There is a recurring fantasy in AI discourse that goes something like this: once the model is good enough, you will not need a human in the loop …
Read MoreThe CTO role was already evolving before AI. Cloud infrastructure reduced the need for deep operations expertise. Platform engineering abstracted away …
Read MoreEvery organization has a knowledge graph. They just do not know it. The information about who owns what system, which team depends on which service, …
Read MoreEvery team building an AI application faces the same architectural question early on: should we fine-tune a model, build a RAG system, or create an …
Read MoreThe Integrated Development Environment has been the center of software engineering for four decades. From Turbo Pascal to Visual Studio to VS Code, …
Read MoreThe compliance team says no. That is usually where the AI conversation ends in regulated industries. Healthcare, finance, insurance, and government …
Read MoreEvery new project starts the same way. Not with the interesting part, not with the problem the client hired you to solve, but with 40 hours of setup. …
Read MoreSingle-agent systems are simple to reason about. One agent, one task, one output. But most real-world problems are not single-agent problems. They …
Read MoreLong-running AI pipelines fail. This is not a possibility to plan for; it is a certainty to design around. API rate limits hit. Provider outages …
Read MoreMost engineering organizations approach observability like this: deploy Datadog (or Grafana, or New Relic), instrument services, create some …
Read MoreThe way we define work in software engineering has not kept pace with how we execute it. We are asking AI agents to implement features from user …
Read MoreThe hiring freeze memo arrives on a Tuesday. Your roadmap does not change. Your headcount does. This is not a hypothetical. We have watched this play …
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