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Tutorials

Hands-on material you can use right away: prompting techniques, workflows, tool usage and lessons learned.

20 picksLast 30 days 17 itemsCollected 149 items

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Picks about Tutorials

Today10/17Sat
  1. AWS Machine Learning Blog60

    Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore

    AWS explains how its SMGS Operations team built a nine-stage evaluation framework with Amazon Bedrock AgentCore for an internal sales intelligence system. The framework combines domain-specific evaluators, versioned golden datasets, CI/CD gates, continuous monitoring, and trace-driven improvements across tools, agents, and multi-agent workflows. AWS reports that automated evaluation reduced quality regression detection from days or weeks to under four hours.

    Why it matters: The framework offers a transferable way to connect domain-specific failure categories with release gates and production monitoring across tools, individual agents, and multi-agent workflows.

10/16Fri
  1. AWS Machine Learning Blog73

    Introducing Reflection Beam, an open-weight model for agentic coding on AWS

    Reflection AI introduced Beam, an open-weight model for agentic coding and multi-step tasks, with 501 billion total parameters and 23 billion active parameters. Reflection reports an effective context length of 1 million tokens and releases Beam 1.0 under Apache 2.0 in BF16, FP8, and NVFP4 variants. The guide explains deployment through Amazon SageMaker JumpStart or HyperPod Inference with JumpStart, SGLang, or vLLM, including capacity planning, invocation, performance measurement, and cleanup.

    Why it matters: The deployment comparison clarifies when managed endpoints suffice and when Kubernetes infrastructure or custom runtimes offer useful control over serving configuration, distributed execution, and scaling.

  2. Hugging Face · X72

    Hugging Face announces livestream on how Reflection built Beam

    Hugging Face says a livestream with the Reflection team about how they built Beam starts in minutes. Stream: https://t.co/q6OKvCiB9o https://t.co/scNsrfsC3s In the quoted announcement, Reflection says Beam is available across platforms with open weights under Apache 2.0, in collaboration with 40+ partners, including a tokenizer, reference implementation, BF16, FP8 and NVFP4 weights, and a DFlash2 drafter.

    Why it matters: The listed artifacts and Apache 2.0 license provide concrete context for assessing how Beam could fit into an existing model deployment workflow.

  3. AWS Machine Learning Blog60

    Meta’s Muse Spark Open now available in Amazon SageMaker JumpStart

    Meta’s coding-optimized open-weight model Muse Spark Open is now available in Amazon SageMaker JumpStart in BF16 and NVFP4 variants, with a 1 million-token context window and 816B total parameters, including 42B active per token. AWS provides deployment instructions using SageMaker Studio or the SageMaker Python SDK, plus inference and cleanup examples. At launch, availability covers US East (N. Virginia) and US East (Ohio).

    Why it matters: The deployment walkthrough connects BF16 and NVFP4 memory requirements to supported instance types, giving teams a concrete basis for planning infrastructure when evaluating the model on their repositories.

10/15Thu
  1. AWS Machine Learning Blog64

    AWS explains Trust in Depth for giving agents autonomy within enforceable boundaries

    AWS’s Shawn Bice explains Trust in Depth, a framework for governing agents through four layers: Scope, Know, Bound, and Observe. He describes how AWS tools support policies, agent identities, external enforcement, and tracing, using employee offboarding to illustrate sequence-dependent safeguards. Automated reasoning can prove precisely stated rules within a system’s defined assumptions, while context-dependent expectations require evaluation and human judgment.

    Why it matters: The offboarding example offers a transferable method for translating business requirements into enforced prerequisites, showing how action order can matter as much as individual tool permissions.

10/14Wed
  1. 宝玉 · X81

    Why Anthropic built Scion to replace git for its monorepo

    Drawing on Anthropic’s engineering blog, Baoyu explains why the company replaced git with Scion as daily merged code changes grew from around 2,500 in May to more than 30,000 in September. Scion retrieves files on demand, moves operations to the server, and uses layered caches with hit rates above 99%. Anthropic reports that median agent workspace startup fell from 7.1 minutes to 5.1 seconds, while emphasizing that Scion serves exceptionally large monorepos and is not a general-purpose git replacement.

    Why it matters: The comparison illustrates how assuming continuous server access changes source control architecture, connecting on-demand file retrieval and server-side operations to the infrastructure demands of agent-generated code.

