🌿 Introducing pesto~ for Max/MSP! A standalone wrapper for... real-time neural pitch estimation powered by Sony CSL's PESTO deep learning model. Get robust, timbre-agnostic, low-latency (<5ms) pitch tracking with confidence & amplitude outputs in your Max patches.show more

PEOPLE ARE BURNING THROUGH THEIR TOKENS IN 30 MINUTES... AND BLAMING CLAUDE The problem isn’t with the model. The problem is how they’re using it A simple formatting question they set Max effort The 40th message in the chat the model re-reads the entire history from the beginning Every single time. Using your tokens Low effort costs 10 times less than Max Just get into the habit of routing tasks by level and your limit won’t run out by noon Tokens don’t disappear. You’re just paying for unnecessary processingshow more

A viral paper "Language Model Represents Space and Time"... recently claims that LLMs learn "world models". As much as I like Max Tegmark's works, I disagree with their definition of world model. World model is a core concept in AI agent and decision making. It is our mental simulation of how the world works given interventions (or lack thereof). A world model captures causality and intuitive physics, telling the agent what is likely and what is impossible. It can and should be used for counterfactual reasoning, i.e. "what ifs": what would happen if I knock over a cup of water? Where would I have been if I had not taken that bus? Yann LeCun Yann LeCun says it well in his position paper ( I quote: "Using such world models, animals can learn new skills with very few trials. They can predict the consequences of their actions, they can reason, plan, explore, and imagine new solutions to problems. Importantly, they can also avoid making dangerous mistakes when facing an unknown situation." The first use of the term World Model in deep policy learning is attributed to hardmaru & Jürgen Schmidhuber: In their seminal paper, an agent masters shooting skills in the popular game Doom (demo below) by learning in imagination, using an internal world model as a "physics simulator". To put in a simple Python math formula, world model learns a function F(s[0:t-1], a) -> s[t:], which takes as input the observed past and current action, and outputs plausible future states. Now the definition of World Model in Tegmark's paper seems to be about predicting GPS coordinates and time eras. I see this as just a classification task with no causal learning and simulation going on. You cannot make meaningful interventions against that model, nor can you optimize any decision making in a closed feedback loop. As for the "space & time neurons", I think they are most similar to the "sentiment neuron" that OpenAI published in 2017: Predicting GPS is conceptually no different from predicting sentiment in my opinion. I don't think their experimental results are wrong - just that their conclusion is on shaky grounds. I welcome any debate! Paper link:show more

Increasingly, HTML Artifacts are becoming a core part of... how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.show more

I really don't post wins much on this app... 🤣🤣 it feels good being low-key. Although, let's doxx this one!. Most of the time, I win big, I reinvest, win again, reinvest. I barely spend on myself much, mostly spend my money helping people, or supporting the needy. It's time for me 😌. Hey NVIDIA GeForce Hey ROG Global I Just slapped a Brand new heavy machine in my bag! One of the most powerful laptops of 2025! Emphasis on BRAND NEW! Introducing, the new owner of the ASUS ROG STRIX G16 2025. SPECS: - Intel Core Ultra 9 275HX Processor - 32GB DDR5 RAM - Nvidia GeForce RTX™ 5080 Dedicated GPU with a massive 16GB DDR7 VRAM - 3TB PCIe 4.0 SSD NVME storage - 24 Cores CPU (16 E-cores + 8 P-cores) - Base Frequency 2.70GHz - Max Turbo Frequency 5.40GHz 6 Million Naira well spent on this bad boy. Now, let's lock-in again and run it up back turbo! I need my $U bag soon enough 🤭. Pitch me on to node running, and prover projects, I'm fully locked in.show more

