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Only First party#Tutorials
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. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

Nov 13Fri
  1. 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.

  2. 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.

  3. Manus Blog28

    50 Creative Game Ideas to Kickstart Your Next Project

    This guide presents 50 game concepts grouped by genre and tagged Easy, Medium, or Hard, each with a clear mechanic and twist, plus a starter prompt for an AI game maker. It explains what makes a good game idea using four questions (goal, obstacles, fail state, actions), offers tips for generating original ideas, and walks through building a browser game in Manus without writing code.

  4. OpenRouter Announcements60

    Reranking in RAG: How to Fix Bad Retrieval with a Reranker

    This guide explains that vector search often ranks near-miss chunks first because embeddings compress whole chunks into single vectors, and shows how adding a cross-encoder reranker over the top 30 candidates raised precision@1 from 81% to 97% on a 274-question synthetic benchmark at roughly $0.10 per 1,000 queries. It compares 11 rerankers (voyageai/rerank-3-lite and rerank-3 were the most accurate dedicated models and cheapest, while LLM rerankers like openai/gpt-6-luna hit 99.6% but took about 4 seconds per call), covers candidate-count tuning, input_type settings, and silent failures such as cohere/rerank-v3.5's 4,096-token limit. It includes a Python example using the embeddings and rerank APIs and notes that real-world gains depend on your own documents and evaluation set.

    Why it matters: Offers a measured, benchmark-backed walkthrough of adding reranking to a RAG pipeline, including cost, latency and failure-mode comparisons useful for teams tuning retrieval.

  6. ElevenLabs Blog22

    7 customer service skills every human and AI agent needs (with examples)

    This guide outlines seven customer service skills—communication, problem-solving, conflict resolution, empathy, adaptability, personalization, and product knowledge—and explains how both human and AI agents apply each, citing a Zendesk study that more than 50% of customers switched to competitors after a single bad experience. It describes implementation steps including defining CX goals, choosing an agentic AI platform, architecting the agent workflow, and testing before deployment, and notes Gartner's projection that at least 70% of customers will start their customer journey using an AI interface by 2028. It also covers training and feedback through metrics such as CSAT, first contact resolution, and average handle time, and distinguishes hard from soft skills.

  7. 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.

  8. GitHub Blog · AI & ML58

    Build once, run on any agent: A practical guide to agent skills

    GitHub's guide explains agent skills as versioned directories centered on a SKILL.md file that capture repeatable task instructions shareable across agents, while the environment still supplies tools, credentials, and permissions. Using examples like Proof Agent for adversarial verification and Data Forge for a multi-skill data pipeline, it recommends keeping project configuration in a manifest, sharing execution paths between skills, and placing human checkpoints and CI checks in workflows. It also covers packaging skills for clients such as Claude Code, Cursor, and GitHub Copilot, and notes that applications suit workflows needing stable interfaces, shared state, audit trails, or permissions enforced outside the model.

  9. 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.

  11. 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.

  12. Google Developers Blog54

    Catch Go bugs that fuzzing misses with Gemini and a constraint solver

    The author describes a workflow that compiles Go functions to RISC-V assembly, has Gemini 4 Argon translate the surviving runtime.panicBounds traps into Z3 constraints, and replays any satisfying input through go test to produce a real reproducer. The pipeline caught three planted bugs and three latent bugs in skill-lint, and the post explains why go test -fuzz missed two crashes because fuzz inputs usually have spare slice capacity, fixed by clipping capacity to length with a full slice expression. It also notes that an unsat result only means "maybe correct," since the LLM-written solver model is not a sound proof system.

  13. AWS Machine Learning Blog36

    Tokenomics at scale: Track and cap Amazon Bedrock spend per IAM principal with CUR 2.0 and AWS Budgets

    This AWS post describes how to get per-identity Amazon Bedrock cost visibility and enforceable spend limits using only AWS-native features: IAM principal and session tags for attribution, Cost Explorer or CUR 2.0 exports (with the caller identity option) for reporting, and AWS Budgets with budget actions that attach a Deny IAM policy when a threshold is crossed. It notes that budget data refreshes up to three times a day, cost allocation tags are not retroactive, and a Deny action on a shared role blocks everyone using it.

