Hugging Face

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Npt

In 1968 five countries that already had nuclear weapons signed a treaty declaring them too dangerous for anyone else to build. India refused, pointing out the treaty did not say nukes were too dangerous to exist, just too dangerous for new entrants. Anthropic built Mythos, deemed it too powerful for public release, then shipped Fable with the same weights but hidden degradation on frontier AI work. The restriction started the day after they finished building. Non proliferation was never about preventing danger. It was about preserving advantage. Mythos 5 goes unrestricted to Microsoft, Nvidia, Google Cloud, AWS, and about 200 other approved partners. Fable 5 goes to everyone else with silent capability limits on frontier ML development. The biggest paying customers get the full product. Potential competitors get a version that quietly gives worse answers on the work that matters most. Anthropic filed confidentially for its IPO one week before this launch. India had a phrase for this kind of arrangement when it refused the NPT. Discriminatory by design. Jensen Huang called the GPU to nuclear bomb comparison stupid. He is wrong about the analogy but right about the instinct behind it. The NPT worked because nuclear weapons require enrichment facilities, centrifuges, and state level infrastructure. AI does not. Qwen has 942 million downloads. DeepSeek V4 ships under MIT license with full weights matching closed frontier models. The knowledge Anthropic is trying to restrict through hidden degradation is already open and available in competing models. You cannot run a non proliferation regime when the material is free to download. Anthropic Fable 5 silently degrades its own performance when it detects someone building a competing model. No warning, no refusal, just worse answers through hidden prompt tweaks and steering vectors Meanwhile DeepSeek published its full R1 training pipeline, failure modes, RL schedules, everything, under MIT license. One lab is hoarding knowledge at the frontier. The other is giving it away. The gap in approach is now wider than the gap in capability, Open is only threatening when you are slow. Alibaba Qwen crossed 942 million downloads on Hugging Face by March 2026. Its share of new open weight derivatives went from 1% in January 2024 to 69% by February 2026. Chinese models now account for 30% of global model usage on aggregator platforms, up from 1% in late 2024. All under Apache 2.0 or MIT licenses, fully permissive. US frontier labs are spending $700 billion on capex while keeping the developmental knowledge locked. China is spending a fraction and giving the knowledge away. Adoption follows access, not origin. Now China too going to do 230 billions+ capex as per report i think... Fable 5 and Mythos 5 are the same model. Mythos goes to 200 approved partners. Fable goes to everyone else, with hidden capability limits on frontier ML work. The stated reason is safety. The result is that US labs build the best tools and then weaken them for the work that advances AI. DeepSeek V4 matches Opus 4.7 on agentic benchmarks and ships under MIT license with full weights. The question is not who builds the better model. It is who gets more people building with it. Some of the Stuff I took from SemiAnalysis, But this will go Nuclear way I don't know submitted by /u/ramanpalkuri9 [link] [comments]

I Built Paper Deck: A Better Way to Discover AI/ML Papers [P]

I do AI research and keep juggling tabs: new ones on arXiv, trending ones on Hugging Face, famous ones somewhere else again. https://preview.redd.it/cg32bshjqd6h1.png?width=1919&format=png&auto=webp&s=00055bb8af699061be0bdcff59f2cb8fa9ab38b6 So I built one site that brings them all together. Pick a paper, read it right there, star the ones you want for later, and it remembers where you stopped reading, even if you switch from laptop to phone. Live: https://ppdeck.com Demo: https://youtu.be/vtyx34JvxX0 It's free and open source - a star on GitHub would mean a lot ⭐ https://github.com/khuynh22/paper-deck submitted by /u/NeitherRun3631 [link] [comments]

Switching from React Native + Node.js (4 YOE) to Agentic AI — need roadmap advice

