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.
Operator, Claude Computer Use, and Project Mariner: the browser agent shootout
Three browser agents shipped within twelve months — each with a different abstraction over the browser. Here's how the screenshot-and-click, DOM-and-accessibility-tree, and Chrome-extension approaches stack up on WebVoyager, OSWorld, latency, cost, and real-world reliability.
996 and the fetishization of overwork
996 — nine to nine, six days a week — is lionized again in startups, but the hours data and WHO death toll check the urge to confuse long with serious.
Surviving 10,000 concurrent requests to your LLM API
An LLM request spends 99% of its life waiting. Design around that one fact and 10k concurrent users stops being scary.
The Big Stay: why nobody's quitting, and why that's worse
A falling quit rate looks like loyalty, but the Big Stay can mean the opposite — people who want out but feel trapped, fraying quietly at their desks.
The three kinds of memory production agents actually use
Working memory, episodic memory, semantic memory — the cognitive science taxonomy that every agent memory startup borrows from. Here's what each one means in practice, what Letta, Mem0, and Zep actually ship, and why most production agents only need the cheapest one.
Cache the question, not just the bytes
Your embedding API answers the same query a thousand times a day. Three caches cut LLM cost and latency — and one of them can hand back a confidently wrong answer.
The recognition gap: the highest-ROI lever teams ignore
Managers think they give feedback and praise constantly; reports barely feel it. That recognition gap is the cheapest, highest-return lever most teams ignore.
What actually re-engages people when the perks don't
Ping-pong tables and meditation apps do not move engagement. What the evidence says re-engages people, ranked by leverage, for data and engineering leads.
50,000 documents, one summarization pipeline
You can't loop 50k LLM calls inside a request. The shape that works — a queue, a pool of workers, and the boring reliability details that decide whether it finishes.
The five patterns that ship real agents in 2026
Anthropic's December 2024 paper became the production reference for agent design. Two years on, here's which patterns actually survived contact with shipped products — and why the simple ones won.
Psychological safety: the precondition every wellbeing program forgets
Before any wellbeing program can work, people must feel safe enough to speak — and most teams fail that psychological-safety test before anyone admits it.
Is sitting the new smoking? The desk-bound body of data work
Coding and analysis are some of the most sedentary work ever invented. What sitting does to the body, why 'the new smoking' overstates it, and what helps.
Why ReAct lost — and structured planning won
The 2023 ReAct paper defined an era of agent design. Its Thought-Action-Observation loop is also why so many agents burn tokens, loop in circles, and time out on long tasks. Here's how Plan-and-Solve, ReWOO, and LLMCompiler displaced it in production — and the concrete numbers that explain why.
The right to disconnect: a global map of after-hours work
Right-to-disconnect laws now let workers ignore the boss after hours — France, Australia, Ireland — while the US has none. What the split means for teams.
The return-to-office fight, weighed honestly
Return-to-office mandates became a wellbeing flashpoint: the office aids connection and mentorship, but blanket rules read as control and push people out.
Tool selection at 1000 tools: routing techniques that ship
Cramming hundreds of MCP tools into your system prompt destroys both latency and accuracy. Vector retrieval, hierarchical menus, RAG-on-tools, and code mode each take a different bet. Here are the numbers, the production deployments, and the pattern that's winning.
Where your Python packages actually live
venv isolates them, uv makes installing them almost free, and pex bundles them into one file you can throw at any machine. A field guide to three tools and the one question they each answer.
Browser agents in production: Manus, BrowserBase, and Stagehand
Headless browser agents looked like science fiction a year ago. Today they cost real money, fail real ways, and are mostly an infrastructure play, not a model play. Here's where they work, where they don't, and which layer of the stack is actually winning.
The caregiving squeeze: work and the sandwich generation
Caring for children and ageing parents at once is quietly pushing experienced people, especially women, to downshift or leave as office mandates collide.
Edge AI in practice: Vercel AI SDK + Cloudflare Workers AI
Some inference belongs at the edge — the user's nearest POP — not in a central GPU cluster. The Vercel AI SDK and Cloudflare Workers AI made that practical. Here's where edge wins, how the cold-start tricks work, and what the streaming-from-the-edge architecture looks like.
Neurodiversity at work: the talent case beyond accommodation
Autism, ADHD, and dyslexia are common in technical fields, yet work is built around a neurotypical default. Stop bolting on accommodation — design for it.
The eval loop that actually ships agents
Every team that ships AI agents reliably has built roughly the same eval system: a golden set kept honest by humans, an LLM-judge they don't fully trust, a regression dashboard that gates merges, and a culture of 'no win on the eval, no merge.' Here's what that loop actually looks like in practice.
Evals that actually work: beyond the LLM-as-judge trap
LLM-as-judge evaluators are the second most popular eval pattern in 2026. They are also the most over-trusted. The eval pipelines at the teams shipping the fastest agents look almost nothing like the ones described in vendor blog posts — and the difference matters.
Self-correction without infinite loops: agent stopping criteria that actually work
Reflection loops promised agents that fix their own mistakes. In production they more often produce agents that 'improve' working code into garbage, or worse, loop forever. Here's what the teams shipping real agents do instead.