The case for overhauling American science
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After publishing 66 technical articles on DevOps and AI infrastructure, I realised I had a blind spot. I knew how many people visited my articles. I had no idea how many people understood them. Page views, bounce rates, time on page — none of these tell you whether your explanation of Kubernetes state management actually made sense to the reader. So I built QuizOps. What it does You paste your article URL. GPT-4o reads it and generates 10 multiple-choice questions streamed in real time. You revi
markdown AI agents are everywhere now. But they still struggle with one thing: memory. Most agent frameworks store memory in a vector database, but that's not enough. A vector DB just stores embeddings; it doesn't understand memory types, consolidation, forgetting, or portability. So I built Mneme — a portable memory layer for AI agents. Structured memory: episodic (what happened), semantic (facts/preferences), procedural (how to behave). Semantic recall: find relevant memories using local embed
Consider what a data engineer has to learn before writing a single useful transformation on a typical 2026 data stack. dbt, for SQL models, sources, tests and macros. Airflow or Dagster, for scheduling, sensors and retry logic. dlt, Airbyte or Fivetran, for ingestion. Python scripts for anything SQL cannot express. Then YAML to configure all of it, and a growing pile of glue to hold the pieces together. Each of those tools is good. dbt genuinely standardised SQL transformation. Airflow's operato
Overview Flagr is a useful test of whether Ota can keep a real service The pressure contract pins released Ota v1.6.25, separates deterministic verification Flagr combines several patterns that show up in mature service repositories: a finite Go verification lane; a long-running local service with a declared HTTP surface; an integration topology that needs an image build, Compose startup, readiness, test execution, and teardown; and CI that needs to distinguish a safe verification lane from de
An AI agent fails, so you add a few log statements: console.log('starting agent'); console.log('tool result', result); console.log('final answer', answer); That works until the agent calls tools in parallel, retries one of them, falls back to cached data, and makes several model calls for different purposes. The terminal still contains the events, but it no longer explains the run. The limitation is not console.log() itself. The limitation is flat, uncorrelated events. Agent debugging needs ide
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We’ve released multiple updates to the OAuth app and GitHub App platforms to support more secure app development: OAuth apps can opt in to expiring access tokens and refresh tokens.… The post Multiple redirect URIs and token refresh for OAuth apps appeared first on The GitHub Blog.
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