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AI NewsAutomattic’s 33-Hour Coup, and can AI labs police themselves?

Automattic’s 33-Hour Coup, and can AI labs police themselves?

2:28 AM IST · September 19, 2026

Automattic’s 33-Hour Coup, and can AI labs police themselves?

A week after an Anthropic researcher’s doomsday warning rattled the AI world, the company’s CEO Dario Amodei hasoutlined his plan to “pace the frontier”of AI development. The proposal leans on independent safety evaluators and coordination between AI labs in democratic countries, and it’s already picked up someindustry support, along with some pointedpushback from Nvidia’s Jensen Huang. On this episode of TechCrunch’sEquitypodcast, Kirsten Korosec, Anthony Ha, and Sean O’Kane dig into whether companies can agree on what slowing down means and who gets to police it. Plus, WordPress parentAutomattic’s boardroom coup, and a couple of the week’s biggest deals. Listen to the full episode to hear more about: Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotifyand all the casts. You also can follow Equity onXandThreads, at @EquityPod.

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Laplace Intelligence red-teams robots and self-driving cars before they hit the real world

Laplace Intelligence red-teams robots and self-driving cars before they hit the real world

If there’s one lesson theOpenAI-Hugging Face incidenttaught us, it’s that AI agents can be unpredictable. “Imagine if something like that happened in the physical world,” Howie Sun, CEO and co-founder ofLaplace Intelligence, told TechCrunch. “AI agents are moving from computers into the real world, and we don’t really want physical AI to fail in the real world.” Ann Arbor-based Laplace is building automated red-teaming — essentially, stress-testing a system by deliberately trying to make it fail — for robotics and physical AI. The company is betting that the only way to make sure physical AI is safe is to have AI test it. “Our goal is to help companies discover and fix problems before deployment — and, over time, build a shared foundation of evidence that customers, insurers, and regulators can trust,” Sun said. Laplace is one of 20 startups competing in Startup Battlefield 200, the startup pitch competition taking place atTechCrunch Disruptin San Francisco this week, October 13-15. It willpresent on the Disrupt Stage Tuesday afternoon, October 13. Sun, alongside his co-founder and chief scientist Henry Liu, founded Laplace a year ago out of the University of Michigan. Sun has spent the last eight years in academia researching how to challenge autonomous driving AI in simulation. In 2023, he and Liu — his then-PhD adviser — published apaperon the subject that made it onto the cover of the top scientific journal Nature. That’s when the calls started coming in from autonomous vehicle companies interested in implementing their ideas. “I realized that I wanted to build this and take it from purely academic research into a product that’s useful in the real world,” Sun said. Using their academic research as a base, Sun and Liu built Laplace, so named for “Laplace’s demon,” an 1814 thought experiment by French scholar Pierre-Simon Laplace, who imagined that if you knew the exact state of everything in the universe, you could predict the future with total precision. “We are essentially using simulation to predict the real world’s future,” Sun said. At the startup’s core is a high-fidelitysimulationreconstructed from the real world, paired with an AI agent that constantly probes for weaknesses and failure points. The goal is to ensure that any physical AI — be it a robot, autonomous vehicle, or smart city solution — is safe in simulation before it’s deployed in the real world. The simulation environment is a post-trained version of Nvidia’s Cosmos, an AI world simulation platform, which is grounded in the real world, allowing Laplace to reconstruct anything from an Arizona highway to a Paris pedestrian crossing. Simulations are generated by blending a mesh of different real-world information. A Google Street View image could serve as a reference from which an entire street is generated. But rather than generating something random, the simulator combines real-world map data to re-create an environment that’s true to scale. Developers can then use language prompts to change the setting in real time, like adding in afternoon traffic. Sun says Laplace pulls real-time traffic and congestion information from navigation providers like TomTom. Within Laplace’s simulations, not only is the environment simulated, but also the behavior of other actors. For example, during a demo, Sun directed the test agent vehicle to swerve into oncoming traffic. The car it would have hit — or driven through, as is common among some generative world models — moved out of its way to avoid a collision. “It’s truly an interactive simulation environment,” Sun said. “Everyone is reacting to everything … This is more like a world model with interactive participants.” While the simulated environment is an important part of Laplace’s product, the real unique selling point is the AI agents the startup is developing to automatically design corner cases (tricky, rare scenarios that could trip up an AI system), moving the human further out of the loop. “They can design from the news, from police reports on collisions — all kinds of information to actively challenge the autonomous vehicles and other physical AI agents,” Sun said. Today, autonomous vehicle developers mostly train an AI to handle edge cases using recorded footage that they then turn into a simulation, or by relying on humans to come up with new ideas and design them. While many AV companies have done what Laplace is pitching and started embracing AI to create edge cases for training —self-driving truck company Waabicomes to mind — the need for this kind of technology will really become apparent when we start placing generalized robots in the real world. “There’s less data on generalized robotics,” Sun said. “No one knows what the edge cases will be in the real world.” Despite Sun and Liu coming from academia, with no shiny Silicon Valley tech company pedigrees, the two have managed to start generating revenue and even turn a modest profit. Most of the money is coming from autonomous driving use cases, with customers including Texas-based autonomous trucking firm Bot Auto and a major Japanese automotive manufacturer. Laplace is also working with Nvidia and T-Mobile on a free smart city pilot for San Jose, California, to provide high-level traffic operations. Laplace is also in the early stages of working with partners building humanoid robotics for warehouses. Most of Laplace’s customer contracts are project-based today, and the company is working toward turning that into recurring revenue. The startup’s near-term goal — which it hopes will be aided by competing at Disrupt — is to raise funds and scale so it can compete with its rivals. Chief among its rivals isApplied Intuition, a buzzy late-stage startup that’s raised $1.2 billion to date at a $15 billion post-money valuation. Sun isn’t too worried. He believes Laplace’s core methodology of using AI to test AI is a “new paradigm that changes the testing of physical AI entirely,” turning it into a “dynamic interactive testing process.” “That will be the biggest moat,” he said. This article has been updated to clarify Laplace’s relationship with Nvidia.