  14. Databricks Blog28

    Eclipse Aerospace simplifies flight-data ingestion with Databricks

    Eclipse Aerospace worked with Databricks to replace a multi-stage AWS Lambda and Avro pipeline with an Arrow-native PySpark custom data source that wraps its existing Rust flight-data decoder, reading proprietary telemetry files directly in Spark on serverless compute. The approach, enabled by the Python Data Source API available from Databricks Runtime 15.4 LTS, cut latency, removed duplicate Avro storage and a Lambda tier, and brought decoding into the Spark UI and job metrics. Eclipse reports the connector can process records within a minute of upload and commonly handle multi-gigabyte batches in parallel in under two minutes.

  15. Databricks Blog22

    How Constellation is building a governed data foundation on Databricks

    Constellation, the largest US nuclear fleet operator, is building a governed Enterprise Data Platform on Databricks to give data consistent shared meaning across domains, choosing Databricks partly because it interoperates with existing architecture rather than requiring a rip-and-replace. The platform rests on conformed semantic definitions, a semantic knowledge layer with selective Data Vault modeling, and Unity Catalog for lineage, fine-grained access and ownership of meaning, with early use cases including a nuclear engineering assistant built with Databricks Genie, outage planning, and sales intelligence.

  16. AWS Machine Learning Blog34

    Building a self-service LLM gateway with Amazon Bedrock and LiteLLM

    This post describes a pattern for a self-service LLM gateway that runs LiteLLM on AWS Fargate to expose Amazon Bedrock models through an OpenAI-compatible API, using Bedrock Application Inference Profiles for per-model quota isolation and scoped IAM access. It covers configuration of developer tools, LiteLLM model mapping with per-token costs for budget enforcement, CDK constructs for inference profiles, Langfuse observability, CloudWatch TPM alarms, and lessons learned from scaling to several hundred developers.

  17. AWS Machine Learning Blog34

    Optimizing cost per task and proving agent ROI on Amazon Bedrock

    This AWS post argues that model cost should be measured per completed task rather than per token, citing four hidden cost drivers—tokenizer efficiency, output verbosity, agentic loop efficiency, and reasoning overhead—and an illustrative example where a model with double the list price costs 65% less per task. It describes levers for reducing cost per task (prompt caching, intelligent prompt routing, output constraints, batch inference) and Bedrock features such as CountTokens, the usage block, and invocation logging for measuring token and turn counts. For ROI, it recommends documenting and measuring a human baseline before launch, instrumenting workflows as experiments with run records, joining cost to outcomes via Athena, and using a five-dimension scorecard with green/yellow/red thresholds.

  18. Wiz Research22

    ASPM in the AI Era Part 3: Fixing Risk at the Speed of Code

    The final installment of Wiz's series argues that AppSec's bottleneck is remediation, not detection, citing Verizon DBIR data that median patch time has risen to 47 days from 32. It describes how Wiz Code maps cloud findings to code owners via VCS connections and correlated data, and how the Wiz Green Agent generates tailored remediation plans that can open pull requests or hand off to coding agents like Claude Code. Wiz Workflows then routes tickets and alerts to Jira, ServiceNow, Slack, or Teams with full scanner and runtime context.

  19. Databricks Blog25

    How H6.ai builds AI-powered financial automation with Lakebase Postgres

    H6.ai automates accounts payable on top of customers' existing ERPs by combining AI-assisted invoice understanding with deterministic, auditable policy execution. To make regression testing reliable despite constantly changing financial data, it gives each customer a dedicated Lakebase Postgres database and uses copy-on-write branches to freeze snapshots, run each test on its own disposable branch, and judge results with deterministic assertions plus a panel of AI judges. The company says branching provides isolation, frozen state, cheap disposable copies and schema-safe replays, and it plans to use Lakehouse//RT queries on Lakebase tables for read-only analytics without copying data.

