Jonathan Sugden

See you Monday Shakers 🫡

⏱️ 90+4‘ Runcorn 🟡 0-3 ⚪️ Shakers A MASSIVE THREE POINTS!! GET IN!! #BuryFC | #bfc140

Just read LeCun's latest paper. His team trained the first world model that can't collapse. Let me explain why this matters. It's called LeWorldModel. World models predict what happens next physically. Objects moving, falling, colliding. That's the base layer for robots that plan, cars that simulate before they steer, any AI that acts in reality instead of just talking about it. The catch is nobody could train these reliably. The models kept cheating. They'd map every input to the same output. Like a weather app stuck on "sunny" forever. Technically predicting. Completely useless. So teams piled on fixes. Frozen encoders, stop-gradient hacks, 6+ loss hyperparameters. A fragile stack too brittle for production. This team asked a different question. What if you make collapse mathematically impossible? An encoder turns each video frame into a small vector. A predictor takes that vector plus an action and guesses the next one. First loss: how wrong was the guess. Second loss: a regularizer called SIGReg that checks if vectors spread out like a bell curve. If they start looking the same, the loss spikes. The model can't cheat because the math won't let it. That simplicity is what makes the results possible. Six hyperparameters became one. 15M parameters. Trains on one GPU in hours. Plans 48x faster. Encodes with ~200x fewer tokens. Open-source. I could run this on my own hardware. Which changes who gets to build physical AI. Not just big labs anymore. Any team, any startup, any grad student. LeCun has pushed JEPA as the path forward. The criticism was always training instability. This paper removes that objection. Two directions compete in AI right now. Bigger LLMs with more compute. Or small models learning physics from raw pixels.

Jet suit paramedics can reach the top of Helvellyn in 3.5 minutes instead of 1 hour 15 minutes.

Project Hail Mary opened last week. Great film. But nobody is talking about the credits. They should be. A guy with a telescope spent hundreds of hours collecting light from objects so distant that the photons hitting his sensor left their source before Rome was founded. His name is Rod Prazeres. His images ended up on 70-foot IMAX screens worldwide. Look at what he captured. The Rosette Nebula is a cloud of gas 5,000 light-years away that has arranged itself into the shape of a human eye, ringed by fire. The Vela filaments are a stellar explosion still spreading outward through space – blue threads so fine they look like frost on glass. The dust pillar in the Pelican Nebula is manufacturing new suns right now. While you read this. None of it was rendered. All of it is real. Weir spent years getting the science right. The filmmakers felt the same way about the sky. When they needed something beautiful enough to close the film, they went looking for something that actually exists. They found it. 5,000 light-years out. Gandalv / @Microinteracti1

🚨 Andrej Karpathy just explained the scariest thing happening in software right now.. someone poisoned a Python package that gets 97 million downloads a month.. and a simple pip install was enough to steal everything on your machine.. SSH keys.. AWS credentials.. crypto wallets.. database passwords.. git credentials.. shell history.. SSL private keys.. everything.. and here's the part that should terrify every developer alive.. the attack was only discovered because the attacker wrote sloppy code.. the malware used so much RAM that it crashed someone's computer.. if the attacker had been better at coding.. nobody would have noticed for weeks.. one developer.. using Cursor with an MCP plugin.. had litellm pulled in as a dependency they didn't even know about.. their machine crashed.. and that crash saved thousands of companies from getting their entire infrastructure stolen.. Karpathy's take is the real wake up call.. every time you install any package you're trusting every single dependency in its tree.. and any one of them could be poisoned.. vibe coding saved us this time.. the attacker vibe coded the attack and it was too sloppy to work quietly.. next time they won't make that mistake.

You can now enable Claude to use your computer to complete tasks. It opens your apps, navigates your browser, fills in spreadsheets—anything you'd do sitting at your desk. Research preview in Claude Cowork and Claude Code, macOS only.

🚨 BREAKING: Meta researchers showed a model 2 million hours of video. No labels. No physics textbook. No supervision at all. It learned gravity. Object permanence. Inertia. And it just beat Gemini 1.5 Pro and GPT-4 level models at physics understanding. Here's what just happened:

