🔴 High Significance
Model Releases
🔴 We are finally there: Qwen3.6-27B + agentic search; 95.7% SimpleQA on a single 3090, fully local — score 73
Sources: reddit/r/LocalLLaMA
LDR maintainer here. Thanks to the strong support of r/LocalLLaMA community LDR got very far. I haven't reported in a while because I thought I was not ready for another prominent post in one of the leading outlets of Local LLM research.
But I think the LDR community finally there again. I think it
Developer Tools
🔴 TauricResearch/TradingAgents — TradingAgents: Multi-Agents LLM Financial Trading Framework — score 99
Sources: github_trending
TradingAgents: Multi-Agents LLM Financial Trading Framework
🔴 ruvnet/ruflo — 🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code / Codex Integration — score 96
Sources: github_trending
🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code / Codex Integration
🔴 Hmbown/DeepSeek-TUI — Coding agent for DeepSeek models that runs in your terminal — score 91
Sources: github_trending
Coding agent for DeepSeek models that runs in your terminal
🔴 Open Design: Use Your Coding Agent as a Design Engine — score 90
Sources: hackernews
🔴 1jehuang/jcode — Coding Agent Harness — score 88
Sources: github_trending
Coding Agent Harness
Omitted 5 additional developer tools items from the main section; see raw data and source-specific sections below.
Research Papers
🔴 Efficient Training on Multiple Consumer GPUs with RoundPipe — score 82
Sources: huggingface · arxiv/cs.AI
Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe interconnects. Pipeline parallelism combined with CPU offloading mitigates these hardware bottlenecks by reducing communication overhead. However, existing PP
🔴 Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows — score 78
Sources: huggingface · arxiv/cs.AI
LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, making it difficult to evaluate agents against evolving workflow deman
🔴 Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence — score 75
Sources: huggingface
We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 Nano Omni delivers consistent accuracy improvements over its predecessor, Nemotron Nano V2 VL, across all modalities, ena
Other Signals
🔴 Been using Qwen-3.6-27B-q8_k_xl + VSCode + RTX 6000 Pro As Daily Driver — score 88
Sources: reddit/r/LocalLLaMA
So in response to the Great Token Reconning of 2026, I decided to try out Qwen 3.6 as a daily driver, and although it's only been about a day, I have to say I'm thoroughly impressed.
I had to download the VSCode insiders edition and set up the local models to support - super easy. Then I messed aro
🔴 Qwen3.6-27B at 72 tok/s on RTX 3090 on Windows using native vLLM (no WSL, no Docker), portable launcher and installer — score 81
Sources: reddit/r/LocalLLaMA
The angle here is native Windows, no WSL. Simple installation, open source, no telemetry. Not selling or promoting anything: https://github.com/devnen/qwen3.6-windows-server
Numbers (RTX 3090, Windows 10):
- 72 tok/s short prompt
- 64.5 tok/s long prompt (~25k tokens)
- 53.4 tok/s at 127k ctx (
🟡 Notable
Model Releases
🟡 **[@OpenAI: One week since the launch of GPT-5.5, and it’s already our strongest model launch yet.
API revenue is growing more than 2x faster than any prior release, while Codex doubled revenue in under seven d](https://nitter.net/OpenAI/status/2050250926888468929#m)** — score 60
Sources: twitter_rss
One week since the launch of GPT-5.5, and it’s already our strongest model launch yet.
API revenue is growing more than 2x faster than any prior release, while Codex doubled revenue in under seven days as enterprise demand for agentic coding tools keeps climbing.
🟡 **[@xai: Voice Cloning is now live via the xAI API!
Create a custom voice in less than 2 minutes or select from our library of 80+ voices across 28 languages to personalize your voice agents, audiobooks, vide](https://nitter.net/xai/status/2050355373052223585#m)** — score 60
Sources: twitter_rss
Voice Cloning is now live via the xAI API!
