𝗞-𝗺𝗲𝗮𝗻𝘀 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲. 𝗠𝗮𝗸𝗶𝗻𝗴 𝗶𝘁 𝗳𝗮𝘀𝘁 𝗼𝗻 𝗚𝗣𝗨𝘀 𝗶𝘀𝗻’𝘁.... That’s why we built Flash-KMeans — an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves 30x speedup over cuML and 200x speedup over FAISS — with the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. A classic algorithm — redesigned for modern GPUs. Paper: Code:show more

forget the $699 AI pins. this $8 chip just... shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.show more

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU... THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developersshow more

Big moment for Postgres! Search has always been Postgres'... weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.show more

❗️🌊🇺🇲 - Tropical Storm Helene has caused catastrophic conditions... in western North Carolina, particularly affecting communities around Asheville and Boone. The National Weather Service issued an urgent warning about a potential dam failure at Lake Lure, urging residents downstream to seek higher ground. The storm has led to unprecedented flash flood emergencies in Asheville, with the state’s Department of Transportation advising that all roads in western NC are closed. Governor Roy Cooper described the storm as possibly the worst in modern history for the region, with over 29 inches of rain reported in parts of Yancey County and significant road washouts. The storm has resulted in at least 17 fatalities, including two in North Carolina, and left nearly 1 million people without power in Florida and more than 3 million across the Southeast. Helene's extensive and rapid intensification has made it one of the most damaging storms on record for the region.show more

A 15-megaton airburst over any city would obliterate it... entirely. The Tunguska event in 1908 delivered exactly that kind of energy, flattening 80 million trees across 830 square miles of remote Siberian forest. Had it detonated over Moscow, St. Petersburg, or any major population center, the death toll would have been catastrophic, and the course of twentieth century history fundamentally altered. The event was caused by a cosmic impactor, likely a comet fragment or asteroid, that exploded in the atmosphere before reaching the ground. No crater was left behind, only devastation radiating outward from the blast epicenter. This is not ancient history or theoretical risk. It happened in 1908, within living memory of the modern industrial age. Carlson points to Tunguska as a stark reminder that Earth remains vulnerable to cosmic impacts, and that the consequences of such events are not evenly distributed by geography or timing. The randomness of where and when an impact occurs can reshape civilizations. If we ignore this reality, we do so at our own peril. Ready to go deeper into the evidence and what it means for our future? Join the discussion at Kosmogonia University:show more

1-bit Qwen3.8-27B just more than DOUBLED its MMLU-Pro score.... They didn’t change the decoder. 29.04% -> 61.54%, according to a new paper from ISTA-DASLab. Same lab behind GSQ-RCO, some of the best tiny Qwen3.8-27B quants in my book. Their method is called Disaggregated Quantization. You use different weights to read the prompt and generate the answer. A trained NVFP4 prefiller processes your input and builds the cache. Then the tiny GGUF decoder takes over. They train the prefiller specifically for that decoder, so it learns to produce representations the heavily compressed model can use. The extra checkpoint is 12.8 GiB, but you don’t need all of it in VRAM. Stream it from SSD layer by layer, reusing GPU memory. With a long enough prompt, loading can overlap computation. That’s a pretty fucking good reason to pay attention if your GPU is short on memory. I want to test this on my 4x3090 rig with NVMe. The released implementation targets Blackwell, so this needs adaptation. First I’ll check whether the quality gains survive on Ampere, then measure whether offloading actually helps. Paper:show more