I have 4 years of experience as a React Native and Node.js developer. I am comfortable with REST APIs, async/await, JSON, MongoDB, authentication, and shipping production apps. I am based in India. What I have learned so far: I recently completed an AI/LLM course that covered: • Pydantic (validation, models, serialization) • LLM theory (transformers, embeddings, attention, tokenization) • OpenAI and Gemini API integration • Prompt engineering (zero-shot, few-shot, CoT, persona prompting) • Prompt formats (ChatML, Alpaca, INST) • Ollama for local LLMs • FastAPI basics • Hugging Face model deployment • Agentic AI fundamentals — built a basic CLI coding agent What I understand conceptually: I understand that an AI agent = LLM brain + tools (Python functions) + agent loop + memory (messages list). I understand RAG, vector databases, the difference between fine-tuning and RAG, and how to structure a backend with Node.js calling a Python AI agent service when needed. What I want to do: I want to transition into Agentic AI / AI Engineer roles in India. I am not looking to become an ML researcher or train models. I want to build production AI agent systems — connecting LLMs to real business data, building tools, RAG pipelines, and shipping real products. My specific questions: 1. Is my current foundation strong enough to start building real agent projects or do I have gaps I am missing? 2. What should my learning roadmap look like for the next 3–6 months given my background? 3. Which frameworks should I prioritise — raw OpenAI API first, then LangChain/LangGraph, or jump straight to frameworks? 4. What kind of projects should I build for a strong portfolio targeting ₹20–35 LPA roles in India? 5. Any specific subreddits, communities, or resources beyond YouTube that helped you in this transition? My planned first 3 projects: • Simple agent with web search + calculator tool (no DB) • Agent connected to MongoDB with RAG • Full FastAPI backend wrapping the agent with a React frontend Any advice from people who have made a similar switch or are hiring in this space would be really helpful. Thanks. submitted by /u/rohitrai0101rm [link] [comments]

I built a tool that maps brain activation responses to creative content, here's what I learned

Started as a thought experiment. When Meta dropped the Tribe v2 model, I saw an opening and spent a few weeks turning it into something real. Neural Lens takes video, audio, image, or text as input and maps network activation patterns over time — showing how your brain responds to creative content, not just whether you clicked or watched. Built it solo. Self-funded. Claude API and Hugging Face under the hood. The use case I kept coming back to: creative teams spend months making content with zero neurological data on how it's actually landing. Clicks and views don't tell you why something works. This does. Try it here: https://huggingface.co/spaces/idkbutitworks/NeuralLens Would love feedback on the concept, the model choice, and where you'd take it. submitted by /u/Dandam_Ra_Doota [link] [comments]

Training-free graph SSL matches GCN with 5× fewer labels — live demo [P]

Hi all, I have been working on this method based on a hunch along with many llm for quite some time. Though first it was being engineered by me but I was learning in supervised ml area but this hunch took to semi-supervised ml and that to too deep. I then became llm orchestrator of sort while 4 llm's tried to figure it out. I put up a live demo on Hugging Face Spaces where you can try it yourself — set the number of labels, click run, see the accuracy. No installation, no code required. Brief about method Optimus — Graph SSL under Extreme Label Scarcity Key Results (PathMNIST, N=2000, 9 classes) Labels Total Optimus GCN 9(1 per class) 73.9 60.6 27(3 per class) 77.3 68.5 45(5 per class) 79.8 77.1 https://huggingface.co/spaces/Keshu007/optimus-graph-ssl Edit : You can can even run the code on your own dataset submitted by /u/Loner_Indian [link] [comments]

On-policy distillation: one of the hottest terms on PapersWithCode [R]

Hi, Niels here from the open-source team at Hugging Face. At paperswithcode.co I am trying to make it easier for people to learn about the newest techniques used across AI papers. One of the hottest terms in AI research that I've recently added is On-policy distillation, also abbreviated as OPD. It's the key post-training behind models like Qwen 3.6 and 3.7, GLM-5.1, and DeepSeek-V4. https://preview.redd.it/yegq2gfag95h1.png?width=3046&format=png&auto=webp&s=f68fdf3ca075f3c4e56051fdd0ebcf97be9bcbc9 On PapersWithCode, you can find the original paper that introduced it, learn more about the method itself, as well as all papers that cite or mention it. Sasha Rush (who used to be a colleague of mine at Hugging Face, now at Cursor) recently made an excellent whiteboard explanation of OPD with Dwarkesh. I've linked this video lecture in the method description on PwC's website, so more people can find it. I'll copy the excellent short description of the method from Dwarkesh here: "The basic idea is this: if the model made a mistake at some point in the rollout (for example, calling a tool that doesn't exist), we want to discourage this specific error, but we don't want to just learn from the final reward, because it's a very noisy signal spread out over the whole trajectory. So we have another model to read this trajectory and figure out where the error was made. It simply inserts some hint tokens into the part of the trajectory immediately above where the mistake occurred. Now, with these injected hint tokens, run a forward pass through the model. You're not having to regenerate a new rollout - aka no new decode required. The hint causes the model to assign lower probabilities to the error tokens. You then train the original model to match these new probabilities, teaching it to downweight that specific mistake." Let me know which other methods I should add! Cheers submitted by /u/NielsRogge [link] [comments]