  2. 宝玉 · X76

    How Gemini 4 Argon Built a Japanese Temple Complex in Blender

    Baoyu describes a demonstration in which Gemini 4 Argon used Antigravity to build a Japanese temple complex in Blender from one prompt containing a detailed workflow. The prompt referenced a scene-building skill document and requested architectural plans, four Nano Banana reference images, and coordinated agents that checked renders against those references and iteratively revised the scene. The demonstrator requested animation and programmatically generated bell sounds and ambient audio the next day, making those later additions to the initial modeling workflow.

    Why it matters: The workflow offers a transferable approach to complex 3D generation by combining construction rules, visual references, agent coordination, and repeated comparison of rendered results.

  3. GitHub Blog · AI & ML68

    GitHub’s model for secret detection: Finding more secrets with less noise

    GitHub explains how its secret scanning pipeline combines regex candidate detection with a fine-tuned ModernBERT classifier to distinguish real credentials from lookalikes using surrounding context. Compared with the previous LLM pipeline, offline evaluation showed 13% higher precision and 19% higher recall; a customer A/B experiment showed 11.5% higher repository owner-weighted acceptance. Median post-push scan execution time fell 86%, and serving costs dropped more than 98%, making it practical to extend generic secret detection to push protection.

    Why it matters: GitHub’s pipeline illustrates a transferable design pattern for narrow classification tasks, combining deterministic candidate generation with a compact contextual model trained on realistic hard negatives.

  4. AWS Machine Learning Blog63

    Natural language security queries at Orca Security using Amazon Bedrock

    Orca Security partnered with AWS to build Discovery AI on Amazon Bedrock, translating natural language questions into validated Sonar queries across 900+ cloud infrastructure and application models. The architecture combines example retrieval and reranking, relevant schema selection, agentic generation, and validation, with domain experts continuously updating the example bank. Compared with its initial baseline, Orca reports 90 percent lower costs, 93 percent fewer input tokens, and 75 percent lower latency, with response times falling from approximately 60 seconds to 15 seconds.

    Why it matters: The architecture offers a transferable approach to querying large schemas by retrieving validated examples, selecting relevant schema fragments, and feeding validation errors back into generation.

10/13Tue
  1. AWS Machine Learning Blog63

    Mixture-of-Experts kernel engineering on Amazon SageMaker AI

    AWS explains three fused MoE optimizations used in Amazon SageMaker AI, reporting up to 10 percent higher throughput and up to 9 percent faster Time to First Token (TTFT). On H200, swapping matrix operands improved throughput by 2–4 percent, while routing FP8 tensor-quantized models to Triton improved throughput by 10 percent. Eliminating unnecessary Triton JIT recompilation improved TTFT by 9 percent at 8-way tensor parallelism; the post identifies availability in vLLM and the AWS LMI v28 container.

    Why it matters: The optimization examples offer a transferable approach to MoE serving performance by examining matrix layout, backend selection, and compiler specialization as separate sources of throughput and latency overhead.

  2. AWS Machine Learning Blog75

    Build long-horizon agents with Grok 4.7 on Amazon Bedrock

    AWS demonstrates how to build and evaluate a tool-calling agent using Grok 4.7 on Amazon Bedrock, comparing four reasoning effort levels across three synthetic tasks. The agent answered 67 of 72 runs correctly; xhigh achieved 18 of 18, while low achieved 15 of 18 using about an eighth of the output tokens. The guide covers task design, automated grading, production monitoring, and operational controls, noting that these small-scale results are indicative rather than statistically conclusive.

    Why it matters: Tasks with provably unique answers and repeated runs provide a reusable way to compare reasoning effort, while separate correctness and token measurements make the trade-off concrete.

  3. AWS Machine Learning Blog67

    Guide to accessing OpenAI GPT-6 models on Amazon Bedrock with global cross-Region inference

    AWS explains how to invoke OpenAI GPT-6 Sol and GPT-6 Luna through Amazon Bedrock global cross-Region inference from Thailand, Malaysia, Singapore, Indonesia, and Taiwan, and GPT-6 Astra from Singapore. The guide covers Converse, OpenAI-compatible APIs, coding clients, IAM permissions, SCP exceptions, quotas, and monitoring. Billing and quota tracking remain in the source Region, while requests run in supported destination Regions; each output token consumes 10 tokens of quota.

