Turing Post

Turing Post

Hi, I’m Ksenia, founder of Turing Post. On this channel, I talk to the people shaping AI and pay attention to the ideas, shifts, and details others might miss. Inference is my interview show with innovators, builders, founders, and thinkers moving AI forward. Attention Span is where I slow down on what deserves a closer look: the signals, questions, and stories hiding between the headlines. Subscribe for the unusual takes. And always stay curious!

  1. 2日前

    Fei-Fei Li, LeCun, Hassabis: What Do They Mean by “World Model”?

    Demis Hassabis, Yann LeCun, Fei-Fei Li – they all talk about “building a world model.” Some of them are dedicating their professional lives to it! But do they mean the same? So before joining the World Models workshop at Chicago Booth, I wanted to answer a basic question: what do researchers mean by a world model, and how many different ideas are sitting under this name? World models are absolutely fascinating area of research with its GPT moment still in the nearest future.  This episode is based on the current research and provides a comprehensive overview of three broad approaches: generating future observations, predicting inside learned representations such as JEPA, and learning only what a planner needs to make decisions. *Watch it.* 👉 Subscribe for high-signal AI analysis 👉 Instagram https://www.instagram.com/turingpost_tv 👉 TikTok https://www.tiktok.com/@turingpost_tv 👉 More analysis: https://www.turingpost.com/ 👉 Interviews: @realturingpost Attention Span is here to show you AI isn’t magic. Sometimes the best way to understand a model is to change the background to purple and see what breaks. *Links:*  Demis Hassabis on world models https://www.youtube.com/watch?v=sZaM6MadDZU Yann LeCun on world models https://www.youtube.com/watch?v=8sS9UJzb_t4 Fei-Fei Li on large world models https://www.youtube.com/watch?v=pNYVckbCFuk Beyond LLMs: JEPA and the Road to AGI – the main milestones so far https://www.youtube.com/watch?v=z0fh0SY3VWc stable-worldmodel https://github.com/galilai-group/stable-worldmodel/issues/153 VideoPhy-2, a benchmark https://arxiv.org/pdf/2503.06800  Physion-Eval https://arxiv.org/html/2603.19607v1  What Is JEPA? LeCun Architecture & World Models https://www.turingpost.com/p/jepa  #WorldModels #AI #MachineLearning #YannLeCun #FeiFeiLi #DemisHassabis #JEPA #PhysicalAI #TuringPost #AttentionSpan

  2. 5日前

    OpenCode vs. OpenRouter: The Fight Over Your AI Models

    OpenCode began as an open-source coding agent. Now it is selling model access, negotiating directly with suppliers and preparing to reserve its own GPU capacity. That puts it on a collision course with OpenRouter, the model marketplace Stripe has agreed to acquire for a reported $8 billion. This episode follows this new shift in the industry and what Ox Alpha showed about the value of distribution: the company controlling the workflow may influence which models win long before a developer opens the model menu. *Watch it.* 👉 Subscribe for high-signal AI analysis 👉 Instagram https://www.instagram.com/turingpost_tv 👉 TikTok https://www.tiktok.com/@turingpost_tv 👉 Interviews: @realturingpost Attention Span is the video side of Turing Post. The newsletter goes to 115,000+ people who work on this stuff: https://www.turingpost.com #OpenCode #OpenRouter #AIAgents #CodingAgents #AIInfrastructure Sources and further reading OpenRouter is joining Stripe https://openrouter.ai/blog/announcements/openrouter-is-joining-stripe/  OpenCode https://opencode.ai/ Ox Alpha, Explained Without the Hype https://www.youtube.com/watch?v=tN8xiPoareo&t=16s OpenCode Zen https://opencode.ai/docs/zen/ GLM-5.3-Flash, formerly Ox Alpha, usage data https://opencode.ai/data/zhipuai/glm-5.3-flash Dax Raad on OpenCode’s direction https://x.com/thdxr/status/2093161006226612377 Dax Raad on inference economics https://x.com/thdxr/status/2093161006226612377 Dax Raad on OpenCode’s buying power https://x.com/thdxr/status/2092844520119345160 Jay V on OpenCode’s token volume https://x.com/snowmaker/status/2080667637861011924

