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Articles by Francesco Sardone
AI Denialism In 2026 Is Becoming A Software Engineering Risk
AI denialism in software engineering made more sense when AI tools were mostly autocomplete, boilerplate generators, or a faster way to write unit tests. That version of the debate is now outdated. In 2026, the meaningful shift is not that AI can write code, but that AI agents can participate in the software delivery loop: exploring repositories, planning changes, editing files, running checks, fixing failures, and returning work that a developer can review.
Model Sizing for Coding Agents: Bigger Is Not Always Better
AI coding agents need capable models, and that part is obvious by now. What is less obvious is that model capability is only half the problem. The other half is model fit. A model that works beautifully for one coding task can be wasteful for another. A frontier reasoning model may be the right choice for architecture, migration planning, or debugging a subtle production issue.
Your AGENTS.md Should Not Look the Same Everywhere
AI coding agents need context, and that part is obvious by now. What is less obvious is that context quality is only half the problem. The other half is context shape. An instruction file that works well in one repository, one harness, or one workflow can be the wrong format for another. A dense single file may be perfect for one setup and awkward for another. A concise operational doc may be ideal in one environment and too shallow in another.
Turning Repository Knowledge Into Usable Agent Context
AI coding agents are getting better at writing code, but they still struggle with one thing that every real-world repository has: context. Not syntax. Not boilerplate. Context. That means understanding how a repo is structured, which commands are the right ones, how tests are run, what naming conventions are actually used, what tooling is configured, and what rules are implicit but never properly documented. That is the gap Agentskill is built to solve.
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