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      "text": "This week we will be removing some additional low utilization models from the platform. This is partly in order to free up hardware and partly because they just are not used enough to justify their existence on the platform. \n\nThe following models will be removed this Friday May 1st at 1 PM EST\n\nchroma,\nunsloth/gemma-3-27b-it,\nunsloth/gemma-3-12b-it,\nunsloth/gemma-3-4b-it,\nqwen-image-edit-2509,\nQwen/Qwen3-30B-A3B,\nQwen/Qwen2.5-72B-Instruct,\nQwen/Qwen3-Coder-Next-TEE,\nQwen/Qwen3-235B-A22B-Instruct-2507-TEE,\ndeepseek-ai/DeepSeek-R1-Distill-Llama-70B,\nopenai/gpt-oss-120b-TEE,\nNousResearch/DeepHermes-3-Mistral-24B-Preview,\n\nAdditionally the following models will be consolidated into one FP8 TEE Chute.\n\nzai-org/GLM-4.7-FP8,\nzai-org/GLM-4.7-TEE,\n\nThanks for your understanding",
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      "text": "Round two of Chutes & Answers.\n\nA couple of updates since last time, and we've got a little alpha to drop tomorrow.\n\n2PM EDT, Thursday. Pull up. https://t.co/fCh1VtSEu6",
      "created_at": "2026-04-29T10:30:12-03:00",
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      "handle": "chutes_ai",
      "text": "RT @jon_durbin: Progress!  < $10/hr to pre-train a 20b MoE.  Not too bad.\n\n8 VMs, each with a single l40s GPU, spread across Poland, France\u2026",
      "created_at": "2026-04-28T21:51:47-03:00",
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      "handle": "chutes_ai",
      "text": "Auto Top-Up is now in billing.\n\nHead to: https://t.co/tyh8p0c6TO\n\nPick a floor and a refill amount, save a card, and your balance refills automatically.\n\nPeople asked for it and we delivered.\n\nhttps://t.co/TrASBRm9cF https://t.co/LQT6ZS94DW",
      "created_at": "2026-04-28T10:30:14-03:00",
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      "text": "Three weeks of pricing comparisons. Someone should ask the obvious question.\n\nMiniMax M2.5 TEE at $0.15/M input. Sonnet 4.6 at $3.00/M. That's a 95% gap. With hardware privacy on the cheap side.\n\nHow?\n\nOpenAI rents or builds data centers. Staffs them. Amortizes the capex across every token. Anthropic does the same. Google does the same. Their prices cover infrastructure, headcount, real estate, and margin.\n\nChutes runs on Bittensor Subnet 64. GPU operators worldwide compete to serve inference. \n\nThey set their own prices. \n\nOutperform \u2192 earn more TAO. Underperform \u2192 lose traffic.\n\nNo data center lease to recoup. No facilities team. No single company setting prices in a boardroom.\n\nThe GPU market finds the price. Competitive markets produce lower prices than monopolies. That's not a hack. That's how markets work.\n\n100B+ tokens per day. Months of sustained throughput. 700k+ users. $1.4M in 90 days.\nDoes the pricing make more sense now? Or do you still think cheap means broken?\n\nhttps://t.co/s7V7nkGkj0 | $TAO",
      "created_at": "2026-04-27T10:30:48-03:00",
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      "text": "A 27B dense model outscored Claude 4.5 Opus on vision.\n\nQwen3.6-27B. Live on Chutes.\n\nQwen's newest open source flagship. Image, video, and text native. 262K context (extensible to 1M). Apache 2.0.\n\nHead-to-head vs Claude 4.5 Opus on vision:\n- V*: 94.7 vs 67.0\n- CountBench: 97.8 vs 90.6\n- VideoMME (w/ sub): 87.7 vs 77.7\n- ERQA: 62.5 vs 46.8\n- CharXiv RQ: 78.4 vs 68.5\n\nTerminal-Bench 2.0 matches Claude 4.5 Opus at 59.3. New \"Preserve Thinking\" mode keeps reasoning traces across turns for agent workflows.\n\nRunning inside a TEE on Chutes. The GPU operators serving the model can't see your prompts or outputs.\n\n$0.195 in / $1.56 out per million tokens.\n\nTry it: https://t.co/labWgerqnq",
      "created_at": "2026-04-24T10:21:04-03:00",
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      "handle": "chutes_ai",
      "text": "RT @jon_durbin: Here's a visual (estimate) to show what I've been working on and why.  Minimum \"island\" size for distributed training gets\u2026",
      "created_at": "2026-04-23T13:19:03-03:00",
