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Russian scientists breakthrough in safer rocket design with model that predicts when space fuel ignites The new mathematical model calculates ignition delay time with high accuracy (within 10–15% error) for hypergolic fuels — propellants that ignite on contact without a spark or heat 🔸The model accounts for heat exchange,...

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$IKA 🪂 Airdrop – 60-second brief - covering my first $SUI ecco drop 👇 ➡️Allocation 🔸 600 M $IKA (6 % of 10 B) allocated for a “community drop” at mainnet - May 8 tokenomics post. ➡️ What’s Ika Ika「🦑」 )? 🔸Sub-second parallel MPC network (~10 k sigs/s) that lets Sui dApps sign BTC / ETH / SOL txs natively - no bridges, no custodians. How to farm (core tasks visible in Ink Sack now) 1⃣Stake one MF Squid NFT → ~100 Droplets / day. 2⃣Lock iSUI (10 iSUI = 5 Droplets / day; 90 % withdrawable any time). 3⃣Use Squid Keys (earned by leveling the NFT) to unlock bonus Droplets - each key ≥ 1 Droplet, jackpot up to 10 M. 4⃣Pre-generate a dWallet (one-click, ~0.13 SUI gas) for an instant 50 Droplets. Tasks 5-8 = sign a msg + submit Sui, BTC & EVM addrs for extra crumbs. Everything lives at the official task list mirrors the above.  ➡️Why care? 🔸Allocation is public & fixed. 🔸Testnet is live; several Sui builders already integrating. 🔸Backers include Sui Foundation, DCG, FalconX — > $21 M in runway. ➡️Unknowns / risks 🔸Snapshot & TGE dates still unannounced 🔸Droplet-to-token curve & vesting schedule remain redacted. 🔸As hype grows, watch for fake “Ink Sack” URLs; the only legit one ends .wal.app. 🔸You have to use $SUI or real funds. NFA. DYOR. I'm not asking you, telling you to, or suggesting anything. Not Financial Advice. For entertainment purposes only. ➡️Why people spam GiveRep under Ika threads? GiveRep is a SocialFi bot on Sui; tagging it earns REP points for a separate airdrop. Farmers double-dip: Droplets for $IKA and REP for the future $REP drop. 🔸🔸Stake $SUI with our Sponsor Cosmos | Everstake and support Airdrops. 🔸🔸 Sources: 🔗Links ➡️Ink Sack task board: ➡️Swap SUI ↔ iSUI: ➡️MF Squid NFT marketplace: DYOR! NFA! Full content policy: 🫡

Airdrops

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🚨🇷🇺 WEST SHOCKED: RUSSIA JUST BUILT ITS FIRST CHIP MACHINE The West tried to cripple Russia with sanctions and cut off advanced chips. But Moscow just put its very first domestic photolithography machine — the Progress STP-350 — on open sale for about 400 million rubles. 🔸 This machine makes 350nm chips — bigger, tougher transistors that are perfect for military use. They resist radiation, Electromagnetic Pulse (EMP) attacks, extreme heat/cold, vibration, and high voltage (up to 100V) where super-small modern chips fail. 🔸 Perfect for triple-redundant military circuits (three copies of the same chip working together) that never fail even if one gets hit by cosmic rays or EMP pulse. 🔸 Handles extreme battlefield conditions: huge temperature swings, constant vibration, high voltage up to 100 Volts — things impossible on modern super-thin processes. 🔸 Uses a modern solid-state laser (365 nm) instead of old mercury lamps. It can process up to 63 silicon wafers per hour (150-200 mm size) and lasts much longer — up to 10,000 hours. 🔸 Developed since 2021 with help from Belarus company Planar — cutting Russia’s tech gap from 40-50 years down to about 30 years. 🔸 Ideal for critical defense systems: control units, engines, power supplies in missiles, planes, and radars that need reliability first, not maximum speed. 🔸 Foreign versions cost 2-3 times more. Tiny modern nodes are perfect for phones, but terrible for military use. 350nm is a mature, battle-proven tech that delivers superior reliability, radiation resistance, high voltage tolerance, and durability — exactly what defense systems and civilian sectors (cars, medicine, comms) actually need. Did Western sanctions actually make Russia stronger?