Create a custom voice in less than 2 minutes or select from our library of 80+ voices across 28 languages to personalize your voice agents, audiobooks, video game characters, and more.
http://x.ai/news/grok-custom-voices
🟡 **[@xai: Introducing Grok Voice Think Fast 1.0
A state-of-the-art voice model built for complex, multi-step workflows with snappy responses and high accuracy.
It takes the top spot on the Tau Voice Bench and](https://nitter.net/xai/status/2047441173569216721#m)** — score 60
Sources: twitter_rss
Introducing Grok Voice Think Fast 1.0
A state-of-the-art voice model built for complex, multi-step workflows with snappy responses and high accuracy.
It takes the top spot on the Tau Voice Bench and handles real-world messiness like noise, accents, and interruptions better than any other model in
🟡 **[@MistralAI: 🆕 Today, we're releasing the public preview of Workflows, the orchestration layer for enterprise AI.
🌎 Enterprise teams have capable models. What they don't have is a way to run them reliably in prod](https://nitter.net/MistralAI/status/2049128071874179091#m)** — score 60
Sources: twitter_rss
🆕 Today, we're releasing the public preview of Workflows, the orchestration layer for enterprise AI.
🌎 Enterprise teams have capable models. What they don't have is a way to run them reliably in production. That's the gap Workflows fills. It takes AI-powered business processes from prototype to pro
🟡 Unsloth solved bug in Mistral Medium 3.5 implementation — score 58
Sources: reddit/r/LocalLLaMA
"May 1, 2026 Update: We worked with Mistral to fix Mistral Medium 3.5 inference affecting some implementations, and released updated GGUFs with the fix (NOT related to Unsloth or our quants). The issue was c
Omitted 5 additional model releases items from the main section; see raw data and source-specific sections below.
Developer Tools
🟡 google-research/timesfm — TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. — score 65
Sources: github_trending
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
🟡 What if AI agents weren’t allowed to declare success? — score 64
Sources: reddit/r/AIAgents
Most agent systems trust the agent.
If the agent says “task complete”, the system accepts it.
I’ve been experimenting with the opposite idea:
what if the system treated the agent as untrusted?
Built a small kernel that does one thing:
→ the agent can propose an outcome
→ the kernel decides i
Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped withhttps://github.
🟡 junhoyeo/tokscale — 🛰️ A CLI tool for tracking token usage from OpenCode, Claude Code, 🦞OpenClaw (Clawdbot/Moltbot), Pi, Codex, Gemini, Cursor, AmpCode, Factory Droid, Kimi, and more! • 🏅Global Leaderboard + 2D/3D Contributions Graph — score 50
Sources: github_trending
🛰️ A CLI tool for tracking token usage from OpenCode, Claude Code, 🦞OpenClaw (Clawdbot/Moltbot), Pi, Codex, Gemini, Cursor, AmpCode, Factory Droid, Kimi, and more! • 🏅Global Leaderboard + 2D/3D Contributions Graph
🟡 **[@OpenAI: Bring your workflow to Codex in just a few clicks.
Import settings, plugins, agents, project configuration, and more so you can keep working with fewer interruptions.
Your move.](https://nitter.net/OpenAI/status/2050290618187055175#m)** — score 50
Sources: twitter_rss
Bring your workflow to Codex in just a few clicks.
Import settings, plugins, agents, project configuration, and more so you can keep working with fewer interruptions.
Your move.
Omitted 4 additional developer tools items from the main section; see raw data and source-specific sections below.
Infrastructure & Compute
🟡 I spent years building a 103B-token Usenet corpus (1980–2013) and finally documented it [P] — score 56
Sources: reddit/r/MachineLearning
For the past several years I've been quietly assembling and processing what I believe is one of the larger privately held pretraining corpora around... a complete Usenet archive spanning 1980 to 2013.
