Daily Brief

The constraint on edge AI is not compute, it is RAM, and it is getting worse: phone makers are shipping less of it this year, and a 6GB Raspberry Pi costs 2.5 times what it did at launch. So Cormac Brick's team at Google AI Edge spends its effort making models small enough to fit. A 2 billion parameter Gemma, quantized to 2.9 bits per weight, runs on a Raspberry Pi at about 8 tokens per second and on a Qualcomm NPU fast enough for a few frames of vision a second. Below that sit tiny models, from 500 million parameters down to 50, that reach the older laptops and cheap devices where even a sma

Full episode: https://www.youtube.com/watch?v=OS1NZLgKM2c Me on twitter: https://x.com/dwarkesh_sp

In the world of AI mental health, vibes can be dangerous, with real consequences. Building SonderMind's Mental Health AI Coach required a new playbook for eval-driven development that balances effectiveness and safety. The team explains the clinical feedback loop that turns human therapist insights into machine-readable evaluations across thousands of conversations; the Ethics Engine of modular guardrails that can evolve with clinical guidelines; the move from single-prompt agents to a closed-loop Supervisor/Executor/Evaluator architecture; and the human oversight used to improve safety and q

Good code data runs out, so poolside manufactures more of it, and the hard part is making it teach. Their synthetic pipeline pairs templates with supplementary context and spreads generations across an axis of phrasing, with difficulty tuned so a task is neither trivial nor so hard the model learns nothing from it. Multistage pipelines port existing data into new shapes, swapping character styles or plots and turning single prompts into multi turn chats, while an orchestrator polices every generation and drops the ones that miss. On the training side the team trusts nothing: run two replicas

Data Quality Research at Prime Intellect and State of Data Author. Prior investor at Hummingbird and Costanoa. Speaker: Sean Cai — CEO, Independent / State of Data X: https://x.com/SeanZCai Website: https://www.seancai.com/

Full episode: https://www.youtube.com/watch?v=OS1NZLgKM2c Me on twitter: https://x.com/dwarkesh_sp

DeepSWE is 113 software engineering tasks written from scratch, not scraped from pull requests, so a model cannot have seen them in training. Each one is a long horizon problem drawn from a real open source repository, authored by engineers who actually maintain that code, with isolated environments and program based verifiers that check observable behavior rather than trusting the model's own account. James Shi's point is that once you remove the contamination the leaderboard stops clustering: strong models pull far ahead and others, Gemini 3.1 Pro among them, fall toward the bottom. The mor