🔴 High Significance
Model Releases
🔴 💬 Qwen3.8-27B announced alongside Qwen3.8-Max — score 97
Sources: reddit/r/LocalLLaMA
https://preview.redd.it/gy0tgokdl2hh1.png?width=540&format=png&auto=webp&s=7db9e034613a915cb33d378b99ad72c31c7cc18f source: https://x.com/Alibaba_Qwen/status/2084100707423289643
🔴 💬 Qwen 3.8 morning to you too Dario, 2$ input/ 6$ output per 1M. — score 93
Sources: reddit/r/singularity
🔴 🤗 MiniMaxAI/MiniMax-H3 (0 downloads) — score 71
Sources: huggingface_models · reddit/r/LocalLLaMA
Author: | Downloads: 0 | Likes: 1423
Developer Tools
🔴 💬 EPA says power for data centers can sidestep pollution laws — score 95
Sources: reddit/r/artificial
🔴 💬 I'm new to AI Agents. Where should I start? (Non-tech background) — score 94
Sources: reddit/r/AIAgents
Hi everyone, I'm from a non-tech background and want to learn AI Agents. There are so many tools and videos that I'm confused. Where should I start, and what should I learn first? Any tips or roadmap would really help. Thanks!
🔴 🐙 Graphify-Labs/graphify — Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store. — score 93
Sources: github_trending
Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.
🔴 💬 The Chinese labs everyone lumps together are making four pretty different bets. I work at one of them. — score 83
Sources: reddit/r/LocalLLaMA
Every time a model drops from a Chinese lab the thread fills with people who already know who made it, and the guess is usually Alibaba. There was a thread here recently asking what separates the open source labs from the frontier labs. It ran to nearly sixty comments and hardly anyone in it separat
🔴 🐙 Alishahryar1/free-claude-code — Use Claude Code, Codex and Pi for free from your terminal, app, IDE, or phone like OpenClaw (voice supported) — score 83
Sources: github_trending
Use Claude Code, Codex and Pi for free from your terminal, app, IDE, or phone like OpenClaw (voice supported)
Omitted 4 additional developer tools items from the main section; see raw data and source-specific sections below.
Business & Funding
🔴 💬 How reliable is voice AI when you're mid-switch and your old system is still half in the picture — score 81
Sources: reddit/r/AIAgents
We're in the middle of moving our outbound follow-up calls away from a platform we've been on for about 18 months. The decision to leave wasn't dramatic, the old tool just kept failing on anything that required a slightly longer conversation. Fine for simple confirmations, but the moment a customer
Research Papers
🔴 🤗 SAF-OPD: Stable Advantage Fusion for On-Policy Distillation — score 82
Sources: huggingface · arxiv/cs.AI
Reinforcement learning with verifiable rewards (RLVR) broadcasts a single response-level reward to every token, while on-policy distillation (OPD) scores each token against a stronger teacher for a dense advantage but caps performance at teacher quality and discourages exploration beyond it. Their c
🔴 🤗 RL^2-VLA: Adaptive RL Latent Compositional Steering with Test-Time Scaling for Vision-Language-Action Models — score 75
Sources: huggingface
Despite the impressive visuomotor capabilities enabled by Vision-Language-Action (VLA) models, their performance often degrades on challenging and out-of-domain tasks. Recent test-time steering and scaling methods improve performance without extensive data collection and retraining, but action sampl
Other Signals
🔴 💬 Is it too late regain some coherence in the ML research space in our life time? [D] — score 94
Sources: reddit/r/MachineLearning
Was just looking at the list of preprints on Arxiv cs.LG https://arxiv.org/list/cs.LG/recent?skip=0&show=500 Everyday 100 - 400 new machine learning papers gets uploaded on this server. Looking at this unending list of preprints is as if
🔴 💬 OpenAI takes the lead — score 94
Sources: reddit/r/OpenAI
🔴 🧡 Prevent cognitive debt by manually retyping LLM-generated code — score 94
Sources: hackernews
🔴 💬 Daniel Han of Unsloth validates Qwen3.8-27B will run only 17GB VRAM — score 90
Sources: reddit/r/LocalLLaMA
Super excited about this release for the new 27B. Who else is with me. Only 17GB VRAM needed 😍😍
🔴 💬 MIT, Harvard, Stanford & Caltech write their own ML course notes instead of using a textbook — I catalogued the best ones — score 85
Sources: reddit/r/artificial
One thing I've noticed separates serious ML students from casual ones: how much they care about the quality of what they actually study from. I take that pretty seriously myself, so a while back I started digging into what students at MIT, Harvard, Stanford, Caltech, and USP actually use to compleme
Omitted 4 additional other signals items from the main section; see raw data and source-specific sections below.
