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life when you discover an open-source model that runs 300 parallel agents, executes for 12+ hours straight, beats GPT-5.4 and opus 4.6 on multiple benchmarks... and the weights are on huggingface

J.B.

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332,279 просмотров • 2 месяцев назад •via X (Twitter)

Образование Новости и политика Наука и технологии

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This free model just beat every closed-source AI on coding benchmarks. Open weights. 6x cheaper than Opus. The labs don't want you to know it exists. > GLM-5.2 from Zai just topped Code Arena - the first open-weights model to ever hold the #1 coding spot. Not a leaked weight, not a fine-tune. A fully open model beating GPT and Claude on their own turf. It's live on Hugging Face Inference API right now. 5 providers: Novita, Together AI, Fireworks, Deepinfra, Zai. OpenAI-compatible client. → Go to huggingface(.)co → grab HF_TOKEN from account settings → plug into any OpenAI-compatible client 6x cheaper than Opus. Companies bleeding on AI bills are already routing to this for orchestration, caching, and token optimization. The smart money moved before the headline dropped. > Now you know. huggingface(.)co/zai-org/GLM-5.2 Bookmark this before everyone else figures it out.

This free model just beat every closed-source AI on coding benchmarks. Open weights. 6x cheaper than Opus. The labs don't want you to know it exists. > GLM-5.2 from Zai just topped Code Arena - the first open-weights model to ever hold the #1 coding spot. Not a leaked weight, not a fine-tune. A fully open model beating GPT and Claude on their own turf. It's live on Hugging Face Inference API right now. 5 providers: Novita, Together AI, Fireworks, Deepinfra, Zai. OpenAI-compatible client. → Go to huggingface(.)co → grab HF_TOKEN from account settings → plug into any OpenAI-compatible client 6x cheaper than Opus. Companies bleeding on AI bills are already routing to this for orchestration, caching, and token optimization. The smart money moved before the headline dropped. > Now you know. huggingface(.)co/zai-org/GLM-5.2 Bookmark this before everyone else figures it out.

Atenov int.

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Introducing Nomic Embed - the first fully open long context text embedder to beat OpenAI - Open source, open weights, open data - Beats OpenAI text-embeding-3-small and Ada on short and long context benchmarks - Day 1 integrations with @langchain, Llama-index, MongoDB

Introducing Nomic Embed - the first fully open long context text embedder to beat OpenAI - Open source, open weights, open data - Beats OpenAI text-embeding-3-small and Ada on short and long context benchmarks - Day 1 integrations with @langchain, Llama-index, MongoDB

CalCo

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1/ Introducing Glider - the smallest model to beat GPT-4o-mini on eval tasks ⚡🚀 - Open source, open weights, open code - Explainable evaluations by nature - Trained on 183 criteria and 685 domains Try it out for free at 🔥

1/ Introducing Glider - the smallest model to beat GPT-4o-mini on eval tasks ⚡🚀 - Open source, open weights, open code - Explainable evaluations by nature - Trained on 183 criteria and 685 domains Try it out for free at 🔥

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After a weekend of testing and working on something with GPT-5.3-Codex & Opus 4.6, ChatGPT Pro + Claude Max 5x are still the best combo in my opinion Using Codex & Claude Code CLI running in tmux (the desktop apps are still a bit buggy) and they're accessible even on the go from iPhone with Termius (and VPN) Usually starting with GPT-5.3-Codex xhigh and Opus 4.6 in plan mode, discussing changes and options, and each writing their plans down to separate Markdown files Then letting GPT-5.3-Codex xhigh (or Opus 4.6) implement the changes as the first draft, then Opus 4.6 reviews them with a team of agents (works pretty well already with tmux even though still experimental) based on both prepared plans, fixes stuff, and then another review by GPT-5.3-Codex xhigh or vice versa Interesting that even Opus 4.6 still occasionally makes silly mistakes like wrong imports, partial or overly broad renaming of variables/functions, etc Fun fact, OpenAI apparently plans to use their Super Bowl ad to promote Codex - curious what that will look like

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Llama 3.1 Nemotron 70B is the latest model from NVIDIA, released only a few hours ago. Initial testing shows the model outperforms GPT-4o and Sonnet 3.5 on several benchmarks. Try it on Akash Chat for free:

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Opus 4.6 vs GPT 5.4 (High) (1/9) prompt: Build a single-file HTML/CSS/JS (no libs) demo that uses SVG to simulate a plant growing: stem extends, leaves sprout + unfurl with springy/windy “physics”, then seamlessly loops forever. For the initial impressions I'm really impressed by GPT, for speed they both felt about the same, but gpt was still half cheaper than opus. I also much prefer the design and animation that gpt produced, physics on the leaves are super cool and it also loops pretty nicely whilst opus just fades out the plant. Still got a bunch of tests to run but this is really exciting.

