Turing Post

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.

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#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

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.

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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

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?

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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

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?

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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

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.

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🔗 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

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.

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*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

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

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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

OpenAI says its next model, Astra, produced ten mathematical advances, each backed by a machine-checked Lean proof. The successful searches would cost roughly $2,000 at API prices. 

Are mathematicians done?!

Twenty-four hours later, Anthropic mathematician Levent Alpöge claimed Claude Fable had matched five of them. Meanwhile, startups are raising hundreds of millions to turn proof generation and verification into a business.

In this episode, we explain what Astra actually achieved, what the $2,000 figure excludes, how Lean changes mathematical verification, and why the next scarce resource in mathematics may be human judgment rather than proofs.

Attention Span explains how AI is changing who produces, verifies, and ultimately controls mathematical discovery.

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#AI #OpenAI #Anthropic #Mathematics #AIResearch #MachineLearning #Lean #Astra #claude 

*Sources used to produce this video:*

- Sébastien Bubeck's Astra announcement: https://x.com/SebastienBubeck/status/2083456300692979886

- OpenAI, Ten advances in mathematics and theoretical computer science: https://openai.com/index/ten-advances-in-mathematics/

- OpenAI, Ten Proofs manuscript: https://cdn.openai.com/pdf/ten-proofs-oai.pdf

- OpenAI, Lean repository: https://github.com/openai/ten-proofs

- OpenAI, reasoning walkthroughs: https://cdn.openai.com/pdf/reasoning-walkthroughs.pdf

- Terence Tao, Mathematics in the Age of AI, ICM 2026: https://teorth.github.io/tao-web/slides/age-of-ai-icm-2026.pdf

- Tao's explanation of the Jacobian counterexample: https://terrytao.wordpress.com/2026/07/21/a-digestion-of-the-jacobian-conjecture-counterexample/

- Leiden Declaration on Artificial Intelligence and Mathematics: https://leidendeclaration.ai/

- Leonardo de Moura, Proof Assistants in the Age of AI: https://leodemoura.github.io/blog/2026-2-18-proof-assistants-in-the-age-of-ai/

- Georges Gonthier, formal proof of the Four Color Theorem: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/gonthier-4colproof.pdf

- Levent Alpöge's Astra counterclaim: https://x.com/__alpoge__/status/2083855298239078748

- Axiom Math funding and product thesis: https://menlovc.com/perspective/ai-will-write-all-the-code-mathematics-will-prove-it-works/

- Axiom proofs accepted by journals: https://www.axios.com/2026/05/26/axiom-ai-math-journal

- AxiomProver Putnam 2025 and early-2026 results overview: https://wal.sh/research/axiomprover-2026/

- Math Inc., Gauss: https://www.math.inc/gauss

- Math Inc., FormalQualBench: https://www.math.inc/formalqualbench

Reuters, Harmonic funding: https://www.reuters.com/business/robinhood-ceos-math-focused-ai-startup-harmonic-valued-145-billion-latest-2025-11-25/

One AI request may soon begin on one computer and finish on another. WHAT?!

AMD and Cerebras are separating the two phases of LLM inference: Helios processes prompts and long context, while Cerebras generates tokens. They claim up to 5x more tokens per second per watt, although the figure is based on internal modeling.

Meanwhile, OpenAI reportedly cut inference costs for one segment of ChatGPT by more than half through an undisclosed optimization. The inference race is shifting from installing more chips to extracting more useful work from them.

Attention Span explains how the race to make AI inference faster and cheaper is reshaping hardware and the software that orchestrates it.

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Sources and further reading

The Information on OpenAI's reported inference optimization: https://www.theinformation.com/newsletters/ai-agenda/openai-discovers-new-way-cut-inference-costs-half

OpenAI on GPT-5.6 inference and agent-harness efficiency: https://openai.com/index/gpt-5-6-frontier-intelligence-efficiency/

AMD and Cerebras announcement: https://ir.amd.com/news-events/press-releases/detail/1293/amd-and-cerebras-announce-industry-leading-ultra-low-latency-and-high-throughput-ai-inference-solution

AMD Helios architecture: https://www.amd.com/en/blogs/2026/amd-launches-helios-the-highest-performing-rackscale-ai-infrastructure-solution.html