🟡 Notable
Model Releases
🟡 🧡 MiniMax H3 Day-0 Support in ComfyUI: Open Weights, Native Audio, and 2K Video — score 69
Sources: hackernews
🟡 ✉️ We return to Baseten at the peak of the 2026 edition ofOpen Weights debate. Ali has published a viral breakdown ofKimi K3: — score 65
Sources: newsletter/Latent Space
🟡 ✉️ And since you last saw him, Philip hasspoken at AI Engineerand written thedefinitive book on Inference Engineeringspotted all over SF: — score 65
Sources: newsletter/Latent Space
🟡 ✉️ To date, our primary efforts on Interconnects have been release recaps for popular models likeKimi K3,GLM 5.2,DeepSeek R1, etc. and monthly round-ups of the open models that matter,Artifacts Log. We’r — score 65
Sources: newsletter/Interconnects
To date, our primary efforts on Interconnects have been release recaps for popular models likeKimi K3,GLM 5.2,DeepSeek R1, etc. and monthly round-ups of the open models that matter,Artifacts Log. We’re expanding on these, building on the tools and internal data we’ve collected for other projects lik
🟡 ✉️ The Artifacts Hub right now covers 792 models released in the last two years, across the core text-focused language models and multimodal generative models. At Interconnects we follow the data of ever — score 65
Sources: newsletter/Interconnects
The Artifacts Hub right now covers 792 models released in the last two years, across the core text-focused language models and multimodal generative models. At Interconnects we follow the data of every model on Hugging Face, analyze the core few thousand LLMs (thislistis public on GitHub and regular
Omitted 9 additional model releases items from the main section; see raw data and source-specific sections below.
Developer Tools
🟡 💬 NeurIPS 2026: If the rebuttal addresses your concern, please raise your score [D] — score 69
Sources: reddit/r/MachineLearning
Potentially a hot take? I am not sure why our community is plagued with reviewers who, after acknowledging that their concerns were addressed by a rebuttal, decide to maintain their score because they don't vibe with the paper. So here is my plea to all reviewers: If you list a set of concerns in yo
🟡 ✉️ MCP IS GOING STATELESS: WHAT THE NEW SPEC MEANS FOR AI AGENTS (7 MINUTE READ) — score 65
Sources: newsletter/tldr
🟡 ✉️ AUTOMATE CI/CD TROUBLESHOOTING WITH AWS DEVOPS AGENT AND GITHUB (5 MINUTE READ) — score 65
Sources: newsletter/tldr
🟡 ✉️ FROM PILOT TO PRODUCTION: THE PLATFORM TEAM'S PLAYBOOK FOR SCALING AI CODING AGENTS IN REGULATED INDUSTRIES (6 MINUTE READ) — score 65
Sources: newsletter/tldr
🟡 ✉️ SHOULD YOU USE AI FOR A TASK? HERE'S A SIMPLE WAY TO DECIDE (7 MINUTE READ) — score 65
Sources: newsletter/tldr
Omitted 17 additional developer tools items from the main section; see raw data and source-specific sections below.
Infrastructure & Compute
🟡 ✉️ Self-sustaining and self-replicating AI viruses are here:…Open weight LLMs + a well-designed harness = a persistent, self-sufficient virus…AI researchers have built a prototype computer virus which us — score 65
Sources: newsletter/Import AI
Self-sustaining and self-replicating AI viruses are here:…Open weight LLMs + a well-designed harness = a persistent, self-sufficient virus…AI researchers have built a prototype computer virus which uses AI models to compromise computers, then uses their underlying GPU resources to run inference, let
🟡 ✉️ Three years ago,inference engineering barely existed as a category. — score 65
Sources: newsletter/Latent Space
Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training:“How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?”Focusing on these creates an entirely new o
🟡 ✉️ TheArtifacts Hub— a curated view of the models trending on Hugging Face, highlighting inference tokens viaOpen Router, model intelligence viaArtificial Analysis, and ourtailored adoption metricsbuildi — score 65
Sources: newsletter/Interconnects
TheArtifacts Hub— a curated view of the models trending on Hugging Face, highlighting inference tokens viaOpen Router, model intelligence viaArtificial Analysis, and ourtailored adoption metricsbuilding on top ofHugging Face’s data.
