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Data, indicators and methods

29 selected reportsIn the last 30 days 29 reportsIndexed 105 reports

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Selected: Data Analysis

TodayOct 31SatItems 1–20
  1. Cointelegraph

    Tether Reports $1.5 B Q3 Operating Profit and Nearly Doubles Excess Reserves to $7.95 B

    Tether announced a third‑quarter operating profit of $1.5 billion, with its excess reserves rising to $7.95 billion – almost double the $4.1 billion reported in June. At the end of September the company held $191.5 billion in assets against $183.5 billion in liabilities, leaving a reserve surplus of $7.95 billion. Assets include $132.4 billion in direct and indirect U.S. Treasury bill holdings, comparable to South Korea’s $134.2 billion exposure and exceeding the United Arab Emirates’ $116.1 billion. Over the first nine months of 2026, Tether generated $4.5 billion in net operating profit, largely driven by Treasury bills and reverse repurchase agreements. Bitcoin reserves were valued at $8.37 billion, up from about $5.8 billion at the end of June. USDT remains the largest stablecoin, representing roughly 60% of the $297.3 billion stablecoin market, and its user base has surpassed 700 million, largely in emerging markets.

    Read source cointelegraph.com
Oct 29Thu
  1. The Verge · AI

    Anthropic files S-1 for Nasdaq debut at $1.5-$2 trillion valuation

    Anthropic filed an S-1 to go public on the Nasdaq, seeking a valuation between $1.5 trillion and $2 trillion. Analyst Joey Brookhart of SemiAnalysis highlighted the company's efficient scaling, reporting over $70 billion in annual recurring revenue and gross margins, compute margins, and free cash flow that meet investor expectations. The filing provides the first detailed look at the economics of a frontier AI lab.

    Read source theverge.com
Oct 28Wed
  1. AWS Machine Learning BlogFirst-party

    Exowatt uses Amazon SageMaker serverless model customization to build AI pipeline for solar energy systems

    Exowatt developed a repeatable AI pipeline using Amazon SageMaker serverless model customization to fine-tune Qwen3-1.7B and Qwen3-14B models for edge and cloud deployment. The pipeline achieved higher accuracy than frontier models in alarm triage and energy market analysis tasks, with macro-F1 scores of 0.911 and 0.975 respectively. The approach reduced model customization costs by up to 49 times and enabled deployment from raw operational data to evaluated artifacts in days rather than months.

    Read source aws.amazon.com
Oct 27Tue
  1. The Decoder

    OpenAI claims to disprove Exponential Time Hypothesis with expert verification

    OpenAI enlisted outside experts, including a Nobel laureate, to verify AI-generated proofs in math, computer science, and physics. In mathematics, the AI claims to have disproven the Exponential Time Hypothesis (ETH), a long-standing assumption in computational complexity theory that underpins hundreds of research papers. In computer science, the system claims to have solved the Boolean satisfiability problem (SAT) faster than exponential time, connecting it to the P versus NP problem. In physics, OpenAI states its AI proved the existence of four-dimensional quantum field theory.

    Read source the-decoder.com
  2. Latent Space

    Anthropic files S-1 for Nasdaq listing under ticker ANT, reports $16.3B H1 revenue and $974M Q2 operating profit

    Anthropic filed its S-1 on Monday, planning a Nasdaq listing under ticker ANT. Revenue surged from $41 million in 2023 to $16.3 billion in H1 2026, with Q2 revenue reaching $11.6 billion, up 1,368% year-over-year. The company reported $974 million in Q2 operating profit and $3.9 billion in H1 operating cash flow, marking its first profitable quarter. The $80.7 billion H1 net loss is largely driven by $81.1 billion in non-cash charges tied to financial instruments.

    Read source latent.space
  3. AWS Machine Learning BlogFirst-party

    AWS launches Managed Amazon Bedrock Knowledge Base with AgenticRetrieveStream API for multi-turn retrieval

    AWS introduces a Managed Amazon Bedrock Knowledge Base that transforms retrieval into an agentic capability, replacing single embed-and-fetch calls with multi-turn planning and tool-driven retrieval. The AgenticRetrieveStream API enables a reasoning agent to decide whether to retrieve, which knowledge base to consult, and whether to iterate before composing a grounded, cited answer. This addresses limitations of ordinary RAG, such as answers spanning multiple corpora or requiring follow-up lookups after initial results. The service is now available as a first-class feature, eliminating the need to manually manage OpenSearch Serverless collections. AWS CDK users can now provision the knowledge base and AgenticRetrieveStream as stacks, though observability remains critical due to the non-deterministic paths agents take during retrieval.

