Go 1.27: The Release That Makes Development Feel Smoother
Go 1.27 brings generic methods, smarter tools for testing and maintenance, JSON v2, goroutine leak detection, and a smoother day-to-day development experience.
Section
Archive
All published posts.
AI Productivity Is Not One Number: Why the Gains Are Real but Uneven
AI writes code faster. The harder question is whether it became easier to deliver software that people can understand, review, and maintain.
The next leap in AI software development: from assistance to orchestration
The next wave is not just writing code with a copilot. It is coordinating supervised agents, context, tests, and boundaries across the entire development lifecycle.
Ontologies for AI agents: the missing contract between model, memory, and tools
LLMs can converse, but they do not share a stable domain model. Ontologies, knowledge graphs, and semantic validation can give agents a common vocabulary, safer actions, and verifiable memory.
Excessive Agency Is a Reliability Bug, Not Only a Security Risk
When an agent is allowed to act too broadly, the risk is not only leakage or abuse. It also becomes hard to operate: it works outside scope, repeats actions, burns budget, and fails without a clean way back.
When Commits Scale Faster Than Reviews
Code volume is growing faster than human review capacity. The problem is not using AI, it is pretending review still costs the same.
Meta Harness: the missing layer in agent engineering
The agent is no longer the whole product. The next jump is the layer above it: memory across sessions, coordination across agents, context across repos, and automatic optimization of the harness itself.
Domain-specific agents: composition against the bloated agent
The generic agent looks simple until it becomes a giant context window with too many tools, too many permissions, and too much cost. Domain-specific agents trade that accumulation for composition, clear boundaries, and cheaper execution.
Evaluating AI Agents Beyond the "Vibes Check": How to Measure What Actually Matters
Your agent nailed the demo and everyone loved it. But how do you know it actually works? If the answer is 'we tested it and it seemed fine', you are operating in vibes mode. And vibes don't scale.
Why Your AI Agent Is a Compulsive "Spender": The Truth About Token Consumption
Autonomous agents can turn a trivial task into a financial black hole because token consumption is massive, stochastic, and difficult to predict.