it’s easier to raise the ceiling than the floor

WOOO CANADAAAA ⚽️🍁

https://t.co/PLL0hNPbdv

@teortaxesTex: Good guy DeepSeek gives us accelerated models The most interesting one here is Gemma4-12B, I presume vision included. Might be the best local model in its weight class now, by some margin Qwen 3.5 not included because DS[park] doesn't do linear attention I guess https://t.co/lKtcGBQH3w

Loops Explained: Prompting AI by Hand Is Outdated

the future of AI is multi-model (including a majority of open-source ones provided by @huggingface of course!)!

@brian_armstrong: How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching.

Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work.

Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task.

Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented.

Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted.

Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect.

The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable.

Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.

China Catches Up

Executive Briefing: Cheap Intelligence Won’t Matter If Your Context Is Trapped

It's quite rational to regulate frontier API models, especially to get more transparency for the government, without regulating open-source AI.

Here's why:

1. The most dangerous AI systems right now aren't open models. They're the large frontier LLM APIs distributed through coding tools and assistants, because:

• They're built in secret behind closed doors and stay total black boxes. Zero transparency on what they can or can't do, with "safeguards" that blur everyone's ability to even analyze them.
• They're built and controlled by a few profit-maximizing megacorps, concentrating unprecedented power in very few hands, with every incentive to downplay risks and overstate their safeguards.
• They're distributed to hundreds of millions, maybe billions, of people and trivially easy to run.

2. Open-weight models are orders of magnitude less risky:

• They're not as massively distributed or as easy to use, especially the big ones, as APIs and assistants.
• We (including governments) can quickly and accurately analyze what they're capable of, and for now everyone confirms they're not as good as the APIs at doing bad things.
• They're distributed to everyone, so defenders and law enforcement get as much access as attackers.

The cost-benefit analysis of regulation is completely different too. Regulating frontier APIs is relatively easy and low risk while regulating open source would be much more complex, less efficient, and orders of magnitude more costly.

Regulating frontier APIs would only potentially hurt a few megacorps, if it even hurts them, given all the marketing that it is already generating for them. They can afford armies of lawyers and absorb losing a few billion dollars, especially given they're on track to become some of the most valuable companies in history.

Regulating open source, by contrast, would hurt the very people regulation is supposed to protect: small businesses, startups, researchers, nonprofits, universities, independent developers, and the broader public, while risking killing competition, slowing AI progress, and reducing transparency even more!

Getting regulated by a government because your model is "too dangerous" is the best marketing (especially for enterprise sales) so everyone is trying to get it now 😅😅😅

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