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Being a great AI PM is hard. This newsletter makes it easy.
One clear idea per email. That’s it.
OpenAI's Sora hit No. 1 on the App Store and landed a $1B Disney deal, then shut down six months later. The story of why a viral hit got too expensive to survive.
Perplexity owns neither the models nor the content. Here's how that became a $22B strategy and why the open web is now suing it.
You can't human-review every AI output at scale. So you have one model grade another against a rubric. How LLM-as-a-judge works, and when to trust it.
Why do some AI products get smarter the more they are used, while others stall? It's because of the data flywheel: the loop that separates a feature from a moat.
Uber built an AI "PRD Evaluator" that assembles company-wide context to pressure-test product docs before human review. Here's how the system works.
Bigger context windows didn't fix AI quality but made it easier to overload the model without noticing. Here's what context rot is, why it happens, and the TRIM framework to fix it.
I've spent the last 6 months diving deep into building with Claude Code. And now I've created a FREE course that teaches you Claude Code inside Claude Code.
Most AI training data is no longer written by humans. Synthetic data, generated by AI models, is faster, cheaper, and often better. Here's how it works.
Airbnb replaced rigid phone menus with four ML models running in real time: ASR, intent detection, semantic retrieval, and paraphrasing. WER dropped from 33% to 10%.
Not every request needs your most expensive model. LLM routing sends simple tasks to cheap models and hard ones to smart ones, cutting costs 30-70%.
Netflix's recommendation engine drives 80% of viewing. Here's how they built a post-training framework to adapt LLMs for their catalogue from SFT to RL.
I built a 7-agent AI pipeline that writes my newsletter (JustAnotherPM) in 45 minutes instead of 8 hours. Here’s the full system, design decisions, and lessons learned.