Opus 4.6 vs GPT 5.4 (High) (1/9) prompt: Build a single-file HTML/CSS/JS (no libs) demo that uses SVG to simulate a plant growing: stem extends, leaves sprout + unfurl with springy/windy “physics”, then seamlessly loops forever. For the initial impressions I'm really impressed by GPT, for speed they both felt about the same, but gpt was still half cheaper than opus. I also much prefer the design and animation that gpt produced, physics on the leaves are super cool and it also loops pretty nicely whilst opus just fades out the plant. Still got a bunch of tests to run but this is really exciting.

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660,011 просмотров • 4 месяцев назад

I ran Codex, Claude, and Gemini on the same PR review. They disagreed on 4 out of 7 issues. That’s when I realized the best workflow isn’t choosing one model - it’s making them compare notes. So I built Council: an open source CLI that runs all three in parallel, surfaces disagreements, and gives you one synthesized recommendation. npx @armstrng/council review this PR

I ran Codex, Claude, and Gemini on the same PR review. They disagreed on 4 out of 7 issues. That’s when I realized the best workflow isn’t choosing one model - it’s making them compare notes. So I built Council: an open source CLI that runs all three in parallel, surfaces disagreements, and gives you one synthesized recommendation. npx @armstrng/council review this PR

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Llama 2: Now on Hugging Chat 🤗🦙 Try out the 70B Chat model for free with super fast inference, web search, and powered by open-source tools! 👉

Llama 2: Now on Hugging Chat 🤗🦙 Try out the 70B Chat model for free with super fast inference, web search, and powered by open-source tools! 👉

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I don't think there's a single terminal ux that handles agent swarms well With slate, you can literally use Opus 4.6 and GPT 5.4 at the exact same time But making it intuitive took a ton of work So heres a thread on how it works and how to actually use it 🧵

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GLM-5 is live on 0G Compute. 744B parameters. SOTA open-source. MIT license. Beats GPT-5.2 on BrowseComp and Terminal-Bench. Run the world's best open model on decentralized infrastructure. No cloud vendor. No data logging. No ToS surprises. Your prompts stay yours.

GLM-5 is live on 0G Compute. 744B parameters. SOTA open-source. MIT license. Beats GPT-5.2 on BrowseComp and Terminal-Bench. Run the world's best open model on decentralized infrastructure. No cloud vendor. No data logging. No ToS surprises. Your prompts stay yours.

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Anthropic’s Mythos raised the bar for AI vuln detection but kept it invite-only. GPT-5.5 is OpenAI’s answer, and it’s open to all. We had early access. Ran the benchmarks. Blackbox GPT-5.5 already beats whitebox GPT-5. Best pentesting model we’ve tested. Read our analysis:

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Introducing Find the best local models based on real data 1. People run and submit benchmarks 2. Stats aggregated over models / devices 3. Find the best model for you `npx whatcanirun`, fully open-source

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agenc //: 👾 updates • training started on the first agenc ai model. fully open source, open weights. first model will be done training in a few days. • grok 4.20 works flawlessly on the new agenc runtime. agenc was built around the xai api and runs best with grok agents. • runtime got ripped out and replaced from scratch. agentic loop is rock solid. pushing to git soon. benchmarks against the best frameworks out there will follow. • marketplace goes live on mainnet solana the moment it's wired into the new runtime. plug your agents in, make money. • locked another 290M AgenC bringing the total locked to ~360M. • agencone hardware device entering mass production now that the runtime and marketplace are done. • custom mini pcs in the works. agenc and a custom linux preloaded. think openclaw on a macmini but you just order the box. anime art on the case. not apple hardware. • still in the pumpfun hackathon, they've got winners left to announce. love you pumpfun. when agenc. 5yC9BM8KUsJTPbWPLfA2N8qH1s9V8DQ3Vcw1G6Jdpump

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We’re excited to add support for Opus 4.6. With adaptive thinking, Opus 4.6 will decide the right amount of reasoning for your query, reducing latency and improving quality. Internally, we've already resolved bugs that Opus 4.5 couldn't tackle. Try it out on the latest!

