Many scientific simulations, from molecular chemistry to materials science, spend most of their execution time on high-precision dense general matrix multiplications (GEMM), called DGEMM for real 64-bit floating-point numbers and ZGEMM for complex 64-bit floating-point numbers. cuBLAS is widely used by HPC and AI applications to accelerate GEMM and other Basic Linear Algebra Subprograms operations.
A plugin called simba-skills offers six Claude Code skills for people running Docker, Postgres and a VPS on the same machine as their AI agent, aimed at preventing test runs, repro loops or deploys from hitting live data. Skills include production-safe-loops (automatic), lint-project-skills, report-skill-failure, safe-deploy, server-maintenance and audit-claude-settings, with deploy, delete and settings-changing skills requiring manual invocation. The repo includes an eval suite (Claude Code 2.1.323, November 2026) and is MIT licensed.
NVIDIA describes the challenge of black hole imaging: M87*'s observed ring spans just 42 microarcseconds, and since black holes emit no light, telescopes capture radio emission from superheated gas swirling outside the event horizon, with the black hole's gravity bending light paths to shape the bright ring. The material introduces running GPU-powered black hole interferometry with NVIDIA cuPhoton.
Rainshare is presented as a mutual aid service for AI coding plans and usage limits, according to its site at rainshare.dev. The item was posted to Hacker News with 1 point and no comments.
In a DEF CON 34 AppSec Village piece tied to an updated Container Escapes 101 workshop, the author says AI-driven offensive tooling can now chain tasks like identifying CNI, exfiltrating data over DNS, finding command injection and chrooting out, but argues containers were never meant to contain anything and still depend on the host. The recommended defenses are unchanged: observability, patching, container best practices, and revisiting dismissed alerts, with microVM runtimes now worth another look.
Cloudflare has open-sourced Kitesurf, its stateless browser for AI agents that runs entirely on Workers, under the Apache 2.0 license on GitHub. The repository includes the engine, PageScript, PageRenderer, and CLI, built on Rust-to-WebAssembly projects like Blitz, Stylo, Boa, and Linebender libraries, and supports the Chrome DevTools Protocol for clients such as Puppeteer. Developers can run it locally or deploy their own instance to Cloudflare Workers, which requires a Workers Paid plan because it uses Dynamic Workers.
Why it matters: Useful for developers who want to self-host a lightweight agent-oriented browser engine and inspect or contribute to its open-source components.
This article explains why coding harnesses need observability, since model invocations, tool calls, retries and dead ends happen out of view, and describes how Logfire's coding agent tracing captures model invocations, tool call execution and request/response pairings across sessions. It walks through use cases including dashboards for productivity, cost and time, monitoring a software factory, reusing past session context across harnesses, and turning repeated struggles into skills, with an example where a skill took dashboard panel creation from 3/12 to 12/12 passing cases. It closes with an evaluation workflow that compares reviewer harnesses on a dataset of five code review cases, where Codex with gpt-6-luna scored highest overall at 3.93, ahead of Cursor at 2.80 and Claude Code at 2.60.
Amazon Bedrock records the IAM principal behind every inference call and publishes it to CUR 2.0 and Cost Explorer, so per-user and per-team spend can be queried without instrumenting individual tools. The post walks through configuring IAM Identity Center with an external IdP, session tags, a Bedrock permission set, tool configuration for Claude Code, Claude Desktop, Codex and ChatGPT Work, and Athena queries, then compares usage controls from AWS Budgets to gateway-level enforcement.
AssemblyAI runs GPU-based speech-to-text inference on Amazon ECS using a mix of EC2 Spot and On-Demand Instances across g4dn, g5, and g6 GPU families, with an Amazon SQS-based retry mechanism that returns in-flight jobs to the queue when a Spot Instance receives a 2-minute interruption notice. The architecture is deployed independently in us-west-2 and eu-west-2 for data residency, and AssemblyAI reports 70 percent cost savings on inference compute compared to On-Demand pricing while maintaining SLA commitments.
This post explains that always-on agents are stateful and proactive, running on the same core loop as other agents but surrounded by architecture built from five primitives: an event inbox, timers, memory, permissions, and an outbox. It describes how each primitive works, including inbox best practices, follow-up versus scheduled timers, memory types such as user instructions and task progress, credential proxying and deferred tool calls, and outbox delivery pacing. It concludes with how the primitives form a repeating cycle in which each brief run is triggered by an event and can set timers that wake the agent again later.
Why it matters: Offers a concrete architectural blueprint for teams building proactive, stateful agents, with practical guidance on event handling, memory, permissions, and message pacing.