Here's what it ended up being:
- 103.1 billion tokens (cl100k_base)
- 408 million posts
Research Papers
🟡 Step-level Optimization for Efficient Computer-use Agents — score 68
Sources: huggingface · arxiv/cs.AI
Computer-use agents provide a promising path toward general software automation because they can interact directly with arbitrary graphical user interfaces instead of relying on brittle, application-specific integrations. Despite recent advances in benchmark performance, strong computer-use agents r
🟡 Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models — score 62
Sources: huggingface · arxiv/cs.AI
Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data, which are further elicited via Chain-of-Thought (CoT) practices. However, whether fundamental reasoning patterns, such as induction, deduction, and abduction, can be decou
🟡 Instruction-Guided Poetry Generation in Arabic and Its Dialects — score 52
Sources: huggingface · arxiv/cs.AI
Poetry has long been a central art form for Arabic speakers, serving as a powerful medium of expression and cultural identity. While modern Arabic speakers continue to value poetry, existing research on Arabic poetry within Large Language Models (LLMs) has primarily focused on analysis tasks such as
🟡 Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization — score 45
Sources: huggingface
Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets provide only single, holistic annotations, resulting in severe label noise: images that excel in some dimensions but are deficient in others are sim
Other Signals
🟡 A Dark-Money Campaign Is Paying Influencers to Frame Chinese AI as a Threat — score 66
Sources: reddit/r/LocalLLaMA
Build American AI, a nonprofit linked to a super PAC bankrolled by executives at OpenAI and Andreessen Horowitz, is funding a campaign to spread pro-AI messaging and stoke fears about China.
So Local LLM is important .... always! Need to support who giving us more Open source & weights. [La
🟡 Show HN: Mljar Studio – local AI data analyst that saves analysis as notebooks — score 50
Sources: hackernews
🟡 Why ML conference reviews sometimes feel like a “lottery“ [D] — score 44
Sources: reddit/r/MachineLearning
I’ve been trying to make sense of all the “ML conferences are a lottery” takes, and honestly I think it’s both true and not true depending on what you mean.
If a paper is clearly strong, like genuinely solid contribution, well executed, easy to understand, it usually gets in. And if it’s clearly we
🟡 "Prompt Engineering" certs are a joke. So we built a FREE Agentic AI Practitioner Exam that actually forces you to build working swarms to pass. — score 44
Sources: reddit/r/AIAgents
Hey Everyone,
If you look at the AI education space right now, it’s flooded with basic "Prompt Engineering" certificates that you can pass just by knowing what a system prompt is. But as anyone building in production knows, chatting with an LLM is 1% of the work. The real nightmare is orchestration
🟢 Incremental
Developer Tools
🟢 HKUDS/AI-Trader — "AI-Trader: 100% Fully-Automated Agent-Native Trading" — score 36
Sources: github_trending
"AI-Trader: 100% Fully-Automated Agent-Native Trading"
🟢 bradygaster/squad — Squad: AI agent teams for any project — score 36
Sources: github_trending
Squad: AI agent teams for any project
🟢 njbrake/agent-of-empires — Manage multiple Claude Code, OpenCode agents from either TUI or Web for easy access on mobile. Also supports Mistral Vibe, Codex CLI, Gemini CLI, Pi.dev, Copilot CLI, Factory Droid Coding. Uses tmux and git worktrees. — score 32
Sources: github_trending
Manage multiple Claude Code, OpenCode agents from either TUI or Web for easy access on mobile. Also supports Mistral Vibe, Codex CLI, Gemini CLI, Pi.dev, Copilot CLI, Factory Droid Coding. Uses tmux and git worktrees.
🟢 Show HN: Filling PDF forms with AI using client-side tool calling — score 30
Sources: hackernews
🟢 chroma-core/chroma — Search infrastructure for AI — score 24
Sources: github_trending
Search infrastructure for AI
Omitted 2 additional developer tools items from the main section; see raw data and source-specific sections below.
Infrastructure & Compute
🟢 What about a website to share our model settings and optimisations ? — score 23
Sources: reddit/r/LocalLLaMA
Hello folks,
I'm thinking about creating a website to share our settings and configurations for our beloved models according to the hardware we have.
We could share our setups and vote for them, search them according to various criterias like hardware, RAM/VRAM, GPUs ...
Maybe it already exists ?
