A volunteer red team filed 4,962 AI findings across 390 repositories in 27.5 hours, and not one came from a public US frontier model. Not a capability gap: the capable versions sit behind identity gates. Refusal policy is now where the labs differentiate, and it belongs on your dependency list.
Knowledge distillation is a large model teaching a small one, and it works almost exactly like school: soft labels instead of a bare answer key, pairing weeks instead of reports, and a tutor who marks your own attempt rather than a textbook of last year’s solutions. The limits map too, and the last one costs money when you size the hardware.
A frontier lab optimizes an absolute target and has to explore. A challenger optimizes a distance to the leader and can exploit. That is the whole cost asymmetry, and it shows up in GPU hours and in a bike race.
I used whisper.cpp on an RTX 5070 to transcribe about 100 hours of podcasts in roughly three hours, then turned the Markdown output into a searchable prompt knowledge base.
Public frontier models lean progressive on open benchmarks. Local inference via llama.cpp is still the practical way to keep an uncensored second opinion. Memory is the bottleneck; TurboQuant-style KV compression is what will make local writing workers routine in multi-model agents.
OpenRouter put numbers on a pattern agent builders already use: a panel of models with a judge synthesizer beats the best single model, and a budget panel matched frontier quality at half the cost. What that means for your AI platform.
A small coding model mocked on r/LocalLLM is actually a clean case of execution-verified distillation. Here is how the method works, when distilling a frontier model into a cheap local one pays off, and the three things the hype leaves out.
Most AI infrastructure teams spend 35-60% more on GPU compute than they need to. The cause isn’t cloud pricing. It is architecture, and it is fixable.
When Tesla published patent WO2024073080 describing a new file format internally called “.smol”, the headline was simple: 4x reduction in IOPS for AI training. Most people read this as a hardware story. It isn’t. It’s a data architecture story. And it exposes a structural weakness in how most enterprise data Read more
GPUs are no longer the bottleneck. Data movement is. I recently attended an online talk that stayed with me longer than most.Not because of a new GPU announcement, but because it clearly articulated something I have seen repeatedly over the years, across very different systems. That message, strongly emphasized by Read more