Remember that time @JustinVerlander was really angry about something and threw 102 in the 8th

牛専用の姿勢推定モデル「YOLO26 Pose」🐄🤖 🔹Muhammad Rizwan Munawar氏がYOLO26 Poseを牛向けにカスタム学習 🔹20個のキーポイントで牛の姿勢・動きを推定 🔹姿勢や歩行、行動の監視による健康・異常検知への活用に期待 1,800枚の画像で構成されたデータセットも公開。

I don’t think you understand what ChatGPT Voice unlocks I went on a 4 hour hike yesterday. Through the northern California redwoods. Got more work done in those 4 hours talking to Voice through my AirPods then I do in 8 hours at my desk Work can be done ANYWHERE now 45 minute drive back using self driving? Now 45 minutes I can talk to Voice and work on side projects 20 minutes in the cafe? I can sit and talk to Voice and when I get home I have drafts on my computer for my newsletter for the next month Being able to just use your voice to talk to a super intelligence that controls your computer is unlike anything we’ve ever experienced before I don’t even care that the voice sounds human like, or you can interrupt it, or any of the wild advancements they made to the tech It’s the fact that it can control my computer, meaning all I need to get incredible work done is talk through my AirPods anywhere in the world that is the major game changer for me I don’t think people truly understand the implications of this yet Why even have a desk or monitor anymore. Work can be done ANYWHERE now The key is your set up: 1. Choose one device (preferably an always on desktop) as your main “headquarters” device all your work gets done on 2. Get the ChatGPT app on all your other devices (iPhone, iPad, laptops, Mac Minis) 3. Set up ‘connections’ in your settings so that all your ‘node’ devices can control your ‘headquarters’ device 4. Go legit anywhere in the world. Turn on Voice. Ask it to brief you on your projects, give you a recommended next step, then spin up new threads to do work I really don’t think work looks the same moving forward.

Quick reminder that America isn't actually 250 years old. It was born on March 30, 2003 when legendary American patriots, Limp Bizkit, performed "Rollin'" for The Undertaker's entrance at WrestleMania XIX.

My heuristic is that any diff an agent generates over ~1500 lines is too big and is indicative that the problem needs to be decomposed. This is my general pattern now for feature work: 1. Try to implement the whole feature, loosely guided. I call this the "draw the owl" prompt in reference to the meme. Expect garbage, you're going to get garbage. 2. If the diff is less than 1500 lines, review it and iterate normally. If the diff is more than 1500 lines, prompt the agent to decompose the problem into atomic, incremental, reviewable tasks. Simultaneously, do this yourself. 3. Agents will very often make these tasks way too specific to the shape they solved. You need to massage it into the right general shape. Do that. 4. Kick off new agents to work on those incremental things (as parallelized as possible). Apply the same rules. 5. At a certain, point, repeat the "draw the owl" prompt. At some point, you will get beneath your review-ability threshold. This has been producing consistently high quality, maintainable, reviewable chunks of code that have a good handoff to either merge as-is or human refinement. And with the latest frontier models at xhigh thinking, these are all slow enough that you can usually have multiple going concurrently while you are actively reviewing others or working on your own tasks. HITL (human-in-the-loop) agents are still super important, especially for feature work. Features touch the human boundary in terms of UI, API, etc. And net new stuff can introduce pathologies in the architecture that violate desired invariants (these should be represented in specs or tests but we aren't perfect!). I know a lot of the leading edge agentic discourse is about "loops" and agents driving agents continuously. I do some of that (will report on that later). But, in terms of raw daily get-shit-done type of work, this is my most rewarding pattern at the moment.

Where does PEAK bat speed happen in the swing? 👀 Our Launchpad biomechanics visuals just got a major upgrade with full signal bat tracking data.

Introducing the MATLAB Agentic Toolkit Get started 👉 spr.ly/6019BBw4zH

私がアメリカ人が日本にきたら絶対に食べて欲しい料理が牛カツだ。 日本のステーキを揚げた感じの料理です。 アメリカ人は絶対好きだと思う。

Found the “poison” How can they sell this to humans?????

A few things I've noticed as all devs write code with AI. When you write foundational / architectural code of a new project by hand, you "feel" the code pushing back if your abstraction isn't right. You feel when something is harder than it should be. The code is telling you it's not in the right shape. Good engineers are sensitive to this. When you're using an LLM, you keep pushing right through this in a way that feels like you're making progress, and it may even be directionally correct in a sense, but the underlying foundation of it all is actually bad in a way that either kills progress of the LLM later as it buckles under the complexity it has created or destroys your ability to maintain the code long term. Related to this, I see a general restlessness with just sitting and thinking about a problem for a while. As I've been working on a new library here at Laravel, there have been days where it feels like I mainly just stare at my screen thinking about something. When Claude Code is at your fingertips, it's tempting to just start yapping into the terminal and watching code come out the other end. Again, directionally correct in some ways, but often doesn't land on the elegant solution that is waiting to be discovered.

One of those water infusers that (nicer) hotels always have, but instead of fruit it's filled with hot dogs