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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.

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OpenServ

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29,960 просмотров • 2 месяцев назад •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.
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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.

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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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🇨🇳🇺🇸 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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it's crazy what a 1.5B model can do these days! "VibeThinker-1.5B is a 1.5-billion parameter dense language model. With a total training cost of only $7,800 USD, it achieves reasoning performance comparable to larger models like GPT OSS-20B Medium." runs perfectly on device!
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it's crazy what a 1.5B model can do these days! "VibeThinker-1.5B is a 1.5-billion parameter dense language model. With a total training cost of only $7,800 USD, it achieves reasoning performance comparable to larger models like GPT OSS-20B Medium." runs perfectly on device!

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Introducing Matrix I crawled 100,000+ agents, skills and tools to train a new model which can answer what capabilities are the best match for a task. Think Google, but for agents. A living model that learns from the gossiping network, and gets smarter with every interaction.

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Solana Perps are getting a new player Percolator was submitted to the Colosseum Frontier Hackathon and is working on permissionless perp markets for Solana tokens with DEX liquidity What makes it stand out from most perp venues is the focus on market creation: → instead of listing a fixed set of assets, Percolator aims to let new perp markets be launched around any token with enough Solana liquidity Built on Toly’s open-source Percolator engine and currently in waitlist mode Full look at the Frontier DeFi category later today Which other DeFi projects should I be watching?

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Man in green, "In my generation it's a pipe dream to own a home, it's so expensive.. The consensus hasn't worked.. The Tories had 14 years, they haven't built the homes we need" #BBCQT "If Labour need to change the rules, to make it easier, then that's what we need to do to build more homes"
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Man in green, "In my generation it's a pipe dream to own a home, it's so expensive.. The consensus hasn't worked.. The Tories had 14 years, they haven't built the homes we need" #BBCQT "If Labour need to change the rules, to make it easier, then that's what we need to do to build more homes"

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SECRETARY RUBIO: You see Cubans go all over the world and find success except in Cuba. We need to change the system that runs the country, and we need to change the economic model that it's following. That's the only way forward if Cuba wants a better future.
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SECRETARY RUBIO: You see Cubans go all over the world and find success except in Cuba. We need to change the system that runs the country, and we need to change the economic model that it's following. That's the only way forward if Cuba wants a better future.

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Reach isn't a stat on a landing page. it's how many users you can address, how far they can go and if their swap completes with the best available fill, regardless of which network they opened the wallet on. that's the ‘reach’ that counts. And you need the right tools for it.
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Reach isn't a stat on a landing page. it's how many users you can address, how far they can go and if their swap completes with the best available fill, regardless of which network they opened the wallet on. that's the ‘reach’ that counts. And you need the right tools for it.

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Such is the Gold Standard for European shoemaking, but it's different in the US. American shoemakers commonly use pre-made lasts, which they adjust with cork or wet leather. If you need extra room somewhere, they add material to the last; if you need it smaller, they rasp away.

derek guy

19,945 просмотров • 1 месяц назад

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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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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Moonshot AI is casually giving developers free daily access to Kimi K3 😳 no subscription no upfront payment just sign in and start using one of the largest open AI models available what you get for $0: - Kimi K3 with 2.8T parameters - 1M token context window - strong coding and reasoning performance - native vision capabilities - free daily credits that refresh automatically why this is worth checking: > access a frontier model without paying API fees > long context for large codebases and documents > works on web, desktop, mobile, and CLI getting started takes less than 2 minutes: 1. go to 2. create a free account 3. Kimi K3 is available as the default model 4. start chatting or coding with your daily free credits bonus: Moonshot Together lets you invite friends for a chance to earn 3, 7, 15, 30, or even 365 days of Kimi Membership through its rewards program benchmark highlights: > 2.8T parameter MoE model > 1M context window > strong performance across coding, browsing, and reasoning benchmarks important: free credits reset daily, rate limits apply on the free tier, and the open-weight release is expected on July 27 A simple way to try one of the latest frontier AI models without paying for API access
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1 day to go before BELUCA! WE’RE GIVING AWAY FREE TICKETS! Here’s what you have to do: 1. Answer the featured question below. 2. Like and share this post. 3. Follow New Frontier Theater’s Accounts. 4. Comment a screenshot following our accounts on our Facebook post or X post
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1 day to go before BELUCA! WE’RE GIVING AWAY FREE TICKETS! Here’s what you have to do: 1. Answer the featured question below. 2. Like and share this post. 3. Follow New Frontier Theater’s Accounts. 4. Comment a screenshot following our accounts on our Facebook post or X post

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It's getting real and it's about to be a crazy, fun night 🔥 ACEs, save the date for a concert you won't forget! See you on October 4, 2025 at the New Frontier Theater for BGYO: THE FIRST SOLO CONCERT! Follow us and stay updated with more details coming soon! #BGYO #StarMusicPH #ABSCBNMusic #ABSCBNEvents #StarMagic
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It's getting real and it's about to be a crazy, fun night 🔥 ACEs, save the date for a concert you won't forget! See you on October 4, 2025 at the New Frontier Theatre for BGYO: THE FIRST SOLO CONCERT! Follow us and stay updated with more details coming soon! #BGYO #BGYO_TheFirstSoloConcert #StarMusicPH #ABSCBNEvents #StarMagic
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It's getting real and it's about to be a crazy, fun night 🔥 ACEs, save the date for a concert you won't forget! See you on October 4, 2025 at the New Frontier Theatre for BGYO: THE FIRST SOLO CONCERT! Follow us and stay updated with more details coming soon! #BGYO #BGYO_TheFirstSoloConcert #StarMusicPH #ABSCBNEvents #StarMagic

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92,022 просмотров • 1 год назад

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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 cameras record the world. They don't understand it. 👁️ Meet Trio-Lumen — point it at any RTSP feed and just tell it what to look for in plain English (e.g., "flag anyone in the loading dock after hours"). It runs frontier vision models 24/7, turning raw video into a live, queryable world-state: who's where, what they're doing, and where they're heading. 🧠 Under the hood, Trio is the world model we are building for the physical world (perceive → represent → predict): 🔹 Perceive (The Eyes): Plug in any frontier model (YOLO, DINOv2, V-JEPA) to extract data from pixels. 🔹 Represent (The Brain): Outputs land in Trio-Retina (open-source), organizing raw video into one structured, queryable database. 🔹 Predict (The Intuition): A dynamics head that learns the patterns of your space to anticipate what comes next. This is how AI actually connects to the physical world: not as a black box, but by making reality readable. Stop watching your video feeds and start talking to them. Try it out here 👇
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Most cameras record the world. They don't understand it. 👁️ Meet Trio-Lumen — point it at any RTSP feed and just tell it what to look for in plain English (e.g., "flag anyone in the loading dock after hours"). It runs frontier vision models 24/7, turning raw video into a live, queryable world-state: who's where, what they're doing, and where they're heading. 🧠 Under the hood, Trio is the world model we are building for the physical world (perceive → represent → predict): 🔹 Perceive (The Eyes): Plug in any frontier model (YOLO, DINOv2, V-JEPA) to extract data from pixels. 🔹 Represent (The Brain): Outputs land in Trio-Retina (open-source), organizing raw video into one structured, queryable database. 🔹 Predict (The Intuition): A dynamics head that learns the patterns of your space to anticipate what comes next. This is how AI actually connects to the physical world: not as a black box, but by making reality readable. Stop watching your video feeds and start talking to them. Try it out here 👇

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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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