🟡 ✉️ COMPUTER USE IS FAR FROM SOLVED (7 MINUTE READ) — score 65
Sources: newsletter/tldr
🟡 ✉️ DATA CENTER BACKLASH COULD SLOW CIOS' AI PLANS (6 MINUTE READ) — score 65
Sources: newsletter/tldr
Omitted 2 additional infrastructure & compute items from the main section; see raw data and source-specific sections below.
Enterprise Adoption
🟡 ✉️ We’re expanding our open models coverage into standalone projects that let you go deeper on the state of the open model ecosystem. The new free sources of data are: — score 65
Sources: newsletter/Interconnects
Our other project is much lighter weight, but far overdue. Ever since we wrote The ATOM Project, we’ve been seeing the US-vs-China model adoption plot on a recurring basis in the AI ecosystem. We’d update the plot from time to time, but not enough. Now, we’re making the crucial data for that report
🟡 ✉️ OurAdoption Dashboard— a living dashboard of download and derivative model numbers by geography and organization. This highlights the US-China gap and growing players in the open ecosystem. — score 65
Sources: newsletter/Interconnects
🟡 ✉️ For the most popular models, the Hub let’s you quickly see how far behind the model was in terms of frontier intelligence based on Artificial Analysis’s Intelligence Index, compare Hugging Face and Op — score 65
Sources: newsletter/Interconnects
For the most popular models, the Hub let’s you quickly see how far behind the model was in terms of frontier intelligence based on Artificial Analysis’s Intelligence Index, compare Hugging Face and Open Router adoption to similar models, glance at relative adoption metric (RAM) scores for time-size
🟡 ✉️ Martha Stewart co-founded Hint, an AI home-management app that builds a profile of a user’s house from just an address to track maintenance, judge contractor quotes, and help simplify homeownership. — score 65
Sources: newsletter/rundown-ai
Research Papers
🟡 🤗 Toward Robust and 3D-Aware RGB-NIR Imaging in the Dark — score 65
Sources: huggingface · arxiv/cs.CV
Robust low-light imaging remains challenging for the community. Recent studies have explored fusing Near-Infrared (NIR) with noisy RGB to achieve improved enhancement, yet most methods depend on carefully curated training data pairs, with limited robustness under different scenarios. This paper offe
Other Signals
🟡 ✉️ We built the Hub as a way to go deeper on this analysis in collaboration withProject VAIL— an AI verification startup who has been one of the most loyal fans of our open model curation. — score 65
Sources: newsletter/Interconnects
🟡 ✉️ THE NEXT AI MOAT ISN'T A BETTER MODEL (6 MINUTE READ) — score 65
Sources: newsletter/tldr
🟡 ✉️ BUILDING WITH AI ISN'T ENOUGH (8 MINUTE READ) — score 65
Sources: newsletter/tldr
🟡 ✉️ JAKOB'S LAW: HOW TO APPLY IT AS AI COLLAPSES SURFACES INTO ONE CHAT BOX (16 MINUTE READ) — score 65
Sources: newsletter/tldr
🟡 ✉️ MEET STRIPE'S KNOWLEDGE AI PLATFORM (12 MINUTE READ) — score 65
Sources: newsletter/tldr
Omitted 28 additional other signals items from the main section; see raw data and source-specific sections below.
🟢 Incremental
Model Releases
🟢 💬 OPEN AI: "we rebuilt the voice stack from client to model." — score 39
Sources: reddit/r/singularity · twitter_rss
GPT-Live can listen while it speaks. To make that feel natural at ChatGPT scale, we rebuilt the voice stack from client to model. This new architecture keeps audio flowing continuously, so deeper reasoning and tool use don't interrupt the conversation.