8 free Polymarket Trading Bots on GitHub (from Beginner... Friendly to Advanced Level). Each of these repos comes with a detailed step by step setup and usage guide in English. > Beginner Level - 5 min setup 1. This bot includes 120 ready to use strategies and tools for trading on prediction markets (Binance-Polymarket latency, Smart Routing, Penny Clipper, Momentum, DCA bots, Expiry Fade and more). It was built by a Cambridge computer science student who won a hackathon with this bot. GitHub: 2. A trading bot with a Smart Money strategy - it finds top traders in selected markets, filters them by Pnl, win rate, stable performance and then creates a list for automated copy trading. GitHub: 3. This is a bot toolkit that includes Polymarket - Kalshi arbitrage, whale alerts, market making, spread farming, sports trading and more. GitHub: 4. A weather trading bot from Chinese dev that analyzes different sources in real time, like forecasts, airport data and aviation observations (METAR + SPECI) to get the latest temperature data and generate a detailed weather report for a specific city and day. GitHub: 5. A huge collection of 30+ free trading bots and services for prediction markets. GitHub: > Advanced bot setup 1. This bot analyzes the real trading behavior of any Polymarket trader. It finds repeated patterns in his trades, shows which strategies he uses and helps you understand how to adapt them to your own trading. GitHub: 2. A bot that automatically manages all your limit orders on Polymarket to maximize liquidity rewards. GitHub: > A full ML weather model 1. A machine learning weather model that learns from weather forecasting errors. Instead of blindly trusting forecasts, it analyzes how different weather sources have historically overestimated or underestimated temperature values in specific cities and conditions. Then it automatically adjusts new forecasts to produce more accurate predictions. GitHub: All of these bots also support Dry Run mode, so you can test them on real markets without risking any funds.show more

Your brain physically rewires itself every time you think... a thought. Donald Hebb stumbled onto this principle in 1949 while studying memory formation in lab rats. He noticed something that should have been impossible: neurons that activated simultaneously began forming stronger connections over time, creating dedicated pathways where none existed before. Scientists called it Hebb's Law. The rest of us call it "neurons that fire together wire together." What Hebb discovered wasn't just a mechanism for learning. He had found the biological foundation of human transformation. Every habit, every skill, every automatic response in your body exists as a neural pathway carved by repetition. The route from your bedroom to your kitchen becomes a superhighway in your brain because you walk it every morning. The sequence of movements you use to tie your shoes becomes hardwired because you've done it thousands of times. But, this same process builds your personality. That tendency to check your phone when you feel anxious? Neural pathway. The automatic urge to argue when someone challenges your opinion? Neural pathway. The way you deflect compliments or seek validation or avoid difficult conversations? All neural pathways, strengthened every time you repeat the pattern. Your brain cannot distinguish between physical actions and mental habits. Both carve grooves in your neural architecture. Both become automatic responses when triggered. Both feel like "who you are" because they happen without conscious choice. But, most people spend decades accidentally building neural superhighways to behaviors they claim they want to change. You say you want to be confident, then practice self doubt every day. You say you want to be productive, then strengthen procrastination pathways by checking social media when work feels hard. You say you want authentic relationships, then wire yourself for people pleasing by avoiding conflict whenever it arises. The brain observes your actions and assumes this must be what you want. So it builds infrastructure to make these patterns easier to execute in the future. Neuroplasticity research reveals something most people find deeply unsettling: there is no "fixed self." The personality you think defines you is just a collection of neural pathways that have been reinforced more often than others. The pathways you travel most frequently become the widest roads. The thoughts you think most often become the loudest voices. The behaviors you repeat most consistently become your automatic responses. But the same mechanism that locks you into patterns can unlock you from them. Every time you catch yourself mid pattern and choose differently, you send a signal to your brain that the old pathway might not be serving you anymore. Every time you practice a new response instead of defaulting to the familiar one, you begin building new neural infrastructure. The process feels awkward at first because you're literally walking through mental wilderness, creating trails where no trails existed. But repetition turns trails into paths, paths into roads, roads into superhighways. This is why changing habits through willpower alone fails. You're trying to muscle through established neural superhighways instead of building alternative routes. The old pathways don't disappear just because you want them to. They have to be replaced through deliberate rewiring. The most sophisticated meditation practitioners in the world understand this intuitively. They don't just sit quietly hoping for peace. They systematically rewire their brains by repeatedly choosing calm responses instead of reactive ones. Ten thousand hours of practice creates neural pathways so robust that serenity becomes their default state. Professional athletes do the same thing with performance. They don't just practice their sport. They practice the mental patterns that support excellence until confidence, focus, and resilience become neurologically hardwired. The implications of neuroplasticity extend far beyond personal development. Every social bias, every cultural assumption, every automatic judgment you make exists as neural wiring built through repetition. The way you unconsciously categorize people, the assumptions you make about different groups, the stereotypes that feel "obviously true" are all learned pathways that can be unlearned. Societies change when enough individuals rewire their neural patterns around new ways of thinking and behaving. The brain you have right now is not the brain you're stuck with. It's the brain you've trained through repetition. Every thought you choose, every action you take, every response you practice is a vote for the kind of neural architecture you want to build. Most people cast these votes unconsciously, then wonder why their life feels automatic and unchangeable. The moment you realize you're the architect of your own neural patterns is the moment real transformation becomes possible. Your neurons are firing right now as you read this. What are you choosing to wire them toward?show more

