Once your team gets past the first month of working with Claude, two things start happening at the same time. Useful work gets bigger. And
Anthropic now ships three ways to put Claude to work, and the names make them sound like rivals. They are not. Claude, Claude Code, and
The conversation about AI agents has moved fast. A year ago it was about whether agents worked at all. Six months ago it was about
Stop Building Apps. Start Building Agents. Low-code was supposed to be the revolution. Drag and drop, no developers needed. But the landscape has shifted
Matt Paige and Thomas Schlossmacher discuss a shift from typing to talking as AI makes voice dictation accurate enough to use without constant corrections, arguing speech is faster and more natural and helps maintain thought flow when interacting with AI tools. Schlossmacher is building Resonant, a Mac voice dictation tool designed to run on-device so nothing goes to the cloud, motivated by privacy concerns and data retention/training practices of cloud-based alternatives like Whisper Flow. They explore the tradeoffs of local vs server inference, noting current consumer hardware can struggle to run full speech-to-text plus LLM post-processing fast enough, but expects improvement in 1–2 years. Schlossmacher explains differentiators like taste/brand, his design workflow using inspiration sources and ShadCN, his path into AI-assisted building, his stack (Claude Code, Next.js, Convex, Vercel), and a vision for proactive, context-aware agent features and potential open-sourcing and enterprise/self-hosted options, with beta/free access at https://www.onresonant.com/. Key moments: Making the Switch Why Build Resonant Local LLM Reality Check Standing Out in AI Designing Resonant Brand Building
Careful and measured scientific thinking is crucial even in artistic fields such as horticulture. With the increasing ease of access to materialization of a software
The most expensive agent costs are the ones you never needed to pay. In our experience, most companies discover this after the fact, when the
AI agent costs get a bad reputation (and between you and us, it’s not without reason). Teams that have watched a promising pilot turn into
Every AI-assisted development pitch includes the same reassurance: “Don’t worry, there’s a human in the loop.” Then the AI generates 200 lines of code in
When n8n costs spike, the instinct is to blame the platform. But in our experience, the platform isn’t the problem, it’s the way workflows have
