LeRobot can now see in 3D 👀🤖 Depth cameras... feed straight into training data end-to-end in v0.6.0: 12-bit precision preserved through encoding, or fully lossless raw storage if you'd rather skip compression altogether. Same release also opens up every video encoding parameter for RGB, plus a live benchmark leaderboard to help you tune it. This exact depth pipeline powers Stanford Vision and Learning Lab's BEHAVIOR-1K 2026 dataset: 20,000 simulated manipulation episodes, depth included. More on how it works below:show more

I'll always root for a team that open-sources its... best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyantshow more

Some updates on the multiview vistadream pipeline with Rerun!... Rerun came in extremely useful here, as being able to visualize depths at each stage of the pipeline allowed me to debug some nasty bugs. Since the last time, I was only working with a single image input. I've added in VGGT as my multiview pose + depth estimator. It works REALLY well for getting camera poses, but the depths are not that great. To try and fix that, I estimated depth maps from MoGeV2 for each of the views, and scale+shift aligned them so that they would match up to the confident sections of VGGT's depth predictions. You can see in the video just how much sharper the visualized 2d depth maps are! The biggest issue continues to be the multiview consistency 🫠 That's up next, along with actually training the Gaussian splat. Lots of work went into actually understanding inputs+outputs for VGGT. I had some funky bugs where the confidence values would all collapse to true I'm also really excited for this pipeline to use Difix3D+ Nvidia instead of Flux Inpainting, it seems like a better suited for a multiview pipeline.show more

Robots can now reconstruct 3D scenes in real time... from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:show more

You can't 3D reconstruct glass from images... ...WRONG! Thanks... for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AIshow more

This work makes a humanoid robot do simple parkour... moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.show more

Something big is happening in robotics - and it’s... hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.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

This week is already so hot. 🔥 Massive release... from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."show more

You don't understand... Higgsfield MCP + Claude just automated... AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wildshow more

🚨 A HYDROGEN FUEL CELL SUBMARINE DRONE JUST ACHIEVED... WHAT BATTERY-POWERED AUVs HAVE FAILED AT FOR 15 YEARS LONG ENDURANCE, DEEP DEPTH, AND ACOUSTIC STEALTH ALL AT ONCE. The Envoy AUV from Cellula Robotics completed a fully submerged mission covering 2,023 km over 385 hours. Crucially, it did this with a realistic, punishing profile: more than 4,000 turns and maneuvers, not a simple straight-line test. It also operates at depths up to 3,000 meters and produces almost no acoustic signature. Powered by proton exchange membrane (PEM) hydrogen fuel cells, the only byproduct is water. This breaks the long-standing trade-off in autonomous underwater vehicle design where improving one capability (range, depth, or stealth) usually destroys the others. Why this matters: • Battery AUVs have been fundamentally limited by energy density adding more batteries increases weight and drag, which cancels out the gains • Hydrogen fuel cells deliver more than twice the energy density of lithium-ion batteries while enabling true long-endurance missions without frequent surfacing • The vehicle can loiter on the seabed using a suction anchor for days or weeks, dramatically changing operational concepts for pipeline inspection, cable monitoring, and naval surveillance • It is already in the hands of Defence Research and Development Canada The deeper implication: This is more than just an impressive endurance record. It represents a genuine shift in what is possible for persistent, covert subsea operations. For navies and offshore industries that have spent years compromising between mission duration, depth capability, and detectability, hydrogen fuel cells are now offering a practical way to stop making those trade-offs. As these systems mature and scale, we could see a new generation of autonomous underwater platforms that operate for weeks or months with minimal support fundamentally changing how we monitor critical infrastructure, conduct scientific surveys, and maintain undersea awareness. How do you think hydrogen-powered AUVs will change subsea operations compared to today’s battery-limited systems? Follow for more frontier robotics, energy, and defense technology.show more

