https://macworks.dev/tags/runtimes/Recent content in Runtimes on MacWorksHugoenhttps://macworks.dev/docs/today/tech-2026-04-18/Mon, 01 Jan 0001 00:00:00 +0000https://macworks.dev/docs/today/tech-2026-04-18/<details> <summary>Sources</summary> <div class="markdown-inner"> <ul> <li><a href="https://medium.com/feed/airbnb-engineering">Airbnb Engineering</a></li> <li><a href="https://aws.amazon.com/blogs/machine-learning/feed/">Amazon AWS AI Blog</a></li> <li><a href="https://aws.amazon.com/cn/blogs/architecture/feed/">AWS Architecture Blog</a></li> <li><a href="https://aws.amazon.com/blogs/opensource/feed/">AWS Open Source Blog</a></li> <li><a href="https://brett.trpstra.net/brettterpstra">BrettTerpstra.com</a></li> <li><a href="https://blog.bytebytego.com/feed">ByteByteGo</a></li> <li><a href="https://blog.cloudflare.com/rss/">CloudFlare</a></li> <li><a href="https://dropbox.tech/feed">Dropbox Tech Blog</a></li> <li><a href="https://engineering.fb.com/feed/">Facebook Code</a></li> <li><a href="https://github.blog/engineering.atom">GitHub Engineering</a></li> <li><a href="https://blog.google/innovation-and-ai/technology/ai/rss/">Google AI Blog</a></li> <li><a href="https://deepmind.google/blog/rss.xml">Google DeepMind</a></li> <li><a href="http://feeds.feedburner.com/GoogleOpenSourceBlog">Google Open Source Blog</a></li> <li><a href="https://www.hashicorp.com/blog/feed.xml">HashiCorp Blog</a></li> <li><a href="https://feed.infoq.com/?token=XQ47eEiAJqUtN8043NhEqJ6kZB8XallO">InfoQ</a></li> <li><a href="https://engineering.atspotify.com/feed/">Spotify Engineering</a></li> <li><a href="https://www.microsoft.com/en-us/research/feed/">Microsoft Research</a></li> <li><a href="https://hacks.mozilla.org/feed/">Mozilla Hacks</a></li> <li><a href="https://netflixtechblog.com/feed">Netflix Tech Blog</a></li> <li><a href="http://feeds.feedburner.com/nvidiablog">NVIDIA Blog</a></li> <li><a href="http://feeds.feedburner.com/oreilly/radar/atom">O&#39;Reilly Radar</a></li> <li><a href="https://openai.com/news/rss.xml">OpenAI Blog</a></li> <li><a href="https://developers.soundcloud.com/blog/blog.rss">SoundCloud Backstage Blog</a></li> <li><a href="https://stripe.com/blog/feed.rss">Stripe Blog</a></li> <li><a href="https://rsshub.bestblogs.dev/deeplearning/the-batch">The Batch | DeepLearning.AI | AI News &amp; Insights</a></li> <li><a href="https://blog.dropbox.com/feed">The Dropbox Blog</a></li> <li><a href="https://github.blog/feed/">The GitHub Blog</a></li> <li><a href="https://medium.com/feed/netflix-techblog">The Netflix Tech Blog</a></li> <li><a href="https://blogs.microsoft.com/feed/">The Official Microsoft Blog</a></li> <li><a href="https://vercel.com/atom">Vercel Blog</a></li> <li><a href="https://engineeringblog.yelp.com/feed.xml">Yelp Engineering and Product Blog</a></li> </ul> </div> </details> <h1 id="engineering--scale--2026-04-18">Engineering @ Scale — 2026-04-18<a class="anchor" href="#engineering--scale--2026-04-18">#</a></h1> <h2 id="signal-of-the-day">Signal of the Day<a class="anchor" href="#signal-of-the-day">#</a></h2> <p>Figma&rsquo;s implementation of the Model Context Protocol (MCP) demonstrates that reliable LLM-driven features require exposing strict, deterministic APIs for state extraction rather than relying on generative guessing. By injecting capture scripts to extract running DOM data and programmatically mapping it to native canvas layers, they solved the chronic fragility of code-to-design pipelines.</p>