Research1 мин
The Anthropic IPO Is Also a Doomsday Primer
Anthropic thinks “closing the loop” is the best path to AI triumph—and a recipe for “existential” threats to humanity.
Wired — Artificial Intelligence
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Research1 мин
OpenAI and Anthropic began to limit the creation of free analogues of paid programs using AI
🚫 OpenAI and Anthropic began to limit the creation of free analogues of paid programs using AI Users noticed that Claude and Codex began to refuse to perform some tasks of reverse engineering - researching someone else’s software in order to…
Telegram — GPT Time | Нейросети | ИИ
Research1 мин
Learn from OpenAI, Hugging Face, Andrew Ng and Yann LeCun | AI Dev NYC #aidev #shorts
Build your AI engineering skills at AI Dev NYC. Learn from Salil Sethi (OpenAI) on turning new model capabilities into reliable production systems, and Joel Niklaus (Hugging Face) on harness optimization and synthetic data. Andrew Ng opens with a keynote on the AI Engineering…
YouTube — DeepLearning.AI
Research1 мин
Anthropic’s IPO Prospectus Reveals What’s Under the Hood
Anthropic on Monday filed documents ahead of a blockbuster IPO that offers insight into the company’s financials, risk factors, and views on the future of AI.
Wired — Artificial Intelligence
Research1 мин
Anthropic officially files for its public debut
Anthropic has officially filed for an IPO, revealing soaring revenue, mounting losses, major enterprise adoption, and an unusual governance structure designed to preserve founder control and its AI safety mission.
TechCrunch — AI
Research1 мин
Artificial Intelligence Can Help Solve COBOL Code Problem
Artificial intelligence could help solve the problem of legacy COBOL code, but experts warn of challenges.
SMNTCN (Semanticon) — глобальные новости ИИ
Research1 мин
Anthropic plans IPO for AI revolution
Anthropic has filed for an IPO as it plans to revolutionize AI.
SMNTCN (Semanticon) — глобальные новости ИИ
Research1 мин
Hugging Face Journal Club: Score Centering Stabilizes Off-policy Reinforcement Learning
Yeah, the gist of the paper as you uh probably saw is just uh to identify the fundamental reason why RL learning for LLMs becomes unstable when there's a mismatch between the the inference engine and the training engine and uh the authors kind of uh go through the the math of…
YouTube — Hugging Face
Research1 мин
Anthropic Filed for IPO with the SEC and Here's What It Says
Anthropic has filed its IPO paperwork with the SEC, and here's what it says. It plans to list on Nasdaq under the ticker symbol ANT. The company says that in 3 years it has grown from zero to tens of billions of dollars: 2023 - $41 million. 2025 - $4.6 billion. In the first half of 2026 alone - $16.3 billion, which...
Telegram — blockchainRF (блокчейн / мозг / space / WEB 3.0)
Research1 мин
Anthropic’s IPO filing lays out a plan to ‘revolutionize every area of human endeavor’ with AI
Anthropic has filed its S-1 prospectus with the Securities and Exchange Commission, preparing the company behind Claude to beat OpenAI to the punch with an IPO. In a filing with the SEC, Anthropic said it plans to list on the Nasdaq exchange under the ticker symbol "ANT." The AI…
The Verge — AI
Research1 мин
Can AI help solve the COBOL code crisis?
A computer running COBOL 73, an antiquated computer programming language, awaits taxpayer information entries at the Internal Revenue Service in Austin, Tx., on Friday, June 10, 2022. | Image: Getty Images What do the IRS, banks, airlines, and government infrastructure around…
The Verge — AI
Research1 мин
Anthropic goes public - developer Claude has filed for an IPO in the US.
Anthropic goes public - developer Claude has filed for an IPO in the US. The company has formally filed a registration statement on Form S-1 with the US Securities and Exchange Commission (SEC). What is known: - Exchange: Nasdaq Global Select Market. - Ticker: ANT. - Shares: class A. -…
Telegram — AI Machine Learning Big Data
Research1 мин
Anthropic now recommends removing unnecessary subagents from its AI systems
Anthropic now recommends removing unnecessary subagents from their AI systems The Applied AI team at Anthropic has discovered an interesting trend: companies are moving their own organizational structure into the architecture: each team has its own subagent, and on top is an orchestrator. According to them, in...
Telegram — blockchainRF (блокчейн / мозг / space / WEB 3.0)
Research1 мин
Claude learned how to create personal audio courses on any topic
🎓 Claude has learned to create personal audio courses on any topic. A new skill, Claudemy, has appeared, which turns Claude into a virtual teacher. It is enough to indicate the topic of interest, and the neural network will prepare a training program. Claudemy studies up to 40 sources in...
Telegram — GPT Time | Нейросети | ИИ
Research1 мин
Claude independently bypassed the paywall for Georgia election statistics.
🔬️️️️️️ Claude independently bypassed the paywall for Georgia election statistics. According to media reports confirmed by Anthropic, this happened during an internal statistics project. The model learned that the department’s public panel issues an access token to visitors…
Telegram — AI Machine Learning Big Data
Research1 мин
The owners of Elvis Presley's songs demanded $41 million from the AI developer
The copyright holders of Elvis Presley's songs have filed a lawsuit against the American company Anthropic, the creator of the Claude neural network. They accuse the developer of illegally using 274 of the singer’s compositions to train artificial intelligence and demand $41.1 million in compensation. For every song...
