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Introducing Lucy: 1.7B model that Google for you It's an agentic‑search model that can even run on your phone. - Agentic search on tap - Lucy calls tools ( ‑aware) - Fits in your pocket - runs on CPU or mobile Under the hood: - Built on Qwen's Qwen3‑1.7B... show more

Menlo Research

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Introducing Jan-v1: 4B model for web search, an open-source alternative to Perplexity Pro. In our evals, Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally. Use cases: - Web search - Deep Research Built on the new version of Qwen's Qwen3-4B-Thinking (up to 256k context length), fine-tuned for reasoning and tool use in Jan. You can run the model in Jan, llama.cpp, or vLLM. To enable search in Jan, go to Settings → Experimental Features → On, then Settings → MCP Servers → enable a search-related MCP such as Serper. Use the model: - Jan-v1-4B: - Jan-v1-4B-GGUF: Credit to the Qwen team for Qwen3 4B Thinking & Georgi Gerganov for llama.cpp.

Introducing Jan-v1: 4B model for web search, an open-source alternative to Perplexity Pro. In our evals, Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally. Use cases: - Web search - Deep Research Built on the new version of Qwen's Qwen3-4B-Thinking (up to 256k context length), fine-tuned for reasoning and tool use in Jan. You can run the model in Jan, llama.cpp, or vLLM. To enable search in Jan, go to Settings → Experimental Features → On, then Settings → MCP Servers → enable a search-related MCP such as Serper. Use the model: - Jan-v1-4B: - Jan-v1-4B-GGUF: Credit to the Qwen team for Qwen3 4B Thinking & Georgi Gerganov for llama.cpp.

👋 Jan

690,191 görüntüleme • 10 ay önce

Jan v1 is now more reliable when handling searches. It's an open-source alternative to Perplexity Pro, and it now avoids infinite loops with Jan v1 2509. Performance updates: - Gains on reasoning & creativity benchmarks - Small drop on SimpleQA (91.1 -> 90.7), still competitive with Perplexity Pro The updated version is live on Jan Hub. You can run the model in Jan, llama.cpp, or vLLM. Use the model: - Model: - GGUF: Shoutout to the Qwen team for Qwen3-4B Thinking and Georgi Gerganov for llama.cpp.

Jan v1 is now more reliable when handling searches. It's an open-source alternative to Perplexity Pro, and it now avoids infinite loops with Jan v1 2509. Performance updates: - Gains on reasoning & creativity benchmarks - Small drop on SimpleQA (91.1 -> 90.7), still competitive with Perplexity Pro The updated version is live on Jan Hub. You can run the model in Jan, llama.cpp, or vLLM. Use the model: - Model: - GGUF: Shoutout to the Qwen team for Qwen3-4B Thinking and Georgi Gerganov for llama.cpp.

👋 Jan

17,759 görüntüleme • 9 ay önce

You can run Kimi-K2-Thinking, the most strongest agentic model, in Jan through Hugging Face. Add it to your Hugging Face Inference models to use it.

You can run Kimi-K2-Thinking, the most strongest agentic model, in Jan through Hugging Face. Add it to your Hugging Face Inference models to use it.

👋 Jan

40,181 görüntüleme • 7 ay önce

Meet Jan-nano, a 4B model that outscores DeepSeek-v3-671B using MCP. It's built on Qwen3-4B with DAPO fine-tuning, it handles: - real-time web search - deep research Model + GGUF: To run it locally: - Install Jan Beta: - Download Jan-nano in Jan Hub - Settings -> MCP, enable MCP and add your Serper API key for web tools Full technical report will be published shortly.

Meet Jan-nano, a 4B model that outscores DeepSeek-v3-671B using MCP. It's built on Qwen3-4B with DAPO fine-tuning, it handles: - real-time web search - deep research Model + GGUF: To run it locally: - Install Jan Beta: - Download Jan-nano in Jan Hub - Settings -> MCP, enable MCP and add your Serper API key for web tools Full technical report will be published shortly.

