Small machines. Big possibilities.

Joined April 2026
localsonly retweeted
As I said many times before, TensorFold is a real turning point for local AI. Just try it for yourself, we have a recipe for everyone! All of my TensorFold recipes can be found here: mia-ai.net/models?for=engine…
TensorFold by @ashxhart is one of our newest Inception startups and has been making waves in the local AI community. It's an open-source inference engine for running models on your own hardware. Finally got around to experiencing it for myself. I'm running Qwen3.8 Flash Next in NVFP4 on it across my two DGX Sparks, and built a paper reader to put its concurrency to work. It splits an arXiv paper into its sections and reads them all at once, one request per section, then merges the results into a one-page brief tagged back to each source section. Per-request and combined tok/s update live as it runs.
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Artificial Analysis has been given access by Reflection and is independently benchmarking Beam Early indicators suggest Beam will be one of the most token-efficient open models we've seen for its level of intelligence. Congratulations @reflection_ai on the announcement!
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: reflection.ai/beam
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Me: buys a house in 2020 Fed: prints $9 trillion more dollars Also me: “I’m a real estate genius”
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Replying to @MiaAI_lab
I can tell you this. Right now, there is no development of a single board replacement for the DGX platform. There was (and probably still is) talk about a 2nd Gen. 2028-29 (Vera CPU + Rubin GPU) product tied closely to the RTX Spark, which is an entirely different product. But in talking to many of the major manufacturers who produce the GB10 offerings, there is NO current roadmap deliverable for a replacement DGX Spark AT THIS TIME. Yet.
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The Hugging Face acquisition is important for America and I think Nvidia will be a good steward for the ecosystem. The Poolside transaction may end up mattering even more. I think Jensen is likely to bring American open-weight AI to the frontier, which is going to be awesome for America. We might see a multi-billion $ training run from Nvidia in the next 18 months for Nemotron v5-6 that is easy for customers to post-train and optimize for their own use case. Would be cool to see a 10 trillion plus parameter American open-weight model. If the best open-weight base is American, cheap to run, and actually post-trainable, then people, companies, labs, and governments can own their own intelligence instead of renting it from a company that might not share their values. Would be good for freedom to have a rich variety of AIs that reflect our own individual human preferences. And for the sake of the clarity, I think cheaper, specialized open-weight intelligence might end up making frontier tokens more valuable!
Jensen Huang
@JensenHuangSep 3
Exciting day for NVIDIA and @huggingface. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. They allow every developer, startup, university, industry and country to build with, customize and benefit from AI. Thank you @ClementDelangue for coming to me. NVIDIA is going to be a great home for Hugging Face, its community and the future of open models. 🤗 blogs.nvidia.com/blog/nvidia…
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Wall St Engine
@wallstengineAug 23
$NVDA SIGNS $6B POOLSIDE DEAL FOR U.S. OPEN-WEIGHT AI Nvidia will pay $6B to license Poolside’s AI technology and invest another $1B in the startup at a $12B pre-money valuation. More than 100 Poolside employees are expected to join NVIDIA and work on Nemotron, its open-weight AI model family. The goal is to build a U.S.-based open AI ecosystem capable of competing with Chinese models such as DeepSeek and Kimi, while also offering a lower-cost, customizable alternative to closed models from OpenAI and Anthropic. NVIDIA is betting Nemotron can rival leading frontier models within the next year. Source: WSJ
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Some thoughts on Dario’s post: 1. Dario does not actually address Gavin Baker’s account of what he said – something he could easily deny if it were inaccurate. 2. Dario claims his critics live in a “bubble” where all regulation equals regulatory capture. He calls this an overly simplified view and notes that “Many people outside this bubble think of regulation as something that constrains corporate power and benefits ordinary people.” This argument is a straw man. Of course treating all regulation as capture would be overly simplified – but almost no one holds that view. I have repeatedly argued for strong antitrust enforcement to keep industries competitive, especially Big Tech. If Anthropic continues toward monopoly or duopoly status, I would be among the first to demand those rules apply. 3. Regulatory capture is not vague or in the eye of the beholder. Nobel laureate George Stigler defined it as regulation acquired by an industry and designed and operated primarily for its benefit. Stigler challenged the traditional view that government regulation arises from a benevolent state protecting the public from market failures. Rather, industry groups have concentrated stakes and pour resources into influencing regulators, whereas the public’s stake is diffuse and unorganized. The revolving door between companies and the agencies that regulate them compounds the problem. Anthropic understands these dynamics: it has hired multiple senior Biden AI-policy officials and built a substantial government-affairs operation plus a network of aligned organizations to push its preferred frameworks at state and federal levels. 4. Dario has consistently pushed for a new federal agency to review and approve frontier models prior to release – a proposal framed variously as an “FDA for AI,” an “FAA for AI,” and most recently a “FINRA for AI.” I call it a “DMV for AI” because a review process modeled on the FAA or FDA (which takes years) or FINRA (which issues rules for a staid industry widely seen as protecting incumbents) will create long queues as AI models wait for testing and approval. This process will only become more labyrinthine as rules accumulate to prevent theoretical harms. This would handicap the U.S. relative to China, which will not adopt the same constraints. It would also undermine Anthropic’s own business model, whose pricing power depends on remaining ahead of open models. Whatever Dario states today, it is difficult to believe the company would simply accept outcomes that erase that advantage. 