Tesla robotaxi will be banned in New Jersey. Vision-only... may work 99.9% of the time. But Robotaxis don't just need to handle 99.9% of scenarios—they need to handle at least 99.9999% of them. That's a 1,000x reduction in failure rate. Imagine a fleet of 1 million Robotaxis making just 40 trips per day each. That's 40 million trips every day. Even a 0.1% (1 in 1,000) long-tail failure scenario would occur 40,000 times per day across the fleet. Can you imagine Elon Musk 's face if Tesla Robotaxis ended up causing 40,000 failures/indidents every day? And the reality is, we still don't know whether Tesla's Robotaxi system can achieve 99.9% safety, let alone the level required for mass-scale autonomous deployment. This is exactly why you don't see unsupervised Tesla robotaxis everywhere today. Scaling too quickly would inevitably expose far more edge cases—and potentially far more accidents. That's also why redundancy matters. Adding LiDAR isn't about replacing cameras—it's about covering their blind spots. Ouster Rev8 LiDAR takes this even further: it delivers true colorized 3D perception, 48 bit color depth, 116dB dynamic range, and operates across lighting conditions from near darkness (1 lux) to direct sunlight(2 million lux). It complements vision rather than competing with it, providing another independent layer of perception when cameras are challenged. At Robotaxi scale, sensor redundancy isn't overengineering—it's engineering. $OUSTshow more

A Letter to Our Community: The Road Ahead for... Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷show more

This is the easiest way to make $10k/month with... organic affiliate and AI Arcads launched an ai ugc studio that lets you build an entire army of hyper-real AI actors Then you turn any static image into a high-quality video showcasing any product go to TikTok and make an account + warm it up using arcads you can run an entirely AI UGC account using the same character over and over, making it seem like an authentic TT page Mix the content up with slideshows and videos with the same character Here's the AI stack gameplan: - Claude to help you write scripts - Arcads to generate an image of an AI girlie that fits your product demographic Scroll tiktok and save + download every video / slideshow you see made by clippers promoting a product (there's literally loads) Your going to find an offer on whop for making money online or spirituality and target it towards girls feed all these videos you scraped into a custom google gemini gem trained to deconstruct hooks / angles for you for easy hook inspiration + ideas Deconstruct the hooks, put them into Claude and ask it to give you hooks for the same style of video put for your products your promoting For the videos do caption and reaction + showcase formats Generate the reactions using the character you made in arc ads then manually record the showcasing of the product or proof of the product working Also for caption generate a 8-10 second video you can put text over Include your CTA in the video for reaction style and captions for caption style Plus generate images with the same character and make slideshows directed to your product Now rinse and repeat this make multiple accounts with multiple different avatars and printshow more

How 2 psyop yourself to success 🍸🥂 Visualization >... + unmatchable lvls of desire (for a specific goal other than money) >> get addicted to work. [ and most importantly DREAM big, DREAM about it, think about it, work for it, and do it every single day, while your sleeping think about it, when you eat, do it, when your walking do it, when your working think about it] In lab studies I’ve compiled tons of data for behavior in hopes of using it for marketing purposes However, it also if curated shows you the triggers people react to The mindset changes that curate and sway them to do more of x (in this case work and create insane drive to achieving a goal or YOUR goals) Remember, every stimuli, every reaction curates your mind. When you come in contact with whatever it may be it sways your future behavior & current attitude/ psychological state So at all times if you want it bad enough you should try to curate your environment (special music that gets you in the mood or NO music, depends on the person bc music distracts you thus means you work less OR if you’re brain storming then it helps) Take out all negative energy, comments, or bs that distracts you and breaks your momentum because one it breaks from the smallest thing then that snowballs and has a negative effect going forward Another thing that I personally like to do is read and put myself into those shoes. For me it’s business and mindset related books and nothing compares to John D. Rockefeller’s mindset, work ethic and GRIT, his absolute desire for SUCCESS : I like his 38 letters to his son, John D the founding fathers of the rockefellers, and a study in power. And most importantly you need to just want it bad enough. Lose sleep for it, not because you want it but because you want it so bad that you’re working nonstop to get to it. This is how Rockefeller succeeded How Elon musk succeeded James Dyson Andrew Carnegie And most importantly how Peter kell succeeded All these billionaires and killers got hyper successful because of their drive and mindsets. It’s so important. If you believe something so strongly it’s revisited your mind to making it reality. Ex. Study DOI: 2003.11.018 > strongly visualizing exceedingly finger abductors boosted muscle strength by 35% Another study showed that basketball players who visualized making free throws showed significant improvement over others. And so on and so on. We as marketers have practiced this forever. We know when we tell a emotional story it changes the viewers mind, and we can lead that into a sale at a much higher conversion rate To get a brutal mindset focused on a dream you have that you truly want with your entire being is the result of the same hormones and neuro pathways after they have been curated and triggered by specific stimuli like in a VSL or ad You need to build your mind, you need to build a dream and want it, give yourself inspiration with books and ideas and positive energy. Build motivation and joy by watching others do it and building proof elements that it can really be done in your mind. Break the barriers and limits Movies are good for this, I like edits for this exact reason because yes it can lead you to doom scrolling but also it can be so so valuable in making you see that goal, want it even more and go for it. I personally really like war dogs, lord of war, limitless, and probably a lot more I’m forgetting. But you get the idea. Your success is in your hands alone, if you believe it fully and truly with a pure heart, then it will happen. Good luck🥂 P.s. * I will drop data, sauce, and more info about this and the methods & stimuli I talked about in this thread continuously so bookmark and come back to check them out.show more

