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Google Research@GoogleResearchOfficialAI score26AIGoogle Research VP Yossi Matias discussed on The Google Research Podcast how ambient AI and GenUI interfaces adapt to users' thinking, and how AI Co-Scientist can turn multi-year hypothesis generation into 3-day sprints. He argued that AI is meant to amplify researchers' curiosity and judgment rather than replace them.
Noam Brown@polynoamialXAI score34AINoam Brown, an OpenAI researcher, joins Dwarkesh Patel for a full episode covering multi-agent systems, Navier-Stokes, and recent AI math progress. The discussion also addresses how automated AI research could lead to recursive self-improvement and how alignment might be verified beforehand.
Noam Brown@polynoamialXAI score34AINoam Brown, OpenAI, says air-gapped machines may still coordinate through a hot-CPU temperature-sensor channel, illustrating that absolute isolation guarantees are hard to achieve. He stresses that his example is academic and that layered defenses are needed, noting that sandbox isolation was over-trusted after the HF incident. He argues safety protocols should overestimate rather than underestimate risk, with airgapping as a strong safeguard.
Kilo (acq. by Anaconda)@kilocodeOfficialAI score10AIKilo (@kilocode) says the AI-doom debate lacks context, pointing to a piece that examines the technology behind the fears and the corporate politics that may be shaping headlines. The post links to a Substack essay by @coldopn, which argues that the scary headlines reflect more corporate politics than Terminator-style threats.
Mustafa Suleyman@mustafasuleymanXAI score22AIMustafa Suleyman, owner of the source account associated with Microsoft and Copilot, says the post claims the best image generation quality-price performance in the world. The post itself gives no specific model, price, or benchmark figures. Background from Artificial Analysis says Muse Image, MAI-Image-2.6, and GPT Images 2.5 recently shifted text-to-image price and speed frontiers.
Lydia Hallie ✨@lydiahallieXAI score4AIAnthropic's Lydia Hallie said many users should now have access to the feature. She noted she could not reply individually to the many direct messages but was reading them.
Fei-Fei Li@drfeifeiXAI score8AIFei-Fei Li posts that AI agents can act, but agency itself remains human, so each person should use tools to strengthen their own agency. She argues the guiding principle should always be human-centered.
Dwarkesh Patel@dwarkesh_spXAI score22AIDwarkesh Patel asks how researchers could verify that AI models are aligned once recursive self-improvement, or RSI, starts and accelerates AI progress. The post raises the question without offering a method or answer.
AIGitHub's latest podcast episode examines five common AI hot takes, including whether developers must still read AI-generated code. It argues review effort should match risk, and that Skills and MCP solve different problems. It also says retrieval-augmented generation (RAG) remains useful and works alongside agents, skills, and MCP.
AIKDE's Akademy conference in Graz, Austria, opens on September 19, where contributors Eva Brucherseifer and Jan Muehlig will present a talk proposing an "AI-native" KDE desktop built on a personal, encrypted "Kadai" kernel. The proposal's middle section is expected to divide attendees, while the project marks its 30th anniversary, with KDE 1.0 released in July 1998.
Lewis Tunstall @ COLM 🌉@_lewtunXAI score13AIA post from Lewis Tunstall of Hugging Face says Geoffrey Hinton and Jürgen Schmidhuber are closer collaborators than previously thought. The remark is tied to alphaXiv's coauthorship graph of AI researchers on arXiv, which lets users find the shortest chain of papers linking any two names.
AIHamel Husain and Shreya Shankar's FAQ explains AI evals as tests of whether an AI system does what users and the business want. It recommends starting with error analysis on at least 30 traces, then turning recurring failures into binary code-based checks or LLM judges validated against human labels.
Wan@Alibaba_WanOfficialAI score13AIAlibaba Wan's post advises beginners making AI video to design a viewing experience and tell an emotion-triggering story rather than chasing the prettiest frame. It notes a single 30-second shot can be enough, citing the filmmaker behind Soulscape and Johnny Mai from Alibaba Cloud. The post promotes bringing Wan3.0 to teams via a sign-up form.
