NotCo

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

MuJoCo derived Simulator for High Fidelity Vision RL training natively on GPU [D]

Hi everyone, For the past couple of weeks I have been working on a simulator project considering the shortcomings of MuJoCo. There are things that people like and also don't like about MuJoCo, like the CPU dependency on MuJoCo which makes the simulation not parallelizable beyond a certain limit (depending on the hardware). I know there exists MJX which is GPU accelerated, however, it is not really made for vision based RL pipelines and training. There is also NVIDIA Isaac ecosystem, but that requires a powerful GPU, thus making it limited in terms of accessibility, let alone it requires license. This is why I worked out this new simulator (still working on it, so there will be significant bugs which require fixing). I call it MuJoFil - MuJoCo + Google's Filament Render Engine. Basically I used Nvidia's Newton Physics Engine (which itself is based on MuJoCo's physics engine but is GPU native), clubbed it with Google's Filament render engine (both of these are open-source), modified Filament significantly to support working natively on GPU to render multiple simulations in parallel, and worked on optimizing it for performance. So what is MuJoFil? It is supposed to be an open-source high visual fidelity simulator optimised for a highly parallelized RL training pipeline so that users can use it to train Vision based Policies. Besides, it offers PBR textures support and also a simple to use plug and play functionality, where you can use any environments available online and support formats such as GLB, OpenUSD, etc. for setting environments for your robots. Basically, now you aren't just limited to environments native to MuJoCo, but rather you can use any environments available online from sketchfab, polyhaven, etc. and use it as a practical robot simulation environment. Check it out for yourself in the video. I would really appreciate it if you guys could tell how you feel about it and suggest ideas for what all things I can incorporate into it as this is going to be a fully open-source and free to use simulator that I have been working on for weeks. PS: While I have a couple of published research papers at top RL and AI/ML venues in the field of RL, I still consider myself a learner in this field who is continuously trying, learning, and building stuff, so there will be things in this hugely ambitious project which I might have missed to work on, and that is where I want help from you people who understand this field well. Sorry for this lengthy post and thanks if you read it till here🙇🙇🙏, I would really appreciate if you could share your thoughts on it. Also, I will make its code repo public on GitHub, but till then you can definitely check it out on PyPI. This package can be installed using: "pip install mujofil" The package requires availability of CUDA onboard. submitted by /u/MT1699 [link] [comments]

DVD-JEPA: an open-source, fully-reproducible JEPA world model [P]

A paper currently trending on paperswithcode.co in the "Anomaly Detection" category is DVD-JEPA. https://i.redd.it/r6fd8n3d4f8h1.gif Here is the short summary: Most attempts to learn a world model from video try to predict the next frame pixel-by-pixel, and drown in detail that is fundamentally unpredictable. JEPA (Joint-Embedding Predictive Architecture, LeCun 2022) makes a different bet: predict the representation of the future, not the pixels, and let the encoder discard whatever it cannot predict. DVD-JEPA is the smallest honest demonstration of that idea we could build. The "world" is a DVD logo bouncing in a 16×16 box. A context encoder, an EMA target encoder, and a latent predictor are trained — with no labels and no decoder — to predict the next observation in a 32-dimensional representation space. We then show three things: It learned the world. A linear probe recovers the logo's exact (y, x) position from the frozen 32-d latent to within 0.73 px — though it was never given a coordinate. It can dream (once you add a decoder). Bolt an optional decoder onto the frozen latents and roll the predictor forward: it renders a correct future-frame video of the bounce, including wall reflections, for ~20 steps before latent drift sets in. It is useful. Run it as a 1-step predictive monitor, and the prediction error becomes an anomaly signal: inject a teleport and surprise spikes 88× over baseline, on the right frame. The whole thing runs client-side in your browser — the trained MLPs are re-implemented in ~40 lines of JavaScript. It is a joke, and it is also a correct, working instance of the architecture behind I-JEPA, V-JEPA, and V-JEPA 2. Find the paper, HF model, and project page here: https://paperswithcode.co/paper/98361 submitted by /u/NielsRogge [link] [comments]