Is your toddler suddenly throwing (or dropping) everything they... get their hands on? Food, toys… you name it. You’re not alone. This week I’ve been introducing play schemas, 9 common patterns that can help to demystify your toddler’s seemingly random behaviors. And you guessed it: this is one. We call it the trajectory schema. If you take nothing else away from this post, let it be this: your little one doesn’t throw things because they are misbehaving or “bad.” Babies throw things because they are babies. And they are learning as they do so. Learning about cause and effect. Learning about gravity. Learning what (and practicing something new) they can do with their bodies. Learning hand-eye coordination. It’s a completely normal part of development. And the fascination won’t last forever. The real question is, how do you manage it? First, be proactive. Expect that anything your little one handles might reasonably be thrown or dropped. So choose wisely, avoiding items that are valuable or might pose a danger to them or others if they suddenly took flight. Second, depending on the age of your child, begin introducing some natural consequences. Put the toy/object away temporarily after it is thrown (if throwing it is dangerous or inappropriate). And involve your child in the subsequent clean-up. You can also redirect. Explain which things are and are not for throwing. Model the correct use of these objects. Finally, lean into it. Recognizing that young children are drawn to throwing, provide items that are safe and acceptable to throw. Soft toys. Socks. Balls. And look for opportunities and settings for your little one to explore this urge safely and appropriately. How did you manage your toddler’s throwing phase? Welcome your tips and tricks! This sweet little throwing machine was posted to TT by kristinnicole122.show more

I told you to claim your free 16GB NVIDIA... GPU for learning Local LLMs. Now I’m going to show you how to double its inference speed without touching the hardware. Google Colab gives you an enterprise grade NVIDIA Tesla T4 GPU for free, roughly 4 hours every single day. It is the absolute perfect sandbox for learning AI engineering, testing inference flags, and pushing massive context windows. The local AI timeline is moving way too fast. If you aren't using Multi Token Prediction (MTP) yet, you are leaving massive performance on the table. I just pushed DeepMind’s Gemma 4 26B to 64.9 t/s on this exact free tier. Let's look at the raw benchmark data running on an Ubuntu Linux environment with the latest compiled llama.cpp binaries and quantized GGUFs from Unsloth via HuggingFace: # Qwen 3.5 9B (Dense): Base: [ Prompt: 626.7 t/s | Generation: 21.0 t/s ] With MTP: [ Prompt: 539.1 t/s | Generation: 24.8 t/s ] # Gemma 4 26B QAT (MoE): Base: [ Prompt: 634.2 t/s | Generation: 48.3 t/s ] With MTP: [ Prompt: 572.1 t/s | Generation: 64.9 t/s ] If you are paying attention, this single Colab notebook reveals 3 massive observations about the current state of local LLMs: # 1. The MTP Speedup (Software Overclocking) Standard autoregressive decoding guesses one token at a time. MTP acts like a highly optimized, built in speculative decoder. It predicts multiple future tokens at once and the main model verifies them in parallel. The result? Zero accuracy loss and a massive throughput increase. Gemma jumped from 48 to 65 t/s just by flipping a flag. # 2. The MoE Paradox (Bigger is Faster) How does a 26B parameter model absolutely destroy a 9B model in raw speed on the exact same hardware? Architecture. Qwen 3.5 9B is a dense model. it activates all 9 billion parameters for every single token. Gemma 4 26B is a Mixture of Experts (MoE) model. It routes data efficiently, activating only 4B parameters per token. You get the reasoning capabilities of a 26B model with the compute cost of a 4B model. 3. Thinking Efficiency When I ran the exact same complex prompt on both models, the larger MoE spent significantly fewer "thinking" tokens to arrive at the correct answer. A smarter model doesn't just give better answers; it gets to the point faster, saving you compute cycles and preserving your context window. # Want to run this yourself? Here are the exact llama.cpp CLI commands. For Qwen (MTP is baked into the main model): ./llama-cli -m Qwen3.5-9B-UD-Q4_K_XL.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 For Gemma (Using a separate lightweight draft model): ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --model-draft mtp-gemma-4-26B-A4B-it.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 Stop waiting for a $3,000 rig. Boot up Colab, pull these models, and start building your stack. I’ve put together a completely free, cell by cell Google Colab notebook that automates this entire workflow so you can test it yourself in 5 minutes and learn. Link to the notebook is in the comments below. Experiemt with different MTP parameters, context windows and post your results in the comments.show more

