codesamplez

I just open sourced SnapDrift: visual regression checks for apps in GitHub Actions.

🤣

Meet the new Stitch, your vibe design partner. Here are 5 major upgrades to help you create, iterate and collaborate: 🎨 AI-Native Canvas 🧠 Smarter Design Agent 🎙️ Voice ⚡️ Instant Prototypes 📐 Design Systems and DESIGN.md Rolling out now. Details and product walkthrough video in 🧵

@shadcn Curious what you think is the bottleneck - is it the visual judgment (taste) or the systematic coverage across all permutations? Wondering if AI could at least help generate the test matrix while humans do the final visual call.

To get translucent menus right, we had to manually test components over different gradients & colors, checking visuals & accessibility. Now multiply that by 5 component types across 5 styles in both light and dark modes. It's a lot of work. Work that, for now, AI can't do.

guys cooked like rent is due

@AnthropicAI Critical timing - AI reliance on open source is growing fast. Are there specific focus areas for the OpenSSF funding, or is it broadly targeting supply chain security across the ecosystem?

The open source ecosystem underpins nearly every software system in the world. As AI grows more capable, open source security becomes increasingly important. We're donating to the Linux Foundation to continue to help secure the foundations AI runs on.

@LangChain The new→dev→deploy workflow is super intuitive! How does this compare to deploying with Docker Compose directly — any advantages for local-to-prod parity?

Deploy LangGraph agents using the LangGraph CLI You can now deploy LangGraph agents to production straight from your terminal using the LangGraph CLI! 🛠️ langgraph new → scaffold from a template 🧪 langgraph dev → test locally in Studio 🚀 langgraph deploy → deploy your agent on LangSmith 📋 langgraph deploy logs/list/delete → manage everything after directly from your terminal Blog: blog.langchain.com/introducing-de… Watch the full walkthrough: youtu.be/hcWHufkzicc

TCP vs UDP

.@nodejs has always been about I/O. Streams, buffers, sockets, files. But there's a gap that has bugged me for years: you can't virtualize the filesystem. You can't import a module that only exists in memory. You can't bundle assets into a Single Executable without patching half the standard library. That changes now 👇

@Kimi_Moonshot Fascinating approach—curious how AttnRes interacts with MoE architectures like Kimi Linear where different experts might benefit from different depth-wise attention patterns. Any plans to open-source the pretrained checkpoints?

Introducing 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with learned, input-dependent attention over preceding layers. 🔹 Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth. 🔹 Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale. 🔹 Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead. 🔹 Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains. 🔗Full report: github.com/MoonshotAI/Att…

Does your Coding Agent uses “tabs” or “spaces”?

This image is destroying my brain.

stackoverflowCopyPasteWasTheOriginalVibeCoding redd.it/1rtd5uf

bigTechRightNow redd.it/1rs728m

Your work-schedule is governed by quota limits schedule

@simonw @tobi Compelling example of agents enabling high-interruption roles to do deep work. Does this pattern mainly work for established codebases with mature test suites, or can teams build the "experiment-ready" foundation incrementally on greenfield projects?

Published some notes on @tobi's autoresearch PR that improved the performance benchmark scores of the Liquid template language (which Tobi created for Shopify 20 years ago) by a hefty 53% simonwillison.net/2026/Mar/13/li…

Many "GIL problems" are actually "I didn't use asyncio" problems. Deep-dive into Python concurrency: codesamplez.com/programming/py…

The real issue: People write synchronous code, then blame the GIL when it's slow. Before you complain about the GIL: 1. Profile your code 2. Identify the bottleneck 3. Is it CPU or I/O? 4. Then choose the right tool

Hot take: The Python GIL isn't the problem. Your code is. The GIL (Global Interpreter Lock) gets blamed for everything: • "Python can't do real threading" • "It's slow for concurrent tasks" • "You need multiprocessing for everything" Here's the uncomfortable truth: