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Turing Post

Turing Post
Turing Post
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49 Episoden

  • Turing Post

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

    31.08.2026 | 16 Min.
    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.*
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    👉 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
  • Turing Post

    OpenCode vs. OpenRouter: The Fight Over Your AI Models

    28.08.2026 | 13 Min.
    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.*
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    👉 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
  • Turing Post

    Ox Alpha, Explained Without the Hype

    25.08.2026 | 17 Min.
    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
  • Turing Post

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

    25.08.2026 | 15 Min.
    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
  • Turing Post

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

    25.08.2026 | 14 Min.
    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
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Über 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!
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