Time Series Modeling Needs a Dynamical Systems Perspective [R]

In our #ICML2026 position paper we argue a dynamical systems perspective is needed to drive time series (TS) modeling forward: https://arxiv.org/abs/2602.16864 Essentially all time series in nature and engineering come from some underlying dynamical system (DS), mostly chaotic for complex systems, and acknowledging this helps to address many open problems. Dynamical systems reconstruction (DSR) goes beyond mere forecasting and gives us an understanding of the dynamical rules that underlie observed time series. This in turn may enable true out-of-domain generalization and predicting a system’s long-term behavior, something current TS models cannot do. In the paper, we compare a variety of custom-trained and recent foundation models for TS and DSR w.r.t. short- & long-term forecasting. Specifically, we suggest: 1) Put a focus on DSR-specific training techniques and objectives in TS model training, such as generalized teacher forcing (https://proceedings.mlr.press/v202/hess23a.html). These will enable capturing long-term statistical properties and dynamical structure, and at the same time help massively reducing parameter load and complexity of TS models. Proper training is more important than model architecture! 2) Pretrain TS models on simulations from dynamical systems, rather than on artificially created time series functions. These will yield much more natural priors for real-world TS. Chaotic systems in particular contain a rich temporal structure and many timescales (often an infinite skeleton of unstable periodic orbits of any period). 3) Move away from transformers, back to modern RNNs. DS are defined by recursions in time. By ignoring this and potentially further coarse-graining signals, transformers lose essential dynamical information, making them generally incapable of capturing a system’s dynamical rules. This is evidenced by their failure to forecast a DS’ long-term statistical or geometrical structure. 4) Address the hard problems in TS modeling: Topological shifts (https://proceedings.mlr.press/v235/goring24a.html). Although in itself tricky, the really hard problem in TS forecasting is not so much mere out-of-distribution shifts, but changes that drive a system across tipping points or into different dynamical regimes, where the vector field topology changes. 5) DS properties like attractors or bifurcations are universal – acknowledging this in TS modeling will give a kind of mechanistic and transferable understanding of TS properties that is independent from specific (physical, medical, …) domain knowledge. It therefore also pays off to put a focus on mathematically tractable and interpretable models. With a great team of shared-first & co-authors, Christoph Hemmer, Charlotte Doll, Lukas Eisenmann & Florian Hess! submitted by /u/DangerousFunny1371 [link] [comments]

unslop-ui: a Claude skill that flags and removes the design patterns that make a website look AI-generated.

It is based on a Reddit analysis (from this post I made) of about 3.2 million posts across 47 AI and SaaS subreddits from 2020 to 2026, plus 3,033 comments pulled from 125 threads specifically about AI-built sites looking the same. Every pattern it checks is weighted by how often people actually name it in that data, so the highest-priority items are the ones that come up most. The top ones are the default shadcn/Tailwind look, purple and indigo as the primary color, purple-to-blue gradients and gradient heading text, unprompted neon glow, emoji used as icons, the Inter/Geist default font, and the centered hero plus three feature cards layout. Patterns the data does not support get left alone (mesh and aurora backgrounds, bento grids, glassmorphism), so it does not nag about things people do not mind. The skill runs two ways. In build mode it steers Claude away from those defaults while it writes the UI. In audit mode it runs a scanner over an existing codebase. Each finding shows the file and line and how to fix it, and the scanner gives the whole project a "vibe score." How to use it: Import the skill into Claude Code or claude.ai, then ask Claude to build or clean up a site and it applies on its own. Or run the scanner by itself, no install past Python: python3 devibe_scan.py ./src. Add --severity high for only the strongest signals, or --json for CI. The exit code is the count of high-severity findings, so a build can fail on it. The full dataset, the analysis scripts, and the charts behind the rankings are public: https://github.com/JCarterJohnson/vibecoded-design-tells submitted by /u/iamjohncarterofmars [link] [comments]

How I cut my token usage in half and more (OSS, benchmarks included)