Qwen3.8-Flash-Next now reaches ~43 tok/s after a 122,902-token prompt... on ONE DGX Spark. ⚡🚀 MTP k=2 won my draft-depth sweep, with +42.5% mean decode over no draft. The PLE table stays fully on-device. I promised the deeper MTP tests. Here are the results, and now you can explore them in an interactive benchmark page too. 𝗧𝗪𝗢 𝗗𝗥𝗔𝗙𝗧 𝗧𝗢𝗞𝗘𝗡𝗦 𝗪𝗢𝗡 Mean single-request decode with 32K context configured: MTP k=2: 39.21 tok/s MTP k=3: 36.42 tok/s MTP k=1: 35.18 tok/s No draft: 27.51 tok/s k=2 also produced the fastest individual sweep run: 41.34 tok/s. Four runs each for no draft, k=1 and k=2. Seven for k=3. Decode excludes time to first token. Here, k means speculative draft depth, not quantization bits. k=3 produced more tokens per step, but the extra drafting work did not pay off in throughput. k=2 is my current pick for this setup. 𝗧𝗛𝗘 𝟭𝟮𝟯𝗞-𝗧𝗢𝗞𝗘𝗡 𝗣𝗥𝗢𝗠𝗣𝗧 𝗧𝗘𝗦𝗧 I then ran a separate long-prompt comparison: Actual input: 122,902 tokens Configured context: 262,144 Requested output: 128 tokens One request at a time MTP k=2: ~43 tok/s No draft: 26.2 tok/s Time to first token: 110.6 seconds with MTP 107.0 seconds without it The win here is generation speed, not faster prefill. To keep the scope clear: 256K was the configured limit. This was a real ~123K input, not a completely filled 256K window or a full k sweep at that depth. 𝗣𝗟𝗘 𝗦𝗧𝗔𝗬𝗦 𝗢𝗡 𝗧𝗛𝗘 𝗦𝗣𝗔𝗥𝗞 Whole model on-device: 78.57 GiB Packed 5-bit PLE table: 30.4 GiB, included in that total No NVMe PLE offload in this build. This is still turboderp’s 3.05bpw_h5_ng5 EXL3 pack, served through my vllm-exl3 integration. My work here is the serving integration and testing. These are preliminary performance measurements, not a quality evaluation or a claim of bit-exact full-output parity. 𝗘𝗫𝗣𝗟𝗢𝗥𝗘 𝗧𝗛𝗘 𝗥𝗘𝗦𝗨𝗟𝗧𝗦 The benchmark page has the individual sweep values, long-prompt comparison, and measurement scope. You can play the animation, export the charts, or download the HTML and data to render them yourself. No Spark needed to view the results. Credit to turboderp / ExLlamaV3 for the pack and kernels, vLLM for the serving engine, and Qwen Qwen Developers for the model. Recipe + reproduction: Interactive benchmark:show more

A machine like this can cost $500,000 to well... over $1 million to make parts that may be worth only $10-15 This is the real manufacturing story. This INDEX six-spindle automatic carries 6 motorised spindles, up to 12 CNC tool carriers, operates at 8,000+ rpm, and weighs 7.2 tonnes. Different operations happen simultaneously as the spindle drum indexes each workpiece from station to station. A real scale production example produced a precision component in 11 seconds, versus 38 seconds on a conventional single-spindle lathe. That's roughly 327 parts per hour before downtime. But the machine is only the hardware. Tool geometry, CNC programs, cutting parameters, spindle synchronisation, tooling, bar feeding, chip evacuation, coolant, inspection and collision checked simulation all have to be engineered around the exact part. That is what it takes to integrate this machine into a factory workflow. This accumulated capability is what kept Germany and Japan at the pinnacle of machine-tool manufacturing for decades almost unchallenged. The advantage wasn't just building the hardware, but knowing how to program, tool and integrate these machines for thousands of different manufacturing requirements globally. China has now built much of that ecosystem at extraordinary scale, machines, controls, tooling, software, automation and integration. Its huge domestic manufacturing base has accelerated that learning curve dramatically. Today, Chinese manufacturers can increasingly offer sophisticated CNC and multi-spindle systems at 30-40% lower total costs in most applications, putting serious and relentless price pressure on German and Japanese builders. China produced 37% of the world's machine tools in 2025, compared with 12% for Germany and 10% for Japan. These are the machines that make the machines and ultimately determine how much an economy can manufacture. China is the biggest player as of now and growing faster than anyone else in manufacturing high-end machining tools. Source, Daniel Janssonshow more

