Gabriel Mitchell (@gabeschnitzel) 's Twitter Profile
Gabriel Mitchell

@gabeschnitzel

Design & AI · Currently @newmaterialco prev @IDEO, @RGA, and @CarnegieMellon HCI

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linkhttp://www.gabrielmitchell.com calendar_today14-07-2011 05:07:20

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Gabriel Mitchell (@gabeschnitzel) 's Twitter Profile Photo

Throwback to this to-do app I prototyped where physical notes would create inputs in mixed reality. Your tasks and reminders would sit mapped to your wrist and oriented based on time. Might be worth revisiting with the VisionPro!

Gabriel Mitchell (@gabeschnitzel) 's Twitter Profile Photo

Worried about sending sensitive documents to ChatGPT? I built an app that automatically identifies and redacts sensitive personal information using a custom trained AI model that runs locally on your device. Using Cursor . Cursor helped me… - Write a complete Electon

Aditya Agarwal (@adityaag) 's Twitter Profile Photo

1/ Here's a secret: you hear this a lot because it's true. Most incubator/accelerators and pre-seed VCs expect to see traction even when they say otherwise. If not revenue then at least user growth. Why the doublespeak?

1/ Here's a secret: you hear this a lot because it's true.

Most incubator/accelerators and pre-seed VCs expect to see traction even when they say otherwise. If not revenue then at least user growth.

Why the doublespeak?
Mike Matas (@mike_matas) 's Twitter Profile Photo

Excited to share LoveFrom and OpenAI have come together to form io, a new company creating the next generation of AI products and interfaces. I’ve rarely felt so inspired by a project and team! openai.com/sam-and-jony/

Julie Zhuo (@joulee) 's Twitter Profile Photo

Whoever wins AI personalization wins the consumer. Nobody actually wants to work with multiple agents. Why everyone isn't scrambling to try and get more data and context from users right now to do better personalization is beyond me.

Andrej Karpathy (@karpathy) 's Twitter Profile Photo

+1 for "context engineering" over "prompt engineering". People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window

OpenAI Developers (@openaidevs) 's Twitter Profile Photo

We've raised Structured Outputs limits to support larger schemas: Object properties: from 100 → 5,000 Characters in string: 15,000 → 120,000 Enum values: 500 → 1,000 Total characters across string members in enums with >250 values: 7,500 → 15,000

Gabriel Mitchell (@gabeschnitzel) 's Twitter Profile Photo

We're building a visual canvas for designing prompts. This interaction makes it easy to test prompting concepts simply by dragging and dropping new sections into your existing prompts.