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.
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.
When not to use RAG
RAG is the default answer to 'how do I give my LLM custom knowledge?' — and for a wide class of problems, it's the wrong answer. Long-context prompt caching, fine-tuning, and in-context learning each beat RAG in regimes where the costs and assumptions actually pencil out. Here's the decision tree.
The workplace health gap: menstruation, fertility, and menopause
Menstruation, fertility, and menopause quietly shape the careers of much of the workforce, and the silence around them pushes experienced women out the door.
Claude Code, Cursor, and Aider — three answers to the same question
Three coding agents, three different bets on autonomy, context, and where the human belongs in the loop. The interesting part isn't which one wins — it's which assumptions each one refused to compromise on.
Replit Agent's architecture, two years in
Replit Agent launched in September 2024 and turned a nine-year-old IDE into a $150M-ARR business. The architecture is unfashionably explicit — plan, confirm, execute, checkpoint — and the bet that explicit checkpoints beat full autonomy is paying off. Here's how it actually works.
MCP is the USB-C of AI tools, finally
Anthropic shipped the Model Context Protocol in late 2024 as a small, JSON-RPC-shaped spec for connecting LLM clients to tools and data sources. Eighteen months later, it's the most adopted open protocol in the AI space — and the hype is, for once, an undercount.
v0 by Vercel: how prompt-to-UI actually works
v0 makes 'build me a dashboard' produce real Next.js + shadcn/ui code that ships. The trick wasn't a smarter model — it was a narrow constraint, a streaming preview loop, and an opinionated component library acting as scaffolding. Here's the architecture, the competitive landscape, and why most clones missed the point.
AI SREs in production: Resolve.ai, Cleric, Parity
A new category of on-call agent is being shipped into real production environments — agents that watch alerts, gather evidence, and propose remediations. Most of them are deliberately read-only. The architecture is converging, the MTTR numbers are real, and PagerDuty is responding from the incumbent side. Here's what's actually working.
Speculative decoding in the wild: how labs cut latency by 2-3x
A small fast model proposes, the big slow model verifies. Across Medusa, EAGLE, and draft-model approaches, speculative decoding is the latency-reduction lever that frontier labs reach for when they can't make the model itself any smaller.
Why multi-agent swarms keep failing to ship
Two years after AutoGen and CrewAI promised teams of cooperating AI agents, the production scoreboard is brutal: orchestrator-workers ships, agent teams mostly don't. The reasons are structural, not stylistic — and they explain why every serious agent team has quietly converged on a much more boring shape.
Getting tool calling to 99% reliability in production
Most production agent failures are not model failures. They are tool-call failures — the model picked the right tool but wrote the wrong arguments, or hallucinated an argument that doesn't exist. The fix is unglamorous and mostly about schema design.