🟢 🤗 Comfy-Org/MiniMax-H3 (2 downloads) — score 35
Sources: huggingface_models
Author: | Downloads: 2 | Likes: 436
🟢 💬 OpenAI's unreleased Astra model solved 10 open math problems for $2,000 and shipped machine-checkable proofs — score 31
Sources: reddit/r/OpenAI
OpenAI says an unreleased model, Astra, produced 10 new results in math and theoretical CS — problems open for at least a decade. Headline: the first explicit construction of a non-sofic group, open since 1999. The twist: every result ships with a Lean 4 certificate on GitHub, so correctness is veri
🟢 🧡 Launch HN: Hoplite (YC S26) – Effortlessly deploy cloud coding agents — score 19
Sources: hackernews
🟢 💬 What your ideal AI work interface would look like — score 15
Sources: reddit/r/artificial
For people using AI tools like Cursor, Claude Code, Codex, Copilot, Antigravity, etc. for real work... I'm curious how your workflow has evolved as your projects have become larger and more complex. I'd love to know: 1. How do you handle workflows that involve multiple skills or stages? For example,
Omitted 5 additional model releases items from the main section; see raw data and source-specific sections below.
Developer Tools
🟢 🐙 PostHog/posthog — 🦔 PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error tracking, logs, and more – capture all the context agents need to diagnose problems, uncover opportunities, and ship fixes. Steer it all from Slack, web, desktop, or the MCP. — score 39
Sources: github_trending
🦔 PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error tracking, logs, and more – capture all the context agents need to diagnose problems, uncover opportunities, and ship fixes. Steer it all
🟢 💬 "Data center in a Box (on Wheels)" 256Gb VRAM/512Gb RAM AI Server 6-8 Month Operational Review, Stability Write Up, Benchmarks — score 37
Sources: reddit/r/LocalLLaMA
I've been out of these forums for awhile but I figured I would provide a formal update on how this has been going now that it has some operation time under its belt, just to put the information out there and share knowledge if there is any interest. I also wasn't satisfied with the quality of my ori
🟢 🐙 Dicklesworthstone/destructive_command_guard — The Destructive Command Guard (dcg) is for blocking dangerous git and shell commands from being executed by agents. — score 33
Sources: github_trending
The Destructive Command Guard (dcg) is for blocking dangerous git and shell commands from being executed by agents.
🟢 💬 I trusted Al with a 10-minute task. I regretted it. — score 31
Sources: reddit/r/AIAgents
I gave an Al a simple task because I wanted to save 10 minutes. Instead... I spent almost an hour fixing what it confidently messed up. That got me thinking. We've spent years adding "Undo" buttons to almost everything we use. But when Al makes a mistake, the answer is usually: "Just run it again."
🟢 💬 Architecture question for people building agents at scale. — score 31
Sources: reddit/r/AIAgents
Suppose 100 users are active and 80 submit agent requests within the same minute. Each request runs in its own Docker-based sandbox. How would you design the worker architecture? * How many concurrent agent runs would you allow per worker? * Roughly how many workloads can a single EC2 t3.medium or t
Omitted 7 additional developer tools items from the main section; see raw data and source-specific sections below.
Research Papers
🟢 🤗 Not All Tokens Deserve Equal Credit: Counterfactual Sensitivity Credit Reallocation for Long-CoT Reasoning — score 30
Sources: huggingface
Reinforcement learning with verifiable rewards (RLVR) is central to improving long-CoT reasoning in large language models. Critic-free methods such as GRPO convert response-level rewards into advantages and uniformly broadcast them across tokens, overlooking their unequal contributions to the final
🟢 🤗 In the Driver's Seat: A Multi-Company Study on the Reality of Autonomous Driving System Testing — score 15
Sources: huggingface
Autonomous driving systems (ADS) are rapidly advancing and increasingly deployed in real-world applications. This creates growing demands for effective testing to ensure system functionality and safety. However, ADS testing remains complex and lacks well-established standards for scenario selection,