$25K+ profit daily from 1 wallet, with OpenClaw. I... have the exact step-by-step guide, giving it free for 24 hours. To get it: 1. Comment "OpenClaw" 2. Like and Retweet. 3. Follow me Himanshu Kumar ( So, i can send you DM) I ran a simple script last night with Claude Code. Pull on-chain data from Polymarket, sort by win rate on 15 minute BTC markets. 20 minutes later, 100s of wallets showed up. Most were losing money or barely breaking even. Then I spotted 1 address. 200+ trades daily, every single week profitable, timing so precise it looked robotic. Because it is. I fed the wallet address back into Claude Code. Asked it to reverse engineer the strategy. 20 mins later the full breakdown appeared on my screen. Here is how it works: Bot monitors Binance and Bybit every 100ms. Waiting for BTC volatility compression to drop below 0.08%. When it hits that level, it buys both Up and Down contracts at 25 to 35 cents each. Classic straddle play. 1 contract loses, the other rockets to a dollar. Entry at 30 cents means 3x to 4x return every time. Repeats dozens of times per day. Result: $13K to $25K profit daily from 1 wallet. No human intuition, no insider tips. Just an algorithm exploiting a gap in market mechanics. I searched to see if anyone else found this wallet. Turns out yes. There is a Telegram bot that auto-copies trades from wallets like this. I connected it to the same address. Every entry matched what my terminal showed. You can now copy-trade an algorithm in real time. That capability did not exist 12 months ago. Comment "OpenClaw" and I will send you everything. Must Follow me Himanshu Kumar to get the DM.show more

Gemini Omni + GPT Images 2 + Claude Code... is f*cking cracked i just built an AI animation ad generator that turns any product into a fully scripted AI 3D explainer video drop in your product photos and a one-line pitch. the system analyzes your brand, pitches you 4 proven ad concepts with hooks and shot lists, then renders the whole thing end-to-end. scripted, voiced, stitched, ready to post. if you're still paying editors $200+ per video or waiting days for revisions this replaces that entire workflow. here's how it works: > set up your brand kit with product photos and a one-line pitch > hit analyze and the AI pitches you 4 different proven ad concepts > pick one and hit generate the system then scripts every shot, renders each clip in parallel, adds voiceover, QC checks and stitches it into a finished vertical ad 3 minute setup and 4-12 min per ad. each 30 second ad costs under $3 in API credits which is cheaper than any editor and 10x faster. RT + reply "ANIMATION" and i'll send you the full app + setup guide (must follow so i can dm)show more