Research Papers
🟢 ViPO: Visual Preference Optimization at Scale — score 25
Sources: huggingface
While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but underperform in others. Naiv
🟢 FlashRT: Towards Computationally and Memory Efficient Red-Teaming for Prompt Injection and Knowledge Corruption — score 10
Sources: huggingface
Long-context large language models (LLMs)-for example, Gemini-3.1-Pro and Qwen-3.5-are widely used to empower many real-world applications, such as retrieval-augmented generation, autonomous agents, and AI assistants. However, security remains a major concern for their widespread deployment, with th
🟢 Safety Drift After Fine-Tuning: Evidence from High-Stakes Domains — score 10
Sources: huggingface
Foundation models are routinely fine-tuned for use in particular domains, yet safety assessments are typically conducted only on base models, implicitly assuming that safety properties persist through downstream adaptation. We test this assumption by analyzing the safety behavior of 100 models, incl
Other Signals
🟢 Mistral Medium 3.5 128b ggufs are fixed — score 35
Sources: reddit/r/LocalLLaMA
All ggufs were broken, resulting in bad outputs, especially at long context.
Anyway, it is fixed now: https://huggingface.co/unsloth/Mistral-Medium-3.5-128B-GGUF/discussions/1
Edit: Unsloth Announcement: [https://huggingf
🟢 Real World Physics-Informed AI Applications [D] — score 31
Sources: reddit/r/MachineLearning
I'm curios to find any real-world applications of physics-informed AI.
Conventional AI, talking only about Neural Networks, have already become something casual, they are in hundreds of tools/services we use daily. But I'm curios, apart from academia, are there industries/fields where physics-infor
🟢 MiniMax M2.7 AWQ-4bit on 2x Spark vs 2x RTX 6000 96GB - performance and energy efficiency — score 23
Sources: reddit/r/LocalLLaMA
Hello,
This model/quant is my daily driver and I wanted to have some reference benchs for comparing my setup with a 3x more expensive and 4x time power hungry setup.
Results first, methodology after, link at the end with all results
Model: [cyankiwi/MiniMax-M2.7-AWQ-4bit](https://huggingface.co/c
🟢 I built "Semvec": A Constant-Cost Semantic Memory for LLMs (Looking for testers!) — score 16
Sources: reddit/r/AIAgents
Hey everyone,
If you build LLM applications, autonomous agents, or just use Claude/Cursor for coding, you've probably hit this wall: Conversation history grows infinitely, token costs explode, latency skyrockets, and eventually, the LLM starts forgetting early context anyway.
To fix this, I built
🟢 NEED HELP URGENT I really need to talk to someone who sells chatbots to local businesses, please — score 14
Sources: reddit/r/AIAgents
I've been trying to figure something out for a while now and I just can't find the answer online. If you're someone who sells chatbots or AI tools to local businesses — restaurants, salons, shops, anything like that — I would really appreciate it if you could spare 2 minutes to DM me.
I just have o
Omitted 5 additional other signals items from the main section; see raw data and source-specific sections below.
