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The 80% Opportunity: AI Adoption Without the Code Obsession
Stop obsessing over AI-generated code. The real opportunity is the other 80% of engineering work: understanding, operating, learning, and verifying. Here's how we approached AI adoption at Kentico.
Key Concepts for Working with AI
The essential concepts you need to understand to start working with AI tooling: models, tokens, agents, prompting, context, and the Model Context Protocol.
AI in 2025: What a Time to be Alive!
A snapshot of AI in 2025 covering key concepts, clarifying misused terms, and practical tips for working with LLMs and navigating the current state of AI in the tech industry.
AI in Kentico in numbers
Hard adoption data, tool usage patterns, and survey insights showing what AI actually does (and doesn’t) change inside Kentico R&D.
How I built an AI reporting stack by just... describing it
Built an internal AI reporting stack mostly by describing it: agent‑generated Node app pulling Copilot & Cursor usage, enriching org data, automating metrics so adoption questions get answered fast.
Early Lessons in AI Enablement: People, Patterns & Pitfalls
Early AI enablement lessons: unify scattered adopters & skeptics, tame hype with realistic baselines, target the 80% of work beyond coding, experiment with context, and measure impact—not raw AI LOC.
I don't trust software without versions
I trust software that versions: meaningful, consistent tags create stability, planning, deprecation strategies and better habits—avoid meaningless bumps, unsignaled breaks and tag chaos.
Customer Success Is Part Of Your Product Too!
Customer success is a product feature: proactive design, documentation, testing and focused mission create retention, advocacy and smart prioritization—support alone can't carry flawed UX.
The Struggles of Picking Up an Ongoing Project as a Program Manager
Picking up an in‑flight project: reconstruct history, earn team trust, realign mismatched stakeholder expectations, surface hidden debt, and accelerate your own learning without derailing momentum.
If you try hard enough, you can find something negative in everything.
Why engineers seem 'negative': constant problem framing, critical thinking and improvement loops spill into life; that scrutiny is care—misused it becomes fearmongering and snake oil.
Pro gamer strategies for creating an API style guide.
Pragmatic API style guide tactics: treat your API as a product, favor consistency over perfection, and anchor standards across design, security, DX, auth, types, docs & strategy.
Facts don't inspire. Why your documentation is more important than you might think.
Documentation is product: a standard, marketing engine, recruiting & trust signal; mix factual references with inspiring guides—facts alone don't move developers, story plus substance does.
Your API doesn't have to suck!
Why most APIs feel painful—and practical moves (style guides, reviews, analytics, empathy for varied developers) to move yours from chaotic routes to a trusted, Stripe‑level experience.