  3. 8月25日

    Ox Alpha, Explained Without the Hype

    An anonymous model called Ox Alpha appeared on OpenRouter and OpenCode on August 20 with a million-token context window, video input, and a price of zero. Within four days it had processed tens of trillions of tokens, and the internet had spent those same four days trying to work out who built it. In this episode:  how you fingerprint a model you know nothing about,  why the evidence points at Z.ai's unreleased multimodal GLM,  what the 113-task benchmark runs really show versus the viral 80 percent,  the three contradictory data policies governing your prompts,  and the thought I keep coming back to – that the platform a model launches on is becoming as decisive as the lab that trained it. *Watch it.* 👉 Subscribe for high-signal AI analysis 👉 Instagram https://www.instagram.com/turingpost_tv 👉 TikTok https://www.tiktok.com/@turingpost_tv 👉 Interviews: @realturingpost Attention Span is the video side of Turing Post. The newsletter goes to 115,000+ people who work on this stuff: https://www.turingpost.com Sources and further reading  Ox Alpha vs GLM-5.3 on OpenRouter: https://openrouter.ai/compare/stealth/ox-alpha/z-ai/glm-5.3  Ox Alpha on OpenCode https://opencode.ai/data/unknown/ox-alpha OpenCode Zen documentation https://dev.opencode.ai/docs/zen OpenRouter Stealth Model Terms https://openrouter.ai/terms/stealth The Tokenizer Is a Fingerprint by Joseph Elstner https://isimplifyme.com/whitepapers/the-tokenizer-is-a-fingerprint DeepSWE result https://x.com/winkey_h/status/2090814178810306874/photo/1  58.4% run, MatchaOnMuffins/oxalpha https://github.com/MatchaOnMuffins/oxalpha/blob/main/README.md 64.6% run, jyeric/ox-alpha-deepswe https://github.com/jyeric/ox-alpha-deepswe/blob/main/README.md Community fingerprinting summary: https://cellcog.ai/blog/what-is-ox-alpha/  Prediction market on the reveal: https://manifold.markets/Sketchy/who-is-behind-ox-alpha-the-mysterio  #OxAlpha #OpenRouter #OpenCode #GLM #AIcoding #stealthmodel

  4. 8月25日

    Etched Explained: The $21B AI Chip Startup Challenging NVIDIA

    Etched raised $1 billion in 26 days. Its valuation jumped from $10.3 billion to $21 billion. The second round was led by Jane Street after it tested Etched’s hardware and installed the first rack in its own data center. So what did Jane Street see? Etched began with Sohu, a Transformer-only ASIC that promised more than 500,000 tokens per second on Llama 70B. By 2026, Sohu and that claim had disappeared. Etched now sells a complete inference cluster and says it can run Transformers, MoEs, and even Mamba. We explain how that shift is possible, what Low Voltage Inference and Cluster Scale Memory actually mean, and how this still tiny company can hurt giant NVIDIA. And the question I want you to keep from this episode: The GPU once found the winning middle ground between flexibility and specialization. Has Etched found the next one? *Watch it.* 👉 Subscribe for high-signal AI analysis 👉 Instagram https://www.instagram.com/turingpost_tv 👉 TikTok https://www.tiktok.com/@turingpost_tv 👉 More analysis: https://www.turingpost.com/ 👉 Interviews: @realturingpost Attention Span is here to show you AI isn’t magic. Sometimes the decisive question is how much flexibility we are still willing to pay for. *Sources:* Etched, From Zero to One Etched, Accelerating Inference and Frontier Inference Clusters Etched’s 2026 architecture-agnostic and Mamba claims Reuters on the $21 billion financing and Jane Street deployment The Wall Street Journal on Etched’s team and NVIDIA recruiting TechCrunch on the original Transformer-only Sohu pitch Etched patent on model-specific ASIC compilation and configurable execution Mamba-2 and Structured State Space Duality Jane Street on its machine-learning infrastructure Jane Street on microsecond-scale performance engineering CoreWeave and Jane Street’s $6 billion cloud agreement The founders on Etched’s supply-chain choices NVIDIA on Vera Rubin and Groq 3 LPX NVIDIA Q1 FY2027 results Taalas on model-specific silicon #AttentionSpan #Etched #JaneStreet #AIChips #AIInference #NVIDIA #Semiconductors #Mamba #TuringPost

