Daniel Walsh 
Jev can now make decisions about images, rather than just text. You describe what it should check, supply a picture, and get a structured answer. A new box question type can also return coordinates around an object or relevant area. Read more
I trained a small, open-source decision model on my own machine to do one job: pick the next tool in a customer support conversation. Training took minutes, and the training data never left my network. The model was Laya, which has roughly 420 million parameters. Those parameters are the internal values that training adjusts. Compared … Read more
Small AI decision models are getting interesting. Models such as Winnow, Decider, and Laya can make decisions on your own hardware. That gives you options if you want to keep inputs inside your infrastructure, run without a network connection, or put a decision step directly into software and automations. But a local model is useful … Read more
Jev isn’t a chat model. It’s a decision model. You give it context and a question, and it returns an answer that your software can use. That distinction sounds small, but it changes where AI fits in an application. A chat model might write a customer reply. A decision model can determine which team should … Read more
A RAG agent does a lot of work before it answers a question. It searches documents, decides which passages matter, chooses whether to search again, and checks whether its answer matches the sources. Each step can involve a small judgment call. I used to rely on language models for many of those calls. They worked, … Read more
Claude can create a beautiful-looking website. But at its core, it doesn’t know what great design is. Models such as Fable 5.1 and Opus have learned from millions of websites. When I ask for a “modern-looking website,” I’m often asking for the average of all those sites. That’s why so much AI-generated work ends up … Read more
Fable 5.1 is Anthropic’s strongest model yet for coding and design, and the /design command gives Claude a full visual canvas inside the Claude desktop app. I can also trigger the same workflow from Claude Code in the terminal. That sounds like a shortcut to great design. It isn’t, at least not by itself. If … Read more
Claude Design can create surprisingly good motion graphics in a matter of minutes. I can describe a character, a movement, a burst of confetti, a glow, and a shadow, then refine all of it through a simple chat. The difficult part comes at export. Right now, Claude Design exports video as MP4. That works perfectly … Read more
AI systems can fail while every normal engineering signal looks fine. The app is online. Error logs are clean. Tests pass. No exception gets thrown. Yet the answer is wrong, the agent used the wrong tool, response quality has dropped, or the monthly model bill has suddenly tripled. That is the awkward reality of software … Read more
If I’m building an AI system, using Claude Code, Codex, or working with an agent such as Hermes, I don’t want to rely on prompting alone. Prompts matter, but the workflow shape matters just as much. That’s where agentic design patterns come in. I think of each pattern as a small workflow graph. It defines … Read more
AI coding agents are becoming a normal part of software work. I use them to explain unfamiliar code, draft changes, find bugs, and move much faster through repetitive tasks. Yet every time I point an agent at a folder, I need to ask a more basic question first: what data is leaving my machine, where … Read more
People often treat an AI model as if it is the entire product. They compare Claude, Codex, Gemini, and local models, then assume the biggest model produces the best result. That misses a major part of what makes an AI coding agent useful. I can take the exact same model, give it the same task, … Read more
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