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You don't need a more expensive model. You need better reasoning. Every frontier LLM got smarter with SERV: - smaller ones match frontier performance - frontier ones go past what they were built for The question isn't which model to choose - it's what engine it runs on.

OpenServ

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29,960 views • 2 months ago •via X (Twitter)

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You don’t have to use one model (or one provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.

You don’t have to use one model (or one provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.

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🇨🇳🇺🇸 China's military may be learning from America's AI, without building it from scratch Why spend billions training a frontier AI model when you can let someone else do the expensive part? Chinese military-linked researchers have repeatedly used outputs from OpenAI and Anthropic models to train smaller domestic AI systems for surveillance, cyber operations, drone targeting, battlefield decision-making, and software analysis. The trick is called distillation: instead of copying the model itself, researchers use its answers, and more importantly, its reasoning, to teach a smaller model. The result? AI that isn't as powerful as the original, but is cheap, runs on local hardware, and can be deployed on drones, military networks, and tactical systems without relying on U.S technology. The AI arms race isn't just about who builds the smartest model anymore; it's about who can squeeze the most military value out of everyone else's. Source: Reuters / Writer: Ian

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derek guy

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Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.

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Dan Hurley shares a blunt reminder about what it takes to be great. "If you can't sit through an hour and 15 minute video session...get out of this industry because this is only for the most competitive people." The film room isn't glamorous. It's tedious, repetitive, and easy to zone out, but that's exactly why it separates. The ones who consistently work even when it's boring are the same ones who execute when it matters. Greatness isn't for the interested - it's for the ones who embrace the boredom of consistency. (🎥College GameDay)

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Most AI agent setups treat every message the same. Simple question? top-tier model. complex task? top-tier model. Your token bill just keeps climbing. I tested OpenSquilla this week on a real document drafting workflow, and the routing caught me off guard. It judges each message's complexity locally, then picks the model tier that fits. Simple tasks go to cheaper models. Complex ones still get the heavy lifting done. You're not paying reasoning tokens for a "hello." I ran a longer workflow, and the context didn't collapse the way it usually does. It distills important information before compression, so you're not starting from scratch mid-session. If you run agents regularly, the bill adds up faster than you think. This is built specifically for that problem. They're running the 10M Token Bill Challenge right now. worth joining if you want to see what smart routing actually saves you in practice. #10MTokenChallenge OpenSquilla

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Ealing Broadway "There's never a Metropolitan Police officer around when you need one". However, this morning, these officers were in the perfect position to deal with a driver parked on the white zig zag lines. Except they didn't. They drove past. Probably got better things to do

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