Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models - WSJ
Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models WSJ
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Beyond the Hype: A Practical Guide to Integrating AI into Your Development Workflow
The AI Developer's Dilemma: Tool or Replacement? Another week, another wave of "Will AI Replace Developers?" articles flooding our feeds. While the existential debate rages on, a quiet revolution is already happening in the trenches. The most forward-thinking developers aren't waiting for an answer—they're actively integrating AI tools into their daily workflows to augment their capabilities, not replace them. The real question isn't if AI will change software development, but how we can harness it effectively today. This guide moves past the hype to provide a practical, technical roadmap for weaving AI into your development process. We'll explore concrete tools, integration patterns, and code examples that you can implement immediately to write better code, debug faster, and design more r
Why Markdoc for LLM Streaming UI
Every AI chatbot I've built hits the same wall. The LLM writes beautiful markdown — headings, bold, lists, code blocks. Then someone asks for a chart. Or a form. Or a data table with sortable columns. Suddenly you need a component rendering layer. And every approach has tradeoffs. That's why I built mdocUI: a streaming-first generative UI library that lets LLMs mix markdown and interactive components in one output stream. The Problem JSON blocks in markdown Some teams embed JSON in fenced code blocks: Here's your revenue data: ```json:chart {"type": "bar", "labels": ["Q1", "Q2", "Q3"], "values": [120, 150, 180]} ``` This works until you're streaming. A JSON object that arrives token-by-token is invalid JSON until the closing brace lands. You either buffer the entire block (killing the stre
I had a bunch of Skills sitting in a folder. None of them were callable as APIs
So I built a runtime to fix that. The problem If you use Claude Code, Copilot, or Codex, you've probably created Agent Skills, those SKILL.md files that tell the AI what to do. I had a bunch of them. But they were stuck. I couldn't plug them into a product, trigger them from a webhook, or let any service call them with a POST request. Each skill was trapped inside the tool that created it. What I wanted take a SKILL.md → get a POST /run endpoint No new framework to learn. No infrastructure to set up. Just point at a skill, configure the model, and deploy. What I built Skrun , an open-source runtime that takes Agent Skills and turns them into callable APIs. skrun init --from-skill ./my-existing-skill # reads SKILL.md, generates agent.yaml skrun deploy # validates, builds, pushes # → POST ht
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Why Markdoc for LLM Streaming UI
Every AI chatbot I've built hits the same wall. The LLM writes beautiful markdown — headings, bold, lists, code blocks. Then someone asks for a chart. Or a form. Or a data table with sortable columns. Suddenly you need a component rendering layer. And every approach has tradeoffs. That's why I built mdocUI: a streaming-first generative UI library that lets LLMs mix markdown and interactive components in one output stream. The Problem JSON blocks in markdown Some teams embed JSON in fenced code blocks: Here's your revenue data: ```json:chart {"type": "bar", "labels": ["Q1", "Q2", "Q3"], "values": [120, 150, 180]} ``` This works until you're streaming. A JSON object that arrives token-by-token is invalid JSON until the closing brace lands. You either buffer the entire block (killing the stre
I had a bunch of Skills sitting in a folder. None of them were callable as APIs
So I built a runtime to fix that. The problem If you use Claude Code, Copilot, or Codex, you've probably created Agent Skills, those SKILL.md files that tell the AI what to do. I had a bunch of them. But they were stuck. I couldn't plug them into a product, trigger them from a webhook, or let any service call them with a POST request. Each skill was trapped inside the tool that created it. What I wanted take a SKILL.md → get a POST /run endpoint No new framework to learn. No infrastructure to set up. Just point at a skill, configure the model, and deploy. What I built Skrun , an open-source runtime that takes Agent Skills and turns them into callable APIs. skrun init --from-skill ./my-existing-skill # reads SKILL.md, generates agent.yaml skrun deploy # validates, builds, pushes # → POST ht
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