Blog

Most software feels like a tech product dropped into an industry. Here’s why studying industry tools, patterns, workflows, and design language matters: it creates software users understand right away. I’ve seen this building products for insurance, finance, and education.

Everyone talks about LLM capabilities, but nobody talks about the bills. Here's what AI actually costs in production across GPT-4, Claude, and other models, with real numbers from BalancingIQ, Handyman AI, and voice systems.

Everyone wants to build 'the next Jarvis,' but most voice AI feels clunky. Here's what I learned about latency, interruptions, and conversation design using Twilio, OpenAI, and Azure Speech.

Everyone implements OAuth, but most do it insecurely. Here's how to build production-ready OAuth 2.0 with PKCE, KMS encryption, automatic token refresh, and multi-tenant isolation based on real implementations.

I love both Rust and Python for different reasons. Here's how I decide which to use for Lambda functions, APIs, and backend services based on cold starts, team dynamics, and long-term maintainability.

Most engineers start with CS degrees. I started by interviewing strangers in Istanbul cafes. Here's why taking the scenic route creates more empathetic, user-focused engineers.

Building BalancingIQ taught me that accounting APIs are deceptively complex. Here's everything about OAuth flows, data models, rate limits, and making integrations production-ready.

After building multiple AI products, the toughest problems weren't prompts or LLM choice, they were multi-tenant isolation, cost control, explainability, and guardrails. Here's what actually matters.

Function calling APIs are provider-specific and break when you switch models. Here's a dead-simple pattern using string codes that works with any LLM, never blocks responses, and handles failures gracefully.

Wearing both hats taught me to ask four critical questions about every feature, and made me ruthless about over-engineering, shiny abstractions, and false future-proofing.

Building AI for healthcare, finance, and regulated spaces requires auditability, deterministic workflows, secure data boundaries, and human oversight. Here's why these constraints make you a better engineer.

Early architectural decisions compound faster than model improvements. Learn why event-driven workflows, data partitioning, encryption boundaries, and observability matter more than which model you choose.

Quick primer on Search, Answer, and Generative Engine Optimization.