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An AI Future Without the Lock-InRemy Guercio argues that the past year of AI adoption pushed teams toward expensive, vertically integrated tool chains. Long sessions can consume millions of tokens, while tools such as Claude Code and Codex combine models, agents, interfaces, and connectors in...
Beyond RAG: Build a Relational Context Engine from ScratchPeter Werry argues that vector retrieval cannot answer many practical engineering questions on its own. A request such as "PRs I merged last week" requires the system to understand an author, a merge state, and a time range. His workshop...
Building AI Agents with Real-Time Web DataYohan Raju explains why an agent sometimes needs to collect information from the web at the moment a user asks a question. Static model knowledge or pre-indexed retrieval can answer stable questions, but current travel conditions, product prices, reviews, and...
Coding agents look capable in a fresh repository and come unstuck in a ten-year-old one.
A production agent can give the right answer and still refund the same customer twice, act on a stale record or quietly finish half a job.
The chat box makes users package their work into prompts, wait, inspect the answer and try again.
More context can make an agent worse.
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