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.
Cloud Run is the most underrated platform for AI agents
Everyone deploys agents on GKE or EC2. The teams quietly shipping the fastest, cheapest production agents have moved to Cloud Run + Gemini + Firestore — and the reasons are not what you'd guess from the marketing pages.
Hybrid search, when one retriever isn't enough
Pure vector search misses exact identifiers. Pure BM25 misses paraphrases. Every serious production RAG stack — Notion, Perplexity, Vespa, Glean — runs both and fuses the results. Here's the math, the latency budget, and the production knobs that matter.
vLLM vs TGI vs SGLang: choosing your inference server in 2026
Three open-source serving stacks, three different bets. vLLM optimises raw throughput. TGI optimises ease and HuggingFace integration. SGLang optimises structured generation and prefix-cache reuse. Here's how to pick.
Long-horizon agent failure modes: context rot, drift, looping
An agent that handles ten turns gracefully will likely collapse at fifty. The reasons aren't bugs — they're emergent properties of how transformers handle growing context. Here's a field guide to how agents disintegrate, with the public failure stories that mapped the territory.
Microsoft Agent Framework, six months in
Microsoft consolidated Semantic Kernel and AutoGen into a single Agent Framework in late 2025. Half a year later, the picture is clear: MAF is the Azure-native option for shops that already speak C# and .NET, and its workflow/agent split is the most pragmatic in the industry.
Coding agents in 2026: Cursor, Devin, Sweep, Aider, Claude Code compared
Two years after Devin launched and froze the term 'AI software engineer' in the popular imagination, here's where everyone actually landed. The dominant tools didn't converge — they specialized, and the ones that won did so by picking a workflow lane and dominating it.
Contextual Retrieval, two years on
Anthropic's September 2024 trick — prepend a sentence of context to each chunk before embedding — quietly became the new RAG default. Here's why a 49% retrieval-error reduction held up, what the numbers actually say, and when you still shouldn't bother.
Deep research agents explained: Perplexity, GPT Deep Research, Gemini Deep Research
Deep Research was OpenAI's first agent product to actually ship, and it landed in a market where Google had already been live for months and Perplexity had built the search-citation pipeline the others were chasing. The three converged on similar capabilities but radically different architectures.
How Anthropic serves a hundred million tokens a second
The frontier labs and their serving partners — Anthropic, OpenAI, Together AI, Fireworks — hide an arsenal of inference optimisations behind the simple-looking chat endpoint. Here's the hierarchy, what each layer bought, and the public numbers behind it.
Agentic data analysis: PandasAI, Hex Magic, Julius — and why it's harder than it looks
'Just talk to your data' has been the cleanest agentic AI demo for two years. The shipped reality is much narrower than the demos suggest, because the gap between answering a question and answering it cheaply, safely, and correctly is enormous. Here's where the production line lives.
LangGraph is what you reach for when the agent has to remember
By mid-2026 LangGraph is the default orchestration layer for stateful, long-running, human-in-the-loop agents. Not because of the API — because of the checkpoints, the interrupts, and the fact that the state outlives the request.
The agent observability stack: LangSmith, Langfuse, Helicone, Arize
You can't ship agents without observability. The 2026 stack has settled into three layers — traces, evals, drift — and five vendors who each won by being unmistakably the best at one of them. Here's who picks which, and why.
Agents for legal, finance, healthcare — the high-stakes pattern
Three industries where hallucination is malpractice. The agent companies winning in legal, finance, and healthcare have converged on the same shape — scoped tasks, retrieval over generation, mandatory human checkpoints, audit logging by default. Here's what that pattern looks like in production at Harvey, Hebbia, Abridge, and Suki.
Why XGBoost is still winning in 2026
Twelve years after the original paper, and despite a parade of transformer-based challengers, gradient-boosted trees remain the default for tabular data at most enterprises. Here's why the boring answer keeps winning — and what TabPFN, CatBoost, and LightGBM actually changed.
MLOps platform consolidation: Databricks, Snowflake AI, SageMaker
The MLOps landscape of 2023 — a dozen point tools, three feature stores, four monitoring vendors, two training frameworks — has collapsed. By 2026 the workload lives on Databricks, Snowflake Cortex, SageMaker Unified Studio, or Vertex. Here's what each platform actually does, what got swept under, and who wins which workload.
Feature stores in 2026: Tecton, Feast, Hopsworks — death and rebirth
The feature store hype cycle went peak (2021), trough (2023, 'just use dbt'), and into a quieter rebirth as the data-for-AI layer for both classical ML and LLM agents. Tecton sold to Databricks. Feast survives as the open-source baseline. Hopsworks redefined itself as an AI lakehouse. Here's what the modern feature store actually is in 2026.
The vector database shakeout: Pinecone, Weaviate, Qdrant, Chroma, pgvector
The pure-play vector DB category isn't dead, but it's smaller than the 2023 hype suggested. Postgres ate the low end, Pinecone owns serverless ops, Qdrant is the open-source cost winner, and Weaviate's hybrid search is its real moat. Here's the 2026 picture by the numbers.
Embeddings in 2026: Voyage, Cohere v4, OpenAI text-embedding-3, mxbai, BGE
The embedding model market split clean. Proprietary models (Voyage 3, Cohere v4, OpenAI v3) own the quality top; open-source (mxbai, BGE-M3, Jina v3, E5) own cost and self-host. Matryoshka rewrote the cost economics. Here's how to actually choose.
Reranking at scale: cross-encoders, ColBERT, mxbai
Retrieve-then-rerank is the single biggest accuracy improvement in production RAG. Three approaches dominate — cross-encoders, late interaction, API rerankers. Here's what each costs, what each wins, and the latency budget that decides between them.
Distributed training: FSDP vs DeepSpeed vs Megatron in production
For serious pretraining or fine-tuning, you pick from three: FSDP2 (PyTorch native), DeepSpeed (ZeRO stages), or Megatron-LM (NVIDIA, 3D parallel). The frontier labs have made their bets — Llama 3 went Megatron, FSDP2 is the open-source default under 70B, and DeepSpeed survives where ZeRO offload is necessary. Here's how to choose.
Mixture of experts in production: Mixtral, DeepSeek, Llama 4
MoE went from research curiosity in 2021 to production default in 2025. The new frontier-model template is sparse, with 5-15% active parameters per token. Here's how the serving infrastructure caught up, what vLLM and SGLang actually do with experts, and the cost math that makes MoE the future.
Quantization in production: GPTQ, AWQ, GGUF, FP8 — what to ship
16-bit serving is dead for most production workloads. Here's how to pick between weight-only post-training quantization, activation-aware quantization, and the FP8 native path — without measuring it wrong.
ML platform build vs buy: a decision framework for 2026
Most teams default to one of three answers — Databricks, SageMaker, or 'we'll roll our own.' All three are wrong for the wrong team. Here's how to pick by team size, model variety, and latency budget — with the war stories that explain why.
Model monitoring in 2026: from accuracy to behavior drift
Classical ML monitoring tracks accuracy decay. LLM monitoring tracks something stranger — the model itself silently changing underneath you. Here's what production observability looks like when the failure modes don't fit the dashboards you built five years ago.