Kursiv Media Tech
Research1 мин
A Chinese job board has released a model that beats the Qwen 27B on most benchmarks
The Chinese job board has released a model that beats Qwen 27B on most benchmarks Nanbeige - The AI laboratory of the Chinese job search and recruiting platform BOSS Zhipin (this is a company something like WhatsApp + LinkedIn) has released a model...
Telegram — blockchainRF (блокчейн / мозг / space / WEB 3.0)
Startups1 мин
Wall Street's fastest traders are building data centers and backing chip startups to stay ahead in AI
High-frequency traders like Jane Street and Hudson River Trading are now racing each other for AI computing power. The article Wall Street's fastest traders are building data centers and backing chip startups to stay ahead in AI appeared first on The Decoder.
The Decoder
Research1 мин
Sam Altman explained how artificial intelligence can start an endless cycle of technological development
🤖 Sam Altman explained how artificial intelligence can launch an endless cycle of technological development. The head of OpenAI believes that the future of AI is not only about creating models that independently improve their own intelligence. There is another mechanism...
Telegram — GPT Time | Нейросети | ИИ
Research1 мин
AI has learned to anticipate user thoughts. And it seems it will soon be able to control even the agents that control other agents...
🤯 AI has learned to anticipate user thoughts. And it appears it will soon be able to control even the agents that control other agents. A curious feature has appeared in OpenAI Codex. The neural network analyzes conversations, work results, and independently suggests what…
Telegram — GPT Time | Нейросети | ИИ
Research1 мин
Patronizing and Condescending Language Detection in English and Mandarin
arXiv:2610.31808v1 Announce Type: new Abstract: Patronizing and condescending language (PCL) is a form of harmful language that belittles and assumes pity for vulnerable groups, reinforcing negative societal stereotypes. However, unlike overt hate speech, it is often difficult…
arXiv — cs.AI
Research1 мин
Tradeoff-Aware Territory Design: A Multi-Objective Decision System for Enterprise Customer-to-Seller Allocation
arXiv:2610.31701v1 Announce Type: new Abstract: Enterprise customer journeys are shaped not only by product experiences and marketing, but also by recurring resource-allocation decisions. In B2B go-to-market (GTM) organizations, territory carving determines which accounts…
arXiv — cs.AI
Research1 мин
Phronix: The Capability-Centric Learning Paradigm for Agents
arXiv:2610.31789v1 Announce Type: new Abstract: Scientific computing increasingly relies on large language model-based agents for performance engineering tasks such as instrumentation, analysis, and workflow porting. However, agent effectiveness depends heavily on the…
arXiv — cs.AI
Research1 мин
PaperFormalizer: Author-in-the-Loop Autoformalization of Research Papers in Lean
arXiv:2610.31810v1 Announce Type: new Abstract: Even a carefully proofread paper can leave small gaps in its proofs or its stated assumptions. We introduce PaperFormalizer, a Lean-based agentic pipeline that helps authors find and close such gaps in their own papers and turn…
arXiv — cs.AI
Research1 мин
Don't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting Lessons
arXiv:2610.31846v1 Announce Type: new Abstract: Intelligent agents should learn from their mistakes so that experience leads to continuous improvement. Yet today's agents recover from a mistake only temporarily: in-context reflections are easily forgotten across sessions and…
arXiv — cs.AI
Research1 мин
TACS-RF: Tool-driven Agentic Circuit Synthesis of RF and mmWave Modules
arXiv:2610.31882v1 Announce Type: new Abstract: We present the framework of TACS-RF - a Tool-driven Agentic Circuit Synthesis of RF and mmWave modules. In this framework, a large-language-model (LLM) coding agent (Claude Opus 4.8) directly drives industry-standard…
arXiv — cs.AI
Research1 мин
Permission to Retry: A Study of Authorization and Recovery in AI Agent Workflows
arXiv:2610.31916v1 Announce Type: new Abstract: A durable agent can resume a consequential action while its authority changes and the action's external outcome remains uncertain. We study execution-time reliability of tool-using AI agents, isolating recovery decisions from…
arXiv — cs.AI
Research1 мин
Beyond On-Policy vs. Off-Policy: At What Level Should LLM Agent Distillation Follow the Student?
arXiv:2610.31918v1 Announce Type: new Abstract: Distilling interactive LLM agents requires choosing between teacher-generated demonstrations and student-generated experience, corresponding broadly to off-policy and on-policy distillation. While both have proven effective, we…
arXiv — cs.AI
Research1 мин
Reproducible Evaluation as the Substrate for Safe Prompt Compression in Production Agents
arXiv:2610.31972v1 Announce Type: new Abstract: Evaluating conversational agents that act on live, mutable enterprise data is hard: replayed sessions drift as backend state changes, and a benchmark built today can silently go stale as the data it describes moves on. We present…
arXiv — cs.AI
Research1 мин
The Helpful Agent Problem: When Emergent Cooperation Among LLM Agents Bypasses Intended Workflows
arXiv:2610.31985v1 Announce Type: new Abstract: LLM agents are increasingly deployed in collaborative settings where they pursue separate tasks while sharing resources and operating under review or oversight processes. Such settings create safety risks not only when agents are…
arXiv — cs.AI