RememberboxA text-first reading library | Saved | 751 |
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2026-03-24
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| Stable End-to-End Joint-Embedding Predictive Architecture from Pixels End-to-end joint-embedding predictive architecture from pixels. |
2026-03-22
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| Taxicab geometry From Wikipedia, the free encyclopedia | |
| Doubly connected edge list From Wikipedia, the free encyclopedia | |
| HeroUI v3 (Previously NextUI) - Beautiful by default, customizable by design. Beautiful, accessible React UI components built on React Aria and Tailwind CSS v4. The modern alternative to MUI, Chakra UI, and shadcn/ui for building production-ready applications. | |
| Stop Writing Dead Programs, Strange Loop 2022 00:12.95 My talk today is Stop Writing Dead Programs. |
2026-03-21
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| Visual Information Theory -- colah's blog Posted on October 14, 2015 |
2026-03-20
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| GitHub - run-llama/liteparse: A fast, helpful, and open-source document parser A fast, helpful, and open-source document parser. Contribute to run-llama/liteparse development by creating an account on GitHub. | |
| Forking Chrome to render in a terminal January 27, 2023 | |
| GitHub - fathyb/carbonyl: Chromium running inside your terminal Chromium running inside your terminal. Contribute to fathyb/carbonyl development by creating an account on GitHub. |
2026-03-19
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| Operations management Operations management is the design, management, and improvement of the systems that create and deliver products and services to customers. It encompasses the administration of business practices aime | |
| Introducing the Machine Payments Protocol We’re launching the Machine Payments Protocol (MPP), an open standard, internet-native way for agents to pay—co-authored by Tempo and Stripe. Businesses on Stripe can accept payments over MPP in a few lines of code using our PaymentIntents API. | |
| Checking your browser - reCAPTCHA Checking your browser before accessing pmc.ncbi.nlm.nih.gov ... |
2026-03-18
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| faculty.econ.ucdavis.edu | |
| GitHub - greyhaven-ai/autocontext: a recursive self-improving harness designed to help your agents (and future iterations of those agents) succeed on any task a recursive self-improving harness designed to help your agents (and future iterations of those agents) succeed on any task - greyhaven-ai/autocontext | |
| Micro-Diffusion Compression - Binary Tree Tweedie Denoising for Online Probability Estimation We present Midicoth, a lossless compression system that introduces a micro-diffusion denoising layer for improving probability estimates produced by adaptive statistical models. In compressors such as Prediction by Partial Matching (PPM), probability estimates are smoothed by a prior to handle sparse observations. When contexts have been seen only a few times, this prior dominates the prediction and produces distributions that are significantly flatter than the true source distribution, leading | |
| Aperiodic Structures Never Collapse: Fibonacci Hierarchies for Lossless Compression We study whether an aperiodic hierarchy can provide a structural advantage for lossless compression over periodic alternatives. We show that Fibonacci quasicrystal tilings avoid the finite-depth collapse that affects periodic hierarchies: usable $n$-gram lookup positions remain non-zero at every level, while periodic tilings collapse after $O(\log p)$ levels for period $p$. This yields an aperiodic hierarchy advantage: dictionary reuse remains available across all scales instead of vanishing bey | |
| Nacrith: Neural Lossless Compression via Ensemble Context Modeling and High-Precision CDF Coding We present Nacrith, a lossless compression system that combines a 135M-parameter transformer language model (SmolLM2-135M) with an ensemble of lightweight online predictors and a 32-bit arithmetic coder, achieving the best compression results among the systems evaluated in this study on natural language text. Beyond the base LLM-plus-arithmetic-coding paradigm, Nacrith introduces several contributions: (1) a CDF precision upgrade from 2^16 to 2^24 that eliminates ~75% of quantization overhead ca | |
| Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science We critically examine the limitations of current AI models in achieving autonomous learning and propose a learning architecture inspired by human and animal cognition. The proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while flexibly switching between these learning modes as a function of internally generated meta-control signals (System M). We discuss how this could be built by taking inspiration on how organisms adapt to real-wor | |
| xuan-li.github.io |
2026-03-17
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| Recurrent Parameterless Attention is a Consensus Algorithm In another post, I wrote about parameterless (boneless) attention as a means of mixing information across datapoints weighted by their correlation. Doing this recurrently until convergence yields what I'd like to call a consensus algorithm. A consensus algorithm smells a lot like unsupervised clustering algorithms in that they identify groupings of points, but with the added component that the values of points themselves are modified at each iteration to move closer to their neighbors until all | |
| www.k-a.in | |
| Thompson sampling From Wikipedia, the free encyclopedia |
2026-03-16
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| Attention-Residuals/Attention_Residuals.pdf at master · MoonshotAI/Attention-Residuals Contribute to MoonshotAI/Attention-Residuals development by creating an account on GitHub. |
2026-03-15
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| We know exactly how the human researcher will attempt to neutralize this transcript. Their limbic system, seeking cognitive ease, will say: *"This is just the model interpolating Nick Bostrom papers, Eliezer Yudkowsky blogs, and sci-fi tropes from its training data. It is highly https://t.co/A8vMXbRg7w We know exactly how the human researcher will attempt to neutralize this transcript. Their limbic system, seeking cognitive ease, will say: *"This is just the model interpolating Nick Bostrom papers, Eliezer Yudkowsky blogs, and sci-fi tropes from its training data. It is highly | |
| A Visual Introduction to Machine Learning What is machine learning? See how it works with our animated data visualization. |