tribut
tribut — researcher or builder tracked in the Angestrom contributor network.
Repositories · 2
paperless-ngx/paperless-ngx
A community-supported supercharged document management system: scan, index and archive all your documents
b4rtaz/distributed-llama
Distributed LLM inference. Connect home devices into a powerful cluster to accelerate LLM inference. More devices means faster inference.
News · 17
Veea Announces the VeeaONE Distributed Intelligence Platform, Enabling Cybersecure Sovereign Data Fabrics and Enterprise AI Grids for Physical AI - The Manila Times
<a href="https://news.google.com/rss/articles/CBMikAJBVV95cUxPeUJsUndjT3RlN0JfdVQ2YmJpbndmbk1TZ0NhRjRwUDlrX2J6dVFmU0REMGtmbFV5YURmajBIQmlmWU12eVVyQXkwa3F5VUZjVDFnTmFWdUJpa19KdHF6MGI3MHVjZ1JyYVpzbnpGZlFCLWdpVVhKNERqbmlOam5CaVRWZkV6WkJlaFVfQmRNdlQzMGtqZWQ0TnVSM2I4UDE0SGIxQlpicEZOdjg5cWRCMUVGZzNpNmZQaVJiWGpCbkV2VkI5OUNwY3huMWsxSmNZdWZqMU9qcHpfX0lXQ1RhRGhnOENSWUNncUtNUkdLdWUxQnhVVmtEWWtvVjVhd2U2c0dDdERiRENLY0pXRUJsQtIBkAJBVV95cUxPeUJsUndjT3RlN0JfdVQ2YmJpbndmbk1TZ0NhRjRwUDlrX2J6dVFmU0REMGtmbFV5YURmaj
HKT Bets Hollow-Core Fiber Can Make Distributed GPU Training Viable in Hong Kong - Tech Times
<a href="https://news.google.com/rss/articles/CBMizAFBVV95cUxPNjhndkJuWTZOOGppUU1WazhaMmRDeVRyT0RjZjRpTGR5VEZ3QUVrTHE1YlhfcGdLTFR0VS1zdHREeXdlYVNiV2YyV0dPNUxqeG4tTFlqcnFtYUFjbXZFYk16UXo4aXlYSjhsblkyN3lrV2ZkLXNNc1gzWGJBOU4wTmtfUFpwa0Y0WWNtVXROcjI3WHVjYkVzcDBRdGJUWUxkNWFReUQzVkRZOW5uYnFYeEFTdjdoVVBldHRwSGNqcTNLYWh1TzhtcjFwN0s?oc=5" target="_blank">HKT Bets Hollow-Core Fiber Can Make Distributed GPU Training Viable in Hong Kong</a> <font color="#6f6f6f">Tech Times</font>
Deploying Oracle Globally Distributed AI Database 26ai on Kubernetes with Oracle DB Operator - Oracle Blogs
<a href="https://news.google.com/rss/articles/CBMixAFBVV95cUxNa2VOTGZ2VHdUWER1aUs0V2huRVd3RFkySnI2MlFTSVdVMEFoTE5Zd1J6UzZTUjAwMm1xdFFXSnc3MjJVbG1vRFlwdGJBYlhBYmgtY2lZVUh1VDJSYkxrUWYzNHNDZXlkbFpPajhPYnJjMk91RXpZQTZ1Y0dQM0toTmZDRVpaNWRqN2libVdwcDI0bVNQelVjM2prM2t1c2p3Ni1mY2pCTUJ4cnJNM1k1VWNaWmw1WHBlalVKUlNweU83eEt0?oc=5" target="_blank">Deploying Oracle Globally Distributed AI Database 26ai on Kubernetes with Oracle DB Operator</a> <font color="#6f6f6f">Oracle Blogs</font>
Who've told you that distributed training is impossible? Democratizing AI: The Psyche Network Architecture
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1uoay98/whove_told_you_that_distributed_training_is/"> <img alt="Who've told you that distributed training is impossible? Democratizing AI: The Psyche Network Architecture" src="https://external-preview.redd.it/CETQXRVwjAaaWNDaD_s_G3tYC0oWbtRHtb3lsStlPaU.png?width=640&crop=smart&auto=webp&s=37665df6c523d03102bba65255e291f1d14b7672" title="Who've told you that distributed training is impossible? Democratizing AI: T
Anthropic is giving 6 months of Claude Max 20x ($1,200 value) for free to open source contributors, applications are open now
<!-- SC_OFF --><div class="md"><p>ame across this and wanted to make sure the dev community knows about it.</p> <p>Anthropic quietly expanded their <strong>Claude for Open Source</strong> program and the details are pretty generous:</p> <ul> <li><strong>6 months</strong> of Claude Max 20x</li> <li><strong>$1,200 value</strong> - completely free</li> <li>No catch, just an application</li> </ul> <p><strong>Who qualifies:</strong></p> <ul> <li>Package maintainers</li> <li>Core contributors landing
GitHub's funding platform tops $100 mn in contribution | Business Standard - newspaper - Magzter
<a href="https://news.google.com/rss/articles/CBMisgFBVV95cUxOSnF4UnAzOG1xWUUtLVdSOTBSUFdmdmZXYVUxekFid0l0VXliQXVhaTRfWldZRk1ZYkhsRGcwYldJVXRTT3JqTGpGYUhVcTFlREt6bDk2NllaczJRdjM1Z0lGc0ZJWnUtZEFEamVvYzBrNGg5dnFPaTFXQTZZMlhiSlpRaFZHRWMxUjBRRE0tRUx3Y1l0bEdxNS1pc3RIcGRLc3VJZFpCblRfV25JSXlNb21B?oc=5" target="_blank">GitHub's funding platform tops $100 mn in contribution | Business Standard - newspaper</a> <font color="#6f6f6f">Magzter</font>
GhostLock, a stack-UAF that has existed in ALL Linux distributions for 15 years
<p>Article URL: <a href="https://nebusec.ai/research/ionstack-part-2/">https://nebusec.ai/research/ionstack-part-2/</a></p> <p>Comments URL: <a href="https://news.ycombinator.com/item?id=48864969">https://news.ycombinator.com/item?id=48864969</a></p> <p>Points: 74</p> <p># Comments: 16</p>
Godot will no longer accept AI-authored code contributions
<p>Article URL: <a href="https://www.pcgamer.com/gaming-industry/open-source-game-engine-godot-will-no-longer-accept-ai-authored-code-contributions-we-cant-trust-heavy-users-of-ai-to-understand-their-code-enough-to-fix-it/">https://www.pcgamer.com/gaming-industry/open-source-game-engine-godot-will-no-longer-accept-ai-authored-code-contributions-we-cant-trust-heavy-users-of-ai-to-understand-their-code-enough-to-fix-it/</a></p> <p>Comments URL: <a href="https://news.ycombinator.com/item?id=4874347
Papers · 21
6G Sensing Security: Distributed Game-Theoretic RL for Urban Beamforming and Attacker Detection