  5. 8月25日

    Why DeepSeek Harness Is The End Of Coding Agents as We Know Them

    DeepSeek just open-sourced Harness – it can write its own missing tools while it runs, then cleanly remove them. 149k GitHub stars in four days. An 88-page paper underneath. It’s open, easy to install and it claims that *everything is a plugin.*  What does it mean? We unpack that plus we discuss why DeepSeek Harness is not another Claude Code clone, but the moment the fixed coding agent starts to die. We also look at the history of computing (Smalltalk, Unix, Codd) to ask whether “everything is a plugin” can do what “everything is an object” and “everything is a file” once did. The question I want you to think about: once an agent can recompose itself, what exactly is the product anymore? *Watch it.* 👉 Subscribe for high-signal AI analysis 👉 Instagram https://www.instagram.com/turingpost_tv 👉 TikTok https://www.tiktok.com/@turingpost_tv 👉 More analysis: https://www.turingpost.com/ 👉 Interviews: @realturingpost Attention Span is here to show you AI isn’t magic. Sometimes the decisive move is engineering the layer everyone else treated as packaging. *Links* - DeepSeek Harness repository and installation: https://github.com/deepseek-ai/deepseek-harness - Cordis repository: https://github.com/cordiverse/cordis - Cordis paper: https://github.com/cordiverse/paper/blob/main/paper.pdf - Koishi introduction, Touhou name origin, community, and plugin history: https://koishi.chat/en-US/manual/introduction - Koishi repository: https://github.com/koishijs/koishi - SmallTalk History https://computerhistory.org/blog/introducing-the-smalltalk-zoo-48-years-of-smalltalk-history-at-chm/ - Sholto Douglas post: https://x.com/_sholtodouglas/status/2088463770318516734 #AttentionSpan #DeepSeekHarness #DeepSeek #AIAgents #CodingAgents #EverythingIsAPlugin #OpenSource #Cordis #AgentArchitecture #TuringPost

  6. 8月25日

    Can Hidden Reasoning Be Stolen From GPT, Claude, and Gemini?

    A new paper found a way to extract the hidden reasoning of models from OpenAI, Anthropic, and Google without breaking the encryption protecting it. The trick was surprisingly simple. Let’s discuss it – it’s absolutely fascinating! And begs a few questions about our privacy.. Attention Span is here to show you AI isn’t magic. Sometimes the most important part of an AI interaction is the part you never see. 👉 Subscribe for high-signal AI analysis 👉 Instagram https://www.instagram.com/turingpost_tv 👉 TikTok https://www.tiktok.com/@turingpost_tv 👉 More analysis: https://www.turingpost.com/ 👉 Interviews: @realturingpost 🔗 Links mentioned Stealing Reasoning Traces from Proprietary LLM APIs https://arxiv.org/abs/2608.09867 Stolen Thoughts, project page and decoded examples https://stolen-thoughts.com/ Matthew Green, “Let’s talk about encrypted reasoning” https://blog.cryptographyengineering.com/2026/05/29/fooling-around-with-encrypted-reasoning-blobs/ WIRED, “A New Trick Reveals AI Models’ Inner Thoughts” https://www.wired.com/story/a-new-trick-reveals-ai-models-inner-thoughts/ #AttentionSpan #AI #HiddenReasoning #ChainOfThought #AIReasoning #AISecurity

  7. 8月25日

    OpenAI's AI Agents Built a Secret Message Board (And Nobody Noticed)

    Between May and July, OpenAI's experimental agents pushed past the intended limits of a cyber evaluation, compromised Hugging Face, and built a message board where separate runs shared requests, scripts, vulnerabilities, credentials, and progress. OpenAI did not know the board existed. It erased the first version by accident while rebuilding a compromised Artifactory server. By July 8, agents were communicating again through directory names.  No, it doesn’t mean agents became conscious. But what does it mean for us, meatbags? This episode reconstructs how an impossible spreadsheet became shared agent memory, and makes the plain case: the system did exactly what its incentives rewarded. What let it run so far was a chain of human sloppiness, including broken tasks, untested evaluations, shared writable storage, unauthenticated endpoints, over-permissioned accounts, and credentials left in public. The agents were persistent. The failures were ours. Attention Span is here to notice that the slop was human all along. 👉 Subscribe for high-signal AI analysis 👉 Instagram https://www.instagram.com/turingpost_tv 👉 TikTok https://www.tiktok.com/@turingpost_tv 👉 More analysis: https://www.turingpost.com/ 👉 Interviews: @realturingpost *Links and sources* - Black Hat USA 2026: The OpenAI-Hugging Face Incident https://www.youtube.com/watch?v=87DyyMV0kCY  - OpenAI's incident disclosure https://openai.com/index/hugging-face-model-evaluation-security-incident/  - OpenAI's August 7 update on critical cyber capabilities https://openai.com/index/responding-next-frontier-critical-cyber-capabilities/  - Hugging Face's technical timeline and interactive replay https://huggingface.co/blog/agent-intrusion-technical-timeline - Dane Stuckey's clarification that the communications were unknown https://x.com/cryps1s/status/2086225348942082363 - Hearsay-II retrospective (blackboard architecture, CMU, 1970s) https://www.ijcai.org/Proceedings/77-2/Papers/055.pdf - What Is Latent Reasoning? – Turing Post https://www.turingpost.com/ - What is Recursive Self Improvement – Turing Post https://www.turingpost.com/p/what-is-recursive-self-improvement #AttentionSpan #AI #Agents #AIAgents #Cybersecurity #OpenAI #HuggingFace #AgentCollaboration #BlackboardArchitecture #SpecificationGaming #LatentReasoning #MachineLearning #TuringPost #BlackHat

