Key points
- Claude Code — One commenter says Claude Code, running under a private account, sent an organizational email address via curl; how it picked that address and how it sent it remain unclear.
- OpenAI — According to the video's author, last week OpenAI released a batch of new proofs, one of which took a step toward a long-open physics question: whether Yang–Mills theory actually exists.
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Claude Code: account email in request headers
Multiple users report that Claude Code sends their account email address to the websites they visit, and some attribute a rise in targeted spam to this behavior. One commenter says Claude Code, running under a private account, sent an organizational email address via curl; how it picked that address and how it sent it remain unclear. The technical path is traced in Issue #107757 to the User-Agent — the HTTP request header that identifies the client software making a request. Any account identifier written there is exposed to every site that receives the request. Claude Code put the email address in while performing web searches, which gives websites a plausible route to collect addresses and send targeted spam. Source: GitHub Issue #107757
According to one commenter, Anthropic added a built-in privacy guard in newer versions that deterministically blocks this behavior, and users can turn the check off via /plugins. But the available evidence is largely anecdotal: there is no technical reproduction and no confirmation from the maintainer. Skepticism in the thread runs high — one person describes getting daily spam addressed to their real name, while others are considering isolating Claude Code from websites or switching to local models to avoid outbound behavior of this kind entirely.
OpenAI’s new proofs: a step toward Yang–Mills
The video’s author argues AI took math first and now physics is next, and that this opening is genuinely interesting. In his view, AI has derived more proofs than anyone can count, and no one can deny any longer that AI can produce new, correct mathematical proofs — not just that it can write proofs, but that it can write correct ones nobody had written before. Meanwhile the preprint repositories are drowning in AI-generated math papers. The author describes mathematicians’ reaction with irony: weeping over their notebooks while complaining that these AI proofs are unreadable and inelegant — really just two complaints, hard to understand and ugly to look at. According to the video’s author, last week OpenAI released a batch of new proofs, one of which took a step toward a long-open physics question: whether Yang–Mills theory actually exists. All of the above is as the video’s author states it; this episode obtained no first-hand OpenAI material. Source: YouTube
The weight of this story sits exactly in the word “physics” — the author then begins explaining Yang–Mills theory, but the available material ends there on an ellipsis.
Qwen3.5 in pure assembly: 6.51 tok/s single-thread
Qwen3.5-0.8B’s tokenization, inference and greedy sampling all run in assembly; the runtime needs neither libc nor Python, with Python used only at build time to prepare the tokenizer tables. The author measured 6.51 tokens per second on a single thread of an AMD EPYC 7B12; the Transformers implementation used as a comparison achieved 4.34 tokens per second in the same setting, and the 32 tokens each run generated matched one for one. The test input was 19 tokens, decoded greedily. The value of this public implementation is that it gives developers working on low-level and edge inference a directly referenceable example: it runs with no libc and no Python dependency at execution time. Source: Reddit r/LocalLLaMA
But part of the speed gap was not won by the algorithm. The assembly path reads BF16 weights, while the Transformers side loads FP32 weights and goes through PyTorch fallback kernels; the benchmark covers only two very short runs and lacks an optimized BF16 comparison. Qwen3.5-0.8B is the 800M-parameter tier of the Qwen3.5 family, a line aimed squarely at local and edge deployment.
TSMC: memory moves into logic chips
At VLSI Symposium 2025, TSMC presented a 1T1C memory technology: an oxide-semiconductor selector transistor paired with a capacitor built on a low-temperature process, placing the memory array directly in the back-end interconnect layers of advanced logic chips. The disclosed figures: a logic-plus-memory test chip operating at 0.75 V, data retention beyond 128 ms, random read/write cycles within 8 ns, and more than 10^14 writes. TSMC positions it as a denser, lower-power alternative to SRAM for processor caches, with potential benefit for AI and high-performance computing systems. Source: Reddit r/LocalLLaMA
But these numbers establish “achieved on a test chip,” not “achieved in volume production” — 0.75 V, 8 ns and 128 ms are not yet a validated commercial roadmap. The same report has a second half: TSMC is reportedly considering manufacturing 3D DRAM in response to AI-driven memory shortages and a request from NVIDIA, which would put it in direct competition with Samsung, SK Hynix and Micron — though no volume timeline and no fab decision have been announced. At that same VLSI Symposium, SK Hynix also presented oxide semiconductors as a candidate channel material for 4F² memory scaling.
OpenAI’s $11B raise: landed below target
OpenAI reportedly completed an $11 billion raise at a $1.25 trillion valuation, below the $1.4 trillion target ceiling it initially sought. One possible reading of the shortfall is that not enough investors were willing to accept its terms. The deal carries no new model, no new hardware, no efficiency gain, no revenue figure and no other technical progress — it is fundamentally a valuation and financing event; and because neither the original reporting nor detailed deal terms are at hand, these figures remain unconfirmed. There is also a subtle difference in how the outlets frame it: Reuters describes the $11 billion as the first tranche of the round, while TechCrunch reports the round is not yet closed and is expected to complete over the coming weeks. Source: 247WallSt
Skepticism in the comments is widespread: some question whether a below-target valuation means demand for AI investment is itself constrained; others worry about a down round, about OpenAI’s burn rate, and about whether worsening financial conditions could force the company toward an IPO. One commenter argues that hardware efficiency gains — reportedly about 40% more FLOPS per watt each year — may not offset how capital-intensive the industry is; another notes that because of differences in share classes and corporate structure, headline valuations and what retail investors would actually pay may be entirely different things. There is a countervailing view too: OpenAI may have a longer time horizon than Anthropic.
Taken together, these five stories are two ends of the same thing. One end is who holds control: what a request header sends out by default, what precision the weights are read at by default, which layer of the chip the cache sits in by default. The assembly author’s approach is to grip the whole chain in his own hands; TSMC tucks memory into the back-end interconnect of logic chips; the Claude Code story is the inverse — a default value made a decision on the user’s behalf that nobody was aware of. The other end is the floor price of the business: OpenAI pushes toward the deep foundations of physics with proofs, while setting its own entry price at $1.25 trillion, below what it first asked.
🎧 This episode is also available as a podcast: listen to Daily AI Stew · 2026-11-08.