All AI news

AIvLLM achieved 5000 total token throughput per GPU in high-throughput PD serving of Qwen3.8-2.4T on a GB300 NVL72 cluster under an 8K/1K workload. The low-latency scenario reached 180 generated tokens per user, with both results shown on the Pareto frontier. The post also provides srt-slurm recipes and explains the tuning process used to create them.

AISGLang and RLinf have published a blog post about integrating Cosmos3 for reinforcement learning. The main post provides only a link to the full blog, so no further technical details can be confirmed from this source.

AIA commenter praises a practical setup where a small local model runs a log-debugging pipeline entirely on CPU through OpenClaw and Ollama. They argue that workflow design can make a small model far more useful than its raw size suggests.

AIOpenBMB's 2B-parameter MiniCPM5 model runs as a local news desk on an older i5-9400F PC with 16GB RAM and no cloud API. The developer built a system that collects official sources hourly and sends a 24-hour Telegram recap with a lead story and links.

AIJasper Lu's new blog post walks through training a search agent with GRPO, showing how small reward function changes teach a model to avoid sloppy tool calls, prune unnecessary documents, and balance persistence against token efficiency. The post makes every rollout browsable and releases the code as open source, with the full process from learning rate sweeps to reward shaping documented.

AILMSYS Org published a blog post introducing an SGLang SSD expert pack, with the full details available on its website. The post itself gives no further technical specifics, so the summary is limited to the announcement.

AIGoogle's Envisioning Studio, with Google Labs, co-developed custom Google Flow tools with designers Jane Wade and Sergio Hudson ahead of New York Fashion Week. Wade's Styling Suite let her style runway looks on digital models before producing physical samples, while Hudson's Runway Visualization helped him stage his show within a tight budget. The source says the tools are built with natural language and no coding experience.

AIHamel Husain and Shreya Shankar's FAQ explains AI evals as tests of whether an AI system does what users and the business want. It recommends starting with error analysis on at least 30 traces, then turning recurring failures into binary code-based checks or LLM judges validated against human labels.

AIMike Krieger, Anthropic's account owner, used an interactive Claude artifact to visualize the Iron Tangle level from Dungeon Crawler Carl, saying it helped him finally understand the layout. The post shares a link to the artifact, with no further details about its features.

AIJetBrains describes building Air Context, a RAG pipeline that gives LLM agents semantic code search over real repositories instead of grep. The first installment covers parsing, chunking, and vectorization, arguing that fixed-size line chunks split related code and that structure-aware chunking using language grammar produces better retrieval units.

AIZ.ai reports that GLM-5.3 helped build and optimize the inference infrastructure for GLM-5.3-Flash. The system went from first successful run to production readiness in under two weeks, with end-to-end throughput tripling over the initial baseline. The team credited dense feedback from local correctness tests, execution traces, microbenchmarks, and end-to-end measurements for enabling targeted hypothesis testing.

AIMosaic, Ponder Studio, and Revyl used early access to Google's Gemini Flash models to test agentic video understanding on long footage. Mosaic reports a 97% cut in median token usage and nearly double the ability to handle complex edits, while Ponder Studio reports a 0.967 F1 score and about 72% lower token costs for B-roll selection. Revyl says the approach improved mobile UI bug-catching accuracy by 65%. The capability is available now for video uploads and YouTube videos via the Gemini API.

AIAnthropic is hosting office hours for teams interested in using Claude Tag for on-call work, and it is asking Team or Enterprise plan users to share triage feedback. Claude Tag can start investigating when a Slack alert fires by pulling metrics, diffing deploys, and checking flags to propose a likely cause and fix. Sign-up is through a Google Calendar booking link.

AITogether AI says teams can migrate from closed to open-source models in weeks to months, not years, using a managed service. The guide recommends defining the use case, shortlisting candidates with relevant benchmarks, and replaying existing traffic against them to test accuracy, cost, and latency against current targets.

AIPart 2 of the zero-trust agents series moves security checks from agent code to the Gemini Enterprise Agent Platform runtime. Model Armor screens prompts and responses, Semantic Governance Policies judge proposed tool calls against intent and business rules, and Agent Anomaly Detection flags multi-turn drainage that single-turn checks miss. The same Customer Support and Returns Agent from Part 1 is used, with the companion demo open-sourced on GitHub.

Why it matters: The post walks through a concrete refund agent under four attacks, showing how screening, intent judgment, and anomaly detection each catch what the others miss.

