Tutorials

Higgsfield MCP hands your agent more than 30 image and video models, with the generation harness already built. How to connect it to Claude Code, what each generation costs in credits, and 10 systems where a dev can integrate it.

An artisan command, a queue and Claude Code in headless mode (claude -p) generating 11 sites inside the subscription. How to orchestrate the agent from PHP, where the math works out, and how to plug in Higgsfield as an asset step with a budget controlled by code.

A decision catalog with numbers, not adjectives: ten tasks that run on a frontier LLM today and fit Jev's three primitives. One million calls in each case costs $46,200 on the LLM and $523 on Jev. It has the payload for each case, the cascade pattern, how to measure the migration in shadow mode without labeling anything, and the four tasks where Jev is the wrong choice.

Tool lists rot in twelve months, yours included. That's why each of the ten comes with a swap criterion: the ten slots in the AI engineer's stack in 2026, the default for each one, and the objective signal that tells you when to rip it out. With data from the 2026 OWASP Top 10 and the Pragmatic Engineer survey.

Half of the "AI projects" I reviewed in 2026 could have been solved with an if and a SELECT. The other half became agents because the team wanted to say they had AI. Here's the 3-minute test: 4 questions that decide whether your problem calls for an if, a query, a prompt, a deterministic flow or an actual agent. With 5 real cases, the verdict on each one (spoiler: 1 in 5 was an agent) and the invisible cost of overshooting.

The agent decides on its own which flow to use, but nobody can explain why it picked the wrong one yesterday at 2 p.m. Deterministic router vs. LLM router, intent classification with a closed schema and evidence, an explicit fallback policy, trace attributes to audit the decision, and how to measure routing accuracy without hand-labeling ten thousand conversations.

If your code has a try/catch wrapped around a json_decode of the model's response, you don't have a contract. You have hope. How to get truly guaranteed structured output from an LLM: constrained decoding at the provider, a schema the model can satisfy, semantic validation and retry with a fixed budget, with no infinite loop and no defensive parser.

Your prototype cost R$ 12 on day one. In production, with 200 users, it costs almost R$ 4,000 a month. We open up the token bill of a real agent layer by layer (system prompt, tool definitions, RAG chunks, history, tool results) and show where almost 80% of the money disappears. In the end, prompt compression, history pruning, and context selection cut the bill by 73%, with the before-and-after spreadsheet.

An honest map of the agent orchestration and memory ecosystem. What LangGraph, Mem0, LangChain and MCP actually do, the point where each one stops being overhead, and the 5-question decision tree to run before installing anything.

The third LLM infrastructure provider already has a São Paulo region, a catalog with Grok 4.3, Gemini 2.5, Llama 4, and Cohere Command A, and bills per character instead of per token. The real Oracle Generative AI catalog, how to call the service with the OpenAI SDK, the dedicated cluster math, and the objective criteria for deciding between OCI and Bedrock.

AWS documents how to turn on Bedrock really well. Nobody documents the rest: the difference between CloudTrail and model invocation logging, the fact that São Paulo doesn't give you data residency, and what shows up on the bill in reais at the end of the month. A practical guide to Claude in production on AWS Bedrock: model IDs, inference profiles, the four governance layers and the full cost breakdown for an internal agent.

One OpenAI-compatible endpoint for hundreds of models, with automatic fallback between providers. What OpenRouter is, how to call it from curl, Python, and PHP, how to control routing, provider, and cost caps, and the scenarios where this extra layer hurts more than it helps.