Shifting structures in a software world dominated by AI. Some first-order reflections (TL;DR at the end): Reducing software supply chains, the return of software monoliths – When rewriting code and understanding large foreign codebases becomes cheap, the incentive to rely on deep dependency trees collapses. Writing from scratch ¹ or extracting the relevant parts from another library is far easier when you can simply ask a code agent to handle it, rather than spending countless nights diving into an unfamiliar codebase. The reasons to reduce dependencies are compelling: a smaller attack surface for supply chain threats, smaller packaged software, improved performance, and faster boot times. By leveraging the tireless stamina of LLMs, the dream of coding an entire app from bare-metal considerations all the way up is becoming realistic. End of the Lindy effect – The Lindy effect holds that things which have been around for a long time are there for good reason and will likely continue to persist. It's related to Chesterton's fence: before removing something, you should first understand why it exists, which means removal always carries a cost. But in a world where software can be developed from first principles and understood by a tireless agent, this logic weakens. Older codebases can be explored at will; long-standing software can be replaced with far less friction. A codebase can be fully rewritten in a new language. ² Legacy software can be carefully studied and updated in situations where humans would have given up long ago. The catch: unknown unknowns remain unknown. The true extent of AI's impact will hinge on whether complete coverage of testing, edge cases, and formal verification is achievable. In an AI-dominated world, formal verification isn't optional—it's essential. The case for strongly typed languages – Historically, programming language adoption has been driven largely by human psychology and social dynamics. A language's success depended on a mix of factors: individual considerations like being easy to learn and simple to write correctly; community effects like how active and welcoming a community was, which in turn shaped how fast its ecosystem would grow; and fundamental properties like provable correctness, formal verification, and striking the right balance between dynamic and static checks—between the freedom to write anything and the discipline of guarding against edge cases and attacks. As the human factor diminishes, these dynamics will shift. Less dependence on human psychology will favor strongly typed, formally verifiable and/or high performance languages.³ These are often harder for humans to learn, but they're far better suited to LLMs, which thrive on formal verification and reinforcement learning environments. Expect this to reshape which languages dominate. Economic restructuring of open source – For decades, open-source communities have been built around humans finding connection through writing, learning, and using code together. In a world where most code is written—and perhaps more importantly, read—by machines, these incentives will start to break down.⁴ Communities of AIs building libraries and codebases together will likely emerge as a replacement, but such communities will lack the fundamentally human motivations that have driven open source until now. If the future of open-source development becomes largely devoid of humans, alignment of AI models won't just matter—it will be decisive. The future of new languages – Will AI agents face the same tradeoffs we do when developing or adopting new programming languages? Expressiveness vs. simplicity, safety vs. control, performance vs. abstraction, compile time vs. runtime, explicitness vs. conciseness. It's unclear that they will. In the long term, the reasons to create a new programming language will likely diverge significantly from the human-driven motivations of the past. There may well be an optimal programming language for LLMs—and there's no reason to assume it will resemble the ones humans have converged on. TL; DR: - Monoliths return – cheap rewriting kills dependency trees; smaller attack surface, better performance, bare-metal becomes realistic - Lindy effect weakens – legacy code loses its moat, but unknown unknowns persist; formal verification becomes essential - Strongly typed languages rise – human psychology mattered for adoption; now formal verification and RL environments favor types over ergonomics - Open source restructures – human connection drove the community; AI-written/read code breaks those incentives; alignment becomes decisive - New languages diverge – AI may not share our tradeoffs; optimal LLM programming languages may look nothing like what humans converged on ¹ x.com/mntruell/statu… ² x.com/anthropicai/st… ³ wesmckinney.com/blog/agent-erg… ⁴ #issuecomment-3717222957" target="_blank" rel="nofollow noopener">github.com/tailwindlabs/t…

Door 6️⃣ | 6 - 0 🏆 In 1903, We won the FA Cup in record fashion by beating Derby County 6-0. The result remains the joint largest victory margin in an FA Cup final, only recently equalled by Manchester City. #buryfc | #bfc140

holy shit AI can now decode the human proteome without any reference genome. a new paper published today presents a new model that can read raw mass spec and generate novel peptide sequences from scratch. no database or priors. just biology. 1/

Major AI breakthrough: Diffusion Large Language Models are here! They're 10x faster and 10x cheaper than traditional LLMs. Here's everything you need to know:

I'm loving the chat with Sesame. sesame.com/research/cross… Feels transformational compared to my previous AI voice interactions. What do you think?

Most people use AI wrong. They try to build one perfect agent. Instead, build a crew: • Research agent finds data • Analysis agent processes it • Writing agent creates content • Review agent polishes it Flow / Multi-Agents > Individual capability.

Fantastic to see Ancelotti's eyebrow getting a run out!

@amandamashburn @luke_brocks @RobertMSterling everytime.....

@luke_brocks @RobertMSterling Internet went out one night. No big deal. I’ll just pop in an old Blue Ray. The Playstation wouldn’t play the DVD because it needed a software update. 🙃

I don’t want to connect my coffee machine to the wifi network. I don’t want to share the file with OneDrive. I don’t want to download an app to check my car’s fluid levels. I don’t want to scan a QR code to view the restaurant menu. I don’t want to let Google know my location before showing me the search results. I don’t want to include a Teams link on the calendar invite. I don’t want to pay 50 different monthly subscription fees for all my software. I don’t want to upgrade to TurboTax platinum plus audit protection. I don’t want to install the Webex plugin to join the meeting. I don’t want to share my car’s braking data with the actuaries at State Farm. I don’t want to text with your AI chatbot. I don’t want to download the Instagram app to look at your picture. I don’t want to type in my email address to view the content on your company’s website. I don’t want text messages with promo codes. I don’t want to leave your company a five-star Google review in exchange for the chance to win a $20 Starbucks gift card. I don’t want to join your exclusive community in the metaverse. I don’t want AI to help me write my comments on LinkedIn. I don’t even want to be on LinkedIn in the first place. I just want to pay for a product one time (and only one time), know that it’s going to work flawlessly, press 0 to speak to an operator if I need help, and otherwise be left alone and treated with some small measure of human dignity, if that’s not too much to ask anymore.

@RobertMSterling My oven has bluetooth. It serves no purpose as the buttons and controls are physical on it. It doesn't even let me set the time or timer which would actually be useful. It must be some kind of botnet agent as, surely, no one is that bad at product design right?

I'm torn between being astounded or skeptical that this is publicity before product. I'm not sure I've achieved GI as I don't know to be excited or scared for our future. How are y'all feeling? 🔮🤖 #AGI #AI #OpenAI12Days #OpenAI youtube.com/live/SKBG1sqdy…

I'm torn between being astounded or skeptical that this is publicity before product. I'm not sure I've achieved GI as I don't know to be excited or scared for our future. How are y'all feeling? 🔮🤖 youtube.com/live/SKBG1sqdy…

ARE YOU NOT ENTERTAINED? #MUFC || #WOLMUN