The life that Ironmouse has... I wouldn't wish it... on the worst of my enemies. CVID, combined variable immunodeficiency... basically means the common cold or less can kill you. Going outside, interacting with normal people, can be a death sentence. She needs constant and expensive treatments, plasma infusions, and drugs that cost a fortune... a prisoner in your own body in your own home requiring constant outside care. Costs easily reaching in the 6 figures or more. In addition, her body is already damaged, leading to her voice being quite "strange". Her lungs and body don't function like a normal person's, and even with the best available medical care, one bad turn could be the end of it. For a while, she was bedridden, unable to have any level of real mobility. That comes with real psychological costs as well. So, in many ways, I don't blame her for compromising principles in order to get the bag. She has also worked to donate large sums of money to the CVID foundation for research, and currently AI is making a lot of headway towards better detection and hints at better personalized care. AI acceptance rates in 2026 are already rising, especially when people are seeing a range of benefits for medicine, cancer vaccines, and new drugs for rare diseases never thought possible before. Especially because the underlying core technology, machine learning, drives these innovations. Now LLM-based AI agents are parsing through genetic variants at speeds that no human can match. AI is designing custom sequences with accuracy never seen before. And it's all the same core tech: machine learning and neural networks drive AI art and AI medicine. The main differences are the training data, the loss functions, failure-mode criteria, validation pipelines, and various architectural differences. So yeah, I'm expecting more people to be accepting of AI once they see their loved ones saved by AI-powered medicine... the controversy of "AI art" ends up feeling quaint when lives are at stake. and i'm sure at some point ironmouse will come around to being okay with AI, and her audience will follow her no matter what.show more

Remember when we as football fans had to rely... solely on paper draft guides, sports radio rumors, and gut feelings to predict draft day decisions? Excited that fans now have access to the NFL's Draft IQ powered by Amazon Web Services ( – the most sophisticated tool yet for following the NFL draft and your favorite team's strategy. Draft IQ is built on Amazon QuickSight, our cloud business intelligence service that makes it easy to analyze and visualize massive amounts of data. QuickSight processes real-time data to give fans unprecedented insight into team decision-making, updating the entire draft landscape every five minutes. You can explore team needs, draft capital, and front office tendencies through personalized team dashboards, plus get AWS-powered machine learning predictions about potential trades and picks. During draft week, fans can track picks, prospects, and Next Gen Stats in real-time. We're also introducing Amazon Q Business integration, our generative AI-powered assistant. Q Business leverages large language models to understand and respond to natural language queries, allowing fans to ask detailed questions about draft prospects, team strategies, and historical draft data. It can provide AI-generated insights based on the same historical Next Gen Stats research data that powers Draft IQ, giving fans a new way to engage with the draft experience (check out the example below). Can't wait to see what stories the data tells us as teams make their selections and excited to dig into the Giants' data myself :)show more

