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
- 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 - 10 min read·ArchitectureMicroservicesBackend
Monolith vs Modular Monolith vs Microservices — picking the right shape for your stage
A practical breakdown of when a monolith is the right call, when to modularise it, and when microservices actually earn their operational cost — with the signals that tell you it's time to move.
Read post - 9 min read·LangGraphLangChainOpenAI
LangGraph vs LangChain vs raw OpenAI SDK in 2026 — what we actually pick
An opinionated, code-first comparison of LangGraph, LangChain and the raw OpenAI / Anthropic SDKs for shipping production agents in 2026 — when each one earns its keep, where they bite, and what we default to on new client work.
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- 9 min read·AI EngineeringArchitectureAPIs
MCP vs A2A vs HTTP APIs — what's actually different, and when each one wins
MCP, A2A and plain HTTP APIs solve overlapping but distinct problems. Here's what each protocol is really for, how they compose, and how to pick without cargo-culting the newest acronym.
Read post - 9 min read·AI EngineeringProductSaaS
Why every SaaS will have AI agents by 2027 — and what that changes about how you build
Agents are moving from bolt-on chat widgets to the primary way users get work done inside software. Here's the shift that's driving it, what an agent-native SaaS actually looks like, and what it means for your architecture and moat.
Read post - 9 min read·AI EngineeringArchitectureDeveloper Experience
Building AI features without LangChain — the raw-SDK path most teams should take first
You don't need a framework to ship an AI feature. Here's what the raw provider SDK actually gives you, the small amount of glue you write yourself, and when a framework finally earns its place.
Read post - 10 min read·AI EngineeringRAGRetrieval
RAG is not dead — you're just building it wrong
"Just use a bigger context window" doesn't kill RAG. Bad chunking, no reranking, and retrieval you never evaluate kill RAG. Here's what a retrieval pipeline that actually works looks like.
Read post - 9 min read·AI EngineeringMemoryArchitecture
AI memory explained — short-term vs long-term, and why the difference matters
"Memory" in AI apps means two very different mechanisms with different costs and failure modes. Here's what short-term (context) and long-term (retrieval/state) memory actually are, and how to design both.
Read post - 9 min read·AI EngineeringPrompt EngineeringTesting
Prompt engineering is becoming a software engineering problem
Clever wording was phase one. Production prompts now need versioning, tests, evals, regression tracking and CI — the same discipline as any other code. Here's what that looks like in practice.
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