Heres an actual way to make $10k a month... from TikTok + organic affiliate TT slides is probably the best way to make AI content for a few reasons > easy and not time consuming to make > much harder to detect images are AI > slideshows rarely get the AI label from TT (especially if you do what I’m gonna show you) >slideshows require less engagement to go viral (more consistent virality) >CTA can be more natural this AI slideshow format is going insanely viral consistently on brand new accounts and no one is even detecting it’s AI they’re super easy to make and use a story telling format that’s really smart you can take the exact same formula to promote sweeps offers from Glitchy and make $10k a month pretty easily here’s the blueprint >Content creation to create the slides realistic you can simply take a photo from Pinterest and put it into Gemini or Chat GPT and ask it to give you an EXACT JSON to recreate the image add in any details you want to add like “make her hair blonde” “make her eyes blue” take that JSON and put it into Nana Banana If you want her in certain backgrounds do the same process and add it in with the JSON of the girl you’ve created or just describe it Now clear the meta data from said image to stop getting the AI label If you still get it go to a meta data analysis site and put the data in ChatGPT Ask if their is anything in the meta data that is signalling this to TT >Writing scripts The reason these go so well it’s because they have a negative scroll stopping hook that instantly make you want to know what the slides going to say next “got fired from my job” “failed my exams” Along with the music that sets the emotion of the video It wouldn’t work as well if they had a random viral song that’s up beat plus the cherry on top is that the image correlates with what’s being said in the hook it’s self acting as a visual hook It wouldn’t work aswell if it was just an image of a girl on her bedroom >how to interpret sweeps Choose an sweeps offer from Glitchy for a retail store (Walmart/target) follow the same format of scroll stopping negative hook Example: “broke my arm” then you would tell a story that paints a bad working environment, maybe she got fired for breaking her arm and is now exposing secrets and your CTA would then be “they don’t promote this but they have a secret feedback program” this is just to give you inspiration but there is literally countless ways >account set up - US proxy / US sim - Download TT with US proxy on - Buy aged account (to help with account trust and getting banned due to proxy issues) - warm up for 2 days (scroll vids all the way through, like, comment authentic things relating to video) Then you post This is a very good way to at least reach a couple K a month but I’d be surprised if you don’t reach $10k beyondshow more

As a graphics engine coder I think when you... look at a flickering bug like this one in the video below it’s not immediately obvious what is going on. The key here is observation - to study this flickering/bugged render carefully - what do we see? Firstly for me it was very obvious that nearly all of the scene shadows were flashing on and off - but (but!) there was a secondary issue where some buildings and parts of the sky were also flashing purple. Hmmmm. Interesting. I initially thought then this might be two separate bugs - but because the sky purple element could only based on full screen post fx and not 3D rendering I looked at this first with a few GPU captures to step through all our post processing to find the rendering stage which made these pixels turn purple: When I did this I found the colour 3D texture LUT grading that makes our different biomes have unique colour palettes was going very wrong - colours near 0 or 1 were wrapping and making the purple elements that we see in the said sky and base parts. The only way this could happen was if the texture was corrupt (which it was not) or if the 3D texture sampling was wrapping and not clamped as intended. That was the Eureka moment - because if the post fx had the wrong texture sampler then the disappearing shadows which also require an exact texture sampler for comparing depth might be also wrong because of the same kind of texture sampling issue! So with this idea that the engine was using the wrong texture samplers, but only in very high draw call scenes like the big base here I the looked at some engine limits and found the bug very quickly - a circular dx12 descriptor buffer for samplers running out over multiple frames, reusing the wrong data for new scenes inflight. Hence the flickering, as the GPU randomly got wrong samplers for some post textures or shadow depth. Easy to fix with triple limits for future expansion and also adding an assert/debug spam in case this limit is ever reached again - QA testers would see this message and report if they ever saw a flicker with this style of bug. My bug and my bad from 2017 porting NMS to DX12 without foreseeing how massively complex bases and our game would grow.show more