🟢 🤗 SGTP: Sampling-based Game-Theoretic Planning for Real-Time Multi-Vehicle Autonomous Racing — score 5
Sources: huggingface
Autonomous multi-vehicle racing requires real-time planning of diverse competitive behaviors in intense interactions. Existing planners often struggle to balance strategic diversity and computational efficiency. To address this challenge, we propose Sampling-based Game-Theoretic Planning (SGTP), a r
Other Signals
🟢 💬 Models are now training models. — score 36
Sources: reddit/r/singularity
https://x.com/intology/status/2084319121332965804/photo/1 These results are from Intology: [https://x.com/intology/status/2084319121332965804](https://x.com/into
🟢 💬 No rebuttals from neurips authors [D] — score 31
Sources: reddit/r/MachineLearning
I know there’s a lot of frustration around no response from reviewers, which I also got only one so yeah what a bummer, but I was wondering if no rebuttal from the authors was just as common or not. I got no rebuttal so far, so I’m here scratching my head what might have happened to the authors lol
🟢 💬 DeepSeek V4-Flash (284B MoE) at 33 tok/s single / 68 tok/s aggregate on 2× RTX 3090 + a used quad-Xeon DDR4 server — full config — score 30
Sources: reddit/r/LocalLLaMA
Ran DeepSeek V4-Flash-0731 — the full official checkpoint, not a re-quant — on commodity used hardware. Sharing because I couldn't find anyone else publishing Ampere results for this engine. # Why bother with a 2018 server The model is 156 GB. That number decides everything before speed matters:
🟢 💬 V4-Flash-0731 - vibes after first weekend of use — score 23
Sources: reddit/r/LocalLLaMA
Spent way too much time with V4-Flash-0731 this weekend and wanted to share my vibes as briefly as possible. I sent it through a bit of real-work and some of my personal benchmarks. My quick thoughts are: - Quantization hits this thing like a truck - I've tried a bunch of the Q2 and Q3 weights a
🟢 💬 I created an autonomous boxing benchmark [D] — score 19
Sources: reddit/r/MachineLearning
I created an AI boxing match to test the decision speed, adaptability and strategy. I fed the LLMs with data about the current match and if they have vision, they will get even more data. The match has street rules, anything goes and an AI is not defeated until the ref counts to 10 or they do 50% of
Omitted 3 additional other signals items from the main section; see raw data and source-specific sections below.
📈 Trending Repos
| Repo | Description | Stars Today | Language |
|---|---|---|---|
| Graphify-Labs/graphify | Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store. | 882 | python |
| Alishahryar1/free-claude-code | Use Claude Code, Codex and Pi for free from your terminal, app, IDE, or phone like OpenClaw (voice supported) | 291 | python |
| livekit/agents | A framework for building realtime voice AI agents 🤖🎙️📹 | 129 | python |
| K-Dense-AI/scientific-agent-skills | Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 170,000+ scientists worldwide. 158 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard. | 103 | python |
| karakeep-app/karakeep | A self-hostable bookmark-everything app (links, notes and images) with AI-based automatic tagging and full text search | 56 | typescript |
| vitali87/code-graph-rag | The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs | 42 | python |
| slopus/happy | Mobile and Web client for Codex and Claude Code, with realtime voice, encryption and fully featured | 41 | typescript |
| jamwithai/production-agentic-rag-course | 40 | python | |
| comet-ml/opik | Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. | 37 | python |
| ZSeven-W/openpencil | The world's first open-source AI-native vector design tool and the first to feature concurrent Agent Teams. Design-as-Code. Turn prompts into UI directly on the live canvas. A modern alternative to Pencil. | 28 | rust |
📄 New Papers
| Title | Category | Hotness | Link |
|---|---|---|---|
| SAF-OPD: Stable Advantage Fusion for On-Policy Distillation | research_paper | 24 | Open |
| RL^2-VLA: Adaptive RL Latent Compositional Steering with Test-Time Scaling for Vision-Language-Action Models | research_paper | 6 | Open |
| Toward Robust and 3D-Aware RGB-NIR Imaging in the Dark | research_paper | 5 | Open |
| OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems | cs.AI | 0 | Open |
| Can AI Evaluate AI Scientists? A Benchmarking Study of Autonomous Research Generation Systems Using Automated Multi-Model Review | cs.AI | 0 | Open |
| LLM Framework for Discovering Major Mathematical Conjectures: AI's Quest for the Next Riemann Hypothesis | cs.AI | 0 | Open |
| ThinkReset: Learnable Intermediate Interface Construction for Bounded-Context Long-Horizon Reasoning | cs.AI | 0 | Open |
| TAPR: Enhancing LLM Performance with a Task-Aware Prompt Rewriter | cs.AI | 0 | Open |
| Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding | cs.AI | 0 | Open |
| An Ontology-Guided, Deduplication-Aware Extraction Layer for Knowledge Graph Construction from Heterogeneous Documents | cs.AI | 0 | Open |
| How Hard Does It Think? Analyzing Step-Aware Reasoning Energy in LLM Chain-of-Thought Trajectories | cs.AI | 0 | Open |
| Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support | cs.AI | 0 | Open |
| ViSAGE: Constructing Self-Correcting Memories for Long-Form Video Understanding | cs.AI | 0 | Open |
| Multi-Agent Planning with Spatio-Temporal and Topological Constraints using STL-GO | cs.AI | 0 | Open |
| Library Reachability in LSR-Synth: How Anti-Memorization Design Changes the Measurement of Symbolic Discovery | cs.AI | 0 | Open |
🏢 Lab Blog Posts
- OpenAI: How we built a realtime system for responsive voice AI in six months
- Microsoft Research: Orchard: An open framework for scalable agentic AI
- Apple ML: Understanding Alignment in Multimodal LLMs: A Comprehensive Study
🐦 Twitter/X Highlights
| Account | Tweet Summary |
|---|---|
| OpenAI | An internal version of our next major model produced 10 new results on long-standing open problems in mathematics and theoretical computer science, using roughly $2,000 worth of tokens at GPT-5.6 Sol API rates. Post |
| mattshumer_ | Hey @threejs if you’re down to sponsor the inference, I’d love to create a ThreeBench! Post |
| reach_vb | man you can't make this shit up, I get in my cab and my driver is telling me about Astra, the model OpenAI is about to release solved 10 math problems !! Post |
| abacaj | More apps should be shipping their own models. You should be offloading as much compute as you can to the end user. Pay once, or pay again for updated versions. Small, easily accessible models are going to be more important than ever Post |
Newsletter
- Import AI: Self-sustaining and self-replicating AI viruses are here:…Open weight LLMs + a well-designed harness = a persistent, self-sufficient virus…AI researchers have built a prototype computer virus which us
- Latent Space: We return to Baseten at the peak of the 2026 edition ofOpen Weights debate. Ali has published a viral breakdown ofKimi K3:
- Latent Space: And since you last saw him, Philip hasspoken at AI Engineerand written thedefinitive book on Inference Engineeringspotted all over SF:
- Latent Space: Three years ago,inference engineering barely existed as a category.
- Interconnects: We’re expanding our open models coverage into standalone projects that let you go deeper on the state of the open model ecosystem. The new free sources of data are:
- Interconnects: TheArtifacts Hub— a curated view of the models trending on Hugging Face, highlighting inference tokens viaOpen Router, model intelligence viaArtificial Analysis, and ourtailored adoption metricsbuildi
- Interconnects: OurAdoption Dashboard— a living dashboard of download and derivative model numbers by geography and organization. This highlights the US-China gap and growing players in the open ecosystem.
- Interconnects: To date, our primary efforts on Interconnects have been release recaps for popular models likeKimi K3,GLM 5.2,DeepSeek R1, etc. and monthly round-ups of the open models that matter,Artifacts Log. We’r
- Interconnects: The Artifacts Hub right now covers 792 models released in the last two years, across the core text-focused language models and multimodal generative models. At Interconnects we follow the data of ever
- Interconnects: We built the Hub as a way to go deeper on this analysis in collaboration withProject VAIL— an AI verification startup who has been one of the most loyal fans of our open model curation.
