Engineering Manager / Technical Lead
A topic hub collecting every article tagged Engineering. Use it to explore related posts and follow this theme across the site.
12 articles
A practical architecture for math tutoring agents: generate random problems with seeded templates, compute ground truth outside the LLM, use the model for explanations, and verify every answer before showing it to learners.
A hands-on, source-backed tour of the modern ML/AI infrastructure stack, centered on self-hosting LLM inference on Kubernetes with vLLM and KServe, plus the GPU economics and skills that actually matter in 2026.
AI can generate most of the code. It still cannot decide what to build, sense when 'correct-looking' output is quietly wrong, or own the consequences. A field guide to the tasks that stay human — for engineers and PMs.
A practical guide for engineers and PMs who want AI coding agents to move fast without breaking architecture decisions, module boundaries, ownership, and long-term system quality.
A practical guide for engineers and PMs who want useful LLM personalization without a giant recommender team, expensive fine-tuning program, or fragile memory layer.
AI can help teams move faster, but the real unlock is designing agents, skills, evals, traces, and self-correction loops around your app.
A practical ladder for growing a software system from one app and one database into observable, event-driven, permission-aware, AI-ready architecture.
AI is changing the center of gravity in software engineering: less manual typing, more specification, validation, system design, and human judgment.
A practical workflow for engineers who want to digest long, technical articles without drowning in detail. Start with quick-view prompts, then move into grounded notes, multi-pass summaries, and long-context research workflows.
A practical roadmap for software engineers who want to move from toy chatbots to production-grade AI agents, with the right study order, common gaps, and portfolio projects.
How AI agents are collapsing the gap between 'I wonder how X works' and 'I have a working implementation of X' — and the research workflows that actually hold up under pressure.
AI isn't just making engineers faster — it's changing what engineering means. Here's what that shift looks like, and why the best days for software teams are still ahead.
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