    Read source aws.amazon.com
Oct 24Sat
  1. Google ResearchFirst-party

    Google Research deploys open foundation models like MedGemma for global TB screening

    Google Research and Google for Health are deploying open foundation models, such as MedGemma, to democratize tuberculosis (TB) screening globally, addressing the scarcity of radiologists in high-burden settings. Initial work demonstrated deep learning algorithms could detect active pulmonary TB on chest radiographs with accuracy matching expert radiologists (2019). The model was ranked among top global TB screening algorithms by the Stop TB Partnership (2021), and a prospective multi-site validation in NEJM AI (2024) confirmed high sensitivity across diverse populations. In partnership with Nexus AI, the tool has received regulatory clearance (CE MDR Class IIb) and has been deployed in 40 medical institutions across countries like South Africa and Sierra Leone, screening over 25,000 chest X-rays.

    Read source research.google
Oct 23Fri
  1. Hugging Face BlogFirst-party

    ThinkingBox turns database grading into RL rewards for post-training agents

    Researchers from Microsoft and Hugging Face introduced ThinkingBox, a system that evaluates agent performance by comparing final database states with expected outcomes. Using executable checks as reward signals, they fine-tuned Qwen3.8-27B with GRPO for 50 steps. This training significantly improved repeatable success, increasing pass@1 from 51.70% to 60.78% and tasks completed correctly on all 20 attempts from 38 to 107.

    Read source huggingface.co
  2. Hugging Face BlogFirst-party

    Hugging Face Releases OlmoEarth v1.3, a 128‑dimensional Satellite Embedding Model for the Entire Planet

    Hugging Face has launched OlmoEarth v1.3, a vision‑transformer based model that compresses a full year of Sentinel‑1, Sentinel‑2 and Landsat imagery into a 128‑dimensional vector for every 10‑meter pixel on Earth. The release includes pre‑computed annual embeddings for the whole planet, a 64‑dimensional subset, and a browser‑based Embeddings Explorer that lets users map, compare, and classify land‑cover without GPU access. In 27 evaluations across six continents, OlmoEarth’s embeddings outperformed AlphaEarth Foundations and Tessera v2 on 20 of 27 tasks, with the 64‑dimensional version still leading on 18 tasks.

    Read source huggingface.co
Oct 22Thu
  1. Hugging Face BlogFirst-party

    VocabX method cuts SLM inference cost by 77% through vocabulary specialization

    A Portuguese essay grader built on Qwen3-4B reduced average output tokens from 350 to 101 by rebuilding its vocabulary for the specific task. The VocabX method specializes token selection around generation cost rather than frequency, eliminating waste from domain-irrelevant tokens while keeping the base model unchanged.

    Read source huggingface.co
Oct 21Wed
  1. Latent Space

    DeepSeek open-sources DeepInfer inference engine, claims 155T tokens/day at 80% margin

    DeepSeek released DeepInfer, the inference engine behind V4.1 Flash, as open source on the first day of PyTorch Conference. The engine supports distributed inference, speculative decoding, and structured output on NVIDIA and Huawei Ascend platforms. According to DeepSeek's translated blog post, their deployment achieves 155 million tokens per day with a claimed inference margin exceeding 80%, generating approximately $22 million in daily revenue at off-peak prices. The architecture combines vLLM with SGLang and TRRTLLM, though DeepSeek notes their most ultra-optimized KV cache and load balancing components remain proprietary.

    Read source latent.space
  2. AWS Machine Learning BlogFirst-party

    i14 Trains and Serves Laplace 3B Linear‑Attention Model on AWS Trainium2

    i14’s Laplace 3B, a 3‑billion‑parameter linear‑attention language model, was trained on a single trn2.48xlarge AWS Trainium2 instance at roughly 120,000 tokens per second. During inference, the model delivered 11,544 output tokens per second across 1,280 concurrent sequences while keeping a fixed 29.5 MB recurrent state per sequence, independent of context length. The work leveraged TorchNeuron Native to port i14’s Gated Linear Attention (GLA) kernels to Trainium2, preserving the model’s Muon optimizer and custom architecture.

    Read source aws.amazon.com
  3. AWS Machine Learning BlogFirst-party

    Amazon Quick and Bedrock AgentCore Enable Conversational Supply‑Chain Decision‑Making

    Amazon Quick and Bedrock AgentCore let supply‑chain managers ask plain‑language questions and receive real‑time, cross‑system answers from live ERP, monitoring, procurement, and email data. By exposing enterprise Model Context Protocol (MCP) servers as composable tools, the system eliminates manual stitching of data and reduces a 45‑minute multi‑tool workflow to a few conversational prompts. The solution demonstrates how conversational AI can directly access live operational data and generate actionable requests such as emergency RFQs.

