Field reports from the TechKis team.
Engineering notes, architecture deep-dives and honest takes on what we’re learning while shipping AI-first software.
Top highlights
- 9 min read·AI EngineeringCostEngineering Practice
What an AI pilot actually costs — a line-item breakdown
Most AI pilot budgets are wrong because they only price the build. Here's the honest line-item breakdown — discovery, data prep, evaluation, build, inference and the run cost nobody quotes — and where pilots quietly overrun.
Read post - 9 min read·PerformanceCore Web VitalsSEO
A Lighthouse 100 doesn't mean your site is fast
Lighthouse is a lab simulation on a throttled mid-range phone. Google ranks on field CrUX p75 for LCP, INP and CLS. Here's why the two disagree, and what to measure instead.
Read post - 9 min read·AI EngineeringSEOContent
Getting cited by AI search — how to be the answer, not the tenth blue link
ChatGPT, Perplexity and Google AI Overviews cite sources instead of listing links. Here's how answer engines pick what to quote, and how to be the citation.
Read post
All posts
29 posts · page 3 of 5
- 10 min read·AI EngineeringArchitectureBackend
How AI changes backend architecture — the parts that are genuinely different
Adding an LLM to your backend breaks assumptions that held for a decade: latency, determinism, cost per request, statefulness. Here's what actually changes and how to design for it.
Read post - 9 min read·AI EngineeringDeveloper ExperienceEngineering Practice
What AI coding agents actually change about shipping client software
AI coding agents make generation cheap and review expensive. What actually gets faster on client work, what doesn't, and verification discipline that keeps it safe.
Read post - 10 min read·AI EngineeringArchitectureEnterprise
The AI gateway pattern for enterprise applications
Letting every service call model providers directly is how enterprises lose control of cost, security and compliance. An AI gateway is the single seam where routing, auth, budgets, logging and guardrails live. Here's how to build one.
Read post - 10 min read·AI EngineeringObservabilityArchitecture
AI observability — logs, traces, cost and hallucinations
You can't run an LLM system on hope. Traditional observability misses the things that actually go wrong with AI: silent quality drift, runaway token cost, and confident wrong answers. Here's the observability stack an AI system needs.
Read post - 9 min read·AI EngineeringCostArchitecture
AI rate limiting and cost control — before the bill surprises you
One buggy loop or abusive user can turn an LLM feature into a five-figure invoice overnight. Rate limiting and cost control for AI aren't the same as for a normal API — here's how to cap spend without breaking the product.
Read post - 11 min read·ArchitectureScalingBackend
Scaling from 10 users to 10 million — the decisions that matter at each stage
Scaling isn't one problem. It's a sequence of different problems that appear at different stages. Here's what actually matters at 10 users, 10,000 users, 100,000 users, and 10 million — and the mistakes that come from solving tomorrow's problem today.
Read post