Search headlines, summaries and articles to connect the dots.
There are 57 million people worldwide living with dementia today. For the researchers working to help them, the obstacle isn’t a lack of effort — it’s too much data, too few experts to synthesize it and a pace of discovery that can’t keep up with the disease. The Consortium for Biomedical Research and AI in […]
This episode of Lex Fridman was divided into 13 topics. The guests were three: Steven Brunton, who researches fluid dynamics at the University of Washington; Ankur Moitra, who works on theoretical computer science at MIT; and Alex Kontorovich, who studies number theory and Lean at Rutgers University. The video discussed the Navier–Stokes equations, the Riemann hypothesis, and computational complexity theory, prompted by AI’s claims—within just a few weeks—to have solved long-standing open problems in each of these fields. Drawing on their respective areas of expertise, the three mathematicians dissected...
Edge Sound Research Inc. today announced it has raised $2 million in new funding to build audio infrastructure that isolates useful sounds and combines them with other sensor data to help physical AI systems understand what’s happening in real time. Stormbreaker Ventures led the round, bringing the company’s total funding to $9 million. When the […] The post Edge Sound raises $2M to help physical AI make sense of audio appeared first on SiliconANGLE.
General Compute is betting on inference chips from outside the Nvidia ecosystem.
Venture funding to robotics startups has soared in 2026, with the bulk of investment going to companies based in China and the U.S., Crunchbase data shows. All told, global venture funding to robotics startups totaled $9.9 billion in Q3, more than double the $4.3 billion invested in Q3 2025, per Crunchbase data.
SEAL and the architecture of persistent adaptation
Why isn’t your quota being used? Most of it is going toward “holding ineffective meetings” for sub‑agents.
The first domino in the AI bubble.
The next round of reshuffling in Silicon Valley may well begin right here.
Model credits are showing up in festival promotions and carrier-style packs. Thin resellers that thrived on opaque markups face quality doubts, enforcement, and better-funded rivals. Durable intermediaries will charge for routing, trust, and integration—not for a secret price cut.
The big one has finally arrived. On October 26, Anthropic publicly filed its S‑1 registration statement with the U.S. Securities and Exchange Commission, preparing to list on the Nasdaq Global Select Market under the ticker symbol ANT. Media reports suggest the company’s valuation is being discussed in a range of $1.5 trillion to $2 trillion, though the proposed offering price range remains undisclosed. Within 24 hours, this more than 300-page prospectus had already been scrutinized inside and outside the tech and finance communities, with everyone focusing on the most eye‑catching figures: revenue of $16.286 billion in the first half of 2026, compared to just $1.163 billion in the same period last year; operating profit of $974 million in the second quarter, marking the company’s first-ever quarterly operating profit; and a net loss of $80.7 billion, largely driven by $72.388 billion in changes to the fair value of convertible bonds, derivative liabilities, and SAFE notes—non‑cash accounting expenses. Yet beyond these Wall Street narratives, domestic developers and everyday users experience something quite different. In the eyes of mainland Chinese users, Claude is arguably the AI product currently facing the most account bans, the strictest risk controls, and the most frustrating user experience. Why does a company that has built a state‑of‑the‑art large model consistently appear so indifferent when dealing with ordinary individual users? After poring over the financial footnotes and risk disclosures in the prospectus, the answer is actually right there in the numbers. In Anthropic’s business landscape, personal users contribute only a very small share of its revenue. 01 Less Than 20% from Individual Users To understand who a company truly prioritizes, the simplest approach is to examine its revenue breakdown. In Note 2 of the financial statements attached to the prospectus, Anthropic splits its revenue into two main categories: usage‑based revenue and subscription revenue. The contrast is stark: for the full year 2025, usage‑based revenue totaled $3.796 billion, while subscription revenue was just $789 million—roughly an 83% to 17% split. By the first half of 2026, usage‑based revenue surged to $13.064 billion, with subscription revenue at $3.222 billion, still leaving usage‑based revenue accounting for about 80% and subscriptions making up roughly 20%. Even more telling details lie hidden within the composition of subscription revenue. Of that 20%, there are both individual users’ Cl
