From theory to the systems that demand real understanding.
Long-form pieces on how production-AI teams orchestrate agents, serve models, and build the stack, plus GATE DA essays that turn probability, linear algebra, DBMS, ML, and AI into durable concepts.
Inference routing: sending each query to the cheapest model that can answer it
By 2026 the biggest lever on inference cost isn't quantisation or batching — it's deciding which model touches each query. Four routing patterns, three rounds of vendor consolidation, and a real case study where a customer support agent cut spend 80% with no measurable quality drop.
Serverless LLM platforms: Modal, Together AI, Fireworks, Replicate
Four platforms, four different bets on what serverless inference should be. Modal is the GPU-as-Python-function. Together is the model marketplace. Fireworks is the speed obsessive. Replicate is the prosumer API. Here's who picks which, and the cost numbers behind each.
Structured outputs engineering: JSON mode, function calling, constrained decoding
Three families of techniques get LLMs to return parseable data. One of them is a guarantee. The other two are negotiations. Here's when each one earns its keep — and the production regression that nobody warns you about.
CrewAI vs LangGraph vs AutoGen: the ecosystem reality of 2026
Three years into the agent framework era, only two of those three names belong in the same sentence in 2026. Here's the actual usage picture — stars, downloads, who's running what in production, and where each one quietly wins.
DSPy: declarative prompting in production
Stanford's DSPy proposes 'programming, not prompting' — declare a signature, compose modules, let an optimizer write the prompt. The pitch is irresistible; the production reality is more selective. Where DSPy actually ships, and why most teams quietly keep their prompts.
Pydantic AI: typed agents for the Python ecosystem
The Pydantic team built the framework most FastAPI engineers were quietly wishing for. Schema-first, model-agnostic, async-native — the agent framework that feels like Python production code instead of a research demo.
Inspect AI: the UK AISI's eval framework everyone copied
A government safety institute released an MIT-licensed eval framework in 2024. By 2026 it's the de facto standard at Anthropic, OpenAI, DeepMind, and the enterprise teams that take evals seriously. Here's why it won — and what the alternatives still get right.