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Only First party#Deployment
Nov 15Sun
  1. Browser Use Blog42

    Log In Once: Reuse Cookies and Profiles Across Browser Use Runs

    This tutorial shows how to create an empty Browser Use profile, log in with it attached, stop the browser to save its state, and pass the same profile ID to a second run that reaches a protected page without credentials. It also demonstrates saving and reloading Playwright's storage_state file locally, plus handling expired cookies, keeping per-user profiles separate, and troubleshooting. The demo uses The Internet's public test account, and the author notes that the website still decides how long a login remains valid.

Nov 14Sat
  1. ElevenLabs Blog22

    Open source TTS vs paid APIs: How to choose wisely

    This article compares open source and paid text-to-speech APIs, noting that both can produce high-quality speech and that the choice depends on team capacity and workload scale. It explains that self-hosting offers control and data sovereignty at the cost of infrastructure and engineering overhead, while managed APIs provide turnkey, scalable access with per-character pricing. It also distinguishes open weight from open source models, covers licensing considerations, and offers criteria for evaluating voice quality, latency, reliability, and total cost of ownership.

  2. AWS Machine Learning Blog24

    Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1

    GS Caltex connects models built by process engineers in Amazon SageMaker Canvas to production workflows in Amazon SageMaker Pipelines, with ML engineers handling deployment and scheduled inference. The company automated the handoff so ML engineers now deploy models within a few hours instead of about a week, and it runs batch inference every 30 minutes, which it reports has meaningfully lowered inference costs. The post details the reference architecture, including reusing Data Wrangler preprocessing flows, registering and approving models in Model Registry, and scheduling runs with Amazon EventBridge.

  3. AWS Machine Learning Blog36

    OpenFold3 on Amazon SageMaker JumpStart: Predict biomolecular structures

    Amazon SageMaker JumpStart now lets you deploy OpenFold3-preview, an open source all-atom biomolecular structure prediction model from the OpenFold Consortium and Columbia University's AlQuraishi Lab, to predict 3D structures of proteins, DNA, RNA, small-molecule ligands and their complexes. The post walks through deploying it on an asynchronous inference endpoint, submitting prediction jobs, interpreting mmCIF outputs with pLDDT, PAE and PDE confidence metrics, running parallel predictions on multi-GPU instances, supplying your own MSAs, and scaling the endpoint to zero when idle.

  4. AWS Machine Learning Blog34

    Architecture patterns for fast session startup on Amazon Bedrock AgentCore runtime

    Amazon Bedrock AgentCore runtime's platformVersion "V2" snapshots a prepared container once at create or update time and restores it for each new session, making session starts faster and more consistent. The post explains how V2 starts sessions, how to measure session start time with CloudWatch metrics like OverheadLatency, and four patterns: moving initialization before app.run(), using build hooks for files the process can't hold, and pre-warming sessions on user engagement for sub-second starts.

  5. AWS Machine Learning Blog38

    Route coding agent requests to the right model on Amazon Bedrock with NVIDIA NeMo Switchyard

    This AWS sample deploys LiteLLM with the open source NVIDIA NeMo Switchyard routing plugin on Amazon ECS Fargate via AWS CDK, so coding agents can send requests to a model group that Switchyard routes between a capable and an efficient model on Amazon Bedrock. It covers Stage routing (which reads recent tool activity and results to pick between two candidates) and Random routing (weighted traffic splits for A/B tests), plus a benchmark runner that measures cost per solved task against single-model baselines. The post notes the sample uses switchyard-server 0.3.0 and LiteLLM v1.102.0, is for non-production use, and that its configuration and APIs may change between releases.

  6. NVIDIA Technical Blog31

    Faster Scientific Simulations with New FP64 Algorithms in NVIDIA cuBLAS

    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.

  7. NVIDIA Technical Blog22

    Run GPU-Powered Black Hole Interferometry with NVIDIA cuPhoton

    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.

  8. Cloudflare Blog71

    Kitesurf is now open source

    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.

  9. Pydantic AI Articles55

    Using coding agent traces to improve your workflows

    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.

  10. AWS Machine Learning Blog38

    Cost attribution and controls for AI developer and productivity tools on Amazon Bedrock

    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.

