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- Trading Margin for MoatWhy a software company would deliberately hire expensive engineers to do customer work. The services-led-growth thesis: spend gross margin on deployment depth because the resulting integration is hard to displace and the outcome is provable — the margin hit is an acquisition cost for defensibility, not an inefficiency. The arithmetic that decides whether it holds, as a worked napkin example: an FDE at ~$300K loaded doing 3 deployments a year costs ~$100K per deployment, which is 67% of a $150K ACV and 17% of a $600K one — and REUSE is the only term that improves over time (0.6x, then 0.4x). The failure mode built into the thesis is paying the margin and not receiving the moat, which arrives one reasonable exception at a time. Diagnostic: does deployment N take measurably less time than N−1? Plus what the trade means for your career and the two questions to ask an employer.Read →
- 1,000 FDE Jobs, AnalysedReading a thousand job postings beats reading a thousand opinions. One title covers at least THREE distinct jobs — builder (few customers, deep, milestone-driven), pre-sales (many customers, shallow, quarter-driven, often with quota), and internal/platform — and roughly a third of postings using the FDE title are the pre-sales shape, which the posting rarely makes explicit. A separate census counted 1,206 strict-definition postings across 669 companies at a ~$185K median posted base. What every posting asks for: strong general engineering, LLM app patterns, integration reality, deployment where you do not own the cloud, and increasingly MCP servers as deliverables — with EVALUATION the most-cited hard skill and the least taught anywhere. How to read a posting properly, the two questions that settle which species it is, and the honest caveat that posting counts measure demand signals rather than filled roles.Read →
- ToolformerHow a language model taught itself to use APIs. Toolformer learns, self-supervised, to call a calculator, search, QA, translation, and calendar — inserting API calls into text and keeping only the calls that make its next-word prediction better. The recipe that seeded modern tool-using AI agents.Read →
- GRPO (DeepSeekMath)The RL algorithm behind DeepSeek-R1. PPO needs a critic as large as the model itself; GRPO deletes it — sample a group of answers, score them, and reward whoever beats the group average. Half the memory, none of the critic's instability, and the engine that later made reasoning emerge from reward alone.Read →
- TitansModels that memorize at test time. A neural long-term memory keeps learning during inference — writing what surprises it into its own weights, with momentum and adaptive forgetting — so attention handles the recent window while the memory holds the far past. Beats Transformers and Mamba on 2M+ token recall.Read →
- Constitutional AIAlignment from principles, not labels. Write the values down as a constitution, have the model critique and revise its own outputs against them, then run RL on AI-judged preferences (RLAIF) — harmlessness that scales with compute instead of crowds, and refuses without stonewalling. The backbone of Claude's alignment.Read →