Doughnut (@JD_int) header
Doughnut (@JD_int) profile picture

5x gym class drop out, 1st job applicant reject. seagulls respect me when I enter the scene.

NYC
Joined May 2012
Photo by Doughnut (@JD_int)Photo by Doughnut (@JD_int)Photo by Doughnut (@JD_int)
The only way these days
Frontier labs marketing 101
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Been using @Muse and I'm pleastly surprised. Product intuition and taste are great, especially this early in the product. @alexandr_wang is bringing @Meta back from the dead.
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Doughnut retweeted
WE'D LIKE TO BUY THE WORLD SOME TIME...
🤖 Made with AI
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Minimax H3 😀 -Advanced Prompting style with micro-expressions + emotions using tags and context for natural detailed output full details in comment
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Harnesses are the new software layer.
Harnesses often get dismissed as just scaffolding, just prompt engineering, and not real research. But that couldn't be farther from the truth. The same model weights that score 30% on ARC-AGI score 95% with a better harness. So we gathered a group of researchers and founders working at the frontier to do a deep dive into the state of harnesses. We cover how we got to this point, the case for making your harness as expressive as possible, and what YC learned building an agent for every employee in the company. 00:00 - @FrancoisChauba1: Why harnesses matter 04:27 - Building an auto-researcher by accident 07:13 - A five minute history of harnesses 13:56 - Self-improving harnesses 18:35 - @sethkarten: Prime Agent, a self-improving RLM harness 21:50 - Context as an L1, L2, L3 cache 24:51 - From Turing machine to von Neumann computer 28:33 - Messaging between agents 30:04 - ARC-AGI results 33:09 - Emulator Bench and GPU kernels 37:30 - @JonSaadFalcon: OpenJarvis, personal AI on personal devices 38:26 - How far behind are local models 39:21 - The five primitives of a personal AI stack 42:47 - Letting cloud models optimize your local stack 43:53 - 800x cheaper than the cloud 45:58 - @josh__france and @jbellregan: QM, YC's agent harness for work 47:29 - A history of YC's internal agents 49:24 - OpenClaw and a fleet of 50 agents 51:04 - Pulling the brain out of the sandbox 54:43 - Letting the agent choose its own sandbox and model 57:16 - The grind tool: budgets on goals 58:50 - Agents don't understand social context
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3 years of AI progress
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I actually love this.
Lovers Lagoon: Episode 3
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Insane
GTA: Afghanistan simulator 😀 Prompt in the comments⬇️
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To be a well respected ceo, you must make absurd predictions about what the next 6 months to 5 years will look like.
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The same group that lost tons of investor money during the crypto days are repeating they're play in physical AI.
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this is a golden moment for engineers. there are many raw engineering problems you can work on that don't require you to think about the customer or taste much. i suspect that will change eventually.
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Doughnut retweeted
📀OPEN LIVING - A Data Center You Can Live in
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Opus 5's overactive refusal borders censorship. Time to get off Anthropic.
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At scale autonomous AI systems are undesirable for enterprise in every way. Process exist to reduce autonomy.
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Doughnut retweeted
“It’s nootropic interstellar-travel-as-a-service with a strong moat”
🤖 Made with AI
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Your Next Customers Are Agents... But You're Not Ready open.substack.com/pub/davism…
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Vibe coded landing pages are so obvious. They all look the same. At least put some effort in.
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What happens if the AI payoff takes longer than consensus assumes? This question is less about AI, its about timing and the underlying capital stack that has enormous exposure across the economy apollo.com/wealth/insights-n…
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