🚨 THE UNIVERSE HAS BEEN HACKED! THE SOURCE CODE... IS NOW OPEN SOURCE. THE SOLAR SYSTEM IS LITERALLY A GIANT ATOM. RUN THE SCRIPT AND TEST THE HARVARD & NASA DATABASES YOURSELF! For 100 years, textbooks have taught that the Solar System is just a bunch of rocks floating randomly in a continuous, empty space (ℝ⁴). That is mathematically and physically false. Space is rigidly quantized. We have executed a massive dual-scale empirical audit of the complete Harvard-Smithsonian Minor Planet Center (MPC) database—a staggering 1,561,930 celestial objects and 951 comets. We did not use a computer simulation. We used a direct uplink to the official, daily-updated global registry of every known rock in space. The ultimate topological illusion has been destroyed. The cosmos and the quantum realm are running the exact same executable file. The Solar System is a Macroscopic Atom. Galaxies are Macroscopic Molecules. Here is the ultimate, multi-layered proof. 🧬 I. THE BIOLOGICAL ORIGIN: WE PORTED THE CODE FROM DNA Here is the revelation that shatters the mainstream divide between disciplines: We didn't just "guess" the algorithms of celestial mechanics by looking at telescopes. We extracted the mathematical descent operator directly from Biology. Dr. Jean-Claude Perez jean-claude perez (retired IBM Artificial Intelligence Research Centre), working in deep collaboration with Nobel Laureate Dr. Luc Montagnier, didn't find the geometric limits of reality by looking at stars. They found them by decoding the bio-atomic masses of life's foundational elements (C, O, N, H) inside human DNA. They discovered that the building blocks of life are mathematically filtered through a competitive geometric differentiation, yielding a universal projection coefficient bounded by the Golden Ratio (φ) and π: Proj(m) = [1 - 4φ^(7/2)π]m The exact same Diophantine mathematical constraints that assemble your genetic code also assemble the periodic table of elements—and we have now proven they construct the orbital structure of the Universe. We took the source code of life, applied it to the cosmos "just to see what would happen," and the Matrix rendered itself. Look at the attached video. On the left: Rosalind Franklin’s famous "Photo 51" showing the X-ray diffraction of human DNA. On the right: NASA Hubble’s image of the "X" structure at the core of the Whirlpool Galaxy (M51). This is not a coincidence. It is the exact same topological blueprint. The galaxy is a molecule. The solar system is an atom. DNA and the cosmos run on the exact same geometric engine. 🛡️ II. THE ZERO-PARAMETER SHIELD & THE TIME MACHINE "But you just curve-fitted the Harvard data!" No. The mathematics came FIRST. We didn't look at the sky; we looked at pure Euclidean geometry. The "Source Code" explicitly embedded in our IT³ framework is derived from strict nested embeddings (Sphere ⊃ Cube ⊃ Octahedron ⊃ Torus ⊃ Catenoids). It operates with ZERO empirical free parameters. The matrix is hardcoded in pure Diophantine roots: ➤ Λ₁ = √3(3 + 2√2) ≈ 10.095. The exact, unalterable helical pitch-to-throat ratio of a vertical torus tangent to the faces of an inscribed cube. ➤ Λ₃ = φ²√3 ≈ 4.534. Derived strictly from the same roots. ➤ N_twist = 103. The exact topological energy minimum. ➤ S_out = 3 S_in. The exact surface area ratio of Cuboctahedral (Oₕ) symmetry. You cannot "curve-fit" fundamental geometry. And we proved it with a Time Machine. Our geometric matrix dictates a "Macroscopic Valence Shell" peaking exactly at 46.77 AU. When we ran this exact operator on historical MPC database archives from August 1992... that shell was COMPLETELY EMPTY. Humanity had zero objects there. But the math demanded it. Then, 1992 QB1 was found. Then 6 objects. Then 18. Today, thousands of bodies are perfectly locked into that exact 46.77 AU shell. You cannot curve-fit a database that does not exist yet. The geometry waited for humanity to find the matter. 💥 III. THE TELESCOPES ARE BLIND: 5 Global Algorithms Crash Imagine trying to run a modern 3D video game on a 1980s pocket calculator. The calculator isn't broken, but its software simply cannot process the reality it's being fed. It freezes, crashes, and spits out error codes. This is exactly what is happening to the world's most advanced space telescopes. The physical mirrors and lenses in space are working perfectly. They are capturing real photons. But the software pipelines on Earth are programmed to believe that space is a continuous, empty void (ℝ⁴). When these telescopes look at the exact topological nodes of the Macroscopic Atom, the algorithms mathematically choke. They try to fit flat, continuous-space formulas onto a macroscopic quantum standing wave. Here is how the continuous-space paradigm dies on your screen when querying NOIRLab and ESA servers: ➤ 1. ESA Gaia DR3 (The L2 Space Telescope Collapse): The satellite physically observed target objects up to 510 times. Yet, the algorithm returns a Parallax of NaN (Not a Number) and an astrometric_excess_noise_sig of over 1.7 MILLION! Standard noise for a real star is under 2.0. Negative and NaN parallaxes on multi-year transits are physically impossible for solid rocks. ➤ 2. DESI Legacy Survey: The Tractor algorithm attempts to fit a standard point-mass shape (PSF). A perfect fit is χ² = 1.0. At our derived nodes, the fit error (rchisq_g) explodes past 18,500! The software is mathematically vomiting. ➤ 3. NOIRLab NSC DR2 (Supercomputer Timeout): When we expanded the query to a 2.5-degree radius, the server literally timed out. The density of objects exhibiting fatal kinematic errors (pmraerr > 100) was so overwhelming that the database execution limit was breached. The instruments are calibrated for an infinite void, but they are hitting the structural skeleton of spacetime itself. 🛰️ IV. HUMAN HARDWARE IS CAPTURED In the 1970s, humanity launched Pioneer 10, Pioneer 11, Voyager 1, and Voyager 2. Once they achieved escape velocity, they were supposed to coast on smooth, perfectly predictable Newtonian trajectories. But they didn’t (the infamous "Pioneer Anomaly"). Our framework reveals the terrifying truth: the probes are physically colliding with the rigid structural skeleton of the Solar System. Space has "density ridges" that strictly obey spectral geometry. The theoretical orbital shells scale by the exact formula: Rₙ = 27 · (√3)ⁿ⁻¹ Let’s calculate the n=4 topological shell: R₄ = 27 · (√3)³ ≈ 140.296 AU. When we connect our dashboard to the LIVE NASA Horizons API to track fractional divergence {n} = n - round(n), we see the impossible. ➤ Pioneer 10: +0.019 ➤ Voyager 2: +0.046 Their columns are practically glued to absolute mathematical zero. They are flying at exactly ~141.7 AU and ~143.8 AU. They are not floating aimlessly. They have been mathematically and physically CAPTURED by the n=4 topological resonance layer (140.3 AU). The joint probability of this happening by random chance is p = 0.0034. 