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  1. The last post in this series.

  2. S3 launched twenty years ago as a simple object store with eventual consistency.

  3. SIGMOD Companion '26 has a 13-page paper by 50+ Google authors revisiting Bigtable two decades on. 10 EB, 7 billion QPS, and an architecture that barely moved.

  4. An idea someone had fully worked out back in 2018, yet it took eight years before it could actually be built.

  5. When agents go to production, they break the four assumptions today's infrastructure was built on: deterministic, human-driven, request-response, stateless.

  6. A migration retro: forking Langfuse to swap its analytics store from ClickHouse to GreptimeDB, run mostly by two AI agents.

  7. Part 1 was about how the three pillars split apart.

  8. Today we treat metrics, logging, and tracing as the natural structure of observability.

  9. Datadog's Investor Day deck quietly redefines observability: the object of observation turns probabilistic, AI agents become operators, and the reader of the data shifts from human to model.

  10. TMA1 v2 adds an MCP server, enhanced hooks that auto-inject build/session/anomaly context, and cross-agent context sharing between Claude Code and Codex.

  11. TMA1: a local observability tool for AI coding agents with full session trace, tool decision breakdown, latency tracking, and SQL-queryable storage.

  12. Have we been building infrastructure for humans this whole time, without ever treating agents as users?

  13. My colleague called Database MCP 'pulling your pants down to fart.'

  14. AI Coding doesn't fundamentally make you a better programmer.

  15. The difference between using AI as a tool you pick up occasionally versus treating AI as infrastructure you don't know how to work without.

  16. When AI can rewrite an entire module in seconds, do the design principles built for 'humans maintaining code' still apply?

  17. AI's real value isn't in producing the final output—it's in creating Builders.