NewRulesGeopolitics

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THE TESLA MODEL S: THE CAR THAT MADE ELECTRIC VEHICLES SERIOUS When the Model S launched in 2012, the entire world still saw EVs as slow, boring, short-range toys for tree-huggers. The Model S changed that narrative overnight. It wasn’t just an electric car — it was a statement. Here’s why the Model S was so important for EV adoption: • It proved EVs could be faster and better than gas cars 0–60 mph in under 4 seconds (later Plaid versions under 2 seconds) while being completely silent and smooth. It beat most supercars off the line and made “electric” synonymous with performance. • It delivered real long-range capability Over 300 miles of range when most EVs at the time struggled to reach 100 miles. Suddenly, road trips became possible and “range anxiety” started to feel outdated. • It introduced over-the-air updates The first production car that could get major performance upgrades, new features, and safety improvements wirelessly — like a smartphone on wheels. This changed how people think about car ownership forever. • It forced the entire auto industry to respond Legacy manufacturers who had been dragging their feet on EVs suddenly rushed to catch up. The Model S basically lit the fuse for the modern EV revolution. • It made luxury electric desirable Premium interior, massive touchscreen, ridiculous acceleration, and futuristic design turned EVs from “compromise” into “aspiration.” Without the Model S proving that electric cars could outperform and out-luxury gasoline vehicles, we wouldn’t have the Model 3/Y explosion, the Cybertruck, or the flood of competitors now racing to go electric. The Model S didn’t just sell cars. It changed the future of transportation. It took EVs from niche to mainstream and showed the world what was possible.

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Luca Greco

169,989 Aufrufe • vor 1 Monat

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:

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📢 Today marks a major milestone as Cat is one step closer to decentralizing $MEOW. Cat has officially set the sell tax for the token to 0% and renounced control over the contract. This means the token tax can no longer be altered, and Cat no longer has the ability to blacklist/whitelist addresses or pause token transfers. However, this also means that any funds sent to the contract address cannot be recovered, so exercise caution and avoid sending funds to $MEOW's token address. With that being said, let Cat leave you with some reasons on why you should hyper gamble on $MEOW 👇 🔸 We have held our position as the leading meme coin on ZKsync, both pre- and post-airdrop. 🔸 $MEOW never faded; it simply traded within a wide range for months, forming a solid bottom on the chart, followed by a steady trend of higher lows over several months. 🔸 The Zeek Army stood strong through a 90% dip. Cat's battle-hardened community never wavered, continuing to vibe and hang out even during the toughest times. 🔸 Despite a low market cap, Cat maintained significant mindshare on ZKsync. 🔸 $MEOW's market cap is currently low enough to have realistic potential for a 10x gain. 🔸 Wealth transfer happened. After 10 months of token distribution, all jeets are out. The supply is now held mostly by long-term believers. Before the $ZK airdrop, 25% of $MEOW was staked, and that number increased to 40% post-airdrop—(3,3) vibes. 🔸 We are not a Cabal coin. While a Cabal can fake trading volume and exchange listings, they can’t fake time. Cat has always been transparent and grinding alongside everyone in the trenches since day one. Now, imagine where $MEOW's price would be if ZKsync catches a narrative and hits the $2B market cap mark. There is no second best.

