create your HF account and start training models! that's the future of personal AI!
INNOVATION
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AI automation creates more human work
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We’ve automated every single thing we could with AI agents. And yet, there’s way more human work to do than ever. We’ve grown from 4 to 30 human employees since GPT-3. I wrote a report on the structural reasons: how AI makes expert competence cheap and why that drives up demand.
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Optimize Your AI Agents with the Right Context
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This applies to any AI coding agent, not just Codex. Cursor, Claude Code, and Antigravity all follow the same approach. Stop asking it to write code from scratch. Provide it with the elements that help you focus on the problem you actually want to solve.
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Google TurboVec: Shrink Vector Embeddings from 31GB to 4GB
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Google's new algorithm just shrunk 31GB of vectors into 4GB. Storing embeddings for RAG eats memory fast. Ten million documents in float32 takes 31 GB of RAM. A new open-source Rust vector index changes that math. TurboVec fits the same corpus into 4 GB. It runs on
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Fixing trusted AI benchmark standards
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feels like a real problem we as an ecosystem need to fix, how do you get deeply trusted and rigorous benchmarks, at the end of the day this is what researchers use to hill climb (plus live experiments)
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Gemini’s focus on real-world use cases
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we are deeply focused on real world use cases for Gemini, its also exciting to see so many benchmarks get better at capturing these use cases
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Frameworks and Prompts to Validate an AI MVP
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7 prompts. 3 frameworks. One Perplexity deep research session. You go from "what should I build?" to validated MVP scope with a launch plan. Before writing a single line of code. I built these from Christensen's Jobs-to-Be-Done and Kim & Mauborgne's Blue Ocean Strategy. The
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Apply JTBD Demand Detector to an AI Opportunity
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Prompt 5: "The JTBD Demand Detector" "For the strongest opportunity you identified, apply
Clayton Christensen's Jobs-to-Be-Done framework. Research what 'job' the target user is actually hiring
a product to do. Not features. The underlying progress
they're trying to make. -
AI Agent Architecture: Three-Layer Pattern Across Models
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Harness engineering finally got its 100-page academic survey paper from UIUC, Meta, and Stanford. Claude Code, Codex, and SWE-agent share the same 3-layer architecture under the hood: Interface · Mechanisms · Scaling Which layer is yours missing?
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Gemini 3.5 Flash Tops APEX-Agents-AA Benchmark
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Gemini 3.5 Flash ranks first on the APEX-Agents-AA benchmark, outperforming significantly larger models.