Meta appears to have soft-launched a new app called Pocket that's aimed at getting people to vibe-code their own minigames. Mobile developer and reverse engineer Alessandro Paluzzi spotted Pocket and posted about it to X today, but reporting platform AppFigures told TechCrunch that the app has been available on both iOS and Android since June 29. Though the app is listed publicly, it's not available in the US on any of the half dozen phone models associated with our Google accounts, and a help page on Meta's site says the Pocket app is not yet available everywhere. The company has not made any public announcement yet about the launch or where the app is being trialed. We've reached out for comment and will update this post if we receive a response.

The drama reveals just how deeply Silicon Valley has sunk its claws into Hollywood. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time. You're already subscribed to the aa_Nitish_Pahwa newsletter. You can manage your newsletter subscriptions at any time.

The Download: a startup has a solution for AI's groupthink problem Plus: Scientists say they have built a cell from scratch for the first time. LLMs are stuck in a groupthink groove. This startup is trying to get them out. Open up your chatbot of choice--Claude, ChatGPT, Gemini--and type "Give me a random number between 1 and 10." You're going to get 7. Almost always. That won't work every time--but if it did for you, you may wonder if I have superpowers. The truth is that most large language models are stuck in a rut.

PCWorld examines whether Microsoft's core productivity apps like Word, Excel, and PowerPoint can withstand disruption from advancing AI technology. External AI applications such as ChatGPT and Claude now offer similar document formatting, content creation, and synthesis capabilities that rival Microsoft's own Copilot feature. The analysis suggests Microsoft's traditional productivity suite may become obsolete as AI chatbots increasingly handle tasks previously requiring dedicated office applications. Are Microsoft's core productivity apps -- Word, Excel, and PowerPoint -- endangered by the rise of AI? That's the point that Bloomberg and its sources addressed in coverage this week, noting that Microsoft is being buffeted by AI disruption as its stock plunges. "Whether Microsoft Word or Excel will be rendered obsolete by AI remains to be seen," said Jack Ablin, chief investment strategist at Cresset Wealth Advisors, which owns the stock, according to Bloomberg. "We don't know what the environment is going to look like in a few years, which opens up very real questions like, will we even use a Microsoft suite anymore?" Keith Fitz-Gerald, principal at the Fitz-Gerald Group, added.

Finance Minister Satsuki Katayama speaks during an interview on Monday. The Group of Seven nations will discuss standards on artificial intelligence security and defense, Finance Minister Satsuki Katayama has said. Speaking in a recent interview, Katayama said that financial institutions "need to decide the order of priority for fixing their systems," in order to prepare for the possibility of advanced AI models detecting a large number of vulnerabilities in their systems. She added that the G7 nations, which include Japan, will discuss related criteria and work together to tackle cyberattacks. State-of-the-art AI models, such as Claude Mythos, developed by U.S. startup Anthropic, are believed to be highly proficient in identifying system vulnerabilities. Katayama has been negotiating with the United States to ensure that major financial institutions in Japan have access to these technologies.

Sam Altman is in talks with the US government in a bid to clear political hurdles, says the Financial Times. OpenAI's Sam Altman has reportedly been in talks with the US government to ensure his company's path towards achieving its goals remains free of political hurdles. According to the Financial Times, Altman has suggested giving the government a five percent stake in the company, in order to share the spoils of the AI boom with the public. But his idea doesn't only involve OpenAI: Under his proposal, other top AI companies like Google, Anthropic, xAI and Meta would have to agree to give the government a similar stake in their businesses. AI companies like Anthropic and OpenAI have recently encountered roadblocks from the US government when it came to releasing their latest AI models.

What if a reasoning model could decide when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the field of parallel reasoning, especially adaptive parallel reasoning. Disclosure: this post is part landscape survey, part perspective on adaptive parallel reasoning. One of the authors (Tony Lian) co-led ThreadWeaver ( Lian et al., 2025), one of the methods discussed below. The authors aim to present each approach on its own terms. Recent progress in LLM reasoning capabilities has been largely driven by inference-time scaling, in addition to data and parameter scaling ( OpenAI et al., 2024; DeepSeek-AI et al., 2025). Models that explicitly output reasoning tokens (through intermediate steps, backtracking, and exploration) now dominate math, coding, and agentic benchmarks.

OpenAI CEO Sam Altman has been in talks about public ownership with Donald Trump, according to the report. OpenAI CEO Sam Altman has been in talks about public ownership with Donald Trump, according to the report. OpenAI'in early talks to give 5% stake to US government' OpenAI is reportedly in early stage talks to give a 5% stake in the ChatGPT developer to the US government as artificial intelligence companies attempt to smooth relations with Donald Trump's administration. The OpenAI chief executive, Sam Altman, has argued that giving the US public a financial stake in the company is the best way to share the benefits of AI, according to the Financial Times, which cited two unnamed people familiar with the discussions. The proposal would also involve other US AI companies giving a similar stake to the government, the FT reported, although it is not clear yet whether companies such as Anthropic, Google and Meta would agree to the plan.

We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the model are recovered within a deep learning framework, which makes it possible to retain a transparent parametric structure while simultaneously accounting for complex and nonstationary patterns in the observed phenomenon. Our analysis covers two specifications of the noise process. Besides the standard Gaussian setting, we also consider Laplace-distributed noise, which can offer a more adequate description in the presence of heavier tails and sharper local fluctuations. For both cases, we formulate the predictive scheme of the model and analyze the associated uncertainty quantification, including the construction of prediction intervals. The results illustrate that a relatively simple model, when combined with time-dependent parameter estimation, can serve as a mathematically tractable and practically flexible tool for forecasting complex dynamics under different noise assumptions. The general model is stated for TVAR($p$), while the prediction-interval formulas and the numerical experiments are developed for the TVAR(1) case.

The success of deep learning models in classification and regression is widely attributed to the low-dimensional structure that real-world data tend to exhibit, despite their high-dimensional representation. This work attempts to provide a mathematical framework for binary classification on low-dimensional data, building on Cover's (1965) function-counting theory. With our framework, we aim to address the question of how the low-dimensional structure of the data affects the classification capabilities of learning models. Cover's theory relies on a general position assumption that blinds it to the underlying data structure. We refine this assumption to account for the low-dimensionality of the data and derive dichotomy counts that reflect the data structure. We further extend Cover's separation capacity and problem of generalization to the low-dimensional setting, enabling the impact of the underlying data structure on both to be analyzed.