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