    Read source aws.amazon.com
  4. Hugging Face BlogFirst-party

    NVIDIA Unveils Cosmos3‑H‑Dreams, a Real‑Time Generative Simulator for Clinical Cholecystectomy

    NVIDIA has released Cosmos3‑H‑Dreams, a real‑time generative surgical simulator trained on clinical cholecystectomy videos from the CMR Surgical Versius platform. Built on the Cosmos 3 foundation and accelerated with FlashDreams, the model runs interactively on a single NVIDIA RTX PRO 6000 GPU, producing temporally consistent, anatomically coherent video that responds to instrument kinematics. The system demonstrates a viable alternative to traditional physics‑based simulation for live robotic surgery control.

    Read source huggingface.co
  5. AWS Machine Learning BlogFirst-party

    AtScale Builds a Dimensional Semantic Layer for Agentic AI on AWS

    AtScale introduces a dimensional semantic model on AWS, replacing graph‑based layers used in earlier posts. The model, defined in the open Semantic Modeling Language (SML) and versioned in Git, is exposed via SQL, XMLA, and the Model Context Protocol (MCP). A Strands agent on Amazon Bedrock AgentCore queries this layer, with identity and role‑based access control propagated through AgentCore’s identity features. The solution is deployed on Amazon EKS using AtScale’s Helm chart.

    Read source aws.amazon.com
  6. AWS Machine Learning BlogFirst-party

    Strategy Mosaic Enables Agentic AI on AWS by Providing a Unified Semantic Layer for Bedrock AgentCore

    Strategy Mosaic is used as a semantic layer on AWS to support agentic AI via Amazon Bedrock AgentCore. The solution models a Customer‑360 domain across Amazon Redshift and Amazon Aurora with two linked models, avoiding ETL. Mosaic’s dimensional model exposes a Model Context Protocol that lets a Strands agent reach the semantic layer, while row‑level and object‑level security propagate end‑to‑end. The same governed definitions can be shared with Power BI, Tableau, notebooks, and custom apps through JDBC/ODBC, XMLA, and REST.

    Read source aws.amazon.com
  7. AWS Machine Learning BlogFirst-party

    AWS Integrates Denodo Semantic Layer with Bedrock AgentCore for Federated Customer 360 Queries

    AWS demonstrates how to build a semantic layer using Denodo that federates Amazon Aurora, Amazon Redshift, and an on‑premises PostgreSQL database into a single Customer 360 view. The Denodo MCP Server exposes governed views to a Bedrock AgentCore‑hosted agent via the Model Context Protocol, while AgentCore handles authentication and credential management. The setup supports row‑ and column‑level security and can run on Denodo Agora or self‑managed EC2/EKS instances.

    Read source aws.amazon.com
  8. Hugging Face BlogFirst-party

    FairStress Benchmark Reveals Hidden Demographic Biases in Large Language Models

    Hugging Face has released FairStress, a new benchmark that tests how large language models (LLMs) respond to persuasive arguments—called adversarial prefixes—when deciding between candidates of different demographic groups. Across 1.3 million test cases and models such as Olmo, Llama, Qwen, Gemma, Claude, and GPT, the study shows that models are more likely to abandon a clearly stronger candidate in favor of a minority candidate when presented with a persuasive argument, indicating a reverse over‑correction bias. The benchmark also uncovers differences in how models explain decisions and in their internal activations across hiring, lending, healthcare, education, and housing scenarios.

    Read source huggingface.co
Oct 20Tue
  1. Hugging Face BlogFirst-party

    ALTK‑Evolve Uses Learned Memory to Top τ²‑Bench Retail Customer‑Service Leaderboard with GPT‑4.1

    ALTK‑Evolve builds a playbook of short behavioral rules by analyzing past task attempts with GPT‑4.1, then appends those guidelines to the system prompt of a stock ReAct agent. The agent achieves a 67.5% Pass^4 score on the τ²‑Bench retail domain, surpassing all other retail entries in the October 12, 2026 leaderboard snapshot, without any model weight updates or human annotation.

    Read source huggingface.co
  2. AWS Machine Learning BlogFirst-party

    Deepgram Boosts Speech‑to‑Text Efficiency 11‑70% Using AWS Trainium2

    Deepgram ported its Nova‑3 speech‑to‑text model to AWS Trainium2, deploying eight replicas on a single trn2.3xlarge EC2 instance. The optimized configuration processed 11–70 % more audio per dollar on batch transcription workloads compared to previous compute options. The team used TorchNeuron for native PyTorch integration, performed kernel‑level tuning, and built a Rust‑based data‑parallel runtime to eliminate Python overhead. These gains enable Deepgram to serve more audio for the same infrastructure budget and lower costs for customers who self‑host on SageMaker AI.

    Read source aws.amazon.com