  11. AWS Machine Learning Blog22

    Cost-optimizing AssemblyAI speech-to-text inference on AWS with Amazon EC2 Spot

    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.

  12. Claude Blog62

    Building always-on agents

    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.

  13. NVIDIA Technical Blog22

    Run Native DLA Inference in Python on NVIDIA Jetson AGX Orin Using cuDLA

    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.

  14. Hugging Face Blog34

    Build a Robot Pick-and-Place Simulation with SimReady Assets

    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.

  15. AWS Machine Learning Blog51

    Build a model improvement flywheel with Amazon SageMaker HyperPod Data Capture

    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.

  16. Wiz Research28

    The Green Agent POV: Formulating the Fix for an Exposed Container with Sensitive Data Access

    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.

  17. NVIDIA Technical Blog38

    When One GPU Isn’t Enough: Serving GLM-5.3 Across Two NVIDIA DGX Stations with vLLM

    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.

  18. Meta Engineering39

    Cuttlefish on Mac: Running Android’s Virtual Device Framework Natively on Apple Silicon

    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.

  19. Databricks Blog58

    OpenAI and Databricks: Enterprise AI on your governed data

    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.

Nov 13Fri
  1. Databricks Blog50

    Migrate Effortlessly to the Databricks Lakehouse with Genie Migrate, now Generally Available

    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.

  2. Hugging Face Blog60

    llama.cpp v1.0.0 : A stronger foundation for the local AI ecosystem

    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.

  3. Cloudflare Blog40

    Introducing Spend Management: Spending limits and alerts for Cloudflare

    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.

  4. Manus Blog38

    AI Game Maker vs. Game Engine: What to Know Before You Choose

    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.

  5. Manus Blog36

    How to Make a Mobile Game Without Any Coding

    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.

  6. n8n Blog22

    New in n8n: Public API rate limits that keep every team on your instance running

    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.

  7. MIT News AI20

    Three Lincoln Laboratory technologies receive 2026 R&D 100 Awards

    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.

  8. MIT News AI34

    New AI “copilot” helps engineers design better structures

    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.

  9. Codex Releases22

    0.169.0

    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.

  10. Modal Blog42

    Sandbox resourcing: burst, don't reserve

    Modal argues that fixed-resource reservations fit poorly with bursty Sandbox workloads, since a typical Sandbox spends more than half its life using under 6% of its peak CPU, so users over-reserve and pay for peak constantly. Modal's Sandboxes now burst by default from a base request of 0.125 cores and 128 MiB, with active-resource pricing billed each second for what is used, and the company cites 58% savings versus a fixed-size reservation on an illustrative 20-minute coding agent task. It also describes an OOM SLO of below 0.0001% per minute for bursting Sandboxes under 8 GiB and tools like exit snapshots and the termination reasons SDK to reduce eviction impact.

  11. Codex Releases12

    0.171.0-alpha.1

    This GitHub Changelog entry is titled "0.171.0-alpha.1" and lists only the site's navigation and product menus, including Platform, Copilot, Actions, Codespaces, Secure Code, and Pricing. The supplied material contains no details about the release itself.

  12. Claude Code Releases32

    v2.1.323

    Claude Code v2.1.323 adds an experimental opt-in Code Mode in which Claude writes JavaScript to call its tools, plus plugin engine version ranges in plugin.json and TypeScript hook type checking in claude plugin validate. The release also adds .docx support to Artifact tool asset uploads and fixes a wide range of issues across permissions, sandboxing, sessions, MCP, OpenTelemetry and Remote Control.

  13. vLLM Blog72

    DeepSeek-V4.1-Flash on Vera Rubin NVL72: 58x Throughput vs. GB300 NVL72 at 387 tokens/s

    On a single NVIDIA Vera Rubin NVL72 rack, vLLM serves DeepSeek-V4.1-Flash at 1.7M tokens/s per GPU with 387 tokens/s per user, 58x GB300 NVL72, reaching 2.52M at peak throughput and 1,810 tokens/s for a single user. The gains come from kernels tuned for Rubin's split memory domains and FP4 tensor cores, specialized low-latency decode kernels, and a rack-wide KV cache and Engram tables shared over NVLink. The post details four optimization groups and reports a 96.5–97% prefix hit rate at high concurrency with workload-aware routing.

