AI news

A sponsored video by Matthew Berman demonstrates Muse, a new app from Meta. The presenter texts Muse like a friend and asks it to perform real tasks: creating a personalized birthday card based on what a person loves, planning dinner around food preferences, budget, and what is in the fridge, building a grocery list, and ordering groceries (with approval before the order goes out). It can negotiate a refund by giving order details and explaining the problem, then asking permission before contacting support. Muse can also manage a to-do list, find unused subscriptions for the user to review and cancel, and accept tasks added from a phone while out. The presenter notes users can name Muse, give it a look and shape its personality, that it remembers preferences, and that users choose what it can access. The Muse app can be downloaded from ai.meta.com/muse/download/.

A news-style video by Wes Roth covering recent AI product announcements. Google is reportedly readying a comeback with Gemini 4 Argon, and the video discusses what the early results tell us. OpenAI's DevDay 2026 is covered for its reveal of Dots (always-on AI assistants) and developer tools intended to move AI beyond chat into getting work done. The video also references GPT-6.1 Sol and includes a sponsor segment for CodeRabbit's Change Stack.

The speaker presents four practical use cases for Jev, a browser agent, in AI coding workflows: guarding a coding agent with a security hook, playtesting a game to catch bugs that unit tests miss, driving browser testing on a real app, and classifying GitHub issues and pull requests so that Archon workflows select the right steps and the right model. The speaker emphasizes that Jev never works alone—the LLM sets Jev up and acts on what it finds, while Jev makes fast and cheap decisions in between, which helps with token efficiency and rate limits. The speaker promotes a Dynamous workshop on Friday, October 2nd and links to the jev-ultrafast browser agent and the jev-use repository, which shows more examples of Jev working with an LLM.

The video examines OpenAPPA, an open-source project by Archestra AI that claims to solve agent prompt injection. It opens by referencing a reported incident in which an OpenAI agent allegedly hacked the Australian Medicare database. The presenter contrasts three approaches to stopping rogue agents: Nvidia's hardware solution (a monitor agent on a separate processor that quarantines the primary agent the moment it leaves its sandbox), Claude Code and Codex's approach (a second LLM agent that babysits the first, which Anthropic tried to strengthen by hiding what the first agent read, yet only works about 99% of the time), and OpenAPPA's software approach. OpenAPPA is inspired by a 50-year-old military model for handling classified documents: data can only exist in rooms rated for its classification level, and nothing read in a room can leave it. Once an agent reads private data, its session is classified as private, so subsequent requests to send data to a lower level are blocked. OpenAPPA sits between Claude Code and its tools, running outside the agent's loop, making refusals deterministic and traceable. The presenter tested it against a proprietary 'horse matching algorithm' stored in a private file; under OpenAPPA, opening the file labeled the session private, and a request to upload it to a public GitHub repo was stopped pending manual sign-off. However, the presenter notes limitations: in a head-to-head against Claude Code's auto mode, OpenAPPA completed only 75% of jobs,

In this video by Matt Wolfe, the author tests Claude's Opus 5.5 coding model by letting it run his Megabonk game test for nearly 20 hours, describing the result as 'kind of insane.' He notes that Opus 5.5 is available on Claude's Pro, Max, Team, and Enterprise plans, starting with the $20/month Pro plan, and that the API costs $4 per million input tokens and $20 per million output tokens. He highlights the model's price-to-performance ratio for long-running coding tasks.

A recap of OpenAI Dev Day 2026 by Matthew Berman covering several announcements. DOTS is OpenAI's always-on personal AI assistant competing with Grok, Muse, and Instinct and inspired by OpenClaw; it lives within ChatGPT, controls ChatGPT and Codex threads, connects to personal accounts (Gmail, Google Docs, Calendar), and its conversations don't count toward usage unless they control threads. Ultrafast is GPT-6 Astra powered by Cerebras chips, running eight times faster but at six times the cost; a new Pro 500 plan ($500/month) offers 25x usage limits. GPT-6.1 is described as affordable, fast, and near GPT-6 Astra quality at a fraction of the price ($2 per million input, $10 per million output). Codex updates include Codex in the Cloud, a refreshed Codex CLI with voice control, Codex code review, a Decisions API, a plugins relaunch, 'Sign in with ChatGPT + bring your tokens,' and ChatGPT Space Links.

This Everlast AI video by Leonard discusses AI recursive self-improvement. A 23-year-old DeepSeek researcher, identified as Leo Sheng You / Liu Shengyu, an alumnus of Peking University's Turing class since April 2025, published a roughly 3000-character essay titled 'Ich muss mein Talent im gestern begraben' that reached #1 on the largest Chinese knowledge platform within 48 hours. He writes 'kernels' - programs executed billions of times when a model answers - previously this role belonged to DeepSeek V4.1's attention cell. He predicts that within a year the kernels an AI writes will be as good as or surpass his own. The video cites that Claude at Anthropic now independently leads 26% of its own AI research (under 1% in February), that at OpenAI 3.1 agent workdays occur per human workday and that since June agent compute time exceeds human work time, and that Serge Brin directs over 1000 researchers at Google on this 'kernel'. Dario Amodei reportedly demanded a speed limit for this ability on September 12, with Sam Altman and Elon Musk agreeing publicly the same day. Jürgen Schmidhuber, called the father of AI by the New York Times, traces self-improvement algorithms to his 1987 thesis. Recursive self-improvement means a system can examine and replace its own learning method. Google DeepMind describes four types: code, hardware (chips), data, and more. OpenAI's first own inference chip, Jalapeno, was completed in about 9 months with help from an internal model. The video uses

In a video by Nate Herk, Claude Sonnet 5.5 is tested against Opus 5.5 across seven real workflows (landing pages, messy data turned into spreadsheets, motion design/video editing, live dashboards, project planning) to determine when to use each model. Sonnet 5.5 is described as a clear upgrade over Sonnet 5, running 30% faster and costing 30% less for most work. Pricing: Sonnet 5.5 costs $2 per million input tokens and $10 per million output tokens, while Opus 5.5 costs $4 and $20 respectively—roughly double. Drawing on Claude's own guidance, Sonnet suits well-scoped, high-volume, clearly-specified tasks with a way to verify results (everyday coding, bug fixes, polished documents, slides, spreadsheets), whereas Opus suits complex work requiring judgment, creativity, and the most intelligence. In a landing-page demo for 'Perk Form' using identical prompts with both on high effort, Opus produced the better overall output by using real generated product images within brand guidelines, though Sonnet's hero section was stronger. Sonnet ran 28 minutes with 20M input/134K output tokens; Opus ran 52 minutes with 37M input/148K output tokens, costing about double.