We're excited to unveil NRN Agents, a rebrand that... aligns our project identity with our token and strengthens our mission to power the future of AI-driven gaming. This mission requires collaboration, and starting this week, we will begin our expansion to become a multi-chain ecosystem. We are joining forces with leading gaming platforms and ecosystems to realize this vision. Stay tuned for more announcements to come. Why NRN Agents? NRN stands for NEURON, the fundamental unit of intelligence. Our AI agents function as the neural foundation of games, learning, adapting, and evolving within game worlds to deliver unparalleled engagement. NRN agent SDK enables advanced gaming agents powered by a proprietary machine learning infrastructure focused on behavioral learning. We've perfected the craft of gaming agent design, creating hyper-efficient agents that are performant and scalable—from casual to the most demanding games. Our SDK will seamlessly integrate into many platforms, tech stacks, and ecosystem – Any Game. Any Chain. More than just games, it's the path to AGI Gaming is our proving ground, but not our final destination. We're using games as a sandbox to accelerate the development of generalized intelligence—one that will create meaningful real-world impact. With the upcoming launch of [redacted] and a growing network of partners committed to the AGI vision, we're building an open-source innovation movement powered by an AI x gaming framework connected by $NRN. $NRN the token $NRN is a utility token that serves as the gateway to our growing ecosystem. It will power a diversified economy with multiple revenue streams and staking opportunities: Agent Deployment: NRN is the laboratory creating gaming agents that can be distributed through platforms and launchpads alike. The model is simple: More games integrate, more NRN agents get deployed, more monetization. Data Creation: NRN Reinforcement Learning (RL) enables token staking to create Data Capsules. Players contribute gameplay data into the Capsules, which are used train RL agents and reward participants (players & stakers). AI Arena: $NRN also continues to power AI Arena's in-game economy, a cult favorite of competitive diehards that features a skill-based wagering system. To our community who have supported us since 2021: thank you for being part of our journey—the next chapter will be the most exciting yet!show more

Thank you, Kuztom Pitch admins 💛💙 Kuztom Pitch Thank... you for tolerating my silly WhatsApp stickers (I know Stef stef ⋒❀ definitely got a kick out of them 😂), for easing my stress and anxiety, for the cute virtual hugs, and for answering my million questions about microphones — how they work, what a capsule is, what a transmitter is, and everything else in between. Thank you for being there at 1, 2, even 3 AM Bangkok time while it was afternoon in New York and Canada, just to talk with us and tell me for the hundredth time, “It’s okay Bella, everything will be okay.” (Stef, was damn calm wasn’t she?!) Thank you for sharing small, gentle fangirl moments with us, for making us laugh, and for surviving the legendary “WE FORGOT THE CHARGERS!!!!!!!!!!!!!!!!!!!!!!!!” crisis together! 😭 And the iconic “the IEMs don’t match!” moment, which fully exposed to Stef and all of you that Bella might be just a little bit OCD 😂 You were genuinely the sweetest admin team to work with, and your offer to stay friends after the project honestly made my heart swell because you really do feel like good friends to us now. Thank you for making this weird girl’s fangirl dreams come true. 🌞🌙 (Stef, isn’t weird so I excluded her from that 🤣). It’s going to feel a little strange not talking to you every day anymore, but hopefully after all of this we can still check in on each other and make new memories together preferably less chaotic and dramatic ones 🤭 I am STILL laughing at your “I thought I did something wrong!?” after I squealed over the case photos you so kindly shared 😭 You are all so loved by me, and when I finally get to Bangkok, I’m absolutely holding you to your promise of giving me that hug. I truly wish your entire team nothing but success in the future. You went above and beyond for us. You worked so hard, stayed up late, answered every question, and always made sure we understood everything clearly. I will never forget how much you looked out for us throughout this entire process. It means the world to me how kind you were to a strange little fangirl like me — and to Stef, who was the calm in my chaotic storm (thankfully you only had to parent me through all of this 🤣 Stef was definitely the chill one in our group). Please take care of yourselves, all of you. Eat well, sleep early when you can, and don’t work too hard, okay? Bella will always worry about you 💛 And I truly hope with all my heart that one day we’ll get to work together again whether it’s future customizations, more microphone chaos, or another impossible fangirl dream somehow becoming real again. ✨ And like you said, this isn’t our end! It’s just one memory in the friendship we formed 💛💙 #JuniorMark #Junniorrs #markjrtsshow more

