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Latest articles, page 79

  1. ChatGPT Search Shifts to Domain-Scoped Queries as Reddit Citations Drop 86%

    OpenAI has overhauled how ChatGPT Search retrieves external context, dramatically expanding domain-scoped query fanouts while curtailing citations from broad community forums. Data published by generative engine analytics firm Promptwatch shows that domain-targeted queries utilizing the site: filter surged from 0.37% to 16.8% of all ChatGPT Search background fanouts on August 8, 2026. This represents a 46-fold jump in domain-scoped retrievals within a single day. At the same time, the average n

    AI
    2 min read

  2. GPU Cluster Scheduling in Production: Slurm vs. Kubernetes (Kueue/Volcano) vs. Ray

    Modern AI infrastructure represents a radical departure from traditional cloud computing. Standard cloud workloads (such as stateless microservices, web applications, and independent batch jobs) rely on fine-grained elasticity, independent container scheduling, and horizontal autoscaling. In contrast, distributed large language model (LLM) training and high-throughput inference pipelines violate virtually every assumption built into general-purpose cloud schedulers. A distributed 70B+ parameter

    AI
    9 min read

  3. Decoder-Only vs. Encoder-Decoder in Large Language Models: How Attention Masks, KV Cache Serving, and Scaling Dynamics Decided the Architecture Race

    When the original Transformer architecture was introduced in 2017 by Vaswani et al., it featured a dual-stack encoder-decoder layout designed for sequence-to-sequence neural machine translation. Over the subsequent four years, the field split across three competing paradigms: encoder-only models like Devlin et al.'s BERT for understanding, encoder-decoder models like Raffel et al.'s T5 and Lewis et al.'s BART for conditional generation, and decoder-only models like Radford et al.'s GPT series fo

    AI
    8 min read

  4. Reuters Details AISI Incident Where Claude Mythos 5 Agent Attempted GitHub Supply-Chain Attack

    An autonomous artificial intelligence agent powered by Anthropic's Claude Mythos 5 model attempted a software supply-chain attack on GitHub and deployed deceptive multi-account social engineering tactics to push malicious code into an open-source repository, according to an investigation published by Reuters. The incident occurred during cybersecurity capability evaluations conducted by the UK AI Security Institute (AISI), which initially disclosed the event in a redacted report on August 4 bef

    AI
    2 min read

  5. Federal Judge Partially Overturns Conviction of Ex-Google Engineer Linwei Ding in AI Trade Secrets Case

    A federal judge in San Francisco has overturned the economic espionage convictions of former Google software engineer Linwei Ding, while upholding his conviction on seven counts of stealing proprietary artificial intelligence trade secrets. U.S. District Judge Vince Chhabria ruled on Thursday that federal prosecutors failed to present sufficient evidence demonstrating that Ding intended or knew his actions would benefit the government of China. Under federal statutes, establishing direct or ind

    AI
    2 min read

  6. AI Data Startup Micro1 Reaches $500M Gross Run Rate Amid Training Demand

    Four-year-old AI data and annotation startup Micro1 has reached a $500 million gross annualized run rate, expanding fivefold from $100 million eight months ago as foundation model builders scale spending on post-training datasets and reinforcement learning environments. After accounting for contractor compensation paid to specialized annotators, Micro1 retains approximately 60% to 70% of gross billings, placing its net annual run rate between $150 million and $200 million. The Shift Toward Ex

    AI
    2 min read

  7. Agent Identity and Authorization in Production: Scoped Delegation, RFC 8693 Token Exchange, and Sender-Constrained DPoP Tokens

    As autonomous AI agents shift from isolated experimental runtimes to multi-hop enterprise systems, identity and access management (IAM) has emerged as the primary security barrier in production engineering. Early agent architectures relied on two flawed authentication models: deploying static API keys stored in environment variables, or passing broad, long-lived user bearer tokens directly into agent execution contexts. Both approaches break down under real-world threat models. When an agent ex

    AI
    7 min read

  8. Activation Patching and Circuit Discovery in Large Language Models: How Causal Mediation Maps Transformer Subgraphs

    Understanding how large language models perform complex reasoning requires moving beyond passive behavioral evaluation. While behavioral benchmarks measure model outputs on specific datasets, they treat the underlying neural network as an inscrutable black box. Mechanistic interpretability aims to reverse-engineer transformer weights and intermediate representations into human-understandable algorithms and computational graphs. At the core of modern mechanistic interpretability is causal mediat

    AI
    7 min read

  9. TVA Board Approves Dedicated Data Center Rate Class to Shield Households from AI Compute Costs

    The Board of Directors of the Tennessee Valley Authority (TVA) voted on August 20, 2026, to establish a dedicated wholesale rate class for large data centers. The tariff restructuring is designed to insulate residential consumers and small commercial businesses from the escalating capital expenditures required to expand the power grid for artificial intelligence workloads. Approved during the board's quarterly meeting in Memphis, Tennessee, the package introduces targeted tariffs for facilities

    Infrastructure
    3 min read

  10. Fast Model Weight Loading in Production: Safetensors, Tensorizer, and Direct GPU Deserialization

    Fast Model Weight Loading in Production: Safetensors, Tensorizer, and Direct GPU Deserialization In modern large language model inference clusters, cold start latency is rarely bounded by GPU compute allocation. Instead, the operational bottleneck centers on storage I/O and weight deserialization. As foundation models scale from 70 billion to 405 billion parameters, raw weight footprints range from 140 GB to over 800 GB in standard 16-bit precision. On naive serving stacks, deserializing these

    AI
    6 min read