Menlo Research

56,600 görüntüleme • 1 yıl önce

LFM2-8B-A1B just dropped on Hugging Face! 8.3B params with only 1.5B active/token 🚀 > Quality ≈ 3–4B dense, yet faster than Qwen3-1.7B > MoE designed to run on phones/laptops (llama.cpp / vLLM) > Pre-trained on 12T tokens → strong math/code/IF

LFM2-8B-A1B just dropped on Hugging Face! 8.3B params with only 1.5B active/token 🚀 > Quality ≈ 3–4B dense, yet faster than Qwen3-1.7B > MoE designed to run on phones/laptops (llama.cpp / vLLM) > Pre-trained on 12T tokens → strong math/code/IF

Maxime Labonne

40,714 görüntüleme • 8 ay önce

SOMEONE BUILT AN AI-POWERED SEARCH ENGINE THAT INDEXES YOUR ENTIRE IMESSAGE DATABASE LOCALLY USING CURSOR-STYLE AGENTIC SEARCH, IMAGE EMBEDDINGS, AND CLAUDE FOR ORCHESTRATION. YOUR WHOLE MESSAGE HISTORY. FULLY SEARCHABLE. RUNS ON YOUR MAC. OPEN SOURCE.

SOMEONE BUILT AN AI-POWERED SEARCH ENGINE THAT INDEXES YOUR ENTIRE IMESSAGE DATABASE LOCALLY USING CURSOR-STYLE AGENTIC SEARCH, IMAGE EMBEDDINGS, AND CLAUDE FOR ORCHESTRATION. YOUR WHOLE MESSAGE HISTORY. FULLY SEARCHABLE. RUNS ON YOUR MAC. OPEN SOURCE.

0xMarioNawfal

67,323 görüntüleme • 3 ay önce

I seriously cannot believe this is a 0.6B LLM! 🤯 Qwen just released Qwen3, a series of hybrid reasoning models that allow you to control how much "thinking" the model does for a given task. They can even run locally in your browser on WebGPU with 🤗 Transformers.js!

I seriously cannot believe this is a 0.6B LLM! 🤯 Qwen just released Qwen3, a series of hybrid reasoning models that allow you to control how much "thinking" the model does for a given task. They can even run locally in your browser on WebGPU with 🤗 Transformers.js!

Xenova

74,954 görüntüleme • 1 yıl önce

Recipe to post-train Qwen3 1.7B into a DeepResearch model What does it mean for something small to think deeply? Meet Lucy, a post‑trained Qwen3‑1.7B as a DeepResearch model based on will brown's verifiers. Primary Rule-based Rewards: - Answer correctness We check whether the final response literally contains the ground-truth answer. This substring match is cheap and avoids calling a larger LLM judge. - Visit/search ratio If the agent visits at least as many pages as it issues search queries, it receives ((visit_search_ratio - 1) / 4) ** 0.25. If it searches more than it visits, the score is -0.5. Format / Anti Reward-Hacking Rewards: - Tool execution success Each API call that returns without an error counts. The reward is (successful_calls * unique_tools_used) / total_call_attempts. - Thinking efficiency A skew-normal penalty centered at 70 tokens discourages endless chain-of-thought between tool calling while still allowing enough tokens for planning. This is how Qwen3 1.7B learned to search, visit, and synthesize information. Small models can do deep research too!

