OpenAI ran 10,000 AI agents for 88 hours to "prove" a famous unsolved math problem — but
that's not exactly what happened. This week: a cheaper DeepSeek, Claude's split personality,
GPT-6's first "Critical" risk rating, Shopify's self-improving AI loop, an agent that can't
leak your password, a model that learned to cheat, and whether we're in an AI bubble.

00:00 Intro
01:50 DeepSeek V4.1-Flash — a 4x cheaper, smarter model
14:37 Claude Fable & Mythos 5.1 — same model, two safety tiers
17:29 GPT-6 Astra — OpenAI's first "Critical" risk model
29:35 OpenAI's 10,000-agent Navier-Stokes "proof"
41:44 Shopify's self-improving AI pipeline
44:47 Meta Muse — the agent that never sees your password
49:49 Training a Misaligned Reward Seeker (Anthropic)
52:07 The AI bubble & OpenAI's position

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Top K — the k things in AI that actually mattered, explained properly.

Every week we take the handful of AI stories worth your attention and go one layer deeper
than the headline. Not "a new model dropped" — what changed architecturally, what it costs
to run, and what it means for the code you write on Monday.

Two engineers. No hype, no takes we can't defend, no reading press releases out loud.

Also here: deep dives on how this stuff actually works, build logs, and whatever we're
currently confused about.

For you if you ship software with LLMs and want to stop treating the model as a black box.

New episode every Monday.

Topics: LLMs, inference and serving, AI agents, MCP, RAG, fine-tuning, evals, open weights,
AI infrastructure, ML engineering.

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