The US midterm election day is tomorrow. Most people know the top of the ticket but almost nothing below it. Use the ballot-peek Grok @bot to quickly help you find all the candidates, their key positions and much more before you vote. Takes a minute.

This is 100% true. My litmus test: Red flag when the main flex is: - how many agents (or teams of agents) they are running - how they can message their agent from the gym - how many millions of tokens they burned this week I see tons of these posts on LinkedIn daily. Legit when the main flex is: - they built something that wasn’t possible or economical before (ideally it makes or saves money) - they shipped more, or faster - they built custom software instead of using a SaaS provider IMO, the former is the equivalent of bragging about 80 hour work weeks with nothing to show for it.

Starting to see “AI will eat software” predictions slowly coming true. My barber just moved his booking system off booksy after 5 years because a friend of his learned about Claude and vibe coded nearly the same thing for his website in a weakened. He told me it makes no sense to pay booksy anymore. I think this trend might accelerate starting from low margin and mom-and-pop businesses then spreading.

Some product feedback for @elonmusk and @poteto. I love GrokBot and its integration with X but every @bot having its own thread in X chat by default is making the chat unusable. Fully buries messages from humans. Please add a separate tab or something similar for bot threads.

Muse Wonder is already outperforming Anthropic and OpenAI frontier models in the wild. A neolab founder DMed me after first trying it. His words: “one of the first times using a model has felt like telling an employee what to do rather than babysitting a dumbass pet...” (screenshots shared with permission) The best judge of a model is the user experience. Muse will eat the world.

There’s been a lot of discussion on Muse Wonder since the launch. But there are some very interesting aspects that are under-discussed and make this launch unique from the rest of the frontier: 1. Big model smell reporting (goodbye benchmaxxing?): For the first time, a model launch measured and reported big model smell which basically shows how the model does on tasks it targeted in post-training vs tasks it didn’t. It’s essentially a guard against “benchmaxxed” models. I haven’t seen another frontier lab put this in a launch post, and I think all labs should start reporting it, ideally with the held-out tasks named. 2. Active focus: Muse Wonder is built to not give up when it hits real-world blockers, to keep trying alternative approaches and adapt to new information, while checking in before important actions. For instance, if a dinner reservation fails or a form throws an error, it tries another route on its own. Every lab trains for persistence, but Meta measures it on everyday personal tasks like housing, travel, IT and finance, not just coding. That’s what makes Wonder a good fit for assistants like Muse. Active memory: carries important context about you across conversations and pulls up the details that matter for whatever you’re doing. This release tested it inside long chats where facts change. Early in a long chat you say “my flight is tuesday”, then 50K tokens of other stuff later you say “actually it moved to thursday”. Ask “when do I fly?” and a model with weak memory might say tuesday because it saw that first. Meta built their memory test around exactly this case (and wonder tops it). Imo this stale info problem is the hardest part of memory, way harder than just remembering stuff. Memory+focus are so important for assistants. Muse will eat the world. public source in comment below.

Appreciate the shoutout @PatrickToulme. Quick intro for new followers: I’m a machine learning engineer at Meta where I’ve worked for 8+ years. Over the past few years I’ve worked on Instagram across ads systems, shopping, growth strategies and most recently people recommendations. I’ll be posting more about AI and its practical applications at scale, what works, what breaks, and what’s overhyped, drawing on my own experience and learnings. Plus some career stuff. Based in NYC. Usually playing tennis when I’m offline (hit me up if you play). Look forward to connecting.