AIThe vLLM team trained a DSpark speculative decoding draft model for Kimi K3, a 2.8T-parameter model, using the Speculators library on GB300 NVL72 hardware. They added a MooncakeHiddenStatesConnector to stream hidden states from disaggregated vLLM inference nodes to training nodes across multiple machines. The released speculator raises single-stream interactivity from about 110 to about 435 tokens per second per user on math reasoning, with up to about 3.5x higher output throughput under concurrent load.

Why it matters: The post shows how hidden-state extraction and Mooncake transfers let a 2.8T-parameter model's speculator be trained across multiple nodes, a reusable pattern for similar setups.

AIA hands-on guide by @pandemicsyn walks through writing evals that detect when an AI agent claims to have completed a task it never did. Working through a demo agent that fails on purpose, the author refines the checks until they can distinguish real work from mere claims of work. The post includes a coding agent skill that can guide readers through the exercise.

AIAugment Code reports that size-adjusted output per active developer rose from 12.3 to 55.7 between November 2025 and July 2026, while median time to merge fell from 11.2 to 3.1 hours. The post says the company added specialized agents wherever work was piling up, across planning, review, verification, feedback, and incident response, and kept engineers responsible for product decisions, architecture, and production risk.

Why it matters: The post pairs internal productivity and quality metrics with the order in which agents were added, showing how review and verification bottlenecks shaped a software delivery pipeline.

AIGoogle Developers Blog describes autofinetune, a project applying autonomous agent loops to LLM post-training with Tunix, Gemma, and Cloud TPUs. In an SFT case study on FunctionGemma, an agent ran 20 automated experiments on a Cloud TPU v5e-1 to adjust LoRA settings, optimizers, and learning rates. In a GRPO case study on Gemma 3 1B for GSM8K math reasoning, the agent ran 40 experiments on a Cloud TPU v6e-1 and improved total reward by about 10%.

AIGiles Thomas extended the GPT-2-style code from Sebastian Raschka's "Build a Large Language Model (from Scratch)" into a 6-expert, 2-active mixture-of-experts model and trained it from scratch over 8 days. Raschka praised the project as interesting LLM work done on a single GPU.

AIHugging Face announced a broadcast titled "Training Agents 4: From reward functions to environments" on X. The post gives no further details on its content or speakers.

AIRadixArk links to a blog post on DeepSeek V4.1 at lmsys.org. The post text provided contains only the URL, so no further details on the model or its performance can be reported.

AIFireworks AI describes a four-stage path for teams moving from renting closed frontier models to training their own, starting with API use and prompt, context, and harness engineering. The post uses the UIPad computer-use dataset to show that Kimi K3 ties GPT 5.6 Sol overall at 87.7 but wins three of four categories while costing about half as much, suggesting routing. After roughly three hours of training on the training split, the tuned Kimi K3 outperforms GPT 5.6 Sol on the held-out test set.

AISherwin Wu, an OpenAI executive, says employees now use Astra to vibe-code custom forms, deploy them on ChatGPT, and share the links instead of Google Forms. The post describes this as an emerging internal use case around ChatGPT sites.

AIGranola says users can add personal jargon in their own settings. The post provides no further detail on how this works or what it affects.

AIMistral AI helped a European energy operator migrate 40,000 lines of Fortran 77 to C++ for a reservoir simulator with no test suite. The post explains a parity harness that checks numerical agreement between the two codebases, and a workflow where agents coder, tester, and reviewer migrate modules under human review. Its authors note the approach covered the self-contained first sprint of 40,000 of 300,000 lines and that dependent systems would bring additional challenges.

AIAmy, a 58-year-old with no coding background, built Wally, an AI voice aid app using Manus, for her brother Jamie after cancer surgery at Brigham and Women's Hospital left him unable to speak. The app combines one-tap urgent and everyday phrases with golf news, live tournament scores, and family messages, all placed on his phone's home screen.

AIGoogle Developers Blog argues that teams building AI coding agents should replace end-to-end benchmark scores with behavioral evaluations that test discrete, observable actions. Examples include asking clarifying questions on underspecified prompts, running a local validator before marking a build change complete, and consulting live search for current information. The post recommends fast, deterministic unit-style checks, outcome-based LLM-as-a-judge checks for complex tasks, and batch runs that track aggregate pass rates over time.

AIInferact says months of vLLM tuning for agentic workloads, validated on SemiAnalysis's AgentX benchmark, let open-source models reach up to 130K tokens per GPU-second. The company claims this is 106 times cheaper than Opus 5 API pricing. The work is described as part of a vLLM blog post covering architecture, framework, and runtime optimizations.