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#Tutorials and practice

All collected and published items, ordered by time. Picks are a selected subset.
10/7Wed
  1. Boris Cherny · X24

    I asked Opus 5.5 to make an interactive website companion for the latest @AcquiredFM Home Depot episode. It came out pretty nice! Water color illustrations all drawn by Claude, too: https://claude.ai/artifact/RdE6uE7WozcP2MLfx3BDky Episode here: https://www.acquired.fm/episodes/home-depot

    I asked Opus 5.5 to make an interactive website companion for the latest @AcquiredFM Home Depot episode. It came out pretty nice! Water color illustrations all drawn by Claude, too: https://claude.ai/artifact/RdE6uE7WozcP2MLfx3BDky Episode here: https://www.acquired.fm/episodes/home-depot

  3. AWS Machine Learning Blog22

    Management and governance best practices for Amazon SageMaker HyperPod

    Amazon SageMaker HyperPod can connect clusters to projects through SageMaker Unified Studio, letting team members launch training and fine-tuning workloads in workspaces while retaining governance controls at the infrastructure level. The article proposes a four-layer control model—organization, project, cluster, and workload—and explains how to design identity, capacity, and observability policies across those layers. Infrastructure teams retain control of cluster operations while ML teams use approved compute in project contexts.

  4. Simon Willison22

    Simon Willison tests Claude Opus 5.5's musical composition: generating playable text music

    Simon Willison asked Claude Opus 5.5 to design a simple text-based music format and build a playable artifact at the level of the original The Secret of Monkey Island soundtrack. The result was more evocative of Monkey Island than expected and surprisingly good. He speculates that a text model's ability to compose music may be an emergent capability, like 3D graphics in the past, that appeared only in recent months, but says more experiments with new and older models are needed to confirm it.

10/6Tue
10/3Sat
  4. AWS Machine Learning Blog38

    Use Amazon Quick and the Adjudicated Query pattern to check thousands of leases for compliance

    AWS proposed the Adjudicated Query design pattern, in which business users ask compliance questions in the Amazon Quick chat interface while a deterministic rules engine makes the actual decision. An integrity receipt ensures that the sum of compliant, noncompliant, ambiguous, and unreadable records equals the total scanned, preventing records from being silently skipped. The pattern applies to high-risk cases such as lease compliance, sanctions screening, and insurance claims.

10/2Fri
  1. GitHub Blog · AI & ML22

    AI is changing how developers work; GitHub highlights three skills to strengthen

    GitHub says developers need to strengthen three skills in the AI era: learn to direct AI agents rather than merely use them, do not trust an AI's first answer, and use time saved with AI to tackle bigger problems. It points to GitHub Copilot's built-in Rubber Duck agent, which uses a second model to critically review plans, code, and tests and catch issues the first model missed.

  2. Andrej Karpathy · X62

    Karpathy shares four prompting techniques for understanding model outputs

    Andrej Karpathy shared several ways to ask LLMs to make their outputs easier to understand: write using the ASD-STE100 controlled-language standard or relax it to 80%; generate diagrams; output an interactive webpage in HTML; or make a custom explainer video, for example with voiceover using an ElevenLabs API key. He thinks that as models improve, more work will move up to supervision and understanding, and models will be able to create large custom software artifacts that previously were not worth making.

  4. AWS Machine Learning Blog32

    How Amazon Payments uses contextual multi-armed bandits on Amazon SageMaker AI to improve acquisition funnel conversion

    Amazon Payments used a multi-objective contextual multi-armed bandit (LinUCB) on Amazon SageMaker AI to personalize content in its product-acquisition funnel. In a seven-week online A/B test, one audience segment achieved a high-single-digit relative increase in final funnel conversion, while another showed no improvement. The team ran one LinUCB model for each stage—application start, submission, and approval—and linearly combined their UCB scores to optimize the full funnel. It open-sourced a repository that can be tried with synthetic data.