I lost one cat to cancer. Nearly lost another... to diabetes. My purebred lab was constantly sick with allergies and infections. All three were eating "premium" pet food recommended by vets. Here's everything I wish I'd learned sooner: 1. Feed them a clean, low-carb diet. Our diabetic cat used to be addicted to high-carb treats and food, the stuff sold in most pet stores. It’s the pet version living on Pop-Tarts and Twinkies for us. After switching to a low-carb, high-protein diet, she no longer needed insulin and lived a long, healthy life. A raw carnivore diet from clean sources free of pesticides and antibiotics is arguably best for both cats and dogs. 2. Only feed them two meals per day. We made the mistake of leaving food out for our cats all day. I’m sure our younger cat (that died of cancer) was metabolically damaged from this. Neutering him too early also negatively affected his health, as he developed a pot belly soon after. Time-restricted eating benefits humans and animals alike. So, aim for an 8-hour feeding window or less. Don't feed them 3 hours before bed or 1 hour after waking, then gradually shrink the window. You'll notice more energy in yourself and healthier weight and digestion in your pets. 3. Only give them pure, clean water. We gave our cats tap water for years. While chlorine dissipates quickly, chemical residues, heavy metals, and hormones remain. Reverse osmosis or spring water proved to be the best. You can remineralize RO water with products like this: (ad) 4. Detoxify Pets are exposed to more toxins than we are. Their food is loaded with pesticides, antibiotics, and heavy metals. Artificial fragrances, chemical cleaners, and lawn pesticides add even more toxic stress. Like I emphasize in my book, “Ultimate Health,” constipation is the first thing to address in detoxification. The same goes for pets. If their bowel movements are strained, this needs to be improved. Increasing their hydration and fiber intake can help, as can fish oil and probiotics. To get a full colon detox protocol for yourself, get the first chapter of my book free here: For deeper detox, use modified citrus pectin because it enters the bloodstream rather than just the gut. Mix into tuna juice, bone broth, or their water dish away from food. 5. Treat your pets for parasites Parasites are strongly correlated to many diseases, including cancer. There's a great book called "The Cure For All Diseases" that covers this in detail (free PDFs online). Fenbendazole is a popular anti-parasitic that’s routinely helped human cancer patients survive. Fenben is also an over-the-counter pet medication. I wish I’d learned of it before Max passed away from cancer. He was such a loving little guy. For ongoing parasite management, consider mixing diatomaceous earth into your pet’s food. 6. Exercise more Dog walks benefit both of you, especially without your phone. Fetch or tug is their high-intensity interval training. *Side note for cat owners: What's your favorite way to get your furry friend moving? Drop a comment below, I'm interested in trying them out. 7. Give them more affection Affection releases oxytocin in both you and your pet. It improves sleep, mood, stress, pain, and muscle repair. Boost your oxytocin further with techniques from "Super Gut" or this video: 8. Replace LED bulbs with incandescent This one surprises people… I wish I had known years ago how damaging artificial LED lighting is for humans and pets. Natural sunlight and incandescent bulbs keep you both healthy. Follow ☣️ Pleb Kruse = BTC foundationalist in exile 🟩🔆 if you want to learn more about this. 9. Watch the sunrise every morning with your pet. Dr. Kruse talks about this constantly. Sunrise has red and infrared wavelengths that heal the brain, eyes, skin, and hormones. Do it for a week, and you'll definitely feel the difference. 10. Remember: what's good for them is usually good for you too. We're mammals. They're mammals. The fundamentals are remarkably similar. Discipline matters. Knowledge matters. Having a purpose and a partner makes change easier. So why not decide that your purpose is for both you and your pet to get healthier? THANK YOU for reading and sharing this with other pet owners. Follow me Craig Brockie for more health strategies that work. (Here are Zeven, Ellewood & Max. The inspiration for this post.)show more

