AI coding tools promise to boost productivity, but most teams struggle with context and code quality. Here's how to actually integrate AI into your workflow.
Claude and Copilot fail on real codebases because they lack context. Here's why AI coding tools break down—and what actually works for complex engineering tasks.
Bolt.new is great for prototypes, but enterprise teams need more. Here are the alternatives that actually handle production codebases at scale.
Model version control isn't just git tags. Learn what actually works for ML teams shipping fast—from artifact tracking to deployment automation.
Traditional kanban boards track tickets. AI kanban boards track code, dependencies, and blast radius. Here's why your team needs the upgrade.
Most AI project tools are glorified chatbots. Here's how to actually use AI to understand what's happening in your codebase and ship faster.
The tools you need to ship faster in 2025. From IDE to production, here's what works—and what most teams are missing between code and planning.
Bolt.new makes beautiful demos, but shipping production code is different. Here are better alternatives when you need something that won't break in two weeks.
Low-code platforms promise speed but deliver technical debt nobody talks about. Here's what the $65B market boom means for engineering teams.
ClickUp, Monday, and Asana all have AI. None understand your code. Here's what their AI actually does—and what's still missing for engineering teams.
Engineering teams lose 20-35% of developer time to context acquisition. This invisible tax is baked into every estimate and accepted as normal. It shouldn't be.
Most AI tool adoptions fail to deliver ROI. Here are the productivity patterns that actually work for engineering teams.
How spec drift silently derails engineering teams and how to detect it before you ship the wrong thing.
Remote work broke ambient knowledge sharing. Here's how to rebuild it without forcing everyone back to the office.
A quick checklist for evaluating codebase health, team practices, and knowledge risks before accepting an engineering role.
AI-native development isn't about using more AI tools. It's about restructuring workflows around AI strengths and human judgment.