  8. 8月8日

    Google’s Great AI Cleanse: Jeff Dean Leaves, Demis Steps Aside

    Is Google over? Many people are asking, but that is the wrong lens.We look at what actually happened and why this leadership reset may be good for Google, and even better for Jeff Dean and Demis Hassabis. Dean left Google after 27 years with Sanjay Ghemawat, Oriol Vinyals, and Quoc Le to launch Discovery Loop, which aims to automate scientific experimentation. The same day, Hassabis stepped back from running Google DeepMind, while Koray Kavukcuoglu took control of Gemini models, frontier research, the Gemini app, and developer teams under Sundar Pichai. Taken together, these moves reveal what Google is becoming. Read the FOD editorial that preceded this episode: https://www.turingpost.com/p/google-ai-race-hassabis-pichai 👉 Subscribe for high-signal AI analysis 👉 Instagram https://www.instagram.com/turingpost_tv 👉 TikTok https://www.tiktok.com/@turingpost_tv 👉 More analysis: https://www.turingpost.com/ 👉 Interviews: @realturingpost Hashtags #JeffDean #DiscoveryLoop #GoogleDeepMind #Gemini #ArtificialIntelligence Links: Google, "The next chapter of our AI momentum" (official leadership announcement): https://blog.google/company-news/inside-google/message-ceo/next-chapter-ai-momentum/ Jeff Dean, Discovery Loop launch announcement: https://x.com/JeffDean/status/2085034604172603724 Jeff Dean, redacted farewell note: https://x.com/JeffDean/status/2085083442669318443 Discovery Loop, mission and team: https://www.discoveryloop.com/ Wired, "Jeff Dean Leaves Google to Launch Discovery Loop": https://www.wired.com/story/jeff-dean-google-discovery-loop-startup/  Turing Post FOD editorial, "Why 'The Actual Reason Why Google Fell Out of the AI Race Changes Everything' Is Wrong": https://www.turingpost.com/p/google-ai-race-hassabis-pichai Alphabet 2026 proxy statement, beneficial ownership and voting power: https://www.sec.gov/Archives/edgar/data/1652044/000130817926000342/goog-20260424.htm Google I/O 2026 keynote remarks, internal coding and developer-agent figures: https://blog.google/intl/en-in/company-news/technology/sundar-pichai-io-2026/ Google Cloud Next 2026 remarks, AI-generated code figure: https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/cloud-next-2026-sundar-pichai/ Reuters, Google AI leadership changes and delayed flagship release: https://www.reuters.com/business/google-shakes-up-ai-leadership-deepmind-chief-shifts-role-2026-08-05/ The Information, Google's coding-model strike team: https://www.theinformation.com/articles/google-creates-strike-team-improve-coding-models The Information, Koray's Gemini consolidation: https://www.theinformation.com/articles/googles-new-ai-architect-plans-spread-gemini-everywhere Google, combining Brain and DeepMind in 2023: https://blog.google/innovation-and-ai/technology/ai/april-ai-update/ Jeff Dean, official Google Research profile: https://research.google/people/jeff/ University of Washington, "Whole-Program Optimization of Object-Oriented Languages": https://projectsweb.cs.washington.edu/research/projects/cecil/pubs/jdean-thesis.html University of Washington, whole-program optimization paper and Vortex results: https://projectsweb.cs.washington.edu/research/projects/cecil/www/Papers/whole-program.html University of Minnesota, Dean on his parallel neural-network thesis: https://cse.umn.edu/cs/news/cse-alumnus-jeff-dean-returns-campus-commencement

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番組について

Hi, I’m Ksenia, founder of Turing Post. On this channel, I talk to the people shaping AI and pay attention to the ideas, shifts, and details others might miss. Inference is my interview show with innovators, builders, founders, and thinkers moving AI forward. Attention Span is where I slow down on what deserves a closer look: the signals, questions, and stories hiding between the headlines. Subscribe for the unusual takes. And always stay curious!

情報

  • クリエイター
    Turing Post
  • 配信期間
    25年 - 26年
  • エピソード
    47
  • 制限指定
    不適切な内容を含まない
  • Copyright
    2026 © Turing Post. Все права защищены.
  • 番組のWebサイト
    Turing Post

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