Build vs Run and zero-sumness: a simple framework to reason about how to scale teams post artificial intelligence abundance.
Will AI annihilate all head-counts? I posit it's not quite the case especially for run functions that are inherently a zero-sum game in the market.
Engineer - software, data, AI/ML
- big indeed. A new dimension for training data collection with clearer reward definition. Exciting times for the oai RL folkstweeting this from chatgpt atlas! very excited about this. this is the single biggest step up for OpenAI in collecting your full context and giving fully personalizable AGI. Context is the limiting factor and as @pmarca said, the browser is the new operating system. the only
- interesting to see jetbrains trajectory with their products being severely challenged by a IDE-less future with terminal based dev - they're reaching out to their user for feedback on in-IDE LLM tools usage and pretty happy with their progress last semester tbh
- interesting to see Z dot ai propose quarterly plan for their claude (clawd?) code API like code agent. No one serious is committing to a yearly plan on a model provider given monthly SOTA rotations among labs
- love this rich format blog post with actual data points in the articleToday, we are releasing FineVision, a huge open-source dataset for training state-of-the-art Vision-Language Models: > 17.3M images > 24.3M samples > 88.9M turns > 9.5B answer tokens Here are my favourite findings:






