Colin Devroe

Of course that’s your contention. You’re a first-year inference engineer. You just read SemiAnalysis’s blog and saw megakernels coming to TPUs and AMD, so naturally you believe that’s the way to get to speed of light. Next month, when you get your hands on an inference engineering cookbook—Baseten’s, probably—you’ll be singing from the treetops about the 500 nanoseconds of critical-path latency you save on every GEMM pipeline drain. That’ll last until sometime next year, when you watch a pirated geohotz lecture livestream and become convinced that PDL gives you a strict superset of megakernel capabilities. “Well, as a matter of fact, I won’t, because PDL introduces—” “PDL introduces unnecessary stalls because its synchronization mechanism is very coarse.” Yeah, I read that too. HazyResearch’s blog, “Look Ma, No Bubbles!” Just below Figure 2. Do you have any thoughts of your own on the subject, or should I just point my agent at the SGLang repo and have it do it for me?

For the past 9 months, I’ve been making Tug, an app to create a great user experience for AI coding. I made a video to introduce it. Tug is for macOS, uses your Claude Code account, and is open source. Now in beta! Let me know what you think! youtu.be/MyGyiKSv-QQ

Most of you probably haven't noticed this, but dots have a new feature called multi-exec that we quietly released on friday. I've been using it all weekend on my personal dot and it's incredibly powerful. I'd like to share why this is a big deal and how to use it. At a high level, multi exec lets your dot run commands directly on any computer. This immediately turns your dot into a real virtual employee. Here's why it's a gamechanger: - you can attach a powerful machine with persistent storage to your dot (I attached it to my maxed out Mac Studio Ultra) - the dot can now drive your computer directly via Computer Use - this extends to browser use - all your websites stay logged in, residential ip etc - you set up all your CLIs and authentication ONCE and the dot can use them directly (no more fighting the default ephemeral cloud computer) - the dot can save skills locally for repeated workflows and subagents can use those directly - if you have a local obsidian memory vault for your agents, your dot can use that too - building ios app end to end and testing it in xcode, and showing me screenshots of progress as it works - all of the above also applies to any subagents your dot delegates to in the cloud (subagents share context unlike separate threads). To enable it, open the desktop app on your computer and hit "allow computer", then give your dot this prompt: "Hey I just connected my computer. Can you attach to it directly so it's your default computer and you can run commands directly on it? Do it in your root thread so that all subagents inherit this computer for running commands. Run whoami to run water through the pipes and verify it works end to end." I recommend attaching your dot to the most powerful machine you have and investing time into it up front: - Log into all the emails & websites you need e.g. stripe cli, gcloud, firebase, etc - Install and auth all the CLIs, git you need - Make sure computer use is turned on and allowed to use your computer while its locked - I also enabled Allow All for Browsing and Downloads so my dot could use computer use to download whatever it wanted from websites without asking me for approval for each domain (use at your own risk). - Important: I HIGHLY recommend to ask your dot to build out repeated workflows as skills, and iterate to make those skills bulletproof by running water through the pipes with its subagents. As of this weekend, my dot is sending me screenshots of its progress building my iOS app end to end on my Mac Studio Ultra via xCode, and clicking around and testing things end to end. It's able to configure things in stripe via the CLI and use websites when it needs to download images for my workflows. It’s night and day. Don't sleep on it.

I bet nobody drives a Lambo in Crocs more than me tbh

Things will change again and again and again. We’re still very early and agents today are incredible, and yet so frustrating still. Can’t wait to crack things to get that flow state back. Long journey ahead.

Fixes available in -proposed pocket for CVE-2026-98432. Read the blog for more details: ubuntu.com/blog/fixes-availa…

Can MCP connectors help save WordPress? 🤖 Next week on @WPProductTalk, @cdevroex joins @learnwithmattc & @zackkatz to unpack how MCP integrations could transform WP workflows & product businesses. wpproducttalk.com/podcast/ca…

Introducing 𝙻𝙵𝙼𝟹: our new generation of Liquid Foundation Models. With a new tri-hybrid architecture and new multimodal encoders, the LFM3 family is the ideal model suite for specialized intelligence. > 6 model instances, across text, audio, vision, and omni modalities, generative and decision models. > 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱: text, 256k context window. > 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱: vision-language model with video-input support for spatial understanding > 𝙻𝙵𝙼𝟹-𝙰𝚞𝚍𝚒𝚘-𝟸𝙱: tool calls, voice chat, reasoning in 18 languages. > 𝙻𝙵𝙼𝟹-𝙾𝚖𝚗𝚒-𝟸𝙱: an omni model combines vision, audio, and language. > 𝚍𝟷-𝟸𝙱 and 𝚍𝟷-𝙾𝚖𝚗𝚒-𝟸𝙱: decision models with typed outputs and calibrated probabilities in a single pass. Omni adds audio input. > fine-tune on 1 GPU. > a new tri-hybrid architecture > architecture optimized for 14 processor classes across GPUs, CPUs, and NPUs. > 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱 is a beast. It performs competitively with Gemma 4 12B (6x larger), and Gemini 3.5 Flash Lite, and GPT-5.6 Luna, with 1.9B parameters. > 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 is #1 on the Pareto frontier of device intelligence 𝚙𝚒𝚙𝚎𝚝𝚝𝚎.𝚕𝚒𝚚𝚞𝚒𝚍.𝚊𝚒 Every modality for specialized intelligence. Pick the one that fits your use case, customize it, and run it on-device. 1/ 🧵

If your CEO is actively vibecoding Run.