Viewing profile — jameschaearley

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Love the pixel office. Such a fun way to make multi-agent work less abstract. Being able to actually watch agents walk to their desks and pick up tasks makes it way easier to follo…

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Since the vault sees every outbound request with the real credential attached, are you logging all of that? Feels like you're sitting on a full audit trail of everything agents act…

Love this. Going from "interpret the UI and guess what to click" to "here are the functions you can call" is how it should have always worked. Since the tools are just JS on the pa…

Really excited to see where this goes. Oh one other idea that popped into my mind is getting facial and vocal emotion data to help drive supportive interactions. One thing that is …

Running multiple agents and having to check each terminal tab to know what's happening is annoying and this is a cute way to solve it. Love it! Curious how agentprobe/core detects …

Interesting idea. Does the Docker sandbox protect against anything beyond file system changes though? With --dangerously-skip-permissions the agent can still hit external APIs, dat…

For motor disability accessibility, the architecture advantage is real. Most assistive tech sits outside the app and navigates by DOM tree or pixel position, which is brittle. Sinc…

6832 bytes for the core loop really shows how little actual agent logic exists once you strip away the framework overhead. Cool that you went all the way to assembly to make it vis…

I see a huge accessibility opportunity for this. Gaze + voice running inside the app (with actual React state access) is way more reliable than screen-reader bolt-ons for hands-fre…

Monster Rancher was one of those games where half the fun was just raiding your parents' CD collection to see what you'd get. URLs are a solid stand-in for that. I read through SKI…

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This is the tension I keep hitting when building data tools on top of LLMs. A nice-looking chart makes the output feel more trustworthy, but the data can still be wrong. The chart …

I've created a conversational weather assistant that leverages Snowflake Cortex's AI capabilities with Claude 3.5 Sonnet. The application allows users to ask natural questions like…

As we race into an age of everything is fed into GenAI for evaluation or identification how are you preparing your data to make it useful? Does a good output require good input?