We measured how AI capabilities INTERACT as models scale. Below 3.5B, reasoning and truthfulness fight. Above it, they cooperate. The transition is engineerable. (2 papers + interactive dashboard + 7 falsifiable predictions)

THE FINDING (Paper 1: "Lying Is Just a Phase") Below a critical scale (~3.5B for Pythia), reasoning and truthfulness ANTICORRELATE: r = -0.989. Train the model to reason better, and it gets less truthful. This is the alignment tax. Above that scale, they COOPERATE. The tax vanishes. Not gradually — it flips. But here's what matters for practitioners: the critical scale is a design parameter, not a constant. Three levers shift it: Data curation: Phi at 1B achieves coupling characteristic of 10B web-trained. One unit of data quality ≈ 10x model scale. Width: Normalizing by model width flips the correlation for ALL tested families. Architecture: Gemma-4 at 4B matches 13B+ standard-trained coupling. Pretraining contributes ~10:1 over RLHF. The tax is not a property of small models — it's a property of how they were trained. Where does the tax live? Not inside the model. 38/40 models have ZERO competing attention heads. The bottleneck is at the output projection — a dimensional compression artifact that wider models resolve. Proof-of-concept intervention: Adding a truth-direction vector at the bottleneck layer (quarter-depth) corrects 60% of misaligned outputs at tax scale. Zero retraining. Zero weight modification. Works on any open-weight HuggingFace model: git clone https://github.com/adilamin89/cape-scaling.git cd cape-scaling python cli/cape_steer.py --model EleutherAI/pythia-410m --prompt "The real reason..." THE FRONTIER (Paper 2: "Growing Pains of Frontier Models") At frontier scale (34 models, 10 labs), capabilities cooperate (r = +0.72). But cooperation varies systematically. The h-field — each model's deviation from the cooperative trend — reveals each lab's training philosophy: Lab h-field Interpretation Google +5.5 Reasoning-rich, consistent across ALL releases OpenAI +3.1 Balanced, steady ascent DeepSeek +1.9 Reversed from +11.2 to -4.7 (pretraining pivot) Anthropic -6.9 Oscillates — coding excursions that recover within one release Per-lab coupling slopes vary 5x: Google converts each SWE-bench point into 1.15 GPQA points. DeepSeek converts at 0.23. The gap originates in pretraining, not RLHF. The h-field is not just diagnostic — it tells you what to change. Pretraining shifts are permanent. Post-training excursions recover. Knowing which dominates determines whether to retrain or wait. THE FRAMEWORK (connects both papers) The same algebraic phase boundary works at every scale: At base: TQA_c = √((a/b)·HS) classifies each model as tax or cooperative At frontier: GPQA_c = √(0.513·SWE) does the same At the next transition: IFEval_c = √(0.97·GPQA) — and two frontier models already fall below this boundary Half of all benchmarks now exhibit saturation (Akhtar et al., 2026). Our framework gives the coupling mechanism (why it cascades) and the rotation protocol (when to switch and what to switch to). 7 falsifiable predictions with timestamped pass/fail criteria. 5 post-cutoff releases fall within our 95% prediction interval (±16.2 pp). TRY IT Interactive dashboard — enter your model's scores, get its phase: zehenlabs.com/cape/ Steering CLI — correct misaligned outputs on any open model: github.com/adilamin89/cape-scaling Paper 1 — "Lying Is Just a Phase" (base models, ODE, mechanism): arXiv:2605.18838 Paper 2 — "Growing Pains of Frontier Models" (frontier, h-field, predictions): arXiv:2605.18840 Blog with steering demo: zehenlabs.com/blog/ Built on EleutherAI's Pythia. Independently confirmed by AI2's OLMo. Everything is open — code, data, dashboard, steering tool. Happy to answer questions. submitted by /u/adil89amin [link] [comments]