AMD Helios networking: https://www.amd.com/en/blogs/2026/amd-helios-resilient-scale-up-networking-for-ai.html

Cerebras WSE-3: https://www.cerebras.ai/chip

Cerebras CS-3 datasheet: https://cdn.sanity.io/files/e4qjo92p/production/0d73d528371618c0372fcb9de9b3c0da703adf9e.pdf

Cerebras inference architecture: https://www.cerebras.ai/blog/introducing-cerebras-inference-ai-at-instant-speed

Kimi K2.6 model card: https://huggingface.co/moonshotai/Kimi-K2.6

AWS and Cerebras disaggregated inference: https://www.aboutamazon.com/news/aws/aws-cerebras-ai-inference

AMD and vLLM MORI-IO disaggregation test: https://vllm.ai/blog/2026-04-07-moriio-kv-connector

PagedAttention: https://doi.org/10.1145/3600006.3613165

Splitwise: https://arxiv.org/abs/2311.18677

DistServe: https://arxiv.org/abs/2401.09670

TetriInfer: https://arxiv.org/abs/2401.11181

Mooncake: https://www.usenix.org/conference/fast25/presentation/qin 

NVIDIA Dynamo: https://www.nvidia.com/dynamo/

NVIDIA Groq 3 LPX: https://developer.nvidia.com/blog/inside-nvidia-groq-3-lpx-the-low-latency-inference-accelerator-for-the-nvidia-vera-rubin-platform/

CDC 6600: https://www.cisl.ucar.edu/ncar-supercomputing-history/cdc6600

#ArtificialIntelligence #AI #MachineLearning #LLM #Inference #OpenAI #AMD #Cerebras #AIInfrastructure #FutureOfAI

Jensen Huang joined X and immediately rallied Silicon Valley around open-weight AI. First came a letter signed by 77 companies and industry leaders. Then came a security alliance with 37 members. Anthropic was absent from both.

This was not a random disagreement. Kimi K3 had narrowed the gap with American frontier models, Anthropic had accused Moonshot AI of extracting 3.4 million Claude exchanges, Hugging Face had turned to an open-weight Chinese model during a security investigation, and Congress had proposed an AI kill switch.

In this episode, we examine why Jensen and Anthropic represent two competing approaches to AI safety, what their companies gain from those positions, and who ultimately gets to control the model layer.

Attention Span explains the technical and business decisions shaping AI.

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*Links:*

Jensen Huang on X https://x.com/JensenHuang

NVIDIA: Open Secure AI Alliance https://blogs.nvidia.com/blog/open-secure-ai-alliance/?ncid=so-twit-957725

Moonshot AI: Kimi K3 announcement https://x.com/Kimi_Moonshot/status/2081760186235289764

Kimi K3 model page https://huggingface.co/moonshotai/Kimi-K3

Anthropic: Detecting and preventing distillation attacks https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks

Turing Post: What Is Knowledge Distillation? https://www.turingpost.com/p/kd

Nathan Lambert on distillation https://x.com/natolambert/status/2079616308203942332

Little Tech Association https://littletech.org/

Jensen Huang’s open-weights letter https://x.com/JensenHuang/status/2080643682408321103

Jensen Huang on the Open Secure AI Alliance https://x.com/JensenHuang/status/2081698060330250294

AI Kill Switch Act press release https://lieu.house.gov/media-center/press-releases/reps-lieu-and-moran-introduce-bill-require-kill-switch-ai-systems-can

Jeremy Howard on NVIDIA, Anthropic, and open weights https://x.com/jeremyphoward/status/2081280852454166680

Turing Post: Anthropic Fable 5 and Mythos 5 episode https://www.youtube.com/watch?v=o9d12zG6kNM&t=1s

Axios: Jensen Huang on China and open-source AI https://www.axios.com/2026/07/22/nvidia-jensen-huang-china-open-source-ai

The Information: Silicon Valley Unites Against Anthropic Over Chinese AI Restrictions https://www.theinformation.com/articles/silicon-valley-unites-anthropic-chinese-ai-restrictions

#AI #OpenWeights #Anthropic #NVIDIA #KimiK3 #JensenHuang