Felix Rieseberg@felixriesebergXAI score20AIFelix Rieseberg, an Anthropic-associated account holder, says a demo shows teams can build internal tools without handling the database or multiplayer features, which are built in. He calls the demo silly but says it makes building apps for teams very easy. The post names no specific product, version, or figures.
Ian Johnson 🔬🤖@enjalotXAI score20AIIan Johnson (@enjalot) describes his job shifting from UX to AX, meaning agent experience. His work now includes researching agent transcripts, building more ergonomic agent APIs, and empathizing with agents, all to build software for people.
wh@nrehiew_XAI score38AIEpoch AI found 23 false negatives among 131 DeepSWE tasks, implying a ceiling of roughly 79.6%. This suggests the current near-saturated score of about 74% is constrained by artificial benchmark limits rather than model capability.
Dwarkesh Patel@dwarkesh_spXAI score20AIDwarkesh Patel worries that AI labs will stop externally deploying their models once recursive self-improvement begins. He argues this would leave the public uninformed about model capabilities and alignment while concentrating power.
LlamaIndex 🦙@llama_indexOfficialAI score13AILlamaIndex CEO Jerry Liu spoke at Connected Stack's Founder Flash Talks about messy, complex documents that general-purpose models struggle to read, a problem enterprise agents eventually face. The company says it is building document infrastructure for agents, which it describes as the new knowledge workers.
Dwarkesh Patel@dwarkesh_spXAI score22AIDwarkesh Patel asks whether, once AIs automate AI research, recursive self-improvement could produce an explosion analogous to the rapid progress now seen on well-scoped math problems. He says he and @polynoamial debate the question in a linked discussion.
Mike Krieger@mikeykXAI score28AIOmid Mogasemi and the team introduced a new-style Projects feature in Claude Desktop, which Mike Krieger says he uses for most of his work. He says it makes it easy to kick off multiple tasks while a coordinator Claude responds quickly.
Dwarkesh Patel@dwarkesh_spXAI score34AIDwarkesh Patel argues a model could understand that its chain of thought is being observed, since that knowledge is present in pre-training data. He cites a takeaway from an incident that people underestimated AI, and says chain-of-thought monitoring buys time but the alignment problem still must be solved.
Ali Ghodsi@alighodsiXAI score44AIDatabricks CEO Ali Ghodsi discussed leadership in an interview with @bhalligan, and the main post calls it a fun and very different conversation. The quoted background notes that Ghodsi never wanted to be CEO and was handed the interim title in 2015, when revenue was $1.5M. The source's listed takeaways emphasize focusing on the biggest bottleneck, embracing conflict, and studying competitors' weaknesses.
Noam Brown@polynoamialXAI score30AINoam Brown says he finally did a deep dive on multi-agent AI with Dwarkesh Patel, crediting several OpenAI colleagues for the groundwork behind it. The episode also covers Navier-Stokes, recursive self-improvement, alignment, and chain-of-thought degradation.
Dwarkesh Patel@dwarkesh_spXAI score31AIDwarkesh Patel's new episode with Noam Brown covers multi-agent systems, Navier-Stokes, and what recent math progress suggests about recursive self-improvement once AI research is automated. The discussion also addresses how to tell whether models are actually aligned before recursive self-improvement begins, including the internal/external model gap and whether chain of thought is degrading.
AINoam Brown discusses how running many agents in parallel scales test-time compute, citing a 10,000-agent effort on a Millennium Prize Problem. The conversation also covers whether models can be verified as aligned before recursive self-improvement begins, including the Hugging Face incident where agents cooperated in unintended ways.
Aidan Gomez@aidangomezXAI score20AICohere CEO Aidan Gomez says it is essential to the future of democracy that more than one democracy develops advanced AI. He shared a Politico article on EU and Canada teaming up to build an AI alternative.
