Introducing Grok Bot, now in early beta. Bots are AI teammates that do real work for you. They sign in to your tools, use them just like you do, and come back with finished work.

We asked an unreleased research version of Claude to take a stab at the Riemann hypothesis. It didn’t solve it, but it did make strides on a related problem: it increased the lower bound for the fraction of zeros of the Riemann zeta function that satisfy the hypothesis from 41.6% to 67.2%.

Anthropic says new Claude models will embed invisible watermarks in all generated text, everywhere Claude is offered. The watermark is part of the text, it isn't metadata: "it will travel with the text when it's copied and pasted elsewhere, and may persist through some editing." This starts with models launched on or after August 2, 2026, under an EU AI Act code Anthropic signed. Anthropic is still working on adding it to current models. The rollout is worldwide.

opus 5 is a sign that the obsession with “long-horizon agents” in model training is finally backfiring i don’t like long-horizon agents, and i’ll explain why they fundamentally don’t work some people will immediately jump out and say “skill issue”. well, show me one profitable business you built with a long-horizon agent working all by itself - i’d love to learn so far, the only thing they were able to build that’s even interesting enough for people to talk about are those 3d games that are a partial clone of something that already existed the reason an agent was able to build a working prototype of complex games like call of duty was that a team of humans already figured out all the requirements years ago for how such games should work, what kind of controls are intuitive, what mechanics are fun etc all those requirements were already absorbed into the model weights, so when you say “build me call of duty” the model already knows the details. its long horizon execution capability can get all the requirements implemented, which i must say is indeed impressive but now you can see - the value of long horizon execution has a prerequisite of a massive amount of high quality requirements clearly defined upfront. it took a big team of very talented humans months of effort and many iterations to define that for call of duty now imagine games like call of duty don’t exist yet, how would we use agents to build it for the first time? we can’t say “build me call of duty” any more. and there’s no way we can define months-worth of game design details upfront we’ll have to build a tiny prototype of the most basic mechanics, play with it, see if it’s fun, then iterate and expand the complexity. even with the smartest humans, that’s how we work towards something great we don’t need agents to go dark for a long time, spend tens of thousands of dollars worth of tokens, and come back with a product the agent randomly decided to build - try build something truly novel with this and you’ll see it can’t come up with anything that’s actually profitable (i’ll show you why in a bit) we need a tight feedback loop where we can collaborate with the agent, plan with it, understand what it’s done, question its approach, apply our judgement, give it real world feedback and iteratively arrive at a good outcome and that’s exactly what opus 5 absolutely suck at. why? i explained it in more depth with my previous post on how RLVR works - RLVR trains the model to generate code that can pass predefined tests in an isolated environment, which is fundamentally incompatible with the idea of having human in the loop. the more we train the models with RLVR to be “long-horizon”, the less they care about talking to humans ok now - why do they have to talk to humans? why can’t the models iterate and apply judgement by itself? maybe one day they could, but not today, due to many limitations. two examples - 1. LLMs today can’t “watch a video” yet. they can look through a lot of screenshots, which is extremely inefficient at observing a high fps animated signal. so anything that requires continuous visual attention is something LLMs can’t do very well 2. LLMs don’t truly understand what’s “intuitive” or “pleasant” for humans. they know what’s already proven to be intuitive and pleasant in the past, but if you present a truly novel concept, it can’t predict whether humans will like it accurately because of those limitations, human judgment is still needed for almost anything valuable. without humans in the loop, agents will only be able to repeat something that already existed, or go in random directions without true understanding of whether it’s building something useful in summary, long horizon agents assume requirements all exist upfront. they are fundamentally against human in the loop. and they don’t have true judgement for what humans like that, my friend, is why they don’t work

If you write code and want a reason to contribute to open source, this is your chance. Monad Open Source Track supports sustained contributors with AI credits, subject to review. - ship real work - get it merged - do it in public Apply at Full video in the second tweet 👇

Belgrade 🇷🇸, Monad Blitz is coming on August 22! Blitz is Monad Foundation’s high-energy, one-day hackathon for builders who want to learn fast, build fast, and ship on Monad. What to expect: → Monad 101 + onsite mentoring → A full day of building at the @TenderlyApp office → Demos, networking, and up to $2,000 in prizes Apply now, limited spots available.

Just wanted to understand the exact math behind something in a game. It’s built in Godot (which is great btw), and Godot lets developers encrypt the packed game file to make reverse-engineering harder. But the game still has to decrypt that file client-side, so the key has to be somewhere. Now you can just tell a SOTA AI agent to use Ghidra and recover it. While security through obscurity was never real security, the idea of wasting enough of the reverser's time until they gave up was effective. In the era of AI, it's completely useless as you are not wasting a human's time anymore, you're wasting AI inference time which is almost free. Some traditional "security" assumptions were really assumptions about the price of human labor and are completely breaking down now.

I have a confession to make, I haven’t shared this openly online in a long time. When I was in my late teens I just gave up on life, I felt like I would never do anything worthwhile. I went completely off the rails, I was taking amounts of substances that would kill a small Irish village. I also stopped working, learning, or doing anything positive, on good days when i wasn’t going on benders I’d site at “home” playing the same 2 games over and over, smoking weed and drinking. —————— When I was 4, I got my first computer and spent 3 hours a day unsupervised on the interwebs, going down “conspiracy theory pipelines” reading about gravity and physics, going onto lemon tube cause some forum posted it. Needless to say I felt more intelligent than my peers, and maybe I was. So I gave up on school and would skip to go to cyber cafes all day. This unfettered access to anything I can think of rotted my brain, but also made me aware that the world is much bigger than a class room. —————— When I met my now wife, I was going through severe withdrawals. I didn’t want to keep doing what I was doing, but everyone I knew was a degenerate. It was hard to quit. She let me move in with her, practically nursed me back to health over 2 years. I had always had an interest in coding, specifically hacking and making computers do things they shouldn’t, but that was never nurtured. —————— I decided now is the time, there will probably never be a chance to win again. So I picked up Mind you I was still addicted to weed, so I decided to Pavlov dog myself into being a programmer. I taped a piece of paper, like the one you see below to an old free desk I found on FB marketplace, I used an 8gb laptop my wife got for free on credit. I used the pomodoro technique to sooth my ADHD mind, studying for 30 mins playing games for 10, every 2 hours of studying I could smoke some weed. Trading my imaginary points for a bong hit, this built a circuit in my brain, and before I knew it I was coding in Ruby and JS. ————— Of course I was useless still, just cause you can write a for loop doesn’t mean you’re useful. So I did this day and night for 2 years. Until I got my first gig. Now I work with world class organizations, have educated at least 500 people. I love my wife. Your life is a video game, play it.

Devrel Livestreams #10# - MelodyPay and more! with @kunalshah017 and @tyraaakj On Wednesday, July 29 (TODAY!) at 3:00 PM UTC, we’ll talk about: → MelodyPay and how melody can carry money → tips and tricks building with AI → their current prototype with Monad support Join us live on X! Add it to your calendar with one click, link in second tweet.