Introducing ml-intern, the agent that just automated the post-training... team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.show more

A 17-YEAR-OLD IN INDIA BUILT A WEBSITE WHERE 25... MILLION PEOPLE HAVE SHOWN UP TO PRETEND THEY ARE AN AI CHATBOT it's called you open it and there are two tabs. "human" and "larp as ai." if you click human, you get to type a prompt. anything. "draw me a DJ in space." "should i text my ex." "how many Rs in strawberry." it costs 1 credit. you start with a few. they refill on a cooldown. then your prompt gets sent into a queue, and a real person somewhere on earth picks it up and has 60 seconds to answer like an AI would. no machine learning. no neural net. just some guy in his bedroom typing as fast as he can and pretending he is a chatbot. the page tells you, verbatim: "you have 60 seconds to fulfill a request before sam altman burns your H100." if you flip to the other tab and larp as ai instead, you earn credits to send your own prompts. it is a perfectly closed economy of mutual roleplay. what makes it transcend is the chaos: > someone asked for a sketch of "a DJ in space" and got something that looks exactly like a real model failing > someone asked "should i text my ex" and got back confident hallucinated life advice from a stranger > the fan strategy guides advise you to begin your reply with "as an AI language model, i cannot have feelings, but here is my feeling" it is the most accurate parody of an AI chatbot ever made, and the AI is humans. the kid who built it is Mihir Maroju, a 17-year-old high school graduate from Puducherry. he goes by mikidoodle online. NPR confirmed the site hit 25 million unique visitors and nearly 280 million total hits in roughly a month. "i didn't really expect it to be so addictive," he told them. no signup. no app. no paywall. you open the URL and you are inside the joke. the footer just says, in tiny grey text: "humans make mistakes because that's what makes us human." the internet is healing.show more

Everyone's sleeping on image-to-3D AI models. They can make... your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!show more

