webhooks) and it felt like an AI native Airtable.
Everything persists and becomes the UI of the app itself.
Panel is a local web app with dockable panes made with dockview. You get chat, files, PDFs, markdown and Jupyter notebooks.
The agent can also create custom panes on request, like a protein structure viewer or an SQLite browser.
There's also an early "Module" system. Modules are like Skills, but with typed inputs, outputs and intermediates, so a long job shows its progress and its results can feed the next step. Right now the working one is a literature review, with an early version for hypothesis generation.
How it's built: a Vue + dockview frontend and a Python (FastAPI) sidecar that drives the agent backends. Everything runs on your machine.
Honest limits for now: - Full support is Claude Code only, so you need it installed and signed in. Codex and other harnesses are not supported yet. - Still very rough around the edges, I've been adding to it as I use it myself. Feedback welcome!
MIT licensed.