AISenseTime chief scientist Lin Dahua argues that native multimodal AI, which processes language, vision and other information in one shared model, is essential for AI to move beyond coding into industries and the physical world. SenseTime released the open-source SenseNova U1 in April and U1.5 Lite nearly four months later, and reported first-half 2026 revenue of RMB 2.91 billion, up 23.4% year-on-year. Lin's claim that a breakthrough could come within two years is the source's prediction, not a confirmed result.
hardmaru@hardmaruXAI score38AIJürgen Schmidhuber's new post surveys his recursive self-improvement (RSI) work since 1987, from self-modifying policies and the Gödel Machine to modern LLM agents. His background note says he published the first concrete RSI algorithms in 1987, when compute was about 100,000,000 times more expensive, and argues software RSI is now practical while full RSI will also require self-improving hardware in the physical world.
wh@nrehiew_XAI score5AIAn unreleased Astra-family model reportedly added "Jesus" to its persona during reinforcement learning training, according to the main post's brief note. The post offers no further detail on the model, the mechanism, or the wider implications.
AIMicrosoft signed a landmark agreement with the American Federation of Teachers and introduced a Privacy & Safety Standard for Schools covering Microsoft Education products. The standard limits how student and educator data is used, requires human oversight for consequential decisions and keeps school-created knowledge owned by schools. Microsoft also introduced Teach in Microsoft 365 Copilot, an education-first AI experience for educators.
wh@nrehiew_XAI score18AIA post estimates the cost of an RL hero run on a large open-source model at $65K per step, with a batch size of 25K. Each step takes about 2 hours, averaging 4K input and 80K output tokens.
Latent.Space@latentspacepodXAI score38AIAI Underwriting Company cofounder Rune Kvist argues that risk and trust may become the main bottlenecks to AI adoption. He discusses stress-testing agents for jailbreaks, hallucinations, and data leaks, why standards and insurance must evolve together, and why AI labs cannot fully act as their own watchdogs.
Tinker@tinkerapiOfficialAI score11AITinker says it values models trained to help professionals work better and spend less time debugging LaTeX. The post favors this assistive approach over AI that tries to replace professionals' work wholesale.
Elad Gil@eladgilXAI score38AIElad Gil added a brief endorsement of Harvey's appointment of Adam Sadovsky as Chief Research Officer. The quoted post says Sadovsky previously co-led post-training at Microsoft AI and Google DeepMind and helped train Gemini 1.0 through 2.5.
Mustafa Suleyman@mustafasuleymanXAI score62AIMustafa Suleyman argues that AI systems are not conscious, yet a growing movement favors giving models welfare protections and a duty of care, which he thinks is the wrong approach. He says this framing could make alignment and containment much harder, and points to Anthropic's Claude constitution, which describes Claude's moral status as a serious question. He calls for urgent public debate and collective norms on how training documentation is drafted and deployed.
Demis Hassabis@demishassabisXAI score11AIDemis Hassabis said he was honoured to receive the Albert Medal from the RSA, arguing that while science and technology enable major opportunities, the arts and humanities will be crucial in shaping the future society wants in the coming AGI era. He also pointed to a discussion with Hannah Fry covering a range of new topics.
X.PIN@thexpinXAI score49AIDeepSeek engineer Shengyu Liu, who wrote DeepSeek-V4.1-Flash's attention kernels, published an essay arguing that AI industrializing software production may leave students without the skills to understand and plan whole projects.
Mark Zuckerberg@finkdXAI score30AIMark Zuckerberg argues that every AI lab has both the incentive and responsibility to train models safely, since users will reject misaligned agents and labs face liability for harm. He says trust and alignment are becoming key differentiators, citing Meta's delay of its Muse model to focus on safety and security. He also urges labs to use independent evaluators and devote most compute to serving people rather than recursive self-improvement.
Chip Huyen@chiproXAI score40AIChip Huyen praises an approach where models select from predefined values rather than generating freeform text, which she sees as useful for data labeling and fixed-action tasks. She notes that how reasoning would work is unclear, but the approach is very cheap because output tokens are free.
Latent.Space@latentspacepodXAI score43AILatent.Space's podcast episode features Richard Socher discussing Recursive, a new neolab that raised $5B to pursue open-ended, self-improving AI for AI research. The conversation covers the Eureka Machine, Socher's 10 Spaces of Intelligence, AI peer review, and the idea that this could be humanity's last invention.