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Omar Shaikh

@oshaikh13 • 2,097 subscribers

member of sociotechnical staff @Stanford

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Anya Rossi

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What’s the point of a “helpful assistant” if you have to always tell it what to do next? In a new paper, we introduce a reasoning model that predicts what you’ll do next over long contexts (LongNAP 💤). We trained it on 1,800 hours of computer use from 20 users. 🧵

What’s the point of a “helpful assistant” if you have to always tell it what to do next? In a new paper, we introduce a reasoning model that predicts what you’ll do next over long contexts (LongNAP 💤). We trained it on 1,800 hours of computer use from 20 users. 🧵

123,679 次观看 • 3 个月前

We upgraded Tabracadabra 🎉 to bring an entire context-aware assistant (not just tab to autocomplete!) to any textbox. It's pretty great if you hate switching between the chat interface and what you're working on. We're also open-sourcing, so you can try it out!🧵

We upgraded Tabracadabra 🎉 to bring an entire context-aware assistant (not just tab to autocomplete!) to any textbox. It's pretty great if you hate switching between the chat interface and what you're working on. We're also open-sourcing, so you can try it out!🧵

40,762 次观看 • 1 个月前

Tabracadabra 🎉 is a system that brings tab-to-autocomplete literally *anywhere* there’s a textbox. Instead of relying on a single codebase for context, Tabracadabra uses a General User Model to autocomplete with everything you see or do on your computer.

Tabracadabra 🎉 is a system that brings tab-to-autocomplete literally anywhere there’s a textbox. Instead of relying on a single codebase for context, Tabracadabra uses a General User Model to autocomplete with everything you see or do on your computer.

87,323 次观看 • 9 个月前

If you thought referencing past chats was cool, we built an MCP that lets Claude use *anything you see or do on your computer* as context.

If you thought referencing past chats was cool, we built an MCP that lets Claude use anything you see or do on your computer as context.

64,225 次观看 • 10 个月前

What if LLMs could learn your habits and preferences well enough (across any context!) to anticipate your needs? In a new paper, we present the General User Model (GUM): a model of you built from just your everyday computer use. 🧵

What if LLMs could learn your habits and preferences well enough (across any context!) to anticipate your needs? In a new paper, we present the General User Model (GUM): a model of you built from just your everyday computer use. 🧵

71,224 次观看 • 1 年前

LLMs sound homogeneous *because* feedback modalities like rankings, principles, and pairs cater to group-level preferences. Asking an individual to rank ~1K outputs or provide accurate principles takes effort. What if we relied on a few demos to elicit annotator preferences?

LLMs sound homogeneous because feedback modalities like rankings, principles, and pairs cater to group-level preferences. Asking an individual to rank ~1K outputs or provide accurate principles takes effort. What if we relied on a few demos to elicit annotator preferences?

52,304 次观看 • 2 年前

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