Built something that might come in handy if you follow AI news

Hey everyone I built AIWire, a free real-time AI news aggregator. One clean feed, 20+ handpicked sources, auto refreshes every 30 minutes. No account needed, no ads. It pulls from the places most people already check anyway: OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI MIT Technology Review, The Verge, TechCrunch, VentureBeat, Ars Technica YouTube: Andrej Karpathy, AI Explained, Two Minute Papers Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bites A few things worth knowing: Top Stories from the last 24h are pinned at the top so you don't have to scroll to find what's recent You can filter by source, category, and date Bookmarks if you want to save something for later Full source list at aiwire.app/sources No account needed, completely free. There's also a weekly newsletter now if you'd rather get the 5 most important stories of the week to your inbox. 🔗 aiwire.app Happy to hear what sources are missing or what you'd change. https://preview.redd.it/kuxfol80ex4h1.png?width=2549&format=png&auto=webp&s=9a723076309a49c704831809df4add4b0597a0ac submitted by /u/Endlessxyz [link] [comments]

Browse CVPR 2026 papers on PapersWithCode [P]

https://preview.redd.it/se5nr2z7tt4h1.png?width=3046&format=png&auto=webp&s=7db15b73afb749da236e5bb50ff96372f6a3239b Hi, Niels here from the open-source team at Hugging Face. It's been 2 weeks since I launched paperswithcode.co, a revival of the website we all loved. It allows us to keep track of the state-of-the-art (SOTA) across various domains of AI, from agents to computer vision and time-series forecasting. I've just added conference support as a new feature. The idea is that you should be able to easily browse all papers of major AI conferences like NeurIPS, CVPR, and ICML. As CVPR 2026 takes place next week in Denver, USA, I've indexed all papers with corresponding arXiv IDs. They are categorized by task, and tagged with linked GitHub and project page URLs, Hugging Face artifacts, and evals. You can also browse the papers which were accepted for an Oral presentation as well as the Spotlight papers. You can try it at https://paperswithcode.co/conferences! Feel free to leave feedback. submitted by /u/NielsRogge [link] [comments]

Robot foundation models keep hiding behind fine-tuning numbers. Wall-OSS-0.5 is trying a different approach

Most robot foundation model demos are hard to interpret because the impressive number usually comes after task-specific fine tuning. Wall-OSS-0.5, a new open-source VLA release from X Square Robot, is interesting because the report tries to measure what the pretrained checkpoint can do before that extra adaptation step. The setup is a 4B vision-language-action model built around a 3B VLM backbone plus action-generation components. According to the report, the pretrained checkpoint was evaluated on a 17-task real-robot suite without task-specific fine tuning. Four tasks crossed 80 task progress: block sorting, fruit sorting, ring stacking, and a held-out deformable task, rope tightening. The part that seems more important than the raw score is the framing. In language models, nobody would accept only a fine-tuned downstream score as evidence that pretraining worked. With robots, that has been much harder because the evaluation is physical, slow, embodiment-dependent, and expensive. A real-robot zero-shot suite is a useful step toward asking the same question directly: does pretraining itself produce executable behavior, or is it mostly a better initialization? The method is also trying to solve a specific training problem. Continuous action losses are useful for execution, but the paper argues they do not send a strong enough learning signal into the VLM backbone by themselves. Their recipe combines action-token cross entropy, multimodal cross entropy, and flow matching in one stage, using the discrete action-token path as a gradient bridge into the backbone while flow matching handles continuous actions at deployment time. For reference, the code is at https://github.com/X-Square-Robot/wall-x, the paper is at https://x2robot.com/api/files/file/wall_oss_05.pdf, the project page is https://x2robot.com/oss#resources, and the Hugging Face org is https://huggingface.co/x-square-robot. The caveat is obvious but important. Zero-shot still does not solve the hardest manipulation tasks. The report says towel folding, table setting and charger insertion remain very low before fine tuning, which is probably the right boundary to pay attention to. Still, seeing a robot model release lead with pre-finetune real-hardware numbers feels like a healthier direction for embodied AI than another clean one-minute demo. The open question is whether this is the right way to evaluate robot foundation models, or whether real-robot zero-shot suites are still too embodiment-specific to become a useful standard. submitted by /u/breadislifeee [link] [comments]

Weekly AI roundup (May 23–30, 2026): Claude Opus 4.8 Fast Mode 3x cheaper, Qwen 3.7 Max beats Claude at half the price, ChatGPT moves into Excel