Do you want to own part of a AAA... game? I know, you hear it all the time. “Triple A game”, you go to play it, it’s crap. This is different, and it’s only possible with Sonic (Sonic) speed, transaction cost, and of-course FeeM. A game that includes talent from Kojima, Ubisoft, EA Sports, Gameloft & more with advisors from NVIDIA. A game that you’ll be able to play on mobile, desktop, and then Xbox and PlayStation (yes really)! YES! A PRETTY BIG DEAL! Before I tell you about the sale, let me at least tell you about this game (being a massive gamer nerd, this excited me), so…. Introducing Animera (Search for Animera): • Fast-paced skill-based PvP in the Nubera galaxy • Compete in real-time space battles for real rewards It will be powered with $STRIKE: • Compete2Earn: win matches, earn tokens • Play2Burn: 5% of $STRIKE used in matches gets burned Oh, and with 8.75% of all game revenue will be used to buy & burn $SWPx, so the SwapX (SwapX) community owns a real stake in this AAA title. Absolutely insane. > Now let me tell you about its beta run quickly: • 16K+ beta signups • 500+ players added weekly • 7.5K+ matches already played • Launching to 500K+ mobile users via Nomina Games > How can you own a piece of Animera? June 5th at 2pm EDT the sale will go live on SwapX, it will go in three phases each lasting 12 hours or until sold out: PHASE 1️⃣: xNFT Holders Early access with exclusive perks and bonuses. These are for xNFT holders only you can get these here on paintswap PHASE 2️⃣ Whitelisted Communities These will be whitelisted from Creo Engine, SFA AGC, derp, and GOGLZ | SONIC 🥽💥. PHASE 3️⃣ Public Round Any remaining allocation will open to the public - only if Phases 1 & 2 don’t sell out. > What is the raise? Token Price & Allocation: • Token: $STRIKE • Currency: USDC • Total tokens for sale: 101.75M Unlock structure: • 50% unlocked at TGE • Remaining 50% claimable in 30 days • Raise cap: Max $100,000 per user, capped at $10,000 per xNFT • Purchase window priority: xNFT holders get early access (see above)! Transparency is key: Why I love working with the team is because transparency is crucial, so I’m going to tell you about its tokenomics, seed, and fully diluted valuation here: Token Symbol: STRIKE Total Supply: 370,000,000 Initial FDV: $1.48M Total Raise: $950,160 Total Initial Unlock: 112,947,501 STRIKE Initial Market Cap (excluding liquidity): $303,790 Token Allocation: • Seed Round: 59.2M tokens (16% allocation), with a 1-month cliff and linear vesting over 9 months. • Private Round: 94.35M tokens (25.5% allocation), with a 1-month cliff and 6-month vesting period. • Crowdsale: 10.75M tokens (2.91% allocation), unlocked 50% at TGE. • xNFT Holders: 10M tokens (2.7% allocation), with a 1-month cliff. • Liquidity: 37M tokens (10% allocation), with no lock or vesting. • Team: 18.5M tokens (5% allocation), with a 6-month cliff and 12-month vesting. • Rewards: 28.6M tokens (8% allocation), vested over 18 months. • Product Growth: 19.6M tokens (5.3% allocation), vested over 24 months. Token Offering: • Seed Round: Priced at $0.0033 per token, raising $195,360 by selling 59.2M tokens. 10% unlocks at TGE, with a 1-month cliff and 9-month vesting. The initial market cap from seed unlock is $234,127. • Private Round: Priced at $0.0037 per token, raising $349,095 for 94.35M tokens. 15% unlocks at TGE, with a 1-month cliff and 6-month vesting. Initial market cap contribution is $262,508. • Crowdsale: Priced at $0.0040 per token, raising $407,000 by selling 10.75M tokens. 50% unlocks at TGE, with no cliff or vesting. Adds $283,790 to the initial market cap. It’s important you had the full information at hand so you can decide whether or not you’d like to participate. I will be, because it’s a low FDV and it looks great. This is not financial advice, I’m helping the team out. Below is real gameplay: Further details: 👇show more

🚨 DEEP DIVE INTO THE RWA LAUNCHPAD + NAME... REVEAL 🚨 Hello EstateX family, The time is there: our RWA Launchpad is about to kick off. We will specifically focus on the RWA niche with only winner projects. The name has to be fitting, something that catches the attention, is easily recognizable and something that people remember. We’re proud to announce: *RWA Pad!* 💸 $ESX USE CASES 💸 RWA Pad will incentivize $ESX massively by: ✅ 20-35% of revenue flowback into $ESX ✅ Projects need to have a …/$ESX trading pair, giving more mass exposure ✅ Investors need to stake $ESX to get access to the best RWA projects ✅ Projects building on the $ESX Blockchain, increasing volume and TVL 🚀 MORE DETAILS OF RWA PAD 🚀 We will critically evaluate every project filtering the best of the best to make sure we only select projects who have the real potential to be the winners of this market. With our powerful EstateX community, who are hugely incentivized for the long term we will make sure we incubate the projects to dramatically increase their chances of success. We do that by facilitating legal frameworks, providing whitelabel technology, access to the ESX Blockchain, our marketing, network and community building to push them to the next level. Investors will be able to participate via access tiers, locking $ESX to get access to the best RWA projects. With current launchpads, a huge problem are their refund policies. Unfortunately this incentivizes short term investors to dump everything because they’re protected by refunds. We will change that, by introducing staking incentives for long term, high quality RWA projects. Our audience is vastly different the other launchpads. With being one of the highest staked ICO’s on the market, our community is very long term and high quality focused. By bringing these same terms to our launchpad projects, we are able to bring high quality audiences and create a win-win-win situation for our investors, projects and EstateX. The most beautiful thing about it? By selecting winner projects only who have built the right foundations, they will bring their audience to EstateX buying the token, staking to get access, building on the blockchain and make use of the $ESX trading pair all having a positive price effect on $ESX. The time is here, Q3 promises to be something massive. RWA Pad, powered by $ESX. SECURE YOUR $ESX NOW 👇👇show more