5. Anthropic is on track to become one of the most valuable companies in history, with the resources to navigate any approval process and shape the rules while competitors wait. Dario wants open models under heavier scrutiny – he has called them dangerous in Senate testimony, criticized them for not being centrally monitored or withdrawn, and linked them to IP theft. He says he has never sought a ban, but he could achieve a similar result by insisting that identical rules apply to both open and closed models. The U.S. risks becoming an island of costly closed models while the rest of the world races ahead with broader choice. 6. Dario acknowledges that AI is structurally centralizing but attributes this mainly to chips and scaling laws. Access to compute matters, but the deeper risk is who decides which capabilities are available to whom. His preferred pre-deployment testing and FAA/FINRA-style oversight would place that gatekeeping power in a federal bureaucracy working hand-in-glove with a small number of frontier labs – reinforcing centralization rather than countering it. 7. The second part of Dario’s post assumes we have amnesia about Anthropic’s well-orchestrated campaigns hyping AI fears. His May 2025 claim that AI would wipe out 50 percent of entry-level knowledge jobs within five years still lacks supporting evidence fifteen months later. Similarly Anthropic breathlessly promoted its heavily contrived “blackmail” study on 60 Minutes. Yet Dario blames public negativity on a long-standing loss of trust in institutions rather than his own messaging. 8. These narratives have done more than anything to shape public fear. People are left asking the same question Mark Zuckerberg posed: why race to build a future you describe in such negative terms? Thomas Sowell’s "The Vision of the Anointed" captures the mindset – elite intellectuals convinced that only they are enlightened enough to control the outcome. As Zuckerberg notes, concentrating power in the hands of an enlightened few has rarely produced the promised results; the practitioners turn out to be less enlightened in practice than in self-conception. 9. Gavin Baker summarized the disagreement cleanly on our pod: Dario believes frontier AI is too powerful to distribute; we believe it is too powerful to centralize. Dario appears to believe, sincerely, that safety and progress are best served by centralizing authority in a marriage of corporate and state power. The weight of human history gives us reason to fear that outcome.
Dario Amodei
@DarioAmodeiAug 15
1/2 Thanks Gavin for an especially thoughtful exchange. I don't usually spend much time on social media but I wanted to engage here because it really brings out the heart of an important conversation. First, on regulation, I think that “either concentrate it in the hands of a chosen few companies and politicians via regulation or distribute it widely” is a false choice.  I know that there’s a sort of Silicon Valley shorthand where regulation = regulatory capture = concentration of power, but I’ve always found this to be an overly simplified picture of the world.  Many people outside this bubble think of regulation as something that constrains corporate power and benefits ordinary people.  I don’t necessarily agree with that perspective either, rather I think it’s complicated and really depends on what the “regulation” consists of.  But in particular I think that those in the “regulation = regulatory capture = concentration of power” frame often underrate the decentralizing power of objective and fair institutional processes.  A crude analogy is that the formal court system can sometimes feel stuffy and elitist, but it does a much better job of defending the rights of vulnerable individuals than the alternative, mob justice.  At their best, institutions can vest power in ideas rather than people, and thereby decentralize that power. This is why Anthropic has always made its policy proposals very carefully.  We try very hard to make proposals that disadvantage (slow down) frontier AI companies while *advantaging* smaller competitors.  California’s SB53 (which we supported), and even the much-maligned SB 1047 (which we were ambivalent on), completely exempt any company below a certain amount of revenue or model training costs from being covered at all (it was $500M for SB 53, lower for 1047 but we objected to that).  More recently the testing process we’ve advocated for at CAISI and the White House involves more rigorous tests for frontier models than off-frontier models — something that differentially advantages challengers.  Similarly, the “Pacing the Frontier” letter envisions (or at least Anthropic’s preferred implementation of it envisions) modulating the pace of the very best models while not constraining those who are catching up.  This hurts the business interests of the frontier labs and helps challengers, including open-weights! Overall my view is that AI is *structurally* a technology that tends to concentrate power, for reasons that have nothing to do with regulation (more to do with the extreme implications of the scaling laws).  Open-weights do help some with this but are nowhere near a sufficient solution because they simply shift the concentration somewhat to those with the most compute and chips (which are roughly the frontier labs plus maybe hardware providers).  By contrast I think the right “rules of the road” can simultaneously (a) address AI’s cyber/bio/alignment risks, (b) institutionally constrain the power of the frontier AI companies, and (c) leave room for open-weights models while also addressing the specific risks that they bring. BTW I do not think that the events of the last few months have “failed to result in [my] preferred regulatory path”.  The approach that the Trump administration is reported to be taking — pre-deployment testing for frontier models, and also testing of open-weights models when they get closer to the frontier — is one that I am very supportive of, though of course I have to see the details to be sure.  I am also supportive of Demis Hassabis’ ideas around a FINRA-like entity.  This contrasts with six months ago when most of the industry was still pushing for preemption of all state regulation and no apparent federal approach either.
