Anthropic recently had to pull Mythos/Fable due to an edict from the US government. While Mythos was a step up from Opus, I’ve been actively moving smaller in terms of my agentic models—and matching the quality of output of some of the largest frontier models.
The use cases have spanned from hard “hedge fund level” (for want of a better description) market analysis, financial management, and AI personal assistants to even helping a few friends in difficult medical situations. I’ve called this pattern “knowledge agents” with a generic template available to everyone here. They literally inject the right knowledge into the AI agent plugged into it. Anyone can do this, with or without my template. — Read More
Daily Archives: June 23, 2026
Alibaba’s AI video model rises to No. 2 in global rankings, as OpenAI’s Sora and ByteDance’s Seedance fall away
Alibaba Cloud on Sunday released HappyHorse 1.1, a major upgrade to its AI video generation model that the company says delivers production-ready video synthesis across core content creation scenarios. The model is now live on Alibaba Cloud Model Studio with full API access for enterprise customers and developers, accompanied by a 40% sitewide launch discount for the first two weeks.
The release arrives at a moment of remarkable upheaval in the AI video generation market — and Alibaba appears keenly aware of the timing. OpenAI discontinued Sora after it proved financially unsustainable. ByteDance indefinitely shelved the international rollout of Seedance 2.0 following a barrage of copyright complaints from Hollywood studios. For enterprise procurement teams that had been evaluating or integrating those tools into marketing, advertising, and content production workflows, the competitive landscape has contracted sharply in a matter of months. — Read More
The Minimum Viable Unit of Saleable Software
ast week I wrote about leaving Stainless and my intention to work on building my side project River into a small, sustainable business. When I sent that letter, a few people asked about my thought process in trying to run a software company in the age of AI: “Are you crazy?! Anything you ship can be instantly displaced by an internal package built by an LLM!” Having become as much of an LLM convert as anyone at this point, I acknowledge that it’s a very fair question. Indeed I might be crazy, but I’ll talk through my thought process, and you can decide.
… We’ve been talking about buy vs. build in software circles for years, but last year the calculus changed. It used to be that build was a very expensive proposition, especially given the state of engineering salaries and scarcity of great people. One could expect huge upfront cost, schedule overruns, and an infinitely deep rabbit hole to slide down. The general wisdom had always been to build only inside your core domain and avoid getting sidetracked by peripheral projects. Once your company reached enormous size, and the cost of those distractions disappeared comfortably into its margins, then maybe they’d be worth doing.
But LLMs changed all of that. Suddenly it was quite possible to produce substantial pieces of software by getting models to do the work. — Read More
Build the Loop, Not the Agent
Most teams treat agent modernization as a project with a finish line: re-architect around today’s frontier model, ship, declare victory. But model capability advances faster than any single modernization effort can complete. By the time you finish re-architecting around today’s frontier model, the next one has already shifted the ground beneath you. Plan around a fixed end state and you will perpetually ship an architecture tuned to a model generation that is already obsolete.
The better bet is simple: the team that can iterate fastest against the newest models will win. — Read More
I Built a Monster CLAUDE.md, And My Coding Agent Got Scary Good
A coding agent can write a thousand lines before you finish your coffee. The problem is that a good chunk of those lines are confidently, fluently wrong. The code compiles, and it reads like something a careful engineer wrote. It also quietly assumed the wrong thing three functions ago, and now you get to find out where.
So when people told me the fix was a markdown file of rules, I rolled my eyes. A text file telling a model to behave sounded like taping a “please be tidy” note to a tornado.
Then after studying couple of them I built one… — Read More
Rick's Cafe AI 