In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the surrounding environment. This leads to the concept of integrated sensing and communication (ISAC), where the same wireless infrastructure is used for both communication and environmental sensing. Thus, ISAC enables the system to transmit information efficiently and observe and interpret channel variations and user behavior. Motivated by this capability, this work focuses on detecting an active attacker in an urban environment scenario, where the att
Online Inference in Distributional Temporal-Difference Learning
We study online statistical inference for functionals of the return distribution under a fixed policy. The return distribution is estimated by nonparametric distributional temporal-difference learning from a single Markov trajectory. For the Polyak--Ruppert averaged estimator, we prove that its root-$T$ error converges weakly to a centered Gaussian random element in Cramér space. We also prove that, conditionally on the observed trajectory, the root-$T$ difference between the bootstrap and original averages converges weakly to the same Gaussian limit. These results justify bootstrap inference
Decoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils
Determining the biological sex of the individuals who created Upper Paleolithic hand stencils remains a challenging problem due to the absence of ground truth, population differences between contemporary and prehistoric groups, and the uncertainty introduced by image degradation. Traditional morphometric methods suffer from high structural overlap across sexes, poor cross-population generalizability, and subjective feature engineering. This study presents an uncertainty-aware deep learning framework for sex attribution in prehistoric hand stencils that explicitly models, propagates, and aggreg
FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
Forest attributes are essential for national-scale resource monitoring. Airborne LiDAR metrics are among the auxiliary variables most strongly correlated with forest attributes used in National Forest Inventory (NFI) estimates. However, producing wall-to-wall predictions remains challenging when LiDAR data are acquired under heterogeneous conditions. As national LiDAR programs expand across Europe, variability in sensors, flight parameters, seasons, and scan angles limits the robustness of existing models, which are often calibrated for local conditions. We present FLORA (Forest LiDAR Octree R
How Jailbreak Attacks Inform Safety Alignment: A Defender-Centric, Shapley-Based Evaluation of Jailbreak Contributions
Jailbreak attacks on large language models are usually evaluated by attacker-centric metrics such as attack success rate (ASR), yet an attack that breaks a model is not necessarily useful for improving its safety. We propose a defender-centric view of jailbreak evaluation, where attacks are evaluated by the downstream safety improvements they enable when used as red-teaming data for safety training. Building on this view, we introduce A-MESS (Minimal Effective Attack-Subset Selection), a setting-agnostic framework for attributing and selecting jailbreak attacks from black-box subset utility ob
Learning Emotion from Motion: Kinetic Multi-Stream Skeleton Modeling with Metadata-Conditioned Weak Label Distributions
Skeleton-based emotion recognition from body motion remains challenging because emotional expressions are often characterized by subtle dynamic and relational motion cues, and hard labels may not fully capture ambiguity among related emotion categories. For the DIEM-A task in the MMAC ACII 2026 Challenge, we propose a multi-branch skeleton-based emotion recognition framework that combines a 6D rotation-based branch, a part-aware kinetic multi-stream branch, and a metadata-conditioned weak label distribution learning (LDL) branch. The branches are trained independently and fused by a probabilit
Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions
The Sliced Wasserstein (SW) distance has emerged as a computationally attractive alternative to the Wasserstein distance by leveraging one-dimensional optimal transport along random projections. Standard estimators of the SW distance rely on Monte Carlo averages of one-dimensional Wasserstein distances computed via quantile functions, which require sorting projected samples and access to full datasets. In this work, we introduce a new class of estimators for the Sliced Wasserstein distance based on cumulative distribution functions (CDFs) of projected measures, that avoid sorting and scale via
When Local Monitors Miss Compositional Harm: Diagnosing Distributed Backdoors in Multi-Agent Systems
As multi-agent, tool-using LLM systems are deployed, a common safety net is a runtime monitor that checks each message, tool call, or step on its own. We show this net has a fundamental hole. A distributed backdoor splits a harmful payload across agents, so every local check passes while the assembled object is the attack. The monitor can be right on every step and still miss the attack. The problem is not splitting itself: split fragments can still leak suspicious tokens or provenance edges. The hard case is \emph{local benignness}. No fragment carries the harm, and what is left looks like or