Software Engineering in 2026

kept by eddie dec 30, 25

Falling costs of LLM-generated code shift software engineering bottlenecks to infrastructure, CI, human-guided abstractions, and code review in 2026.

Engineering management after the cost of code collapsed
Engineering management must refocus on auditing assumptions, measuring business outcomes, and owning specification and accountability, as AI-driven code production makes traditional output metrics misleading.
also on LLMs, #ai-coding, #software-engineering, #productivity, #developer-tools
LLMs and performative productivity - Josh Collinsworth blog
LLMs often create the illusion of productivity while actually reducing real output through cognitive debt, maintenance overhead, and misaligned incentives.
also on LLMs, #ai-coding, #software-engineering, #productivity, #developer-tools
Design from the inside || Matt Ström-Awn, designer-leader
In high-growth AI startups, product designers must work inside the codebase, ship code themselves, and make incremental changes instead of relying on static blueprints.
also on #ci-cd, #ai-coding, #software-engineering, #productivity
GitHub Actions is the weakest link
GitHub Actions' default features—mutable tags, pull_request_target, template injection, and write-scoped tokens—have enabled a chain of supply-chain attacks on open-source registries, and GitHub's opt-in security roadmap won't fix the root causes.
also on #ci-cd, #software-engineering
How to Work and Compound with AI
Treating AI as a collaborator requires structuring context as infrastructure, encoding taste as config, shifting verification early, delegating bigger tasks, and closing feedback loops to compound improvements.
also on MCP, #ai-coding, #software-engineering, #productivity, #developer-tools