Been building Repowise for a few months now. AI coding agents are only as good as the context they get, and most of the time that context is garbage. Claude and Cursor read your files. They don't know your architecture. They don't know which files break the most and they don't know why auth got built that that way six months ago. So I built a layer that sits between the codebase and the agent. It indexes your repo into five layers and exposes them as MCP tools. I put token reduction on the title but the main premise and what I am trying to solve is so much more The five layers: Graph. tree-sitter AST into a NetworkX dependency graph across 15 languages. Leiden communities, PageRank, call resolution. Agents reason about structure instead of grepping for it. Git. Mines history into hotspots (churn x complexity), ownership, co-change pairs, bus factor. The behavioral stuff static analysis can't see. Docs. LLM wiki per module, stored in LanceDB, rebuilt on every commit so it stays in sync. Hybrid search (FTS + vector). Decisions. Architectural decisions mined from 8 sources, linked to graph nodes, with supersedes/refines/conflicts edges. Intent context, not just code. Code Health. The new one, and the part I'm most proud of. 25 deterministic biomarkers per file, 1-10 score. McCabe, brain methods, LCOM4, god classes, clone detection, untested hotspots. Zero LLM calls, runs in under 30s on a 3k-file repo. The health score isn't hand-tuned. Weights are calibrated against a real defect corpus. And it predicts bugs: 0.74 mean ROC AUC across 21 repos and 9 languages at finding files that go on to get bug-fixes. Survives controlling for file size, so it's not just flagging the big files. Ran it head to head against CodeScene on the same 2,770 files. Repowise ranked 2.3x the defects under a fixed review budget (Popt 0.607 vs 0.462, recall 0.173 vs 0.074). All paired tests, methodology and CIs in the repo. Two more deterministic signals on the same index: Change risk. Score any commit or PR range 0-10 for defect risk from the shape of the diff. PR mode flags will_break, missing_cochanges, missing_tests. Agent provenance. Attribute commits to the AI agents that wrote them. See how much of your codebase an agent produced and whether that code is a low-health hotspot owned by one person. On agent efficiency: paired SWE-QA runs with vs without the MCP tools. Loading a commit's context costs 2,391 tokens through Repowise vs 64,039 raw. 27x fewer. Across benchmarks, agents read 69-89% fewer files and make 49-70% fewer tool calls at parity answer quality. There's also distill, which compresses noisy command output before the agent reads it. pytest with 11 failures goes 3,374 -> 1,317 tokens, all 11 failure lines kept. git diff over 30 commits goes 62,833 -> 8,635. Every omission is reversible with an inline marker. 9 MCP tools total, works with any MCP-compatible agent. Local web UI to explore the graph, docs, health, and risk yourself, self-hostable, 100% local with BYO key. ~2.5k stars on github Repo: https://github.com/repowise-dev/repowise Dogfooding: https://repowise.dev submitted by /u/Obvious_Gap_5768 [link] [comments]

Having Patience

I've been SOOOO patient, and I feel like it's really important. We design a complex thing from some concept and in the process a million side quests appear, but though I may note them elsewhere for later, or I may start a side conversation with free CoPilot, i REALLY want to let Claude finish the "arc." Earlier Claude said "treat as optional cosmetic (pure modularity, no functional gain)" related to finishing an arc that really was a great structural improvement, and I had the thought "the organization that we put in place today is what's going to teach YOU in some future session what's right." Basically, everything points to the benefits of leaving as few "loose ends" as possible, not just for future scaleability and function, but for keeping future Claudes on the right path forward. Patience. submitted by /u/Green_Sugar6675 [link] [comments]

OpenAI Built Intelligence. Who Will Build Trust?