NVIDIA describes how edge AI applications often must run multiple neural networks within limited compute and power budgets, and how the NVIDIA Deep Learning Accelerator (DLA) provides dedicated hardware for deep learning inference. On NVIDIA Jetson AGX Orin, DLA executes supported neural network operations, letting applications offload inference work from the GPU, which can improve power efficiency.
This walkthrough shows how to assemble a UR17G arm, Robotiq 2F-140 gripper, and beverage can from SimReady Central in Isaac Sim 6.2, then use an agent to generate and run a pick-and-place task. It explains SimReady profiles, features, and requirements, and lists checks for joint settings, movement, and contact before running the grasp, lift, transfer, and release sequence. The example uses a scripted horizontal grasp with a 2 N·m drive-effort cap and fixed waypoints, and the authors note the results do not establish robustness to uncertain object positions, different payloads, or real hardware.
Amazon SageMaker HyperPod now offers Data Capture, which records live inference inputs and outputs to Amazon S3 at three independent tiers (SageMaker endpoint, load balancer, and model pod) with minimal latency impact and no impact on model availability. The post walks through a single-cluster example in which a home-appliance support router's production traffic is captured at the model-pod tier, analyzed with Amazon Athena to surface a new-product blind spot, labeled with a SQL policy, and used to fine-tune the router on a second GPU. Accuracy is measured before and after on data that could not have existed before deployment, with scripts, manifests, SQL, and synthetic datasets in an accompanying GitHub repository.
Wiz describes how its Green Agent investigated a critical Issue involving an internet-facing EKS container with CVE-2026-111497 ("Broccoli2Shell"), an overprivileged IAM role, and S3 buckets holding over 5,000 unmasked PII and credit card records. The agent validated each link in the attack path, traced runtime assets back to their Dockerfiles, Terraform, and GitOps manifests to identify owners, and produced Mitigate and Fix plans while rejecting strategies like deleting the ingress that would cause a production outage.
AI Juggler is a free, open-source (MIT), local-first macOS app that shows which AI coding sessions are open across the Claude app, terminals, the Codex app and Cursor, lets you jump to any of them in one step, and reports usage and limits per account. It works with tools you already use, never proxies their traffic, never signs in for you and never stores your credentials, and it requires macOS 14 Sonoma or later plus at least one of Claude Code or Codex installed and signed in.
Artificial Analysis released version 1.1 of its Endpoint Accuracy Index, which compares more than 100 serverless endpoints hosting open-weight models against reference deployments of the official weights, finding roughly one in four score below their reference. The index reruns each endpoint on BFCL v4-500, HLE-250 and AA-LCR-25, and version 1.1 raises long-context repeats from 10 to 25, cutting the average 95% confidence interval from plus or minus 4.7 to plus or minus 2.6 points, while coverage expands to six models including DeepSeek V4.1 Flash 1017, GLM-5.3, GLM-5.3 Flash, Muse Spark Open, Kimi K3 and gpt-oss-120b. Reported gaps trace to output caps that truncate answers, shorter reasoning, and a schema bug encoding string tool arguments as numbers, with GLM-5.3 endpoints scoring from 67.2% to 99.3% and all 22 DeepSeek endpoints statistically consistent with the reference.
Running a frontier-scale model locally requires keeping all its weights in GPU memory, and the largest models no longer fit on one machine. Two NVIDIA DGX Stations connected over ConnectX-8 RDMA serve one model through a single endpoint, with tensor parallelism splitting the model weights and computation across both GPUs. The NVIDIA GLM-5.3 NVFP4 model is described as a quantized version of Z.ai's mixture-of-experts model.
Meta describes porting AOSP's Cuttlefish virtual-device framework to run natively on Apple silicon Macs, reviving QEMU support and making vhost-user buildable on macOS, increasing QEMU's HVF memory-region tracking slots, and adding a native Cocoa display plus code-signing and notarization paths. The unified platform replaces the previous split between Goldfish and Cuttlefish, cutting maintenance and infrastructure overhead and letting developers and CI use the same high-fidelity virtual devices. Meta is releasing a QEMU fork (qemu-wearables) and Cuttlefish patch set (cuttlefish-wearables) and is exploring upstreaming the changes.
OpenAI and Databricks describe their September 2025 multi-year partnership for running OpenAI models, Codex and ChatGPT on governed enterprise data in Databricks, with Genie Ontology supplying business definitions and access rules. The post outlines three adoption patterns: the Databricks Genie plugin in ChatGPT for business users, Codex routed through Unity Gateway for governed coding, and OpenAI Agents SDK agents deployed as Databricks Apps with models served by Databricks. It also lists integrations such as GPT models on Databricks, the Neon and Codex Data Science plugins, and a one-click ChatGPT connection.