  20. Databricks Blog58

    How Block powers collaborative AI agents with Buzz and Unity Gateway

    Block developed Buzz, an open source, model-agnostic collaboration space where employees and AI agents work together in group discussions, focused conversations, and one-on-one exchanges, with users choosing the model or coding harness behind each agent. Databricks Unity Gateway provides the model access layer, letting Buzz users select Databricks as their inference provider and choose from frontier models from OpenAI, Anthropic, and Google plus select open-weight models through a single integration. Block has also been experimenting with multi-agent workflows, such as a more capable model coordinating agents powered by smaller, more efficient models, and developed an internal benchmarking approach for these agent teams.

  21. PyTorch Blog38

    Fast by Default: Compile-Time Heuristics for Helion Kernels

    Helion now uses compile-time heuristics that analyze a kernel's IR and dataflow to classify it as matmul, reduction, or pointwise and emit block sizes, warps, and stages without autotuning. On H100, the computed configs reach 1.18x hand-written FLA Triton for linear attention, 1.27x vLLM's handwritten CUDA reduction kernels, and 1.09x torch.compile max-autotune on a general reduction suite. The heuristics are merged into Helion and enabled by default on H100 and B200 with the Triton backend.

  22. AWS Machine Learning Blog36

    From prototype to production: Engineering agents with Agent DLC

    AWS authors propose the Agent Development Lifecycle (Agent DLC), which ties six activities—Define, Build, Evaluate, Release, Observe, and Feed Back—to an explicit "gold standard" of criteria, thresholds, and approved examples, with an inner loop (score, inspect the path, change one thing, re-evaluate) and an outer loop that updates the standard from production feedback. A reference HR policy assistant shows chunk-level retrieval precision of 1.00 on all three test questions while fact-level precision was 0.59, 0.53, and 0.09, and a prompt-only change improved one question but worsened another, which the authors attribute to missing source coverage. The post also describes a benefits-enrollment answer that scored 0.90 groundedness yet made a false claim about the user's contact preference, and outlines a one-week AWS getting-started plan using Amazon Bedrock AgentCore Evaluations and Observability.

  23. AWS Machine Learning Blog48

    Fine-grained access control for Amazon Bedrock Managed Knowledge Base tools

    This post describes an architecture in which all employees share one agent and knowledge base, but each user can search only the documents they are allowed to view, using Amazon Bedrock Managed Knowledge Base ACL awareness exposed as MCP tools through Amazon Bedrock AgentCore Gateway. The design keeps the LLM from deciding who the user is: a request interceptor injects the verified email from the JWT into userContext, Policy in AgentCore checks it against the token's email claim, and a resource-based policy denies searches from principals other than the gateway execution role. It also covers prerequisites, ACL metadata files, tool schema configuration with parameterOverrides, and a verification script that tests each defense.

  24. AWS Machine Learning Blog52

    Build composable, interoperable multi-agent systems with A2A on AWS

    This AWS post presents a deployed reference architecture in which a LangChain-based enterprise orchestrator in one AWS account calls a Strands Agents-based compliance SaaS agent in another account using the Agent-to-Agent (A2A) protocol, with neither side depending on the other's framework or SDK. It covers cross-framework interoperability, bidirectional agent-to-agent callbacks, and OAuth authentication in both directions using separate Amazon Cognito user pools, with deployment from three Jupyter notebooks and an end-to-end test.

  25. AWS Machine Learning Blog34

    Migrate to Amazon Bedrock Managed Knowledge Base with your coding agent

    AWS added a knowledge base migration guide to the amazon-bedrock skill in the Agent Toolkit for AWS, which lets a coding agent plan a move from a customer-managed knowledge base to Amazon Bedrock Managed Knowledge Base, starting with read-only discovery and a plan you review line by line. On approval, the agent creates the new knowledge base and data sources alongside the original or writes a migrated infrastructure as code template, while ingestion and updating the application's Retrieve calls remain with your team. The post covers why AWS recommends the move, prerequisites, installation, planning, updating retrieval calls, and validating retrieval before cutover.