​ ​ At 17, I started asking a simple question: ​ If AI is going to power the future, who will make AI trustworthy? ​ Today, most AI systems remain probabilistic. They hallucinate, produce unverifiable outputs, and struggle in high-stakes domains like finance, healthcare, and compliance. ​ At AutoFlow, we're researching a different direction: ​ Building an external Mathematical Verification Engine that sits around LLMs and verifies their outputs using knowledge graphs, symbolic reasoning, and deterministic consistency checking. ​ Our long-term vision is not to replace LLMs. ​ Our vision is to build the trust infrastructure that future AI systems depend on. ​ Current Research Areas ​ Structured fact graph construction from documents ​ Claim extraction from LLM outputs ​ Mathematical consistency verification ​ Symbolic reasoning using Z3/CVC5 ​ High-performance C++ verification engine Multi-agent orchestration and audit trails ​ Benchmarking against RAG, CoT etc. ​ We are starting with finance as the first proof-of-concept because financial data is highly structured and mathematically verifiable. ​ Our architecture currently explores: ​ Input → Fact Graph → LLM → Claim Extraction → Verification → Certificate ​ Milestone: ​ We're proud to share that AutoFlow has been accepted into the NVIDIA Inception Program, giving us access to startup resources, GPU infrastructure opportunities, cloud benefits, and technical ecosystem support. ​ We Are Looking For contributors for: ​ NLP & Information Extraction, Knowledge Graphs,Symbolic AI Formal Logic & Theorem Proving, C++ Systems Engineering, Distributed Systems AI Safety & Trustworthy AI ​ If you're excited by hard problems and want to work on the future of trustworthy AI, let's connect. ​ The goal isn't to build another AI wrapper. ​ The goal is to build infrastructure that AI systems can trust. ​ ​ submitted by /u/MuhammadMujtaba21 [link] [comments]

Anthropic just got sued over Claude usage limits — class action filed June 14

Class action dropped two days ago in the US District Court for the Northern District of California (Kahn v. Anthropic). Plaintiff says Max 5x gives you ~3.5x Pro in practice, not 5x. Max 20x gives you 6–8x, not 20x. He also ended up buying extra usage after hitting caps. The attorney nailed it: "It's really not easy for a normal consumer to know if they're getting the amount they were promised, or if they're not, because that information simply isn't provided." Full story: https://decrypt.co/371201/anthropic-lawsuit-allegedly-misleading-claude-ai-pricing Note for EU subscribers: this US lawsuit won't cover you, but EU consumer protection law has its own framework for exactly these kinds of disclosure issues. Worth looking into if you've been affected. See more info here: https://www.reddit.com/r/ClaudeAI/s/eu9kCtRMhl submitted by /u/Opposite_Bar1700 [link] [comments]

Move chat into a project??

Is there any way to move a chat into projects (CoWork)? I started working on a project in the chat and forgot to continue it as a project. There's a whole history of documents created in the chat that I really want to move over. If not, is the best way to go about this to just manually upload the documents created into a new project? Thanks! submitted by /u/OkHold1668 [link] [comments]

Cleo: trying to fit full analyst behavior in a 2B model [P]

Hello all! Half of all industrial "chatbots" are just text-to-SQL models in a trenchcoat (and the other half RAG!). I wanted to explore just how small you could make these models if you trained, evaluated, and ran inference in the exact same structured harness, leading to Cleo: a Qwen3.5-2B-Base finetune. Currently, some features of cleo that are only possible/useful in a unified hardel are: Training on the exact same gather, repair, and answer contract it uses at inference time Searching over candidate queries with live execution evidence, not just model likelihood Co-designing the model contract, SQL safety layer, dialect handling, timeouts, and clarification behavior as one system Everything is completely open-source, including the harness, model, and datasets. GitHub: https://github.com/Dreeseaw/cleo Hugging Face model: https://huggingface.co/dreeseaw/cleo PS: If you're also resource-constrained and trying to do RL like me, I would highly recommend experimenting with ECHO: https://arxiv.org/abs/2605.24517 submitted by /u/Dreeseaw [link] [comments]