Langfuse AI Gateway is a standalone Rust gateway that exposes OpenAI and Anthropic native APIs under /openai/v1 and /anthropic/v1 namespaces, relaying provider JSON or SSE without rewriting bytes while capturing each finalized generation and uploading it to Langfuse via OTLP. It resolves gateway keys to trusted provider connections through Web, adds request IDs and trace/observation IDs to responses, and emits operational OpenTelemetry traces and metrics. Configuration is environment-based, with pnpm dev or cargo run for local use and no real provider credentials needed for building and testing.
Databricks announced that Genie Migrate for Lakehouse, a coding agent built on Genie Code, is now generally available and converts SQL from seven data warehouses—Microsoft SQL Server, Azure Synapse, Teradata, Oracle, Snowflake, Amazon Redshift, and Google BigQuery—to open ANSI SQL. It organizes migrations into projects with lineage review, parallel conversion, and side-by-side comparison, and converted more than 90% of files correctly on benchmarks for all seven sources. MIXI reported that about 85% of tested BigQuery files ran without manual edits, with the rest needing only one-line fixes.
This repository hosts the nil-3b-q4_k_m.gguf file for running the NIL-3B project locally with a GGUF-compatible inference runtime, via a chat script that expects the file in the project root. It also documents an uploader script that uses a Hugging Face token and repo ID from a .env file to create the model repository if needed and upload the GGUF file. The root LICENSE covers only the project source code and does not by itself confirm license or redistribution rights for the model weights or training data.
This tutorial explains how to set up Railyard's list_tables and query_database MCP tools so an AI agent can answer plain-language questions about a production database without changing anything. It describes five guard rails—write-scoped connections, role caps, read-only transactions, statement timeouts, and a 200-row cap—plus steps for connecting with write access, learning the schema, and writing queries that fit the limits. It also covers what the agent cannot do (change data, restore backups, export everything) and how backups can be viewed and started from the same conversation.
llama.cpp released libraries v1.0.0, marking a commitment to stable public APIs for downstream developers, with both llama.cpp and ggml now reflecting API and ABI incompatibilities through major-version bumps. The project states it supports more than 150 architecture variants and all mainstream speculative decoding methods, and distributes pre-release artifacts across Linux, macOS, Windows, Android and iOS plus channels including Docker, Homebrew, conda-forge, MacPorts, Winget and XCFramework. It also describes continued investment in CI infrastructure and self-hosted runners with hardware support from NVIDIA, AMD, Qualcomm, Arm, IBM and Intel.
Why it matters: Signals a versioning milestone for a widely depended-on local inference library, useful for developers tracking API stability and platform support.
This newsletter case study profiles Tarik Guney, a Principal Software Engineering Manager on Microsoft Teams, describing his systems-thinking approach to finding bottlenecks through leading and lagging indicators and his focus on culture, goals, and tooling to multiply his team. Guney uses AI as a practical layer on existing workflows for code generation, faster information access, meeting summaries, tailored communications, context reconstruction, drafting, solution exploration, and routine task automation, while stressing that AI does not replace judgment. He advises starting small, integrating AI into existing workflows, focusing on outcomes rather than tools, and using AI where it meaningfully improves how teams work together.
Cloudflare has launched Spend Management in Beta for pay-as-you-go customers, starting with Workers and Durable Objects, letting users set spending thresholds and have Cloudflare pause activity when limits are reached. Limits can be configured via the dashboard, cf CLI, or REST API, with email alerts at chosen thresholds and near real-time (under 10 minutes) cost visibility. Enforcement is asynchronous, propagating caps through Quicksilver so requests never wait on the Rating Engine, and more products plus application- and agent-based budgets are planned.
CloudHike is a backend platform offering Postgres, auth, S3-compatible storage, Redis, email, background jobs, functions, 100+ AI models via an OpenAI-compatible API, and Linux cloud computers for agents. It connects to AI editors such as Cursor, Claude Code, Codex, and VS Code through MCP so agents can create projects and deploy services from a conversation. Pricing starts with a free plan including $10 in signup credits, Pro at $10/month, and Scale at $29.99/month.
A Medium article by Mir Arshad Talpur discusses how to build reliable agentic AI systems for vertical AI companies. The post was shared on Hacker News, where it received 1 point and no comments.
Google Cloud has released TPU Sync, an Apache-2.0 C++ library with Python wheels for PyTorch and JAX that moves KV caches and model weights across TPU HBM, pinned host DRAM, shared storage and the data center network using layout-preserving asynchronous DMA. Google reports over 90% lower KV transfer latency on 235B to 480B parameter MoE models, with Qwen3 235B median KV transfer latency dropping from several seconds to about 230 ms in internal tests on TPU v7x. The README says it is not yet recommended for general use, and the RL weight-broadcast and storage tiering features are experimental.