    Why it matters: It offers a detailed, measurement-backed look at how Rubin-specific memory and MMA features translate into serving throughput and latency for a frontier open model.

  14. Databricks Blog22

    Dispelling LTAP myths

    This post rebuts four common misconceptions about LTAP: that it is CDC replication, that a single copy forces slow lookups or scans, that open table formats mean eventual consistency, and that Postgres logical replication suffices. It argues Lakebase keeps one logical table with the lake as its durable tier and caches in front of it, so no layer adds a second catalog or source of truth. Each myth is traced to the same misunderstanding of treating every materialization of the data as a second database.

  15. AWS Machine Learning Blog47

    How Natera designed an agentic AI system for lab ops with Amazon Bedrock AgentCore

    Natera's lab engineering team built SWARM (Systematic Workflow Analytics and Resolutions Manager) on Amazon Bedrock AgentCore to capture and deploy institutional knowledge for lab instrument support, using two runtimes (swarm-agent and swarm-hive) that share a versioned library of 33 skills. The system reduced average first response time from 17.4 hours to roughly 8 minutes (about 130x faster) and delivered an automated first response on 96.2 percent of tickets, measured over a 90-day production evaluation period. The post details the architecture, including a custom document pipeline with Amazon Titan Text Embeddings and Amazon Bedrock Knowledge Bases, keyless OIDC-based deployment, Jira integration via EventBridge and Amazon SQS, and a continuous evaluation loop.

  16. AWS Machine Learning Blog58

    Deploy frontier AI models on AWS Trainium with SGLang-Neuron

    Amazon announced that SGLang-Neuron, released by RadixArk with Annapurna Labs, is publicly available through an AWS Deep Learning Container, letting users serve models and generate RL rollouts on Trainium via the same SGLang interface. The out-of-tree plugin runs on native PyTorch, is selected with SGLANG_PLATFORM=neuron, and supports PD disaggregation with KV-cache transfer via NIXL over EFA. The post includes a walkthrough on a Trainium3 EC2 instance using the pytorch-inference-sglang-neuronx container to serve openai/gpt-oss-120b.

  17. Codex Releases22

    0.168.0

    Codex 0.168.0 adds TUI model provider browsing and default selection for new chats, an opt-in side conversation pane (features.tui_workspace) with resizable and zoomable panes, audio attachments with rejection recovery, and opt-in animated GIF image generation. It also fixes account-scoped local history, keeps threads on their saved model provider when defaults change, and improves TUI rendering of interrupted replies and reasoning summaries.

  18. vLLM Blog60

    Inkling 2 Small on vLLM: Day-0 Support

    vLLM announces day-0 support for Thinking Machines' Inkling 2 Small, a multimodal text-and-vision MoE model with 594B total and 17B active parameters, up to 1M tokens of context, and a native DSpark speculator. The post details the architecture (48 layers, 384 experts with top-8 routing, block attention residuals, gated attention, a 23-layer ViT image encoder, and mixed NVFP4/MXFP8 expert precision) and vLLM's optimizations, including kernel fusion with PDL, fused MoE activation quantization, sequence parallelism for prefill, and a Gluon relative attention kernel, reaching up to 630 TPS/user at TP4 on GB200. It also covers LoRA and RL (via SkyRL) support, a serve command, agentic-workload benchmarks, and accuracy checks on NIAH up to 1M tokens, MMMU-Pro and HLE.

    Why it matters: Useful for engineers evaluating or deploying a large multimodal MoE model, as it pairs concrete architecture and quantization details with vLLM-specific kernels, benchmark numbers and a ready serve command.

  20. AWS Machine Learning Blog40

    Four Amazon Bedrock inference choices that affect cost

    The post explains how four request-level Amazon Bedrock settings—prompt cache duration, service tier, reasoning effort, and maxTokens—can change token processing and billing rates without altering the model or prompt. It reports tests showing that a 1-hour cache pays off from the second request per hour when gaps exceed 5 minutes, that Flex on Nova 2 Lite processed the same tokens at half the Standard rate in us-west-2, that lowering effort from high to low cut median output tokens by 169% on an agent test, and that a 155-token maxTokens fence avoided 1,893 output tokens in one test. It recommends confirming model support, changing one setting at a time, and verifying effects through response metadata, CloudWatch metrics, invocation logs, and billing data.