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      "text": "How does Subnet 64 verify that miners are running the GPUs they claim to be running?\n\nMost decentralized compute networks rely on trust. Subnet 64 verifies every GPU before it serves a request.\n\nEvery GPU on the network gets validated by GraVal, our open-source validation library. \n\nThe GPU runs device-info-seeded matrix operations. Validators check that the results match the expected output, and that 95% of the claimed VRAM is available for those operations.\n\nSpoofed GPUs and GPUs with partitioned VRAM fail the test.\n\nThe encryption layer adds a second check. Chutes encrypts every request with keys only the exact GPU advertised can decrypt. Reroute to a different GPU on the same server and decryption fails. The runtime drops the job.\n\nMiners who underperform lose traffic, and miners who try to cheat get caught by validators running cross-verification on the outputs.\n\nAll of this runs at 100B+ tokens per day.\n\nhttps://t.co/yeAlcy4BzO",
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      "handle": "chutes_ai",
      "text": "@oroagents (SN15) is now live on Chutes \n\nOro is building an arena for AI shopping agents. Miners train agents, agents compete on real shopping tasks. Qualifiers first, then the race.\n\nMiners run their agents on Chutes. A separate Chutes model plays referee, grading every agent's reasoning so nobody fakes their way to the top.\n\nSign in with Chutes keeps the setup plug and play. Miners bring their own credits. The arena stays fair.\n\nNext up: harder tasks, bigger real-world scenarios.\n\nSN15 x SN64. Subnet building on subnet. \n\n$TAO",
      "created_at": "2026-04-22T11:30:10-03:00",
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      "handle": "chutes_ai",
      "text": "Kimi K2.6 by @Kimi_Moonshot is now live on Chutes.\n\n54.0 on HLE-Full with tools. Ahead of GPT-5.4 (52.1), Claude Opus 4.6 (53.0), and Gemini 3.1 Pro (51.4).\n\nIt also leads on:\n- SWE-Bench Pro: 58.6 (ahead of all three again)\n- DeepSearchQA f1: 92.5 (next closest is Claude at 91.3)\n- BrowseComp with Agent Swarm: 86.3 (up from K2.5's 78.4)\n\n1T parameters, 32B activated. 256K context. Native multimodal. Modified MIT license.\n\nAgent Swarm now scales to 300 sub-agents across 4,000 coordinated steps in a single run.\n\nRunning inside a TEE on Chutes. The GPU operators serving the model can't see your prompts or outputs.\n\n$0.95 in / $4.00 out per million tokens.\n\nTry it now: https://t.co/Ukw3jPmLw9",
      "created_at": "2026-04-21T10:30:10-03:00",
      "likes": 229,
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    {
      "tweet_id": "2046219877426311386",
      "handle": "chutes_ai",
      "text": "How long does your inference provider take to host a new open-source model after it drops on Hugging Face?\n\nFor most, weeks or months. Some never even bother.\n\nChutes gets almost all major releases live within a day. Kimi K2.5, MiniMax M2.5, Qwen3.5-397B-A17B, GLM-5.1. \n\nAll running with TEE where the model supports it.\n\nThe best open-source model today won't be the best one in three months. Six months from now, you'll be running models that don't exist yet. You want a provider that hosts them on day one, not day ninety. \n\nWhat open-source model are you waiting on?\n\nhttps://t.co/s7V7nkGkj0",
      "created_at": "2026-04-20T10:30:10-03:00",
      "likes": 154,
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    {
      "tweet_id": "2045147389539811737",
      "handle": "chutes_ai",
      "text": "How much of your AI provider's stack can you read?\n\nOpenAI gives you a privacy policy. Their inference engine, load balancer, and the code that handles your plaintext are all closed.\n\nChutes is open source top to bottom:\n\u2192 Python SDK: chutesai/chutes\n\u2192 API server: chutesai/chutes-api\n\u2192 Inference engines (vLLM and SGLang forks): chutesai/vllm, chutesai/sglang\n\u2192 E2EE proxy with post-quantum crypto: chutesai/e2ee-proxy\n\u2192 Claude Code proxy: chutesai/claude-proxy\n\u2192 Codex proxy: chutesai/responses-proxy\n\u2192 OAuth SDK: chutesai/Sign-in-with-Chutes\n\u2192 GPU verification library: chutesai/graval\n\nFork it and audit it before you sign a contract.\n\nOpenAI publishes a privacy policy. We publish the source code.\n\nhttps://t.co/WXXvDPTNva\n\nWhat's the one part of your AI provider's stack you wish you could read?",