📈 Trending Repos
| Repo | Description | Stars Today | Language |
|---|---|---|---|
| TauricResearch/TradingAgents | TradingAgents: Multi-Agents LLM Financial Trading Framework | 2227 | python |
| ruvnet/ruflo | 🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code / Codex Integration | 1258 | typescript |
| Hmbown/DeepSeek-TUI | Coding agent for DeepSeek models that runs in your terminal | 572 | rust |
| 1jehuang/jcode | Coding Agent Harness | 482 | rust |
| tirth8205/code-review-graph | Local knowledge graph for Claude Code. Builds a persistent map of your codebase so Claude reads only what matters — 6.8× fewer tokens on reviews and up to 49× on daily coding tasks. | 323 | python |
| simstudioai/sim | Build, deploy, and orchestrate AI agents. Sim is the central intelligence layer for your AI workforce. | 280 | typescript |
| Q00/ouroboros | Agent OS: Stop prompting. Start specifying. | 185 | python |
| iOfficeAI/AionUi | Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it! | 167 | typescript |
| google-research/timesfm | TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. | 132 | python |
| microsoft/qlib | Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped withhttps://github.com/microsoft/RD-Agentto automate R&D process. | 100 | python |
📄 New Papers
| Title | Category | Hotness | Link |
|---|---|---|---|
| Efficient Training on Multiple Consumer GPUs with RoundPipe | research_paper | 32 | Open |
| Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows | research_paper | 28 | Open |
| Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence | research_paper | 16 | Open |
| Step-level Optimization for Efficient Computer-use Agents | research_paper | 11 | Open |
| Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models | research_paper | 7 | Open |
| Instruction-Guided Poetry Generation in Arabic and Its Dialects | research_paper | 4 | Open |
| Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks | cs.AI | 0 | Open |
| Binary Spiking Neural Networks as Causal Models | cs.AI | 0 | Open |
| When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems | cs.AI | 0 | Open |
| End-to-end autonomous scientific discovery on a real optical platform | cs.AI | 0 | Open |
| Think it, Run it: Autonomous ML pipeline generation via self-healing multi-agent AI | cs.AI | 0 | Open |
| Unsupervised Electrofacies Classification and Porosity Characterization in the Offshore Keta Basin Using Wireline Logs | cs.AI | 0 | Open |
| TRUST: A Framework for Decentralized AI Service v.0.1 | cs.AI | 0 | Open |
| Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming | cs.AI | 0 | Open |
| Optimal Stop-Loss and Take-Profit Parameterization for Autonomous Trading Agent Swarm | cs.AI | 0 | Open |
🐦 Twitter/X Highlights
| Account | Tweet Summary |
|---|---|
| OpenAI | One week since the launch of GPT-5.5, and it’s already our strongest model launch yet. API revenue is growing more than 2x faster than any prior release, while Codex doubled revenue in under seven days as enterprise demand for agentic coding tools keeps climbing. Post |
| xai | Voice Cloning is now live via the xAI API! Create a custom voice in less than 2 minutes or select from our library of 80+ voices across 28 languages to personalize your voice agents, audiobooks, video game characters, and more. http://x.ai/news/grok-custom-voices Post |
| xai | Introducing Grok Voice Think Fast 1.0 A state-of-the-art voice model built for complex, multi-step workflows with snappy responses and high accuracy. It takes the top spot on the Tau Voice Bench and handles real-world messiness like noise, accents, and interruptions better than any other model in the world. https://x.ai/news/grok-voice-think-fast-1 Post |
| MistralAI | 🆕 Today, we're releasing the public preview of Workflows, the orchestration layer for enterprise AI. 🌎 Enterprise teams have capable models. What they don't have is a way to run them reliably in production. That's the gap Workflows fills. It takes AI-powered business processes from prototype to production, with the durability, observability, and fault tolerance that production actually requires. Leading organisations like ASML, ABANCA, CMA-CGM, France Travail, La Banque Postale, Moeve, and many others are already using Workflows to automate critical processes. Post |
| OpenAI | Bring your workflow to Codex in just a few clicks. Import settings, plugins, agents, project configuration, and more so you can keep working with fewer interruptions. Your move. Post |
| MistralAI | Mistral AI made the TIME100 Most Influential Companies list for 2026 — and the top 10 for AI. Why we're proud: customers run frontier models in production on their own terms, on their own infrastructure. Thank you to our customers for their trust and for joining us on the journey. Grateful to our incredible team members around the world and congrats to all the businesses recognized this year. Learn more at: https://time.com/collection/time100-most-influential-companies/2026/mistral/ #TIME100Companies #TIME100CompaniesIndustryLeader Post |
| karpathy | Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights: The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons: 1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing. 2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc. 3. LLM knowledge bases as an example of something that was impossible with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc. I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3). The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base and 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to... Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors. Post |
| simonw | I released LLM 0.32a0 this morning, a major backwards-compatible refactor of my LLM Python library and CLI tool for working with language models - the new changes should help LLM work better with reasoning models and other new frontier capabilities https://simonwillison.net/2026/Apr/29/llm/ Post |
| sama | you can sign in to openclaw with your chatgpt account now and use your subscription there! happy lobstering. Post |