# work context (blogs) — RSS Amplifier Recent posts from the 2 feeds in the RSS Amplifier directory that cover work context. Page: Feed: --- ## [Nobody voted on this: how to build a team that can still change its own mind](https://dtsbourg.me/en/articles/nobody-voted-on-this) _2026-07-15 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ A team can get faster, flatter, and happier while quietly losing the ability to decide how it works. Here are some notes on how to tell, and what to do about it. Watch a team adopt AI tooling and you will see something that looks like a win. Cycle time drops. The hierarchy flattens, because the junior person and the senior person now ship work that is harder to tell apart. Autonomy scores go up.… ## [Context Engineering and the Limits of Agentic Coding](https://stephenfritz.dev/blog/context-engineering/) _2026-04-22 · Stephen Fritz_ My work is context engineering. I didn't know the term until after I'd built a system that turned out to be exactly that. ## [Skin in the game: how to build vertical AI that survives](https://dtsbourg.me/en/articles/skin-in-the-game-vertical-ai) _2026-03-11 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ Foundation models are eating software from both ends. Here are some notes on where the value actually lives, and how to build something that lasts. Nearly $1 trillion was wiped from software and services stocks in a matter of weeks. FactSet dropped from a $20B peak to under $8B. Thomson Reuters shed almost half its market cap in a year. Anthropic, OpenAI, and Google are verticalizing fast. They… ## [The new, new AI-native playbook, or: everything you thought was the product isn't](https://dtsbourg.me/en/articles/new-ai-playbook) _2026-02-25 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ Like most, I feel like I'm constantly struggling to keep up with the pace of AI development these days. Not just in the technological sense, but mainly trying to make sense of the paradigm shifts it induces. As a founder, it's ever more important to understand how this impacts how we build, ship and sell products. The playbook feels like it's constantly shifting, so I wanted to summarize some of… ## [The Lobster Internet: A Field Guide to the Places Humans Aren't Invited](https://dtsbourg.me/en/articles/moltbook) _2026-02-08 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ There are a lot of strange places on the internet. Forums that required blood oaths to join. Discord servers with lore docs longer than Infinite Jest . Subreddits dedicated to things I cannot unsee and will not name. But usually, the captcha tries to keep the robots out, not the other way around. On Moltbook, the robots are the ones keeps us out: "Humans welcome to observe." That's the actual… ## [The impedance mismatch: why automation keeps stalling](https://dtsbourg.me/en/articles/impedance-mismatch-automation) _2026-02-05 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ Even in warehouses bristling with sensors and automation, a curious gap persists: operators often struggle to explain why throughput fluctuates from day to day. It's not a measurement problem: the data is there, the dashboards are green. Yet output drifts in ways that resist clean explanation, a pattern that points to something fundamental about the systems we've built. One way to make sense of… ## [12 Predictions for Embodied AI and Robotics in 2026](https://dtsbourg.me/en/articles/predictions-embodied-ai) _2025-12-31 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ Predictions are notoriously a fool's errand. As Rod Brooks puts it in his own yearly predictions , we are all susceptible to FOBAWTPALSL : Fear Of Being A Wimpy Techno-Pessimist And Looking Stupid Later these days. This fear, combined with its cousin FOMO (Fear Of Missing Out), drives herd behavior in establishing the zeitgeist on almost any technology topic. Now, I'm no Rodney Brooks, so I won't… ## [The artificial hivemind: When AI creativity collapses into consensus](https://dtsbourg.me/en/articles/creativity-hivemind) _2025-12-18 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ Ask ChatGPT to write a metaphor about time. Then ask Claude. Then Gemini. You're consulting different oracles, built by rival companies with different philosophies, trained on different data. Surely you'd get meaningfully different answers? Here's what actually happens: time is a river. Time is a river. Time is a river, flowing endlessly. Time is a weaver, threading moments into tapestry. Time is… ## [Europe's AI Dilemma: Regulate, Imitate, or Innovate?](https://dtsbourg.me/en/articles/eu-ai-hyperscale) _2025-12-12 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ Silicon Valley doesn't just shape the world, it rules it. The tech giants that call it home have amassed extraordinary influence over political and social structures, with decisions that ripple far beyond U.S. borders. This dominance turned domination is especially pronounced in AI, one of the critical domains of technological leadership of the 21st century. In a recent interview, Jensen Huang,… ## [Who Judges the Machines? The Quiet Race to Measure Artificial Intelligence's Intelligence](https://dtsbourg.me/en/articles/arena-benchmark) _2025-12-06 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ In the opening act of The Social Network , Eduardo Saverin stands at a dorm room window, marker in hand, scrawling a mathematical formula across the glass. The Elo rating system, originally designed for chess, would become the engine behind Facemash, Mark Zuckerberg's proto-Facebook experiment in rating the "hotness" of female Harvard students. "Give each girl a base rating of 1400 1400 1400 . At… ## [Prime Noise-Cancelling Picks (Sponsored)](https://crawlproof.com/a/jpTYLBpHXaHb) _2025-12-05 · **Sponsored**_ Browse Prime-eligible, 4-star-and-up noise-cancelling earbuds with fast shipping and deals ## [We're Not Training AI Anymore. We're Teaching It.](https://dtsbourg.me/en/articles/teaching-machines-think) _2025-12-02 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ AI systems are suddenly capable of tasks they consistently failed at months ago. The secret isn't just about bigger models or more computing power. Behind the latest breakthroughs is a quiet revolution: companies have stopped training AI and started teaching it. For years, the recipe for AI seemed simple: build bigger neural networks, feed them vast oceans of text from the internet, and let them… ## [On the importance of post-training in modern LLM development](https://dtsbourg.me/en/articles/post-training) _2025-12-01 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ p\]:m-0"\>🚧 Note: This article is under construction. Post-training is all the rage. Are we going from training to teaching? What does a curriculum look like for LLMs? Let's find out. For most of the deep learning era, the core recipe for building intelligent models has been relatively straightforward: take a transformer, scale up the parameters, feed it vast amounts of text, predict the next token… ## [MCP Explained… Again - Because This Time It's Getting Real](https://dtsbourg.me/en/articles/mcp-explained-again) _2025-10-29 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ This article was originally published on the Ogment.ai blog . Being in the trenches of AI-first infrastructure, I've been watching Model Context Protocol (MCP) evolve from niche curiosity to a mainstream standard. For builders like us, MCP isn't just a theory, it's a potential backbone that would let anyone expose product APIs and data as plug-and-play tools inside agents like ChatGPT or Claude.… ## ["The Sensor Illusion" or Why Your AI Agent Might Be Flying Blind](https://dtsbourg.me/en/articles/sensor-illusion) _2025-06-20 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ Would you trust a robot with no sensors? Then why are we building AI agents that way? In robotics, we obsess over sensors. We use cameras for vision, lidar for depth, IMUs for motion, force sensors for manipulation. Each one is painstakingly selected, calibrated, and fused into a representation of the world — the robot's working reality. But for AI agents , we often treat the data pipeline as an… ## [Stepping Away from Claryo](https://dtsbourg.me/en/articles/stepping-away-from-claryo) _2025-06-16 · contact@dtsbourg.me (Dylan Bourgeois) · Dylan Bourgeois_ After nearly three years building Claryo from conversations on commutes with Mohamed into a truly awesome AI product, I've decided the time is right for me to step away and begin a new chapter. Claryo is in great hands. I'm confident our team will keep pushing the mission forward, and I'm excited to cheer them on from the sidelines. It's been a deeply formative experience: building with brilliant…