Deep dives into software architecture, developer tools, programming best practices, databases, infrastructure, and the technical decisions that define modern software.
85 articles
Fine-tuning has become the default move. It’s usually the wrong one. Here’s the practical 2026 stack: retrieval, tool calling, strict evals, and audit-grade logs.
In 2026, the winners aren’t the teams with the fanciest model. They’re the ones with routing, evals, safety, and cost controls built like real infrastructure.
Most “agent” rollouts fail for boring reasons: no identity, no spend limits, and no receipts. Fix those three and agents become operators, not demos.
Fine-tuning on “your data” is turning into a liability pattern. The winners are shifting to retrieval, logging, and provable boundaries—not bigger models.
Most teams treat LLMs like a library. In 2026 they behave like a dependency with outages, lock-in, and policy drift. Build like you mean it.
In 2026, the best AI systems look less like a single genius model and more like a well-instrumented pipeline. Here’s how to build the pipeline that survives audits, outages, and scale.
The winners in 2026 won’t be the teams with the best model. They’ll be the teams who treat LLMs like unreliable components—and engineer the rest like it matters.
The winners in 2026 won’t be the loudest coding agents. They’ll be teams that treat AI like a compiler: constrained, testable, and brutally observable.
Model Context Protocol (MCP) is becoming the default way agents touch your systems. If you don’t design it like an identity-and-audit layer, you’ll ship a breach-shaped feature.
The agent era isn’t a productivity hack. It breaks code review, CI, and ownership unless you rebuild the pipeline around specs, policies, and reproducible runs.
Model Context Protocol is quietly turning AI features into infrastructure. If you’re still shipping one-off “AI assistants,” you’re building the new Clippy.
Retrieval-augmented generation shipped fast, then hit a wall. In 2026, the durable advantage is structured knowledge you can govern, not bigger prompts.
Cloud LLMs are turning into a tax. The winners in 2026 will be teams that can run useful models on laptops and phones—cheaply, privately, and offline.
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