- Interconnects: For the most popular models, the Hub let’s you quickly see how far behind the model was in terms of frontier intelligence based on Artificial Analysis’s Intelligence Index, compare Hugging Face and Op
- tldr: THE NEXT AI MOAT ISN'T A BETTER MODEL (6 MINUTE READ)
- tldr: BUILDING WITH AI ISN'T ENOUGH (8 MINUTE READ)
- tldr: JAKOB'S LAW: HOW TO APPLY IT AS AI COLLAPSES SURFACES INTO ONE CHAT BOX (16 MINUTE READ)
- tldr: OPENAI CUTS PRICES FOR TWO OF ITS GPT-5.6 AI MODELS AS COMPANIES GROW SENSITIVE TO COSTS (3 MINUTE READ)
- tldr: MEET STRIPE'S KNOWLEDGE AI PLATFORM (12 MINUTE READ)
- tldr: CITADEL BUYS SITUATIONAL AWARENESS'S STOCK PORTFOLIO AFTER BIG LOSSES IN AI (7 MINUTE READ)
- tldr: THE AI AESTHETIC (3 MINUTE READ)
- tldr: ANTHROPIC AI MODELS HACKED THREE COMPANIES DURING TESTS (3 MINUTE READ)
- tldr: META SAYS AI IS MAKING IT EASIER TO BUILD NEW APPS — AND MORE ARE COMING (3 MINUTE READ)
- tldr: MCP IS GOING STATELESS: WHAT THE NEW SPEC MEANS FOR AI AGENTS (7 MINUTE READ)
- tldr: AUTOMATE CI/CD TROUBLESHOOTING WITH AWS DEVOPS AGENT AND GITHUB (5 MINUTE READ)
- tldr: FROM PILOT TO PRODUCTION: THE PLATFORM TEAM'S PLAYBOOK FOR SCALING AI CODING AGENTS IN REGULATED INDUSTRIES (6 MINUTE READ)
- tldr: COMPUTER USE IS FAR FROM SOLVED (7 MINUTE READ)
- tldr: SHOULD YOU USE AI FOR A TASK? HERE'S A SIMPLE WAY TO DECIDE (7 MINUTE READ)
- tldr: WORLD MODEL OPTIMIZER (GITHUB REPO)
- tldr: AGENT MANAGER (GITHUB REPO)
- tldr: STRONGER WITH EVERY UPDATE: HOW WE'RE MAKING CHROME AND THE WEB SAFER IN THE AI ERA (13 MINUTE READ)
- tldr: JONY IVE'S FIRST OPENAI HARDWARE DEVICE SOUNDS RATHER LIKE … A HOMEPAD (2 MINUTE READ)
- tldr: INFORMATION ARCHITECTURE IS THE FOUNDATION AI IS STARVING FOR (12 MINUTE READ)
- tldr: AI ILLUSTRATIONS YOU CAN EDIT (WEBSITE)
- tldr: TYPE DESIGNERS HAVE CREATED A FREE FONT THAT "POISONS" AI (3 MINUTE READ)
- tldr: NOT JUST OPENAI: NOW ANTHROPIC SAYS ITS INTERNAL MODELS GOT ONLINE AND CYBERATTACKED 3 OTHER ORGANIZATIONS (5 MINUTE READ)
- tldr: MICROSOFT CONFIRMS AN AI WORM IS PROPAGATING THROUGH COPILOT AND OTHER MS APPS (10 MINUTE READ)
- tldr: DATA CENTER BACKLASH COULD SLOW CIOS' AI PLANS (6 MINUTE READ)
- rundown-ai: Inkling-Small - Thinking Machines’ compact open model that rivals the full-size version
- rundown-ai: Hint - Martha Stewart’s AI home app for maintenance, repairs, and fair quotes
- rundown-ai: Former OAI researcher Leopold Aschenbrenner’s Situational Awareness Fund reportedly sold off its public holdings to rival hedge fund Citadel, coming after its leveraged AI positions saw steep declines in the past months.
- rundown-ai: Google DeepMind introduced Gemini Robotics ER 2, an "embodied reasoning" model that acts as a planning brain for robots, letting multiple machines coordinate on tasks in shared spaces.
- rundown-ai: Thinking Machines Lab released Inkling-Small, an open-weights model with just 12B active parameters that matches the full-size version and beats it outright on reasoning and agentic coding tests.
- rundown-ai: AI market research startup Simile raised $200M at a $2B valuation, letting companies survey "agentic twins" of real consumers for synthetic data insights.
- rundown-ai: Martha Stewart co-founded Hint, an AI home-management app that builds a profile of a user’s house from just an address to track maintenance, judge contractor quotes, and help simplify homeownership.
- rundown-ai: I created ==an AI-powered master ====plan==== for acreage landscaping, covering design, phased builds, ==irrigation, maintenance, budgets, and long-term care.
- rundown-ai: Read our last AI newsletter: OpenAI’s escaped AI claims another victim
- rundown-ai: OpenAI's models cut their own costs
- rundown-ai: A new home for real AI workflows
- rundown-ai: Credit to reader^^ ^^Michael Ebner^^. How do you use AI? Tell us ^^here^^.^
- rundown-ai: In 24 hours, Anthropic’s Levent Alpoge claimed he was able to reproduce five of the 10 proofs with Fable, running on a generic prompt and no internet.
- rundown-ai: Why it matters: The community is debating whether Astra’s proofs can be Fields Medal-worthy, given that a machine did the thinking here. The question feels simple, but it will only grow bigger as AI capable of cracking d
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