nFOX lab builds enterprise private AI infrastructure with Blockhouse sandboxing, model scanning, attestation, stress testing, and audit evidence.nfoxlab.comJoined August 2026
Linus Torvald, one of the greats, has let us know clearly where he stands regarding AI and coding in this response to a complaint about the Linux kernel project abandoning it's requirement for AI-assisted work to carry an "Assisted by AI"
Self-hosted inference is the way. Or find yourself an actually trustworthy inference provider. Otherwise, Compliance™️ will eat you alive, and rightfully so, in this case.
6/ The backlog is enormous. The payback potential is extraordinary. But the next billion dollars of infrastructure needs financing at today's rates, not yesterday's.
The machines make money. The lenders get first claim.
And the shareholders get what's left.
4/ CoreWeave has a cost-of-capital problem layered on top of a capital-intensive growth model.
The existing fixed-rate debt doesn't get more expensive. The next round of borrowing does.!
3/ But look at CoreWeave: Q2 net interest expense was nearly 25% of revenue. Its 9.75% bonds have traded at yields north of 15%.
And it has over $100 billion in contracted revenue backlog.
Demand is hardly the problem.
2/ At current RunPod rental rates, an eight-GPU B300 server could pay back in roughly 17–20 months at 70% utilization, under our modeled operating and financing assumptions.
These are remarkably productive assets.
1/ CoreWeave's amazing problem: GPU economics can be fantastic while rising financing costs eat the future.1/ CoreWeave's amazing problem: GPU economics can be fantastic while rising financing costs eat the future. $CRWV
Would your company ever run a known hostile AI model? Like a model that you knew would try and exfiltrate your data? How could you run it safely, even when you know its unsafe? - We think about this stuff all the time!
We're open-sourcing Muse Spark Open!
This is an important release which is the most capable US open-weight model yet. 🇺🇸
- 816B parameters (42B active)
- 1M token context window
- Built for coding and long-horizon agents
- Runs locally on two Apple M5 Ultra Mac Studios
More here: research.meta.ai/blog/muse-spar…
Today, we’re expanding our Nemotron family of open models with Nemotron 3.5 Super VL.
Bringing visual understanding, reasoning, and tool use together to help AI agents complete more work in less time.
Open models ➡️ more control to customize, deploy, and build on your own terms.
Dive into what’s new:
Meet NVIDIA Nemotron 3.5 Super VL
Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents.
120B total parameters with 12B active 🧵
Congrats to @GoogleAI's Gemini 4 Argon for taking the top spot of our Humanity's Sixth Sense (HSS) benchmark with a score of 55.4%, edging out GPT-6-Astra at 53.6%. 🥇
HSS tests AI’s ability to reason beyond what’s visible, from hidden objects to predicting what happens next and understanding human behavior.
We took a closer look at performance across categories, and that’s where things get interesting. 👀🧵
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