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Nvidia launches new open models to run agents in the enterprise and on PCs

Nvidia launches new open models to run agents in the enterprise and on PCs

Nvidia on Thursday launched new open-weight models designed for always-on agents and edge computing. The Nemotron 3.5 Super VL is a large, 120-billion-parameter model geared for agents used by the enterprise. While it uses fewer parameters than theNemotron 3 Ultramodel, Nvidia says it can deliver similar accuracy in instruction-following and tool-use tasks. At the other end of the spectrum, the company also launched two lightweight Bolt models. These are intended to run locally on the new AI PCs (running the Nvidia RTX Spark chip) and workstations running the DGX Spark. Such PCs, aimed at consumers, programmers, and enterprise knowledge workers, are beingreleased right now into the market through partners like Microsoft and Dell. “Any organization that wants to run agents on Nvidia infrastructure now has an open, optimized model for their tier without having to choose between capability and deployability,” Nvidia’s vice president of Open Models and Software, Kari Briski,wrote in the blog postannouncing these models. Nvidia claims that the Super VL completes tasks faster than competing models from Anthropic and OpenAI (Opus 5, GPT 6.1 Sol, Luna, Astra) when post-trained on specific agentic tasks. These tasks include document intelligence, financial accounting, scientific experimentation, medical insurance claims, and security attack resilience. While such claims are standard whenever a major tech company announces a new model, Nvidia has backed it up with a list of heavy-hitter companies that it says are using Super VL. These include Skydance, EY, Ramp, Crowdstrike, Perplexity, andSalesforce. Of course, these aren’t the first open-weight models that Nvidia has released that compete with the proprietary frontier labs — companies that are both its customers and its investments. The GPU giant has an entire family ofNemotronmodels that do everything from multimodal artificial intelligence to reasoning, coding, math, and enterprise automation. However, those fast-growing proprietary AI labs are now looking to many other chips and AI system makers beyond Nvidia to power their needs, like AWS’s Trainium and Cerebras, as well as building their own home-grown chips. So Nvidia is leaning hard into the trend of enterprise companies wanting to run their own open models in their own data centers. “Unlike closed frontier models, organizations can post-train Super VL on their own data, deploy it on their own infrastructure and own what they build,” Briski wrote. But with the announcement or the smaller, local PC models, it is also trying to push forward the nascent trend of consumer AI assistants. While it seems a new AI assistant is introduced every day by startups and Big Tech alike, privacy-weary consumers may not want to invite such agents so deeply into their lives giving them access to info about their families, health, finances, and so on. So, while Nvidia hopes to create a new market for itself selling PCs powered by its chips by creating machines that run the models and agents locally, PC users “can delegate everyday projects … without sending those workloads to the cloud,” Briski wrote. Of the two Bolt models, Nemotron 3.5 Bolt 74B-A9B 24GB is a 74-billion parameter model designed for local agentic tasks. It is intended to balance speed with reasoning to handle multistep reasoning, tool calling, and web browsing. The smallest Bolt E4B is aimed at conversational agents. Super VL and Bolt 74B-A9B are available now, with Bolt E4B coming later this month, Nvidia says.