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localsonly retweeted
Almost 3 years ago, I believed open-models would become the key threat to the AI incumbents & that unfortunately they would lean into regulatory capture as a response. I underestimated the fervor with which they would use this non-business approach. xitter.cf/theallinpod/status/170…
2,851 Miles by @bgurley from the @allinsummit now available in full on @x (0:00) 2,851 Miles (24:44) In conversation with @bgurley
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Jensen Huang
@JensenHuangJul 24
For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. images.nvidia.com/pdf/Open-W…
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0xSero
@0xSeroJun 13
In light of the US government banning Fable for non US people (and Anthropic pulling it off cc) It’s more and more important that we develop systems that can’t be shut down, nor taken away. I’m grateful I got a glimpse of the future, I hope I get to spend more time with it
0xSero
@0xSeroMar 20
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Explaining to the firefighters union pension why their 401ks need datacenter construction debt exposure:
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A new @bgurley blog post! I have been thinking about how sophisticated executives are using open source in super creative ways. Started writing this three years ago. Excited to finish it up and publish it! And with the new @p3institute brand. substack.com/home/post/p-197…
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Local AI is having its moment! Below is the number of new GGUF models created each month over the past 8 months & insights from our HF internal agent (May is partial): - 176,000 total public GGUF models on HF - Two distinct regimes: Oct–Feb averaged ~5.1K new GGUF models/month. Then March–April jumped to ~9.2K/month — nearly double the previous rate. - March was the inflection point (+55% MoM) — likely driven by a wave of new open-weight model releases being quantized to GGUF. - April sustained the momentum at 9.7K, suggesting this isn't a one-off spike but a new baseline. - The GGUF ecosystem is accelerating — the community is quantizing models faster than ever, likely thanks to better tooling (llama.cpp improvements, automated quantization pipelines, and more models supporting GGUF natively). Let's go!
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localsonly retweeted
Friendly reminder that Google has an official app to run Gemma 4 on your phone. - 100% open source - Fully offline and private - Multimodal with text/audio/image - Works with Gemma E4B and E2B And the app is available on both iOS and Android. Steps and download below
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It’s over. Anthropic just banned OpenClaw. Uncensored thoughts: 1. Massive mistake that will come back to bite them 2. Open source needs to win. If you have a local model running on your Mac mini, no corporation will ever be able to ban you 3. ChatGPT 5.4 is the best model. But it sucks compared to opus in OpenClaw. I will continue to pay for Anthropic api 4. I have no doubt the next OpenAI model will be optimized for Openclaw and be excellent 5. In 6 months the local models will be as good as opus 4.6 and all of this will be forgotten 6. It’s feels like from a consumer sentiment perspective things have flipped for OpenAI and Anthropic. They were the darlings when Opus 4.5 came out 7. Going to the Kanye concert right now please don’t spoil the stage or set list in the replies 8. The best openclaw set up is now Opus as the orchestrator, then much cheaper models as the execution layer. If you do this properly you won’t be paying much more than $200 a month. I’m using Gemma 4 and Qwen 3.5 for execution on my DGX Spark and Mac Studio
Boris Cherny
@bchernyApr 3
Starting tomorrow at 12pm PT, Claude subscriptions will no longer cover usage on third-party tools like OpenClaw. You can still use these tools with your Claude login via extra usage bundles (now available at a discount), or with a Claude API key.
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AMA on local LLMs + self-hosting hardware for the next couple of hours bring your weird edge cases, your cursed configs, and your “why is this slow” questions let’s fix your stack
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How I'm using OpenClaw today. I'll open-source my autopilot system soon.
Don't think I've heard any other CEO describe agent use in such detail before. @pedroh96 out here running a $5bn company on OpenClaw Full episode with lots more detail in the replies
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