MIT just proved that upgrading your AI model only gets you HALF the results. The other half is how you prompt it. 1,900 participants. Controlled experiment. The findings completely destroy the "just use a better model" narrative. Here's what they discovered
MACHINE LEARNING
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Imaginary stakes improve AI’s scrutiny and hedging
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Step 6: Create Imaginary Stakes
— God of Prompt (@godofprompt) 23 janvier 2026
"Let's bet $100 on this: Is my code efficient?"
Something about stakes makes the model scrutinize harder.
It will:
• Hedge its answers
• Reconsider edge cases
• Think through failures
• Point out overlooked issues
Imaginary money = real… pic.twitter.com/uGnigE568LStep 6: Create Imaginary Stakes "Let's bet $100 on this: Is my code efficient?" Something about stakes makes the model scrutinize harder. It will: • Hedge its answers
• Reconsider edge cases
• Think through failures
• Point out overlooked issues Imaginary money = real -
Technical insights on Codex model learning and memory
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interesting from my experience (and except very few exceptions) codex just goes and learns whatever it forgets/ needs after compaction
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floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
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#PaperADay 9
floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL https://arxiv.org/pdf/2509.06863 In theory, value based reinforcement learning is a regression problem, which is most naturally addressed with an MSE loss. However, there are a bunch of subtle -
Running Llama 3.2 3B on Raspberry Pi 5 with AI HAT+
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And here we go – running Llama 3.2 3B on Raspberry Pi 5 with AI HAT+ 2. This thing is not going to break any speed records, but it’s incredible that we can now get such a powerful model on such a constrained system. Just a little over 3 years since ChatGPT launched. pic.twitter.com/Cb5wfUEGnD
— Bojan Tunguz (@tunguz) 22 janvier 2026And here we go – running Llama 3.2 3B on Raspberry Pi 5 with AI HAT+ 2. This thing is not going to break any speed records, but it’s incredible that we can now get such a powerful model on such a constrained system. Just a little over 3 years since ChatGPT launched.
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Discussion on AI system architecture and training data transparency
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There really isn't an algorithm in these new AI systems. More like billions of algorithms. And, no, I can't see Elon open sourcing his dataset that goes into training these billions of algorithms.
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Databricks Named Leader in Gartner Magic Quadrant Reports
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Databricks has been named a Leader in two 2025 Gartner Magic Quadrant™ reports for Cloud Database Management Systems and for Data Science and Machine Learning Platforms! We believe this recognition highlights the strength of our open platform and new capabilities like Lakebase
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Autonomous Delivery Challenges: When Reality Meets Winter Conditions
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Autonomous delivery meets icy roads and suddenly the future looks very… human On slides, simulations, and sunny demo days, autonomy looks flawless.
But throw in ice, unpredictability, and real-world messiness — and things get interesting fast. It’s not a failure.
It’s a -
16-bit Integer Multiplication on Tenstorrent Hardware
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16-bit Integer Multiplication on @Tenstorrent: jasondavies.com/2026/tenstor…
→ View original post on X — @tenstorrent, 2026-01-22 13:54 UTC