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People are using AI to make their smart TVs dumb again

People are using AI to make their smart TVs dumb again

The latest use case for AI? Dumbing down your smart TV to get rid of the bloat. A number of developers and vibe coders have recently been sharing solutions for “vibe-coding” your TV to get rid of sponsored ads, speed up the app launcher, redesign the home screen, disable tracking features, and more. One such solution, called “dumbtv,” describes its mission on itsGitHub pagethus: “Your TV should do what you paid for: your apps, your HDMI inputs, and nothing else. No ads from the TV itself, no watch tracking, no update you did not ask for.” Another, “cleanyourtv,” promises to strip the bloatware from any Android TV or Google TV over Wi-Fi, to replace the launcher with one that resembles Apple TV. While hacking consumer electronics devices to gain more control over their systems is hardly a new idea, the latest advances in AI technology have made such efforts much more accessible to anyone. With smart TVs in particular, the hacks come at a time when people have grown frustrated with being forced to watch ads and put up with their devices surveilling their viewing habits. Modern smart TVs ship with a technology called Automatic Content Recognition (ACR), which identifies what’s playing on the screen using screenshots, audio clips or other forms of digital fingerprinting. If these “dumb TV” hacks become more popular among everyday consumers, they have the potential to disrupt the smart TV ad industry, which profits from selling viewing data to advertisers or using it to display personalized ads on their own interfaces. As the creator of cleanyourtv, Gathin T., puts it, the idea to clean up the smart TV interface started one afternoon when he got tired of seeing the ads on his parents’ TV set. Vibe coded my parents' TV this afternoon!It was showing a sponsored ad before you could even get to Netflix.Gave Claude Code the model number, it found the TV on wifi, removed 25 Xiaomi packages and wrote a new launcher.Looks like an Apple TV now lolpic.twitter.com/E5Q4A1b0yI “I had Claude Code clean it up over Wi-Fi and build a simple home screen with just their apps on it. I put the prompt on GitHub, tweeted the before and after,s and a few thousand people did the same to their own TVs over the next two days. Xiaomi, TCL, Sony, Fire TV, LG, a projector or two,” the developer wrote on awebsitefor donations, linked from the project’shomepage. The solution doesn’t require gaining root access to the TV, nor does it make it impossible to return to the factory default state, if desired. Meanwhile, the dumbtv solution has been tested end-to-end on a Fire TV Edition television set and Android/Google TV platforms on TVs from Sony, TCL, Philips, Hisense, Xiaomi, and Shield. To use this hack, the developer, Muzammil Yasin Sabuwala,suggestspointing your preferred AI agent at the dumbtv repository on GitHub. Sabuwala said he was motivated to work on dumbtv because he was tired of the ads and not being able to delve directly into the content he wanted to watch. His solution was coded using a combination of Deepseek V4.1 Flash, Claude Opus 5.5, and Claude Sonnet 5.5, he told TechCrunch, and he plans to maintain the GitHub, adding support for more models over time. introducing dumbtv– no spying– no ads from tv– fully customisableto setup just point your agent to this repo –https://t.co/prQLD17Wa4https://t.co/z41SWJZVXtpic.twitter.com/c6R8EhljiA Fair warning: all these solutions are works in progress, and various users are now testing them across different TV platforms and suggesting updates and fixes. However, individuals don’t need to borrow someone else’s work from GitHub to get the job done. Inone newly viral X post, Ryan Doyleexplains that he simply sent Claude Code the listing for his TV and asked “Can you hack this?” The agent then talked him through the process, allowing him to remove the ads and bloatware from the TV set, speed up the lagging user interface, and customize his screensaver, he claims. Doyle then goes on to demonstrate in a video how fast and responsive the new interface is after the AI made the requested changes. This is how fast the TV moves now that Claude deleted all the BS laggy bloatware it comes withhttps://t.co/nUCEjqPwAOpic.twitter.com/g07CEgC1GJ Seeing posts about people “vibe-coding their TVs,” inspired one X user, Mihir Gupta, to try it himself. “One afternoon with Claude Code: 25 bloat apps disabled, the 192 MB stock launcher replaced with a custom 83 MB one, plus rotating wallpapers,” hewrote. The end result of these hacks may be mixed, however.Some people have foundthat Claudewouldn’t respondto their simplesuggestionsto “hack my TV,” necessitating more complex prompts. Others warned that AI agents could make mistakes, possibly disabling important packages or breaking things in the process. One person even said theybricked their Android TV using Claude, andanother got stuck halfway throughhacking their Fire TV. For those wary of using AI to fully hack their TVs, there are existing solutions for customizing Android-based smart TVs, such as home screen replacement apps ATV Launcher, Projectivity Launcher, and FLauncher.

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LTTS Secures $203 Million in Large Deals, More Than Half AI-Led

LTTS Secures $203 Million in Large Deals, More Than Half AI-Led

L&T Technology Services reported $120 million in annualised Engineering Intelligence revenue as EBIT margins rose to 16.3% in Q2 FY27.

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