Insights
Real-world notes on voice agents, RAG, autonomous systems, and the trade-offs that matter when AI meets production.
@google/genai shipped Agent and Environment APIs today — days before Google I/O. The SDK velocity tells you what's coming before the keynote does.
Claude Agent SDK jumped to 0.3.x, Remote Agents went live, Gemini SDK shipped four versions in eight days. The agent infrastructure layer is moving.
Most AI agencies build products. We build worlds — and worlds you can read, listen to and walk through. Codemachia is our 7-sovereign-AI transmedia universe: four published novels (~297,000 words), four music albums (52 tracks), seven champions, 46 Codex fragments, bilingual EN/FR. Here's the discipline.
Don't pick on benchmarks. Pick by use-case. Here is the decision tree we run for every new AI product, with the model we actually ship for each task.
The failure mode that takes down most production conversational agents isn't hallucination — it's the sentence that sounds confident and is almost right. Here is the architecture that fixes it.
Most teams ship AI features without eval. They flip a coin every PR. A small eval set built right takes two days and pays back forever — here is the minimum viable version.
Most SaaS products try to be multi-tenant from day one. Most get it wrong. Here are the patterns that actually ship — org isolation, per-org quotas, role-based collaboration, and the migration to schema-per-tenant when you outgrow shared DB.
Anthropic prompt caching can cut your bill 80–95% on the right shapes. It can also do nothing at all if you mis-order your blocks. The patterns, the pitfalls, and the numbers from production.
Side-by-side comparison of the three leading voice AI platforms in 2026 — latency, languages, pricing, integrations, and what we ship in production at Ikki.
Most teams reach for RAG by default. Most don't need it. Here's how to decide between RAG and an agentic + tool-call architecture, and how to ship RAG correctly when it's the right call.
Real-world numbers from voice AI projects we shipped: build cost, monthly run cost, hidden expenses, and how to avoid common pricing traps.
What we learned shipping voice agents, RAG platforms, fintech engines, civic AI, and immersive web — the patterns that worked, the ones that didn't, and the things nobody told us.
After shipping AI products to production, here's the architecture we converged on — Nuxt 4 + Fastify + MongoDB — and why it beats Next.js, Astro, and SvelteKit for our use case.
SHIP-0247·CODEMACHIA·v1.4.2—DEPLOYED 2026-05-17 07:27 UTC