Agentic Alienation
I haven't automated myself out of the loop. I've automated myself out of the experience of making.
I haven't automated myself out of the loop. I've automated myself out of the experience of making.
Slop is born from unverified code. Here's a pipeline of increasingly independent gates, from cross-model review to deterministic audits to manual QA, where each gate is harder to talk its way past than the one before.
Tan's fat-skills/thin-harness architecture maps almost one-to-one onto an AI coworker for non-developers. The catch: when users don't have a repo, skills can't be files. Skills have to be the product.
The big AI chat apps ship heavy rendering libraries to every device. Cheddy Chat renders markdown server-side and streams finished HTML, eliminating 160-440KB of client JavaScript while keeping the main thread free.
Started evaluating 3 frameworks for work, ended up building 10. Next-gen frameworks (Marko, SolidStart, SvelteKit, Qwik) all deliver instant 35-39ms performance. The real differentiator? Bundle sizes range from 28.8 kB to 176.3 kB compressed. Choose based on your priorities, not microscopic FCP differences.
React-by-default has hidden costs. Here's a case for making deliberate choices to select the right framework for the job.
Level 4 excels for isolated rich components, but heavy coordination signals when to intentionally escalate to a unified framework.
Master production-ready Astro middleware with dependency injection, testing strategies, and caching for enterprise applications.
Make HTMX apps feel instant with optimistic UI: instant feedback, automatic rollbacks, and no frameworks required, just simple declarative HTML.
A manifesto for progressive web complexity. Reject the false binary between static sites and SPAs. Embrace the powerful middle ground with server-rendered HTML, HTMX, and intentional complexity escalation.
Forget complex state libraries. Use the URL as your single source of truth for filters, sorting, and pagination in HTMX applications
Build streaming LLM responses with Server-Sent Events, request cancellation, and real-time status updates to create engaging, responsive AI applications.
Transform your MCP based AI agent from an open system to a secure, user authenticated application ready for production deployment.
Learn how to evolve from direct LLM tool integration to the Model Context Protocol (MCP) architecture, enabling scalable AI tools that maintain conversation context across multiple services.
Learn how to give LLMs the ability to call functions and interact with APIs for real-world problem solving using TypeScript and type-safe tool integration.
Don't just reach for your favorite framework. Let's explore how a constraint-based mindset can help you choose the right tool for the job and avoid over-engineering.
Tired of frontend frameworks and complex state management libraries? Learn how a few lines of JavaScript and the power of Proxies can automatically sync your UI with your application state.
From spaghetti code to clean, decoupled code: building a powerful event system that scales with your application
For decades, the request-response model of REST has been the backbone of the web. But what happens when the server needs to initiate a conversation? Discover how NestJS WebSockets enable real-time interactivity.
Generating a simple, unstyled PDF is one thing. But what if you need to create complex, beautifully styled documents? Learn how to leverage the power of HTML/CSS with headless browsers.