On October 26, Zhang Tao (online alias HideCloud), co-founder and product partner at Manus, recently published a lengthy article on his WeChat public account “Qianyun Sisi,” titled “Transforming Manus with Manus.” The piece opens with an image that left a deep impression on him. He had submitted several requirements and bug reports to an engineer; when he checked back later, he found that the engineer’s computer had multiple Codex and Manus sessions open. Clicking on any one of them, he saw that the input was precisely the original requirement he’d just posted on Slack—without a single word changed. Even more surprising to him was that this engineer was simultaneously working on over ten or twenty tasks. At Manus today, this has become the norm. The article is packed with information, but a few key points are especially worth noting. Internally, Manus has created a unified code entry point called Manus-mono, accompanied by a manual designed for AI, enabling the AI system to view the entire project landscape across repositories. With a single request, one person can complete all client-side development. The mobile team has consistently consisted of only seven members, yet they manage two products and four apps, keeping their progress largely in sync with the web version. All product managers have submitted code to Manus, and roughly half of them can independently bring their requirements to production. Some product managers even turn their written requirements into video presentations. Meanwhile, the marketing team now has a “digital colleague” that schedules its own tasks, proactively reminds everyone of project deadlines, and even reaches out across teams to clarify feature details. In September, the company’s per-capita AI spending exceeded $10,000, with a small number of individuals consuming as much as $70,000 to $80,000 in a single month. Zhang Tao dedicated an entire section to addressing external skepticism. The timing of the article’s release is also noteworthy. At the end of September, Manus launched version 2.0 along with its personal AI agent app, Cue; in early October, the parent company Butterfly Effect announced the completion of a new round of financing exceeding $500 million, while simultaneously opening 17 positions in Beijing. Seen against this backdrop, the article reads less like a routine update and more like a manifesto marking the Manus team’s entry into the 2.0 era. It discusses how the organization leverages AI to evolve, while revealing the company’s product roadmap, cost structure, and hiring standards. 01 Manus itself serves as a showcase for 2.0 If you compare the internal practices described in the article with the feature list of Manus 2.0, you’ll find…
The prospectus shows that in the first half of 2026, the company generated revenue of US$16.286 billion, roughly 14 times that of the same period last year, while its operating loss narrowed from US$2.135 billion to US$693 million.
Jensen Huang is still struggling to prop up the Neocloud wall, while Musk has already predicted its collapse. The first domino in the AI bubble may well be among these companies. On October 25, Musk issued a warning on X: in recent weeks, the debt markets have virtually stopped extending financing to firms that lack substantial revenue streams beyond their AI data centers. He forecast that more than 200 Neocloud players will face “guppy‑level” deaths and consolidation, with only a handful ultimately surviving. Yet just three days earlier, NVIDIA CEO Jensen Huang offered a nearly opposite assessment. On October 22, during an interview with CNBC, Huang insisted that demand for Neocloud computing power remains extremely robust; the real bottleneck is insufficient working capital, and funding challenges will eventually be resolved. Both men remain optimistic about the demand for AI compute, but they differ sharply on how to manage the business of renting out computing power...
The S-1, the numbers, and the reactions.
On the very same day that Anthropic officially filed its IPO prospectus, OpenAI unveiled its new model, GPT-6.1 Terra. This marks the first update to the Terra series in more than three months, and it has already been 27 days since the release of GPT-6.1 Sol on September 29. In its official promotional materials, OpenAI emphasized: “Delivering fast and powerful computing capabilities at a lower price.” However, users don’t seem to be convinced. “Bro, what we need is a model stronger than Opus 5.5. Nobody cares whether it’s faster or cheaper,” one user bluntly remarked, adding that they’ve almost stopped using the Codex subscription because it’s simply not useful anymore. Over the past period, Anthropic has garnered significant attention—particularly in programming-related fields—thanks to Claude Opus 5.5. Many users are now eagerly anticipating Open...
arXiv:2610.33016v1 Announce Type: new Abstract: Llama-3, Qwen3 and many other open LLMs pre-tokenize text with a regular expression whose word class admits only Unicode letters. Devanagari vowel signs are combining marks, so this regex splits Nepali words at every vowel sign, and models that use it spend 2.6 to 5.8 times as many tokens on Nepali as on the same English text. Merge-based vocabulary extension cannot join the split pieces: for Llama-3 and Qwen3 no number of new merges brings this premium below about 2.2, and ten released extensions already end within 4% of this pre-token floor. We give a small edit to the deployed regex that provably keeps every token id of text without combining marks or Devanagari characters. With 32,000 new merges it brings Nepali to 1.01 times English, abo
Last night, Anthropic finally disclosed its IPO application, posting its prospectus on the U.S. Securities and Exchange Commission’s website. Many people also say that, in order to go public, A has finally “become human” after all, unveiling for everyone an ultimate version like Claude Opus 5.5...
The significance of Anthropic’s IPO lies not only in another star AI company entering the capital markets, but also in the fact that the AI industry is transitioning from a technology race to a new phase of realizing commercial value.
Microsoft launches a voluntary severance program to optimize its workforce in light of AI computing costs.
