Snehal Patel

The first question I got after the Jev post was a fair one: “Isn’t this just a glorified classifier?”

A lot of the GenAI workflows I’ve worked on follow the same pattern: give a model some context, ask it to return JSON, parse the response, and use a field to decide what happens next.

I had a conversation about AI shopping assistants: what signals help a model understand what someone wants, whether better representations or better reasoning move the needle, whether another agent improves the experience.

DeepSeek-V4.1-Flash is built around a practical problem: agents spend enormous amounts of time reading.

Somewhere around the ninth “OpenAI just announced” Slack message in a single week, I gave up trying to hold the timeline in my head.

Every guard model I’ve used ships with someone else’s taxonomy baked into its weights.

On July 6, 2026, Anthropic published A global workspace in language models, based on the paper Verbalizable Representations Form a Global Workspace in Language Models.

Live dashboard: metra.snehal.ai · Code: github.com/spate141/metra-monitor Every morning I drive to the Metra station, pay for parking, walk up to the platform, and stand there.

My wife asked me a simple question: “When should I water the vegetable beds?”

In May 2026, Robinhood shipped an official Model Context Protocol server that lets an AI agent place real trades on your behalf.

I wanted to add audio narration to my blog posts on snehal.ai.

The thing that finally got me to sit down with Ideogram 4 was the text rendering.