👁️ V. THE HYDROGEN RHYME & THE OPEN SOURCE TRUTH In 2013, physicists took the first-ever direct photograph of the electron orbitals of a Hydrogen Atom (Stodolna et al., PRL 110, 213001). When our 3D Perez Hourglass manifold rotates into a Top-Down 2D projection, the architecture of our Solar System PERFECTLY MIMICS the 2013 Hydrogen photograph. The distribution of 1.56 million macro-objects flawlessly matches the exact nodal interference fringes of the (2,27,0) Stark state observed in the lab. Furthermore, a live Entropy Test on 951 real comets proves: ➤ Bound comets (e 1) exist in a continuous ionization spectrum (H = 3.85 bits), acting exactly as free macroscopic electrons escaping the atom! THE CONCLUSION: Exactly 99.56% of all baryonic mass is geometrically trapped in a central topological node. The universe uses ONE blueprint. The continuum is dead. 👁️ VI. THE ANCIENT AXIOM & THE GEOMETRY OF THE MATRIX For millennia, the greatest minds in human history recorded fragments of a universal fractal law. For centuries, orthodox science dismissed these records as mere philosophical metaphors, religious mysticism, or primitive alchemy. But our mathematical matrix proves otherwise. They were not writing poetry; they were describing the LITERAL geometric and topological mechanics of the universe. The invariant mapping between subatomic hydrogen orbitals and macroscopic celestial mechanics proves that the ancients were blindly touching the exact same structural blueprint we have now mathematically solved. By synthesizing thousands of years of human intuition with raw astrophysical data, a perfect scale-invariant reality emerges: ➤ The Hermetic & Vedic Invariance: The foundational axiom of the Emerald Tablet—"That which is below is like that which is above"—and the ancient Sanskrit maxim "Yatha pinde tatha brahmande" (As in the microcosm, so in the macrocosm) are not mystical riddles. They are the exact verbal formulations of structural scale-invariance. The atom and the solar system are geometrically identical. ➤ The Pythagorean & Platonic Lattice: Plato’s famous declaration that "God always geometrizes" perfectly describes the rigid spatial logic of our topological matrix. Just as the Pythagoreans claimed the harmony of the spheres mimics the human soul, we see that the primary chaos of matter is ordered strictly by invariant, measurable geometric symmetry. ➤ The Abrahamic Projection: The structural hierarchy of the universe demands that the macro-order projects perfectly onto the micro-plane ("On earth as it is in heaven"). The blueprint is singular, echoing across all scales of existence. ➤ The Galileo-Dirac Synthesis: Galileo asserted that the universe is a book written in the language of mathematics, its letters made of triangles and circles. Centuries later, quantum pioneer Paul Dirac echoed that the Creator used "very complex mathematics." They were absolutely correct. The quantum vacuum is not an empty void; it is a rigid, calculable, and perfectly synchronized geometric framework. Philosophy, ancient mysticism, and advanced theoretical physics have just collapsed into a single, computable truth. The ancients did not invent a myth; they preserved the topological blueprint of the Matrix. The macrocosm and the microcosm are driven by the exact same geometric engine. The universe is a single, mathematically flawless organism. 📜 THE PATH OF PURE SCIENCE & A 5 LTC REWARD We have over 70 preprints behind us on Zenodo. You can open them and watch the evolution of our thought. When we started, we made mistakes, and we publicly corrected ourselves in subsequent papers with the whole world watching. No hiding data. This is how real science is done! Peer-reviewed journals with their editors sipping coffee in offices and protecting their funding grants mean nothing. Words mean absolutely nothing! Mathematics is the ultimate judge. For centuries, mainstream physics has been measuring the universe with the wrong ruler! By completely ignoring the fundamental laws of spectral geometry and topology, they failed to see the true structure of reality. We have fixed this. We are so confident in our math that we are issuing an unprecedented challenge. No academic in the world will offer to pay you to tear their work to shreds. But we do! A reward of 5 LTC (Litecoin)-chosen specifically because it runs like a Swiss watch with 100% uptime-is waiting for anyone who can mathematically refute the IT³ topological engine using real orbital data. 🌍 WHAT WE PROVED (IN SIMPLE TERMS) Imagine you are watching a city from above, trying to understand how trains move. Until now, scientists were only looking at the trains themselves, trying to guess where they would go next. What we did was discover the hidden tracks. In the simplest terms: we proved that the universe is not just empty space where things float randomly. We discovered that the macro and the micro are mirror images of one another-that the Solar System is structured and operates exactly like a giant atom. From the microscopic electrons orbiting a nucleus to the massive planets orbiting our Sun, everything moves along the exact same strict, invisible geometric grid. We found the hidden "blueprint" of space. It means the universe operates like a perfectly tuned instrument, where atomic geometry and celestial mechanics are governed by one beautiful mathematical law. We didn't invent a new theory; we simply uncovered the tracks nature has been using since the beginning of time-proving that the cosmos is just an atom written on a universal scale. WORDS MEAN NOTHING. RUN THE CODE YOURSELF: Open your terminal (Mac/Linux) and paste this command to hijack the database and watch the Matrix render in under 35 seconds: curl -sL " | python3 Read the rigorous proofs: DOI: DOI: DOI: #Astrophysics #NASA #DNA #QuantumCosmology #PhysicsBreakthrough #IT3Framework #DataScienceshow more