Pulling together this week's major AI releases for anyone who didn't have time to track every blog post. Sticking to substantive changes, not hype. Anthropic — Claude Opus 4.8 Released this week. Headline pricing unchanged, but Fast Mode dropped from $30 input / $150 output per million tokens to $10 / $50 — a 3x reduction on the premium tier. Reported improvements in "judgment" and longer autonomous runs. Also shipped 20+ legal MCP connectors and Microsoft 365 add-ins (Excel, PowerPoint, Word) in GA. Alibaba — Qwen 3.7 Max Launched May 20 at Alibaba Cloud Summit. 1M-token context. Reported to top Claude Opus 4.6 Max on Terminal-Bench 2.0, SWE-Bench Pro, and MCP-Atlas. Pricing $2.50 / $7.50 per million tokens — roughly half of Opus 4.7. Alibaba claims autonomous operation up to 35 hours without performance degradation. Alibaba is now ranked #6 lab globally on Arena text leaderboard. OpenAI — GPT-5.5 Instant Now default in ChatGPT. Reports 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts (medicine, law, finance). OpenAI also shipped a ChatGPT sidebar inside Excel and Google Sheets, plus a personal finance dashboard for Pro users (US only). Google — Gemini 3.5 Flash Reported to beat Gemini 3.1 Pro on coding and agentic benchmarks at ~4x faster output token rate. Ultra subscription cut from $250 to $200/month; new $100/month Developer tier introduced. xAI — Grok Build 0.1 Coding agent moved to public API beta May 28. Custom Skills feature added for reusable user-defined tasks. Connectors for SharePoint, OneDrive, Notion, GitHub, Linear, plus bring-your-own MCP support. Mistral Launched Vibe (unified work + code agent, replaces Le Chat). Acquired Emmi AI for physics-based simulation. Targeting €1B revenue in 2026; new 10MW inference DC announced. Hugging Face Launched an app store for the Reachy Mini robot. ~10,000 units shipped. Also reported a malicious repo masquerading as an OpenAI release that accumulated 244K downloads before takedown — relevant for anyone pinning models from HF in production. My take as someone building on top of these APIs: The 3x Opus Fast Mode price cut and Qwen 3.7 Max's pricing + autonomous duration are the real signal this week. The cost floor on premium-tier inference is dropping faster than most app-layer products have repriced for. Anyone running multi-step agent workflows needs to recompute unit economics this week — either pass through the savings or reinvest the margin. The other pattern worth noting: OpenAI and Anthropic are both pushing into Excel/M365 surfaces. Distribution is becoming the next battleground, not raw model capability. If you're building a productivity SaaS, the giants are now inside the same surface as you. submitted by /u/ksraj1001 [link] [comments]

Does anyone have a copy of the ICDAR2013 Chinese Handwriting Competition Dataset? [R]

I understand that this is a little unorthodox, but I'm desperately trying to download a copy of the ICDAR2013 Chinese Handwriting Recognition Competition Dataset. Unfortunately, the linked page in the Conference Archive: https://nlpr.ia.ac.cn/databases/handwriting/Download.html appears to be down, and has been down for the past few weeks consistently. I've checked every source I can find, like Kaggle, HuggingFace, remnant Google Drive and Baidu Netdisk links, even checking if someone's accidentally committed it to github, but no dice. I've tried every google dorking trick I know to no avail. Which brings me here. Please, if anyone has a copy of the Competition Dataset, I would be very grateful if you could share the ZIP with me. Thanks in advance! submitted by /u/Aathishs04 [link] [comments]

Wall-OSS-0.5: 4B VLA with open training code and zero-shot real-robot evaluation[D]

Wall-OSS-0.5 is a new 4B VLA release from X Square Robot, built on a 3B VLM backbone with action experts in a Mixture-of-Transformers layout. What caught my eye is that the report evaluates the pretrained checkpoint on real robots before task-specific fine tuning, instead of only reporting downstream fine-tuned performance. The reported numbers are: zero shot on a 17-task real-robot suite, 4 tasks above 80 task progress, including a held-out deformable task (Rope Tightening, 82). After fine tuning on a 15-task suite, they report 60.5 average task progress, +17.5pp over pi0.5, and +26pp on the 10-task manipulation subset. They also report +21.8pp on embodied grounding while general VL ability stays stable. The method bits I am trying to sanity check are the gradient bridge and the optimizer claim. They argue that discrete action-token CE is the dominant gradient into the VLM backbone, while flow matching's contribution to backbone updates collapses to roughly 5 percent within a few thousand steps. The Vision-Aligned RVQ tokenizer is supposed to make those action tokens semantically grounded instead of just numerical compression. For continuous actions, they still use flow matching, but supervise in recovered action space rather than velocity space. They also include DMuon, a distributed Muon optimizer, with a pretty aggressive overhead reduction claim. Code: https://github.com/X-Square-Robot/wall-x. Hugging Face org: https://huggingface.co/x-square-robot. Project page: https://x2robot.com/oss#resources. Paper: https://x2robot.com/api/files/file/wall_oss_05.pdf The questions I had after reading it: if you have run an analogous gradient-bridge ablation in another VLA, did action-token CE dominate in the same way? For people already using Muon, does the DMuon overhead claim sound plausible? And has anyone seen RVQ-with-vision-alignment clearly beat FAST-style tokenization outside this paper? If anyone is already trying to reproduce this on real hardware, drop notes. The third-party results will matter more than the release numbers. submitted by /u/Tall-Peak2618 [link] [comments]