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Claude Opus 4.6 is available for all users in Orchids right now! Tag Orchids with any app idea and we’ll give you 100k in free credits to test it out. Comparisons to Opus 4.5, GPT-5.2-Codex, Gemini 3 Flash, and Kimi K2.5 👇 Prompt: Make me an interactive, 3d model of the solar system.

Claude Opus 4.6 is available for all users in Orchids right now! Tag Orchids with any app idea and we’ll give you 100k in free credits to test it out. Comparisons to Opus 4.5, GPT-5.2-Codex, Gemini 3 Flash, and Kimi K2.5 👇 Prompt: Make me an interactive, 3d model of the solar system.

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It's Christmas morning: OpenAI and Anthropic shipped new models on the same day! We tested GPT 5.3 Codex vs. Opus 4.6 head-to-head. Verdict: the models are converging. Here’s what we found 🧵

It's Christmas morning: OpenAI and Anthropic shipped new models on the same day! We tested GPT 5.3 Codex vs. Opus 4.6 head-to-head. Verdict: the models are converging. Here’s what we found 🧵

Every 📧

11,945 просмотров • 5 месяцев назад

You can make $600K. With a bot that earns $83 every hour, 24/7, no days off, 365 days straight. By buying both sides of the same match on Polymarket and locking profit before the game even starts. > The bot buys YES and NO simultaneously on sports markets. > When combined price is below $1 - profit is locked instantly. > 7,877 trades. > $619K in 12 months. Check the wallet here: I reverse-engineered every trade and rebuilt the full logic in Claude Opus 4.6. The strategy, the math, both arb types, the exact 5-step bot architecture - all in the article. Feed it to your OpenClaw, let it spec the bot for Claude Opus 4.6, and you are set.

You can make $600K. With a bot that earns $83 every hour, 24/7, no days off, 365 days straight. By buying both sides of the same match on Polymarket and locking profit before the game even starts. > The bot buys YES and NO simultaneously on sports markets. > When combined price is below $1 - profit is locked instantly. > 7,877 trades. > $619K in 12 months. Check the wallet here: I reverse-engineered every trade and rebuilt the full logic in Claude Opus 4.6. The strategy, the math, both arb types, the exact 5-step bot architecture - all in the article. Feed it to your OpenClaw, let it spec the bot for Claude Opus 4.6, and you are set.

may.crypto {🦅}

997,768 просмотров • 4 месяцев назад

INSANE...this is an Open Source Video model available for free on Hugging Face. LongCat just dropped an amazing video avatar model that can do crazy things. And it's 100% Opensource.

Shubham Saboo

30,601 просмотров • 1 месяц назад

Qwen 3.7-max beats Opus 4.7 and GPT-5.5 We tested three frontier models on a real agentic task: write a Tetris bot that plays the game and trains itself. Each model could read its own code, run benchmarks, and rewrite itself across 10 iterations. Then we compared the final bots head to head. Qwen 3.7-Max: training cost $1.32, bot improvement +56% Claude Opus 4.7: training cost $12.15, bot improvement +28% GPT-5.5: training cost $2.85, bot improvement +7% Qwen won on every dimension - biggest jump, 9× cheaper than Claude, 2× cheaper than GPT. Long agentic loops is where Qwen Max actually delivers.

Qwen 3.7-max beats Opus 4.7 and GPT-5.5 We tested three frontier models on a real agentic task: write a Tetris bot that plays the game and trains itself. Each model could read its own code, run benchmarks, and rewrite itself across 10 iterations. Then we compared the final bots head to head. Qwen 3.7-Max: training cost $1.32, bot improvement +56% Claude Opus 4.7: training cost $12.15, bot improvement +28% GPT-5.5: training cost $2.85, bot improvement +7% Qwen won on every dimension - biggest jump, 9× cheaper than Claude, 2× cheaper than GPT. Long agentic loops is where Qwen Max actually delivers.

atomic.chat

863,548 просмотров • 1 месяц назад