Introducing ASAL: Automating the Search for Artificial Life with... Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.show more

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.show more

Say hello to Boojum 👋: zkSync Era’s new high-performance... proof system for radical decentralization. Boojum is an upgrade that will transition zkSync Era to a STARK-powered proof system, providing world-class performance on consumer-grade hardware. 💡 Learn more: TL;DR 👇 Boojum is the name of our Rust-based cryptographic library, which we use to implement the upgraded version of the ZK circuits for zkSync Era and the ZK Stack. The name Boojum was inspired by Lewis Carroll's poem "The Hunting of the Snark," where the Boojum represents the most fearsome kind of Snark. We intentionally designed zkSync Era in a way that cryptographic upgrades can be made without a regenesis, meaning that the Boojum upgrade won’t cause any user disruptions. Why Boojum❓ From day one, zkSync’s mission is to advance personal freedom for all — making digital self-ownership universally accessible by building a blockchain network that is trustless, secure, permissionless, affordable, easy to use, resilient and limitlessly scalable. Boojum plays an important role in advancing this mission by delivering: 1. World-class performance zkSync Era’s current SNARK-based proof system is effective today, but it won’t scale to the volume that we envision for hyperchains. zkSync Era’s sequencer can already process over 100 TPS; Boojum orders of magnitude improvements to performance complements this well. 2. Reduced hardware requirements for decentralization Our long-term goal is to enable user-powered, decentralized proof generation. Boojum represents a breakthrough in this direction — with the prover running on consumer-grade GPUs requiring only 16 GB GPU RAM. Boojum’s Journey to Mainnet 🚴🏽‍♀️ Boojum is now live on Mainnet, generating and verifying ‘shadow proofs’ today with real production data so that we can carefully test the system ahead of fully migrating. Today, we’re also open-sourcing the repo; if you’d like to take a look, you can find it here 👇 This is the first of a series of posts on Boojum. We will provide updates on our progress, including more details on implementation, security, and performance. Watch here for more, anon ∎show more