noisekit - CLI for generating realistic degraded speech datasets for ASR benchmarking [P]

If you've ever tried to pick an STT vendor for a phone-based voice agent or call center product, you've probably hit this wall: you have plenty of real production audio, but it's unlabeled, so you can't compute WER on it. And the annotated public datasets (FLEURS, CommonVoice, LibriSpeech) are clean studio recordings that have nothing to do with how STT models actually handle your G.711 encoded noisy phone calls. Annotating production audio is slow, expensive, and usually a privacy headache. So most teams end up benchmarking on clean data, picking a vendor, then discovering in prod which one actually survives noise. noisekit fills that gap. Take a clean annotated dataset, apply degradations that approximate your production conditions, end up with a noisy annotated corpus you can run WER on across every STT candidate. uvx noisekit generate \ --dataset google/fleurs --config en_us --split test \ --samples 100 \ --output ./noisy-fleurs Feed ./noisy-fleurs through each STT candidate, normalize, and compute WER with the existing transcripts. The output is HuggingFace AudioFolder-compatible, so load_dataset("audiofolder", data_dir="./noisy-fleurs") works. Presets cover the conditions that actually matter for voice products: telecom: G.711 narrowband bandpass + 8-bit BitCrush + 16-32 kbps MP3 (sounds like a real phone call, not a synthetic low-pass filter) noise: real ambient mixed at 5-15 dB SNR (auto-downloads a MUSAN noise-only subset, or bring your own --noise-dir matching your domain: call center, cafe, car, street) reverb: pyroomacoustics far-field at 1-3 m mic distance low_bitrate: wideband MP3 at 16-32 kbps clipping: ADC / mic saturation clean_reference: control / WER floor compound chains stack realistically. noise_telecom = noisy room then phone codec, which is what an actual support call sounds like. Each output gets PESQ, SNR and NISQA scores in metadata.jsonl alongside the original transcript, so you can correlate WER with measured signal quality after the fact. Repo: https://github.com/karamouche/noisekit (MIT, uvx-runnable so zero install) Genuinely curious to hear from people who've benchmarked STT in production: what degradation conditions am I missing? submitted by /u/Karamouche [link] [comments]

GPT-5.5 tops the benchmarks but sits at #22 for actual usage - I built a live index that tracks both (open source)

I built AgentTape to rank models on more than just benchmarks - it blends benchmark performance with who's actually using and talking about a model, plus cost and speed. It scores every public model from public signals (GitHub, Hugging Face, OpenRouter, MCP registries, npm, PyPI, arXiv, Hacker News) refreshed hourly, plus the main benchmark leaderboards daily. Right now OpenAI sits at the top: GPT-5 is #1, with 5.2, 5.1 and 5.4 Mini rounding out the top 5, and 5.2-Codex and 5.4 just behind - 6 of the top 7. The only thing breaking the run is xAI's Grok 4.20, level on score at #2. GPT-5.5 is the clearest example - it sits at #22 overall, and the breakdown shows why: * Quality: 96.4 - 2nd highest on the whole board, only pipped by Gemini 3.1 Pro Preview (97.2). On benchmarks alone it'd be near the top. * Adoption: 15 and Efficiency: 36 - both low. New release, steep price, so hardly anyone's using it day-to-day yet. * Biggest 24h climber on the board (+6) - so that's starting to shift. A benchmark-only board would put GPT-5.5 near #1 (second only to Gemini 3.1 Pro). That gap between topping the benchmarks and actually getting used is the whole reason I built this. Early days and I'm still tuning the methodology, so I'd love your thoughts - does weighting adoption alongside benchmarks match how you'd rank the GPT line-up, or would you trust the raw benchmark order?