A friend of mine just recently got his first... gun, a striker fired Glock 19. He is terrified to leave a round chambered. To anyone reading this who has this same fear, I want to alleviate your concerns. I'm going to explain why there is nothing to be afraid of. First and foremost, I fully understand your concern. It's not irrational. And you're not stupid for being nervous about a bullet being aimed at your leg with the striker cocked back under spring tension about an 1/8 of an inch from the primer. I get it. I shared this concern when I was new to carrying. But your fear is due to a lack of understanding of the internal safety features of a modern striker fired handgun. On a properly maintained modern striker fired pistol the striker CANNOT hit the primer on the chambered round without the trigger being pulled. Can't. Not shouldn't. Can't. (If you have any good Sig P320 jokes, this is the proper place to share them) There is a block that the striker will hit and stop if it is released without the trigger being depressed. It works just like a lock and key. There is a channel in the blocker that the striker can pass through if it is in the fire position. It only goes into the fire position if the trigger is pulled which pushes the blocker into the fire position. A spring keeps the blocker firmly in the block position until then. The blocker is a solid piece of steel that won't break. And if your gun is securely in a hard sided Kydex holster nothing can move the trigger unless it is unholstered. Many guns like Glocks have a trigger safety that prevents the trigger from moving due to force of dropping. Your finger has to be on the trigger safety to move it. So the gun cannot fire unless you pull the trigger. And it's easy for you to test this safety feature to see how it works! See the video below. You can try to push the striker forward by moving that piece at the back of the slide forward. That piece is the back of the striker. Push it forward and it will stop. The tip won't come out through the breach. You see it trying to come through but it gets stuck. Now push that little button in. That is the actual blocker. When you push it in you will now be able to move the striker forward through the breach and see how it will strike the primer. You can test this easily every time you disassemble your pistol to verify it is still working. You can break this safety feature by removing the blocker or losing the spring. But if it's there it will work. (You would be the first person in firearms history who has ever lost a spring. Literally the first one. It has never happened before. 😐) But it doesn't come out as part of regular maintenance. You have to disassemble the firing assembly to get it out. That won't happen by accident. And if you do that on purpose you should do this blocker test when you reassemble it to make sure the safety feature is working. My video shows a Glock slide. All Glocks will look like this. Other brands may put the blocker in a different spot or it may be a slightly different shape. But they all (mostly) have the same design. If you test yours as demonstrated and it works like mine does, your gun WILL NOT fire without pulling the trigger. (If you just thought of another P320 joke, this is another good place to toss that out there)show more

Introducing Glidepath. A new way for builders on Bankr... to take profit -- without nuking their own chart, or their reputation. The problem: Builders earn fees in their own token. The second they sell into the pool, the chart craters, holders get wrecked, and trust evaporates. And they torch their own long-term upside doing it. First -- what Glidepath is not: It doesn't pull liquidity. It never touches your pool's LP. Pulling liquidity makes trading your token inefficient and unappealing. It's your own tokens, fed back into the pool in slices so small the market barely registers them, each one sized by the Bankr AI agent to live conditions. Why that's healthy for the chart, not harmful: Every slice is a tiny fraction of pool depth, spread over time. Organic buy volume absorbs it, price can keep trending instead of taking a wick. A small, steady, absorbable flow is nothing like a full clip. It actually gets better. Once "the dev might dump" is off the table, buyers price in less risk. The overhang that caps every launch disappears. Less rug risk → stronger bid. Committing to a Glidepath can be bullish. And it's not opt‑in. Selling your fee token straight into the pool through Bankr is now turned off -- Glidepath is the only way to sell it on Bankr. So "the dev might dump" stops being a promise holders have to trust, and becomes a rule they can see. Credible commitment -- enforced, not just offered. And here's the part builders sleep on: Before you commit, Glidepath shows what that same stack is worth at higher market caps. You don't have to dump to fund your project. Grind the coin up, and the same tokens fund you many times over. Your treasury grows with your chart, not against it. Once you commit: → tokens are locked to a vesting wallet → after a short heads-up window (48hr), they exit in small slices using the AI generated sell plan → each slice capped to a fraction of real liquidity -- the AI can size under the cap, never over And it's all in the open. Your token page shows a live exit plan for everyone to see -- committed, sold, remaining -- with the exact timing fuzzed so it can't be front-run. Holders see a capped, transparent glide. No hidden float. No 3am chart nuke. Bottom line: Creators -- take profit on your terms, chart and reputation intact. Holders -- "the dev might dump" becomes a known, capped, visible number known up front. For once, you and your holders want the exact same thing: number go up. This is what launching on Bankr should mean: credible commitment, built in. Glidepath now live in your Bankr terminalshow more