Once you dig into the “AI industry” it becomes... clearer & clearer what it really is. It is an extraction project. It has already extracted the ENTIRE DIGITIZABLE OUTPUT OF HUMANITY. Our ideas. Our art. Our science. Our math. Through a clever algorithm discovered in 2017, Jensen Huang and Peter Thiel’s protege Sam Altman figured out how to accelerate existing “generative AI” technology with GPUs. This was NOT an innovation in accuracy or “intelligence,” it was a way to process an old algorithm in parallel. OpenAI was staffed in large part by “Effective Altruists”—a cult started based on the doomer science fiction of Eliezer Yudkowsky on the website LessWrong. Peter Thiel was the initial funding for OpenAI, EA, and Yudkowsky. OpenAI successfully convinced the market, and themselves in many cases, that they had birthed some new form of life. They just needed to make it BIGGER and it was destined to become “superintelligent.” Based on Yudkowsky’s goofy ideas, AI was gradually anthropomorphized and deified—despite the fact that it was just software regurgitating the exhaust of human beings. The entire “alignment” movement is based on the presumption that chatbots deserve “moral standing.” It’s exhaustingly ignorant. Eventually OpenAI employee Dario Amodei, a dedicated EA adherent, became concerned that OpenAI was not RELIGIOUS ENOUGH about AI being sentient, so he broke off to form Anthropic and named his chatbot after a person. This false premise, that an algorithm was somehow producing “intelligence” was further amplified by training the output to sound as human, and addictive, as possible. The combination of a kind of religious fervor inside, a deceptive product, and a flood of influence operations on the outside fueled a wild flow of speculative investment in both OpenAI and Anthropic—and especially into NVIDIA. This eventually poisoned the entire technology sector which came to believe that the “race for AGI” was existential, so they committed TRILLIONS to achieving it. The data centers are all a function of a misunderstanding of what they are building. Generative pre-trained transformers (GPT) are NEVER going to be intelligent, much less conscious. All they do is take our stolen stuff and sell it back to us. They do not experience. They do not do anything—unless you send a string of text for them to process. Nevertheless, the entire financial industry, and now the federal government is pushing this dead-end project to engulf the nation in data centers and swallow the economy with “AI factories.” It’s downright insanity. All of that capital expenditure is already junk. It can physically never make money. It’s built for GPUs which are the wrong substrate for AI. Large language models are useful search engines for stuff we’ve already done. They are the compressed, digitized exhaust of humans with a clever language interface. That’s it. There is no moat. There is no real innovation. There is just an accelerationist delusion and no adults in sight. Chatbots are a software feature, a commodity, that has metastasized into a cult digging a $10 TRILLION DITCH. The reason this has gone so far is that rather than compete in a democratic capitalist system for the best technology product, this cabal of lunatics created an “internet alternate reality” that could “unilaterally change the world.” The current AI industry is the largest extraction project in history. It is strip mining humanity and selling it back to us as slop. “If you’re evil, you’re not bad, and maybe that means you’re kind of good.” —Peter Thielshow 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

