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ExternalSoftware Engineering
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Writing evidence-linked docs exposed two missing regression tests

This is a crosspost of the canonical version on GitHub. I run agent-cost, a small open-source CLI that reads local Claude Code / Codex CLI usage logs and estimates token cost. I wanted documentation for it more trustworthy than a hand-written README: not prose claiming "the reader handles the TTL cache-write breakdown correctly," but claims that each point at the exact test or source line backing them, checked against real git history so the pointer can't silently go stale. To do that I used a s

opensourcepythontesting
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ExternalSoftware Engineering
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I built a tool that tells me if readers actually understood my articles

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

webdevaibuildinpublic
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ExternalSoftware Engineering
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"I built a portable memory layer for AI agents so you don't have to"

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

aiopensourcerust
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ExternalSoftware Engineering
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interlace.sh: Why We Built a Unified Abstraction

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

datadataengineeringopensource
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ExternalSoftware Engineering
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Pressure-testing Ota on Flagr: native and container Go verification with honest boundaries

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

pressuretestingflagrgo
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ExternalSoftware Engineering
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Why console.log Isn't Enough When Building AI Agents

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

typescriptaibeginners
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