I got tired of writing brittle CSS selectors just to automate the browser. So I built an engine that lets you do it in plain English. Meet Slapify 👋 Open-source, AI-powered autonomous browser agents. Give it a goal. It figures out the rest. ✅ Fully autonomous Task Mode 📈 Native performance audits & HTML reports ⚡️ Bring your own LLM keys (@OpenAI , @AnthropicAI , @grok etc) Just run: npx slapify init ⭐ github.com/vgulerianb/slapif… 🌐 slaps.dev/slapify

we're running a hackathon after our conference - happening on Oct 3 anyone attending Select gets automatic admission come hack with the Supabase team and get a bunch of credits from @supabase, @claudeai , @stripe, and @vercel to hack sign up select.supabase.com

shadcn UI Bento sections Bento grids for landing pages. Blocks built for @shadcn @tailwindcss and @nextjs shadcnblocks.com/blocks/bent…

Chrome turned on public ad-load metrics for every site it measures. A week later, Google Search's page-experience documentation started recommending them. The four numbers, live in the Chrome User Experience Report as of September 15: Ad Count (average ads in the viewport), Ad Density (the fraction of the viewport they occupy), and Ad Weight in two flavors — network bytes and CPU milliseconds. Same eligibility rules as Web Vitals. Experimental, no thresholds. The placement is the story. "Does your content avoid using an excessive amount of ads" has been a self-assessment question in the page-experience doc for years, with no public number behind it. The doc's resource list — updated September 22 — now points at the CrUX ad metrics one bullet below Core Web Vitals. The disclaimer rides along on the same page: only Core Web Vitals are used by ranking systems, and other page-experience aspects "don't directly help your website rank higher." The dataset arrived before any signal that consumes it. It is also a measurement that was never public. CrUX reports any eligible origin, so a publisher can pull a rival's ad density today. Core Web Vitals took this path — measured in CrUX, then a ranking signal. The metric shipped this month. The threshold didn't.

solana builders privora runs one exchange contract across web, typescript sdk, rust sdk and mcp same actions, same private account model everywhere what would you build on top of a private perps venue?

Built a document intelligence pipeline that turns unstructured PDFs into validated, structured data — no manual entry. Next.js frontend, FastAPI backend,Ollama Github:github.com/23f3003467/invoic…

Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR. We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use. Read more: anthropic.com/news/claude-di…

Inference scaling part 1. Starting with a modded text generation function (temperature scaling, top-p filtering, multinomial sampling) to generate diverse outputs for self-consistency and best-of-N (improving answer accuracy by>2x) 00:00 Introduction and recap 00:31 Training-time and inference-time scaling 07:52 What we'll implement 11:47 Notebook setup and model loading 17:43 Building a flexible text generation function 24:40 Chain-of-thought prompting 28:26 Sampling and output diversity 33:43 Next-token logits and greedy decoding 38:20 Temperature scaling step by step 42:46 Softmax and token probabilities 47:42 Multinomial sampling 54:51 Adding temperature sampling to text generation 59:31 Top-p filtering step by step 1:10:23 Adding top-p filtering to text generation 1:13:43 Sampling and LLM watermarking 1:16:01 Self-consistency and majority voting 1:20:36 Implementing self-consistency 1:29:02 MATH-500 results 1:35:01 Accuracy and compute tradeoffs 1:36:50 Next steps and self-refinement

Core Web Vitals from raw HTML/CSS/JS — no Lighthouse tax. cwv-speed-engine demo (music + transitions): LCP/CLS/INP signals, optimize plans, Material 3 Studio. github.com/1nc0gn30/cwv-spee… @ChromiumDev @webdev @GoogleChrome

Could be crazy at first and tempting to blame one visible stranger for our fears, but in 2026 alone: TypeScript migrated... Bun migrated... Vite as well Git starts to use Rust, so does Linux Kernel. We're leaving a cult of language where single language is a hammer to build everything, and moving to cult of results, where we care about speed, effectivity, and best-for-the-job language.

Your frontend needs maximum performance. What are you choosing? A. Next.js B. React C. Svelte D. Vue

Your frontend needs maximum performance. What are you choosing? A) Next.js B) Svelte C) React D) Vue

Your agent does not need to own the database connection. Put shared tools behind MCP, and let the agent communicate with the server instead. MCP can expose a common tool like Postgres without embedding it directly in the agent. Instead of including a tool directly, you can expose it through MCP. MCP stands for Model Context Protocol and defines how interaction occurs. In the example, stdio lets terminal input receive responses directly. My interpretation: the important shift is architectural, not merely technical. MCP separates what the agent can ask for from how the underlying system connects. That makes a shared capability easier to own as an independent service while the agent remains a protocol client. Practical implication: when adding a shared tool, define an MCP server as the interface owned by the systems or data team. Keep the agent responsible for invoking that interface, rather than carrying direct database-connection logic. Follow Catman for practical judgment on the architecture behind agent systems.

DeepSeek V4.1-Flash did something I find more interesting than another benchmark score. It cut its KV cache requirements to 1/4 the HBM and 1/8 the SSD storage of the previous generation. For AI agents that constantly carry huge contexts, infrastructure efficiency can matter just as much as raw intelligence.

Managed agents are useful—until platform lock-in dictates your models and deployment. Open Managed Agents is an open-source, self-hosted agent platform for builders who want to run managed-agent-style workflows on their own infrastructure. It helps you deploy agents with durable sessions, sandboxed tools, memory, encrypted credentials, and crash recovery—using Node, Docker, or Cloudflare. Key features: • Flexible hosting – self-host on Node with Docker or deploy on Cloudflare Workers • Familiar client paths – use the Claude Managed Agents API or the OpenAI SDK through the host’s `/openai/v1` endpoint • Durable agent runs – sessions, memory, and crash recovery are part of the platform • Tool controls – supports sandboxed tools, private tools, and MCP servers under your deployment boundary • Model choice – bring your own model keys rather than being tied to one provider It’s open-source (Apache License 2.0). Link in the reply 👇

If you STILL remember all the tailwindCSS classes, raise your hand 🙋🏻‍♂️

Next.js — the React Framework - MIT · JavaScript · ~142k GitHub stars - ~4.2k contributors · canary · v16.3.5 - SSR, SSG, App Router, API routes Best for: production React apps with routing and rendering built in.

👨🏽‍💻 Just shipped tailwind-srounded! Opt-in squircle / superellipse corners for Tailwind v4. 
Drop in srounded-* and get buttery iOS-style smooth corners (with perfect fallback). npm i tailwind-srounded 
→ npmjs.com/package/tailwind-s… #TailwindCSS #CSS #WebDev

Scope them with an agent catalog, run them in parallel, and fold the reports into 1 answer.

Opening @ChromeDevTools's Performance panel for the first time is overwhelming: two axes, a wall of color, hundreds of stacked frames So I built an interactive guide to learn to read it, from the waterfall and flame chart to LCP, CLS and INP perf.reviews/profile-guide #WebPerf

2 weeks into learning Rust, and one thing has surprised me: My TypeScript background actually helps a lot. Types, generics, enums, async/await, modules, functions with explicit contracts… The syntax is different, but the mental model feels familiar.