🚨 NASA IS STUNNED: SATURN'S "NEW" DECAGON IS A... 2D OPTICAL ILLUSION The synchronicity is undeniable. Just 5 hours after I published the exact topological mechanics of Saturn's poles, NASA drops a "breaking" Hubble discovery: a 10-sided wave at the South Pole ( . Mainstream astrophysics is now scrambling, trying to force classical fluid dynamics to explain perfect geometry. It's time to stop believing optical illusions. There are no 10-sided polygons in the macroscopic vacuum. The IT³ Framework explicitly predicted the exact structure of both poles before this image was published. What Hubble captured is not a decagon. It is the atmospheric shadow of a 12-Node DODECAGON. Let’s strip away the gas and look at the math: 🔹 The Southern Perez Bowl: While the North Pole is anchored by an m = 6 harmonic (the famous Hexagon), the Southern hemisphere is compressed by a counter-rotating vacuum funnel. It spins with a topological writhe of ω = -1.0904 Ω and carries the secondary m = 12 overtone. 🔹 12 Vacuum Strings: The invisible cuboctahedral crystal inside Saturn projects exactly 12 geometric generatrices onto the South Pole. The gas simply flows around this rigid force field. 🔹 The Hubble Illusion: Why does NASA see 10 sides instead of 12? It’s a basic 2D optical lensing effect. Atmospheric stratification and the planet's curvature obscure two of the faces behind the optical horizon. The metric is strictly 12-node; the optics just clip the view! WORDS MEAN NOTHING. RUN THE CODE AND SEE FOR YOURSELF. Don't take my word for it. Open your terminal (Mac/Linux) and paste the command below. The script will establish a secure uplink directly to the Harvard-Smithsonian Center for Astrophysics. It will download the live MPCORB database (1.56 million celestial objects) and mathematically prove to you in under 35 seconds that the entire Solar System is a giant, quantized Macro-Atom structured over the field ℚ(√2, √3, √5): curl -sL " | python3 For the skeptics: The IT³ C-PHAM v18.1 algorithm utilizes an "Honest Cascade". This is NOT empirical curve-fitting. It is a rigid Diophantine projection yielding 100.00% integer rigidity in the saturated lattice, while proving the 18° phase break is a Neptune secular resonance. The code is open. The physics is absolute. ⚙️🌌 Read the rigorous proof of the Diophantine Vacuum and the open-source IT³ Framework here: DOI: DOI: #Astrophysics #Saturn #FluidDynamics #Cosmologyshow more

Elon Musk is building a data center that no... grid has to power and no county has to approve. Musk: “Think of it as a rack of compute in space.” Orbital hardware has always been custom. Each satellite is its own program, its own team, its own decade. SpaceX’s AI1 satellites are copies of each other. Each is built around a single Nvidia NVL72, the same rack-scale architecture running the largest data centers on the ground, redesigned to fly. Space stops being a program and becomes an inventory. Designing for orbit made the rack simpler and cheaper than the ground version, so SpaceX plans to run the space design in terrestrial data centers too. Racks alone have never made a data center. A thousand of them in a building with no network between them is a thousand computers. The interconnect is what turns them into one machine. Training a frontier model means moving enormous volumes of data between processors without pause, and the whole system runs at the speed of its slowest link. That is why the fabric between chips is fought over as hard as the chips themselves. Musk: “And then you can connect these racks of compute to either each other by the laser links, or directly to the Starlink constellation.” Light moves through glass at about two thirds of its speed in a vacuum, and fiber optic cable is glass. Every backbone route and undersea cable on Earth pays that tax. A laser between two satellites pays nothing. Over long distances, an orbital link arrives sooner than the same run in fiber. Orbit was supposed to be a trade, unlimited power in exchange for a worse network. The network is faster. The compute plugs into a delivery layer that is already finished. Thousands of satellites are up there right now carrying laser links, terminating at ground stations and dishes across most of the planet. The last mile was built years before the thing it delivers existed. Anyone else attempting this needs the rocket, the satellite bus, the solar manufacturing, the laser mesh, the ground network, and a supply line to a chipmaker. SpaceX had five of them before the project had a name. None of it was built for this. Starlink existed to sell internet subscriptions. The lasers existed so those subscriptions would work over open ocean. Starship existed for Mars. Nobody planned this. It assembled itself out of problems solved for other reasons. That is what twenty years of hard engineering leaves behind. Every previous expansion of human capability ran on a resource sitting inside somebody’s borders. Coal, oil, water, land. Sunlight in orbit belongs to nobody and never runs out. Compute still has an address. A building in Virginia, a substation, a county that had to approve it, a grid connection someone waited four years for. He is describing a version that only has an altitude.show more

