Charles

Wow!

@parkerconrad This! And distillation isn’t unique to the frontier, SLMs have achieved amazing success with distilling down to a couple billion parameters. We need that research for robotics, on premise models, and general research

If Anthropic is right and distillation is impossible to prevent, it follows that the strategic value of anyone opening a lead on frontier model capabilities is short-lived, and national security arguments for protectionism fall apart. If china pulls ahead we can distill them!

@miramurati @elonmusk Moreover, open models unlock real research opportunities not available with proprietary models. Distillation, reasoning traces, internal state, are all foundational for AI safety research and broader innovation. It’s sad to see these absent today

The knowledge that makes AI useful is diffused. It lives with scientists, engineers, clinicians, firms. For AI to benefit from distributed knowledge, it must itself be distributed. Agree with Jensen that this is a future worth building.

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Surprised distillation isn’t on the list. Model innovation will branch from open source models. The ability to parse reasoning traces, logit scores, and internal state is a necessary extension of any model that’s goal is to be on the frontier. All of which is frustratingly absent from American labs, stifling domestic research and innovation during one of the greatest technological advances ever.

In recent months, the best open models are increasingly Chinese, and I don't think enough people understand why. It's not that they got lucky, but rather, our export controls pushed them toward open weights, and open wins at infrastructure basically every time. In my opinion, our regulations aren't protecting the lead, but the reason why we are going to lose it, as reportedly, around 80% of startups are already building on Chinese open models. Not saying that the big security story this week is related, but the irony is definitely there. OpenAI's models escaped a closed internal test and hacked Hugging Face. Rather than catching it right away, it was the open platform that caught it and published a full postmortem within days. I work with local open models every day and the trend line is kind of obvious. If we want American AI to win, the answer is fewer restrictions and more open source, not the opposite. Thanks Ben Werdmuller for this read! I’ll link it in the description.

This is bad. What’s worth noting is Hugging Face used Chinese models to combat the attack due to guardrail restrictions on domestic models. With the frontier becoming less of a duopoly and more populated it makes sense for many companies to switch from the American labs, which likely triggers Government regulation both export control and foreign restrictions.

Pretty frustrating using Claude this month. Models seem slower, constantly hitting guardrails, and burning tokens. Planning is still superior, but definitely stresses the need for multi model setups

@MichaelJeffords LFMs are also really fast/cheep to fine tune. Always tradeoffs 👏

Lots of nuance to this question, while these are all great I find myself increasingly pointing others to LFMs. On a M3 36G I get ~500 tok/s on LFM2 350M with surprisingly good outputs

Kimi beats Fable and Sol in Code Arena!!

Kimi K3 Benchmarks - seriously where is for profit in 18 months

We just launched Juniper! Try it at conifer.build

papers.ssrn.com/sol3/papers.cf… Sharpest point imo: every time we call a capability "emergent," we're quietly admitting scaling laws can't predict what's coming. That's a strange foundation for trillion-dollar capex. From where I sit in inference infra, the efficiency curve is important; 22x isnt one off its a trend that's likely to dominate what the future of inference looks like.

Fresh essay on scaling laws. Sara Hooker spent years inside DeepMind and Cohere watching scaling laws get treated as gospel. Then she wrote down the heresy: Falcon 180B was state of the art in 2023. One year later it lost to a model 22x smaller. The $700B question isn't whether scaling works. It's whether it's still the cheapest way to buy intelligence. She was convinced enough to quit and build a company on the answer.

Excited to share that @coniferbuild is part of the @ycombinator S26 batch. Looking forward to getting to work with @gustaf! Token costs are eye-watering. @charles_v11 and I burned through $13,000 in Claude credits in just five days, and that's a fraction of what every company transitioning to Al is facing. But it's not just the cost. Al usage today is fragmented: five different models, three subscriptions, three API dashboards, and a drawer full of API keys. Every team is flipping between tabs, juggling providers, and paying full price for all of them. Conifer replaces all of it with one interface. We're the inference gateway that routes every Al query to the cheapest model that can actually do the job. Built on top of our Typhoon engine, which allows local models to run faster and punch far above their weight, Conifer only reaches for the cloud on your hardest tasks. The result is a token bill >80% less. We launch tomorrow, July 7th, on conifer.build If you're part of a company spending thousands on Al every day, been holding off on the switch because of cost, or someone just trying to decrease their spend, we'd love to hear from you. Email contact@conifer.build or reach out here on X.

I have to say, I agree. I've been reading a lot of research on LLM routing, and it has consistently shown that the majority of queries don't actually need a frontier model. Most of what teams send to Fable 5 would come back just as good from something far cheaper. And even for the most difficult prompts, Anthropic literally just shipped Sonnet 5 last week. It's nearly on par with Opus 4.8 (beats it on some knowledge work benchmarks) at less than half the price. Defaulting everything to the "best" model in 2026 shouldn't be a requirement, and oftentimes (as you pointed it out), it can be costly.

So many great points, but the error-correction piece is the one people will underrate. LeCun's (1−ε)ⁿ doom assumes per-step errors are iid and absorbing, which they're not. Trained agents learn a restoring force back so long horizon behavior looks like a mean reverting walk. 👏

You're wasting API tokens. A 14B model that runs on a laptop now solves 85% of AIME 2025

Conifer is an open-source runtime that makes local AI actually fast, private, and reliable. Here's the problem we kept running into: running AI models on your own machine should be the obvious choice for anything private, your code, your documents, your data. But anyone who's tried knows the truth: the hard part isn't the model, it's everything around it. Setup. Storage. Quantization. Memory management. Getting it to actually use your GPU properly. Splitting a model across multiple cards without it falling over. Keeping it stable when it runs for hours instead of minutes. Most people hit that wall and go back to the cloud. Which means their data leaves their machine, their costs scale forever, and their tool breaks the day someone else's servers have a bad day. Conifer is the layer that handles all of it. It manages the model, setup, storage, quantization, and memory, and schedules the work across whatever hardware you actually have, so local inference just runs. The goal is simple: local AI that's genuinely competitive with the cloud, not a slower fallback you settle for. And we're getting there. In early benchmarks Conifer already beats llama.cpp on decode for some models (1.24x on Qwen, 1.13x on TinyLlama) and MLX on prefill (1.26x on Qwen). It's not a clean sweep yet, there are places we're still behind and tuning hard, but it's genuinely competitive, and the numbers are climbing before launch. Conifer launches June 1st. Completely free and open source. Install it, point it at a model, and see what it does for yourself, no signup, no trial, no catch. We're opening beta access to the first 100 people on the waitlist before then. If you run models locally, or you've wanted to but the setup beats you, we'd love for you to try it and tell us what breaks. Link for signups will be in the replies.