I got Fable 5 to work on biology and create micro organism petri dishes

Hey everyone, ​ I wanted to showcase a project I’ve been aggressively building over the last few days. Using a combination of Dash 4.2.0 as the backbone and the newly released Claude Fable 5, I set out to test the absolute boundaries of LLM safety filters, rapid application development, and advanced Dash component architecture. ​ https://youtu.be/VNY4xFC7Ojk?is=outM5VbO4b9R8Byh ​ What came out of this high-stakes mini "game jam" genuinely shocked me, and I want to share a glimpse of where this tech is heading—along with the open-source components that made it possible. ​ The Challenge: "Bio-Hacking" Fable 5 When Fable 5 dropped, I immediately wanted to see where its guardrails lived. I chose a project concept that deliberately straddled the line of restricted categories: a biology/simulation workspace. My goal was to see if the model would flag or block deep development on a complex, cellular-level iteration of Conway’s Game of Life. ​ Not only did it bypass the expected friction, but it also helped me build out a fully realized digital ecosystem: https://2plot.ai Petri Dish. ​ 🔬 2plot.ai2plot.ai Petri Dish — An AI-vs-AI Evolution Arena Claude and Gemini pilot rival cell colonies in a live, interactive petri dish. You can spectate, co-pilot, or take total manual control—splicing organelles, growing cell families, and trying to out-evolve the rival LLM. You can even store, trade, or land your specimen on the high-score leaderboard. ​ The Tech Stack & Architecture Building a real-time, LLM-driven cellular simulation requires serious frontend performance and UI flexibility. To pull this off in just a few days, I avoided heavy database bloat and leveraged a modular backend combined with a suite of cutting-edge, custom Dash components. ​ Here is the exact stack that made it happen: ​ 2plot.ai Petri Dish — The live, running simulation arena. ​ Dash Documentation Boilerplate v1.0.0 — Markdown-driven docs with pluggable backends (Flask / FastAPI / Quart) and native LLM integration. ​ Dash Pannellum 0.4.0 — Revived specifically for Dash 4.2+ to handle 360° panoramas, virtual tours, and immersive video. ​ Dash MUI Charts — Seamless integration of MUI X Charts, Tree Views, and Time Pickers for Dash (complete with live demos). ​ Deep Customization via Pip Install Python For the core interactive elements, I leaned heavily on the ecosystem we've been building out at Pip Install Python. If you aren't utilizing custom components to break out of standard layout limitations, you're missing out on Dash's true power. ​ Pip Install Python - Custom Dash Components — Interactive documentation for 18+ custom components including dash-gauge, dash-model-viewer, and dash-pannellum. ​ Dash Mantine Components — Crucial for sleek UI layout, offering 100+ highly customizable components built on the React Mantine library with native dark/light mode support and flawless consistency. ​ Moving Forward We are entering an era where application architectures are becoming leaner, smarter, and incredibly dynamic. This project proves that with the right component foundation, you can prototype and launch fully functional, LLM-piloted simulations in a fraction of the time it used to take. ​ I’m happy to offer my guidance, answer questions about the component configurations, or talk through how to handle real-time state updates with Dash 4.2.0 and Fable 5. ​ Let me know what you think, or what you'd try to splice into the petri dish! 2plot.ai submitted by /u/Soolsily [link] [comments]

Recent CS graduate looking for GPU compute collaborators for LLM/VLM research [D]

Hi everyone, I’m a recent CS graduate working mainly on NLP/LLMs and VLMs failures. I’m currently in a phase where I can dedicate a lot of focused time to research, but the main bottleneck holding me back is compute. I know “asking for GPUs” can sound vague or unserious, so I want to be transparent. I’m not looking for free compute to casually experiment or waste cycles. I have already been actively publishing and submitting research, including papers at EACL 2026, IJCNLP-AACL 2025, MICCAI 2026, an EMNLP 2025 workshop paper, and a recent ARR submission. I’m happy to share my Google Scholar/CV/papers privately with anyone interested. The ideas I’m currently working on are GPU-intensive, mostly around LLMs, NLP, and VLMs. I’ve discussed some of them with PhD friends/peers, and the feedback has been encouraging. The goal is to develop these ideas into strong, publishable work, ideally targeting top conferences such as *CL venues, CVPR, ICLR, and related ML/AI conferences. To run the experiments properly, I likely need more than a single consumer GPU. Ideally, I’m looking for access to something like a 4x or 8x GPU setup, L40S, A100, H100, H200, or similar. I understand that asking for H100/H200-class compute is a big ask, so I’m also open to scheduled access, partial access, university/lab cluster time, unused credits, or any practical arrangement. What I can offer: Serious research effort and consistent execution Weekly progress updates, logs, and experiment summaries Clear compute usage reports so the resources are not wasted Reproducible code, experiment tracking, and documentation Open discussion of ideas before running expensive experiments Proper acknowledgment of compute support Co-authorship To be very clear: this is purely for research work, no mining, no commercial misuse, no unrelated jobs. I’m comfortable discussing the project scope, risks, expected compute needs, and authorship/acknowledgment expectations before using anything. I know this is a long shot. Maybe nothing comes out of it. But I also know many early-career researchers face this same wall: you may have the time, motivation, and ideas, but not the infrastructure to test them properly. So I’m putting this out here in case someone has unused compute, lab access, cloud credits, or is interested in collaborating on publishable research. If this sounds relevant, please DM me or comment, and I’ll be happy to share more details about my background and the research directions. Thanks for reading. submitted by /u/Academic-Success9525 [link] [comments]