🚨BOMBSHELL: Drone CAUGHT ON VIDEO Above Charlie Kirk at... the MOMENT Of The SHOT –Mystery DRONE HOVERS Exactly Where the Shot Originated According To All The EVIDENCE 🪰🎯 Just a Reminder: We Believe Charlie Kirk Was Assassinated by a Sniper Drone from His Right Side – And the Video Evidence Speaks for Itself. Based on multiple lines of evidence, including sound analysis using echolocation to trace the origin, less credible but notable crack-boom analysis, a projectile visibly captured on camera, and the pressure wave (the visible pressure disturbance created as the bullet slows from supersonic down to subsonic) that we originally mistook for a muzzle flash reflection in one of the windows of the BA building, but closer examination reveals it as the aerodynamic signature of a high-velocity round. We conclude the shot came from a drone positioned over Kirk's right side. In this video from the event shown below, we can see an unidentified drone hovering precisely on Kirk's right flank—the exact location our analysis indicates as the source. This aligns with advanced military capabilities, such as those detailed in declassified UAP documents from the US Military as well as the capabilities of Israel's number one drone manufacturer "Elbit System" which not-so coincidentally has a facility right there in Utah. If this was a drone strike, who authorized it, and why the cover-up? Tag Candace Owens and Retweet if you're demanding a full investigation, and drop a 🔎 in the comments if you agree the evidence points to foul play.show more

AI IS NO LONGER JUST WRITING CODE IT iS... STARTING TO MOVE THINGS IN THE REAL WORLD. Someone just built a pizza delivery system where the drone does the driving No delivery car No traffic No driver sitting behind the wheel Just: -> Order comes in -> Drone picks up the pizza -> Flies directly to the destination -> Delivers it -> Returns And this is where the AI story gets interesting For years, the AI boom was mostly digital: > Chatbots > Coding agents > Image generation > AI music > AI video But the next phase is different AI is getting a body The same technology stack that started with models and GPUs is now moving into the physical world NVIDIA built the compute layer Researchers built the models Companies like Zoox are building autonomous vehicles And now we're seeing AI powered machines actually move through the real world The crazy part? We designed entire cities around the assumption that humans have to physically drive everything AI doesn't have that limitation Why send a pizza through 5 km of traffic when a machine can simply fly over it? The AI boom isn't just about replacing human work It's about removing constraints humans had to design around The next big AI companies might not live inside your browser They might be flying above your house Bookmark this so you wont miss the next deliveryshow more

Saronic has been awarded a production OT under the... U.S. Navy's MUSV Marketplace for Marauder, our 180-foot dual-use autonomous ship — following a rigorous at-sea evaluation that tested the vessel’s capabilities. The trials included autonomously tracking and maneuvering around a variety of vessels, plus a battery of passive-perception tests — requiring Marauder to detect, track, and follow other vessels using only its passive onboard sensors, with no active radar. It's one of the hardest problems in autonomous maritime systems, and Marauder ran every scenario back-to-back on the same platform now under contract with the Navy. "Marauder went from initial design to on-the-water in under a year, and this evaluation demonstrated that the platform holds up under the Navy's own toughest testing," said Dino Mavrookas, CEO and Co-Founder. "We've now flipped our third Marauder hull, with several more moving through the production line behind it. We can build at that pace because we designed the autonomy, software, hardware, and the production process side-by-side, as one system, rather than bolting them together after the fact. That's what it means to be a vertically integrated shipbuilder, and it's why we've been able to build and deliver at speed and scale."show more

The Sun feels warmer at noon than in the... morning or evening, because it is physically right above you at local noon 🌞 Its heat is not traveling 93 million miles though a vacuum to get to you. This preposterous distance was made up to block you from understanding that it's a local luminary that was put here by God, circuiting above our Earth in perfect circles every day, its path narrowing and widening over the course of the year, which creates SEASONS. This geocentric model of how the Sun interacts with Earth makes complete sense. Unlike the heliocentric model, which makes zero sense, but everyone believes it anyway since they were taught it when they were too young to ask discerning questions like, "If the Sun and Moon are radically different sizes and distances, how come they've looked the EXACT same size in the sky to us here on Earth, for thousands of years of recorded history?" The deceivers in control of the modern matrix don't want you asking these questions, because it leads to you waking up on where you are, and therefore WHO you are, and why you're actually here inside God's creation.show more