    Why it matters: The guide provides a transferable approach to configuring cross-Region inference permissions by scoping SCP exceptions to inference profiles while preserving Region controls for other services.

  4. Perplexity · X67

    How Perplexity built the Projects filesystem as a Git forge for agents

    This is how Perplexity built the filesystem behind Perplexity Projects as a Git forge for agents. Each agent works in its own sandbox and commits to a shared repository where people and other agents can build on its work across sessions. Read more: https://t.co/jcrTptb2IW https://t.co/dXnhroO4mg

    Why it matters: The shared repository provides a concrete example of how work from separate agent sandboxes can remain available for people and other agents across sessions.

  5. AWS Machine Learning Blog66

    Run multi-node inference workloads using Ray LLM Deep Learning Containers

    AWS shows how to deploy Qwen3.5-9B across two GPU nodes on Amazon EKS using Ray LLM Deep Learning Containers, with separate prefill and decode deployments behind one OpenAI-compatible endpoint. The walkthrough configures the application in YAML and uses NIXL over EFA to transfer the KV cache between nodes. It covers cluster provisioning, model invocation, transport verification, and resource cleanup.

    Why it matters: The guide provides a transferable deployment pattern for separating prefill and decode, including how to verify the transport backend and avoid a silent fallback to TCP-speed communication.

  6. AWS Machine Learning Blog61

    Deploy decision models with llama.cpp on Amazon SageMaker AI

    AWS explains how to deploy GGUF models with its llama.cpp Deep Learning Container on Amazon SageMaker AI, then add a SemIf adapter that returns probabilities over declared options without generating answer tokens. The guide includes CPU and GPU deployment code. Across 231 JevBench tasks using Qwen3.5-4B Q4_K_M, the GPU configuration delivered 4.9 times faster p50 and 32.2 times faster p95 latency than the CPU configuration, with similar accuracy.

    Why it matters: The deployment pattern shows how applications can score predefined choices without parsing generated prose, while keeping authorization, thresholds, and fallback rules under application control.

10/9Fri
  1. Hugging Face Blog73

    Hugging Face author trains six models in two days with ML-intern, spending about $103 in total

    The author used ML-intern in HuggingChat, describing the requirements in prompts, to build six models in two days and publish them to the Hub, at a total compute cost of about $103. One project distilled the 9B prompt rewriter for Qwen-Image 2.1 into a 0.8B version that runs on a CPU, with a 99.7% valid-output rate and about one-quarter of the teacher model's token usage. The author published all seven prompts and recommends asking for baseline comparisons, smoke tests, and a budget cap.

    Why it matters: The author built six models in two days with ML-intern and published all prompts and cost details, offering an approach others can adapt to their own training workflows.

10/7Wed
  1. AWS Machine Learning Blog60

    How to build a context-aware AI assistant on AgentCore and OpenClaw

    An AWS blog post explains how to run the open-source agent system OpenClaw on the Amazon Bedrock AgentCore runtime to build Sprout, a personal assistant with long-term memory.

    Why it matters: The source provides reusable methods for container wrapping, namespaces, and prompt-cache ordering, helping readers build an agent with long-term memory.

9/17Thu
  1. GitHub Blog · AI & ML78

    GitHub uses Copilot to rewrite the Copilot runtime in Rust

    Using the GitHub Copilot app and Copilot CLI, GitHub completely rewrote the Copilot agent runtime from TypeScript into more than 800,000 lines of production Rust. AI agents wrote most of the code, merged incrementally into main across 128 pull requests rather than in a single cutover.

    Why it matters: Drawing on firsthand experience, the author reviews the 800,000-line Rust rewrite and shares agent-orchestration and review practices that can be reused.

8/14Fri
  1. Hugging Face Blog62

    Closing the recording-to-training-to-deployment loop with Strands Agents, LeRobot, and Hugging Face Storage Buckets

    A Hugging Face blog post presents a streaming-data loop with Strands Robots: the same `Robot()` records a LeRobotDataset and syncs it to a Storage Bucket, then `stream_dataset()` reads directly from the Hub for training without downloading the full dataset first. To deploy a trained checkpoint back to hardware, users only need to switch the mode to `real`.

    Why it matters: The Strands Robots example breaks down a robotics data loop and shows how recording, streaming training, and deployment can share one LeRobot format.

3/11Wed