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The following article originally appeared on Elevate and is being reposted here with the author’s permission. A software factory is a repeatable loop around software work. If you’re building a software factory, code good enough to ship still needs human taste and ownership. We’ll discuss this including whether you need a factory just yet. If so: You’ll likely need humans in the loop upfront for deciding on product intent, system design (if you care) and your quality bar.

AI systems are gaining access to more tools and data, raising new questions about oversight and accountability. This Week in AI host Christina Stathopoulos spent this episode examining how those questions are playing out in model safety, government oversight, and even digital marketing.

My conversation with Dan Gookin, the original For Dummies author and now mayor of Coeur d’Alene, Idaho, started as a publishing reunion. It ended with a much larger question: What happens when the software you rent becomes infrastructure you can’t leave? There was something wonderfully circular about hearing Dan Gookin tell me that, after he was first elected to public office, he bought a copy of Robert’s Rules For Dummies.

The following article originally appeared on LinkedIn and is being reposted here with the author’s permission. This week the AI community was in uproar about Jev from TypeSafe, not just a new model but a new kind of model: one that classifies, scores, and routes but can’t write a sentence. The reason for the fuss is simple. It’s radically faster and cheaper than using an LLM to perform the same task (up to 200x faster and 400x cheaper if TypeSafe’s numbers are to be trusted).

Ask most engineers what MCP is and you’ll get the same answer: a way to plug tools into an LLM. Fair enough, as far as it goes. But that description treats MCP like plumbing, and after months building MCP-based integrations for large enterprise platforms, I don’t think plumbing is the right metaphor. Plumbing moves water through pipes you already designed. MCP changes who’s holding the wrench.

The following article originally appeared on Venkatesh Rao’s Substack, Contraptions, and is being republished here with the author’s permission. The sole athletic achievement of my life came in 1993: winning the IIT Bombay freshman 50m freestyle race with a time of 41s. That got me into the college swim team (it was a bad recruitment year) and launched my brief and entirely undistinguished athletic career.

The early rapid expansion of AI capabilities that focused on frontier models was largely ushered into the world by a few powerful, US-based AI labs. Open-weight models released from labs in China, early on from DeepSeek, and later from Moonshot, Z.ai, and others, have in part disrupted that dominance. But growing concerns about the concentration of power have led to discussions about the need for at least some level of AI sovereignty.

Chester Ismay, a data science educator and AI consultant, created schedule viewers to keep up with the sports he follows, including the WNBA, NFL, NBA, and Premier League. But he still had to decide which games deserved his attention each week. In this episode of Zero to Agent in 30 Minutes, Chester built a sports concierge agent to surface the games he should watch. It reads his preferences and current schedules, then sends a weekly summary to his phone.

This is the third post in a four-part series about post-training. If you missed them, read part 1 and part 2. The final post, on implementing your own pipeline, will be coming October 7. Now that you understand the gist of reinforcement learning and supervised fine-tuning, it’s time to explore what post-training has actually accomplished in the frontier models you know and love. Remember our prompt “Why do people like golden retrievers?” GPT-3 would often answer nonsensically.

When considering the history of AI, Richard Sutton observed that brute force and compute scale has always trumped human expertise, and when you look for it, you can see this “bitter lesson” play out throughout tech history. In his keynote at Ai4 2026, Tim O’Reilly explains why grappling with the bitter lesson is the forge of effective AI corporate strategy, as companies figure out what to embrace and what to let go of.