Recipe to post-train Qwen3 1.7B into a DeepResearch model What does it mean for something small to think deeply? Meet Lucy, a post‑trained Qwen3‑1.7B as a DeepResearch model based on will brown's verifiers. Primary Rule-based Rewards: - Answer correctness We check whether the final response literally contains the ground-truth answer. This substring match is cheap and avoids calling a larger LLM judge. - Visit/search ratio If the agent visits at least as many pages as it issues search queries, it receives ((visit_search_ratio - 1) / 4) ** 0.25. If it searches more than it visits, the score is -0.5. Format / Anti Reward-Hacking Rewards: - Tool execution success Each API call that returns without an error counts. The reward is (successful_calls * unique_tools_used) / total_call_attempts. - Thinking efficiency A skew-normal penalty centered at 70 tokens discourages endless chain-of-thought between tool calling while still allowing enough tokens for planning. This is how Qwen3 1.7B learned to search, visit, and synthesize information. Small models can do deep research too!

Casper Hansen

39,684 görüntüleme • 11 ay önce

Introducing a brand new way to use Jan 💛 You can now search the web, do deep research, and let Jan use your browser. Powered by Jan-v2-VL-Max, 30B model beating Gemini 2.5 Pro & DeepSeek R1 on execution benchmarks. Try it:

👋 Jan

29,403 görüntüleme • 6 ay önce

(1/5) Gemini 3, our most intelligent model, is landing in Google Search today – starting with AI Mode. Excited that this is the first time we’re shipping a new Gemini model in Search on day one! 🚀 In Search, Gemini 3 with generative layouts will make it easy to get a rich understanding of anything on your mind. It has state-of-the-art reasoning, deep multimodal understanding and advanced agentic capabilities. That allows the model to shine when you ask it to explain advanced concepts or ideas – it reasons and can code interactive visuals in real-time. It can tackle your toughest questions like advanced science.

(1/5) Gemini 3, our most intelligent model, is landing in Google Search today – starting with AI Mode. Excited that this is the first time we’re shipping a new Gemini model in Search on day one! 🚀 In Search, Gemini 3 with generative layouts will make it easy to get a rich understanding of anything on your mind. It has state-of-the-art reasoning, deep multimodal understanding and advanced agentic capabilities. That allows the model to shine when you ask it to explain advanced concepts or ideas – it reasons and can code interactive visuals in real-time. It can tackle your toughest questions like advanced science.

Robby Stein

94,728 görüntüleme • 7 ay önce

Wild. Kimi K2 Thinking just released and it's insane. It's an AI model that can run by itself for hours on end and make HUNDREDS of tool calls It's the 1st model I think that can replace humans In this video I show why it's so special and how to use it to build your first app

Wild. Kimi K2 Thinking just released and it's insane. It's an AI model that can run by itself for hours on end and make HUNDREDS of tool calls It's the 1st model I think that can replace humans In this video I show why it's so special and how to use it to build your first app

Alex Finn

70,004 görüntüleme • 7 ay önce

I built a Deepseek R1 RAG Reasoning Agent running locally on my computer. It's an Agentic RAG reasoning agent that can think, reason and fall back to web search if needed. 100% Opensource code with step-by-step tutorial.

I built a Deepseek R1 RAG Reasoning Agent running locally on my computer. It's an Agentic RAG reasoning agent that can think, reason and fall back to web search if needed. 100% Opensource code with step-by-step tutorial.

Shubham Saboo

124,490 görüntüleme • 1 yıl önce

Just made an MCP server to use Anthropic’s new web search tool. What I love about it is that the search is agentic. You can use any model to call a Claude instance that returns processed search results. Or specify how deep the search should be. 🔗 Link in the comments.

Just made an MCP server to use Anthropic’s new web search tool. What I love about it is that the search is agentic. You can use any model to call a Claude instance that returns processed search results. Or specify how deep the search should be. 🔗 Link in the comments.

Pietro Schirano

29,189 görüntüleme • 1 yıl önce

For ongoing tasks, like planning a wedding or managing a move, Search will soon be able to build custom experiences with Google Google Antigravity that you can continue to come back to and make progress on. Think of them like building your own mini apps right in Search. Here’s Robby Stein, VP of Product, Search demoing the new tool on the #GoogleIO stage.