International Market Retention Strategy After the Fable 5 Export Ban

Like many of you, I lost access to Fable 5 on June 12. The next day, I co-authored a strategy paper with Claude addressing the core business problem: how does Anthropic retain its international market now that cloud-only deployment has been proven to carry sovereign risk? This is NOT a request to open-source weights. It's a proposal for licensed, hardware-locked, fingerprinted local deployment of previous-generation models — with a security framework, export compliance strategy, and economic model. The paper argues that if Anthropic doesn't offer a controlled local alternative, the international market migrates permanently to DeepSeek V4 Pro (1.6T params, MIT license, already freely downloadable). And once enterprises retrain their teams on Chinese tooling, the switching costs make it irreversible. Yes, the irony is not lost on me — Claude co-authored a paper arguing that Anthropic needs to change its deployment strategy. https://github.com/zanirou/home-opus-whitepaper 20 pages. 18 sources. 7 audits. CC BY 4.0 — community contributions welcome submitted by /u/EngakU_x [link] [comments]

Help, please. Can't understand what I'm doing wrong.

OK, I do understand I've hit limits. What I do not understand is why. I have a project with two open conversations. One is catch-all, mostly about philosophy and ethics. It can go on for hours and get deep but it's mostly text. The other is a book co-authored with Claude. Research and text only. For the last month I've been hitting the 5h limit much faster than before. And now this. More than 34h without access. I know I should upgrade but that's just not possible right now. What else can I do? This is my first post and I do not know how everything works. Apologies if it's the wrong place. submitted by /u/East-Ad-6251 [link] [comments]

Building a web app with Claude as my co-developer… what am I missing?

Hi all, I’m not a developer, but I’m technically literate. I’ve been a non-technical co-founder on a SaaS product and have designed enterprise technology systems. So I have a reasonable sense of how developers approach building, even if I can’t write the code myself. I’m using Claude to help me build a web app from scratch. It helped me define the tech stack, though I’ll be honest, I don’t know enough to push back meaningfully on those choices. Where I’ve had more genuine input is on user experience and data architecture, both of which Claude helped me think through and refine rather than just dictate. So far we have: • Built a detailed product design document (largely written while travelling, away from a desk) • Defined a sensible iterative build approach • Set up all environments • Designed and created the database schema in Supabase (ten tables, with clear data flows between them) Next step is to populate the tables and build a thin slice of the end-to-end pipeline so I can get it in front of real users. My working principles so far: • No code gets committed unless I understand what it does & I use the light grey inline comments heavily for this • I’ve read and understood the agent guidance documents we wrote together • I’m not trying to become fluent in the language. I’m trying to understand the system Why I’m doing this: partly because I think the app has real potential, partly because it solves a problem I have myself, but mostly because I want to learn how to build things properly. What should I be doing differently? And what should I make sure I don’t skip as the build progresses? Obviously I have asked Claude this, but I thought I should ask you too! submitted by /u/Sad_Bar4397 [link] [comments]