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DesertThrottleDiaries

1,345,474 Aufrufe • vor 2 Monaten

A look back at our mission to do /more. 🔥🛠️ Months ago, we entered a new growth phase for xLaunchpad, driven by a simple commitment: to do more. More exciting startups, more platform improvements, and a deeper, more meaningful connection with our community. /new We’ve introduced multiple initiatives to make participation in xLaunchpad more accessible, while still keeping EGLD stakers at the core of the experience: 🔸 Challenges Portal – Built in partnership with ᕈulsar Money, this platform allows users to earn lottery tickets by completing simple tasks. 🔸 Creators Program – A reward system for users who create valuable content about xLaunchpad and its projects. 🔸 xLaunchpad Users – Loyal participants from previous launches now have a chance to earn lottery tickets. 🔸 Project Users – Projects launching on xLaunchpad can distribute lottery tickets to eligible users based on set criteria. 🔸 EGLD Holders – Tickets can now be earned simply by holding EGLD in a wallet. Beyond these initiatives, we’ve also strengthened relationships with key community members, empowering them to write educational content about upcoming startups with full creative control on their side. And of course, we’ve introduced more projects and focused on accelerating launch timelines. /better The platform and lottery system have undergone continuous improvements, such as making participation more accessible as detailed above: 🔸 Faster EGLD Reclaims - Users now get their EGLD back more quickly after participation. 🔸 Improved Communication – Blog & X Articles covering both general topics and project-specific updates. Increased community discussions and more structured feedback collection. 🔸 Website Enhancements - Various UI/UX tweaks, with /more to come 👀. 🔸 Integrating AshSwap - Users could buy lottery tickets for the last project with any tokens. 🔸 KYC Improvements - Streamlined processes, with /more to come 👀. Additionally, we’ve successfully co-launched projects with other launchpads and supported startups in securing partnerships, listings, business deals, and legal guidance. xLaunchpad remains one of the most compliant launchpads, ensuring projects align with MiCA regulations and broader industry standards. /next Looking ahead, our main focus remains on the product and the community. Every improvement is guided by real user interactions and feedback, ensuring xLaunchpad continues to evolve. At its core, our mission revolves around two key goals: 1️⃣ Bringing exciting startups to life 2️⃣ Providing users with investment opportunities on the best terms For xLaunchpad and its community to thrive together, it’s essential to recognize these two pillars while understanding the platform’s bigger purpose: making something possible that wasn’t before - allowing retail users to participate in the early launch of promising projects and benefit through multiple avenues. From investments to early adoption, and from unlocking key benefits to shaping young startups. We cannot stress enough how much effort the team puts into achieving these key goals, and we’re grateful to have you by our side on this mission. /more

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Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,849 Aufrufe • vor 1 Jahr

The term "continual learning" has become overloaded if you see it as an ML problem. One classic thread is about memorization: regularization-based continual learning methods, such as EWC, MAS, and SI, estimate which parameters mattered for previous tasks and resist changing them too much. One modern thread is about adaptation: test-time training and inference-time learning methods, such as TTT, adapt part of the model on the incoming test stream before making predictions. These are sometimes discussed as separate threads. But in modern scalable architectures, I think they are better seen as complementary constraints: a model that learns quickly at test time also benefits from a mechanism for deciding what not to forget. In our #ECCV2026 paper, we study this in large-scale 4D reconstruction: how to build fast spatial memory that can adapt over long observation streams while reducing collapse and forgetting. Instead of using fully plastic test-time updates, we stabilize fast-weight adaptation with an elastic prior that balances adaptation and memory. Key ideas: - Elastic Test-Time Training: Fisher-weighted consolidation for fast-weight updates - EMA anchor weights that provide a moving reference for stability - Chunk-by-chunk inference for long 3D/4D observation streams We show that this scales across large 3D/4D pretraining settings, including both LRM-style and LVSM-style models, and improves reconstruction across benchmarks including Stereo4D, NVIDIA, and DL3DV-140. We release model checkpoints across different design choices: resolution, post-training curriculum, and whether the model uses an explicit 4DGS intermediate representation. - Homepage: - Paper: - Code: - Models: This work is co-led with Xueyang Yu, contributed by Haoyu Zhen Yuncong Yang, and advised by Michigan SLED Lab Chuang Gan.

Martin Ziqiao Ma

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Brian Krassenstein

258,615 Aufrufe • vor 2 Jahren