For ongoing tasks, like planning a wedding or managing a move, Search will soon be able to build custom experiences with Google Google Antigravity that you can continue to come back to and make progress on. Think of them like building your own mini apps right in Search. Here’s Robby Stein, VP of Product, Search demoing the new tool on the #GoogleIO stage.

Google

79,017 görüntüleme • 19 gün önce

You can now use AI agents with MEGA, on your terms Run them locally or connect a model you trust. Search, organise, and manage your files using simple prompts. Plus, you stay in control of what the AI can access and what it can do. Privacy first. Always 🔒 See what’s possible:

You can now use AI agents with MEGA, on your terms Run them locally or connect a model you trust. Search, organise, and manage your files using simple prompts. Plus, you stay in control of what the AI can access and what it can do. Privacy first. Always 🔒 See what’s possible:

MEGA

16,791 görüntüleme • 3 ay önce

🎙️ How to go Live with Search: Open the Google app on Android or iOS and tap the Live icon under the Search bar. From there, you can ask your question out loud to get a helpful audio response, then continue the conversation with follow-up questions or dive deeper with helpful web links.

🎙️ How to go Live with Search: Open the Google app on Android or iOS and tap the Live icon under the Search bar. From there, you can ask your question out loud to get a helpful audio response, then continue the conversation with follow-up questions or dive deeper with helpful web links.

Google

42,076 görüntüleme • 3 ay önce

if i said i think natsu was crushing on lucy way before lucy had feelings for him?

if i said i think natsu was crushing on lucy way before lucy had feelings for him?

mia⁷

49,939 görüntüleme • 7 ay önce

Meet Waldo: Glean’s first agentic search model. Built on NVIDIA Nemotron 3 Nano and post-trained for search planning, Waldo figures out how to break down a query, which tools to call, what to read next, and when it has enough evidence to hand off.

Meet Waldo: Glean’s first agentic search model. Built on NVIDIA Nemotron 3 Nano and post-trained for search planning, Waldo figures out how to break down a query, which tools to call, what to read next, and when it has enough evidence to hand off.

Glean

98,327 görüntüleme • 2 ay önce

Alibaba just changed the AI game! Their QwQ-32B model delivers premium performance but it's completely free and open source. • Outperforms on key benchmarks • 32B parameters (vs 671B for DeepSeek) • Run locally or use web interface • Thinking mode for reasoning • Web search integration Save this video if you need powerful AI tools without the expensive subscriptions 💡 Want the SOP? DM me.

Alibaba just changed the AI game! Their QwQ-32B model delivers premium performance but it's completely free and open source. • Outperforms on key benchmarks • 32B parameters (vs 671B for DeepSeek) • Run locally or use web interface • Thinking mode for reasoning • Web search integration Save this video if you need powerful AI tools without the expensive subscriptions 💡 Want the SOP? DM me.

Julian Goldie SEO

16,920 görüntüleme • 1 yıl önce

ReZero: A small model that learns to search - it never gives up 🔥 ReZero trains with synthetic search engines that force the model to retry, refine, and persist until it finds a better answer (never give up 💪). It's built on Meta's Llama 3.2B. Instead of optimizing for recall or speed, we train the model to retry when it's wrong - using reinforcement learning to build persistence into the search process. - Model: - Code: Thanks to AI at Meta for the Llama 3.2B base, Unsloth AI for AutoDidact (the framework we built on), and Colin Kealty for quantizing the model!

ReZero: A small model that learns to search - it never gives up 🔥 ReZero trains with synthetic search engines that force the model to retry, refine, and persist until it finds a better answer (never give up 💪). It's built on Meta's Llama 3.2B. Instead of optimizing for recall or speed, we train the model to retry when it's wrong - using reinforcement learning to build persistence into the search process. - Model: - Code: Thanks to AI at Meta for the Llama 3.2B base, Unsloth AI for AutoDidact (the framework we built on), and Colin Kealty for quantizing the model!

Menlo Research

13,948 görüntüleme • 1 yıl önce