Moving compute closer to memory can cut data movement, power consumption and latency, but different AI workloads need different approaches to memory architecture, interconnects and processing. Software support, standardization and thermal management remain major barriers to wider adoption, with processing-in-memory and near-memory processing still early despite efforts such as LPDDR6-PIM and compute on custom HBM base dies. The article surveys industry views on how edge inference, where models are known ahead of time, may benefit from ahead-of-time compilation and near-data compute.
This guide compares AI game makers, which build a playable browser game from a text description with no coding, against traditional engines like Unity, Unreal, Godot, GameMaker and GDevelop that require you to build the game yourself. It covers differences in control, cost, publishing and limitations, then walks through making a first game without code using Manus Game Dev, and lists signs it's time to switch to an engine.
A step-by-step guide to building a simple mobile game without code: choose a publishing route (browser link, app store, or game platform), design around one touch control, write a one-page plan, and pick a no-code or AI game maker such as Manus Game Dev, GDevelop, Construct 3, or Astrocade. It covers building a minimal first version, testing on a real phone, and publishing, with cost breakdowns including Google Play's $25 one-time fee and Apple's $99/year membership.
n8n 2.47.0 introduces per-API-key public API rate limits, available on all plans for Cloud and self-hosted, with separate read and write limits across all endpoints and per endpoint. Clients receive 429 responses with Retry-After and rate-limit headers, and in multi-main setups every main shares one count per API key through Redis. Rate limiting is off by default and can be enabled via Settings > Policies > Public API Limits, the settings API, or environment variables, and works alongside idempotency keys (since 2.45.0) to make retries safe.
Agent-brain is a personal operating model for AI coding agents (Claude Code, OpenCode, Codex, Antigravity CLI) that provides a second-brain knowledge structure (journal, WIP, memory, tasks) built on top of any folder of notes, with Obsidian as one option rather than a requirement. It keeps runtime config and memory in a git-tracked brain symlinked into each runtime, adds a session lifecycle driven by the brain skill, and offers an opt-in boyscout skill for routing improvement opportunities to a remediation workflow or backlog. It installs via a curl bootstrap script that dry-runs by default and requires Python 3.x, git, and at least one supported agent runtime.
Anthropic told investors ahead of its proposed IPO that its Mythos model wrote or ported nearly all the necessary kernels to get Claude running on AMD MI355X GPUs in about three days with basically no human intervention. The roughly three-day timeline covers bringing up prompt processing for one model at a useful performance level, not a full migration of Claude's training and operations. Anthropic says its architecture spans Nvidia GPUs, AMD GPUs, AWS Trainium and Google TPUs, treated as a shared compute pool.
Runitlocal.io is a site about running AI on your own hardware, presented by people who run it locally. It probes your GPU in the browser without sending that data to the site, and notes that speeds are estimates unless marked as measured; users can send in their own measured results.
MIT Lincoln Laboratory announced that three of its technologies are among the 2026 R&D 100 Awards recipients: the Magnetite secure-by-design operating system for embedded computers, the Quantum Microsystems wafer-scale platform integrating superconductor and semiconductor chips, and an algorithm that cancels 5G base-station interference on co-channel RF devices. The awards, managed by R&D World and judged by an independent panel, recognize 100 innovations transitioned to use or made available for license in the past year. Laboratory director Melissa Choi said the technologies address critical problems in cybersecurity, quantum computing, and communications.
MIT researchers developed a human-in-the-loop topology optimization system that presents regions of an algorithmic design to a human designer for feedback as it is generated, using machine learning trained on synthetic human-feedback data to identify likely regions of interest. In demonstrations, the approach improved manufacturability and linear buckling load by 39 percent while adding only 15 seconds to total design time compared to all-algorithmic approaches. The system currently only lets users make structures within identified regions thicker or thinner, and the researchers hope to add other modification options in future versions.
FloxoLab's AI Crawler Access Checker takes a URL, reads its robots.txt under RFC 9309, requests the page with the user agents of 14 AI crawlers, and compares each response with a normal browser request to give a per-crawler verdict. It also reports page directives, text available without JavaScript, and discovery files such as /llms.txt and the sitemap, while noting limits like vendor IP verification and bot challenges returned with HTTP 200. The tool is free with no account and allows 8 checks per 10 minutes.
Codex 0.169.0 adds multiple side-chat tabs in the opt-in terminal workspace, task history search from the agent command center, collapsible turn details in the full-screen transcript, and custom model provider editing from the terminal. It also adds Ctrl+S local diagnostic ZIP saves, opt-in Cloud connection features on macOS and Linux, and fixes including explicit project trust for non-interactive runs and Windows sandbox handling for quoted cmd.exe paths.