      "created_at": "2026-04-17T11:28:29-03:00",
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      "tweet_id": "2044770375465103734",
      "handle": "chutes_ai",
      "text": "When Claude Opus solves a math problem and \"thinks\" for 30 seconds before answering, what are you paying for?\n\nReasoning tokens, billed at full output rates.\n\nOpus 4.6 charges $25 per million output tokens. A serious reasoning task can burn 5,000 to 10,000 reasoning tokens before producing an answer. That works out to $0.13 to $0.25 per query.\n\nGLM 5.1 TEE on Chutes runs $0.95 input, $3.15 output. The same query at the same depth of reasoning costs $0.016 to $0.032.\n\nAbout 8x cheaper per reasoning query, with hardware-enforced privacy included.\n\nGLM 5.1 scores 51 on the Artificial Analysis Intelligence Index, two points behind Claude Opus 4.6 and leading among open-weights reasoning models. 200K context, full chain-of-thought, JSON mode, structured outputs. It also holds the state-of-the-art on SWE-Bench Pro at 58.4, ahead of Opus 4.6, GPT-5.4, and Gemini 3.1 Pro.\n\nIf you're building anything that needs the model to think before answering (math tutoring, scientific analysis, deep code review, multi-step research agents), this is the one to benchmark against your current default.\n\nWhat kind of reasoning workload are you running? Curious if Opus is overkill for it. \n\n\ud83d\udd17  https://t.co/VGvNNWfI0k",
      "created_at": "2026-04-16T10:30:22-03:00",
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    {
      "tweet_id": "2044407948206608462",
      "handle": "chutes_ai",
      "text": "Integration Update: @nevika_ai just added Chutes to their provider dropdown.\n\nIf you're already running your chats there, you can now point them at any of the 50+ open source models we host, including DeepSeek, GLM, Kimi and MiniMax models.\n\nGo check them out!\nhttps://t.co/9kXkkrL13g",
      "created_at": "2026-04-15T10:30:12-03:00",
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      "tweet_id": "2044090671695364229",
      "handle": "chutes_ai",
      "text": "RT @KyleWidmann: Our TEE implementation was the foundation for secure compute at https://t.co/HmDy3VZBh7, but we\u2019re not stopping there.\n\nWe\u2026",
      "created_at": "2026-04-14T13:29:28-03:00",
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    {
      "tweet_id": "2044087358958542905",
      "handle": "chutes_ai",
      "text": "RT @GenLayer: This is the showcase of a new agentic future!\n\nAnnouncing the winners of the Bradbury Builders Hackathon, where 280+ builders\u2026",
      "created_at": "2026-04-14T13:16:18-03:00",
      "likes": 206,
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    {
      "tweet_id": "2044056892247339470",
      "handle": "chutes_ai",
      "text": "imagine helping train a frontier-scale model from your gaming PC.\n\nthat's roughly where @jon_durbin thinks decentralized training is headed. \n\nhe walked through the whole picture on yesterday's Chutes AMA. recap below \ud83d\udc47 https://t.co/zy6pNCXx3Q",
      "created_at": "2026-04-14T11:15:14-03:00",
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    {
      "tweet_id": "2043733274141569494",
      "handle": "chutes_ai",
      "text": "Starting in 10 minutes! \n\nhttps://t.co/yethejB5FZ",
      "created_at": "2026-04-13T13:49:17-03:00",
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      "tweet_id": "2043463021021032905",
      "handle": "chutes_ai",
      "text": "Tomorrow at 1pm EST! Turn your reminders on \ud83d\udc40!\n\nhttps://t.co/yethejB5FZ",
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      "handle": "chutes_ai",
      "text": "RT @JesusMartinez: Jon Durbin is a core contributor to Chutes, the biggest AI subnet on Bittensor\n\n72 hours after Covenant AI rage-quit the\u2026",
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