  6. Simon Willison32

    pwasm 0.2a0 released: Claude Opus 5.5 improves pure-Python WebAssembly engine

    Simon Willison's pure-Python WebAssembly engine pwasm reached version 0.2a0 after Claude Opus 5.5 took over and made 42 commits. It now supports almost the entire WASM specification. The PyPI wheel bundles working WASM builds of MicroPython, QuickJS, and Micro QuickJS. This release is still alpha, and the author says it is completely untrusted.

  7. AWS Machine Learning Blog36

    How to configure multi-environment access to Claude Platform on AWS

    A complete implementation for Claude Platform on AWS (CPonAWS) lets production AWS workloads, developer laptops, and external CI/CD systems share one subscription while isolating production and development traffic through workspaces. It uses three access paths: a dedicated AI Services account with cross-account SigV4, workspace-level API keys, and OIDC federation. External environments use short-lived credentials and require no persistent keys.

  8. AWS Machine Learning Blog34

    Build ambient agents with Amazon Bedrock AgentCore, from event-driven signals to human-in-the-loop workflows

    Amazon Bedrock AgentCore supports building 'ambient agents' that listen to event streams such as Amazon S3 event notifications, EventBridge rules, and Lambda triggers and perform tasks automatically. When needed, they pause for human approval through a single ask_human tool and resume from where they stopped.

10/1Thu
9/30Wed
  4. AWS Machine Learning Blog28

    Generate images and video with vLLM-Omni on SageMaker AI (Part 2)

    An AWS tutorial shows how to deploy two endpoints using the same vLLM-Omni DLC image on SageMaker AI: a real-time endpoint using FLUX.2-klein-4B for image generation and an asynchronous endpoint using Wan2.1-VACE-1.3B for image-conditioned video generation. A text prompt first produces a PNG, then the image and motion prompt are sent through Amazon S3 to the video endpoint. The generated MP4 is retrieved from S3. The example includes a command-line workflow and an optional Streamlit interface.

  6. AWS Machine Learning Blog22

    A practical guide to prompt engineering for Amazon Quick

    Prompt engineering determines how well Amazon Quick's AI features respond to natural-language requests, spanning custom agents, automated workflows, and conversational data analysis. The article presents general principles and the structured CRISPE framework, emphasizing specificity, business context, and examples. It uses enterprise scenarios such as customer-service efficiency analysis to show how to reduce iteration and improve first-pass accuracy.

  7. AWS Machine Learning Blog22

    Prompt engineering for Amazon Quick components: patterns and pitfalls

    AWS breaks down prompt-writing approaches for Amazon Quick components: Quick Research needs a clear objective, audience, and focus area, with sub-questions decomposed and data sources constrained; Quick Flows should specify timing, data sources, calculations, and delivery goals, using numbered steps for complex logic and conversational iteration for revisions; Quick Sight queries should state the business question, metrics, dimensions, and visualization preferences.

  8. AWS Machine Learning Blog28

    Building an AI contract intelligence platform with Amazon Quick and Amazon Bedrock AgentCore

    To address aggregate questions spanning hundreds of contracts that semantic RAG search cannot answer, this approach uses structured extraction instead. An extraction agent powered by Claude Sonnet models pulls eight key fields from each PDF and attaches confidence scores; a lightweight Claude Haiku model independently checks the results, while Amazon Textract uses computer vision to resolve disagreements over signature detection.

9/29Tue
  1. Perplexity · X30

    Agent governance is an engineering problem. We’ve built safeguards into Perplexity’s infrastructure, harnesses, and tools, and put them to work across our products. Today we're sharing how we engineer safer agents: https://www.perplexity.ai/hub/blog/how-we-engineer-safer-agents

    Agent governance is an engineering problem. We’ve built safeguards into Perplexity’s infrastructure, harnesses, and tools, and put them to work across our products. Today we're sharing how we engineer safer agents: https://www.perplexity.ai/hub/blog/how-we-engineer-safer-agents

9/27Sun
9/26Sat
  2. AWS Machine Learning Blog36

    Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP boosts throughput by 40%

    AWS presents an architecture for scaling MoE reinforcement-learning training on Amazon EKS with EFA and DeepEP, improving throughput by 40%. It addresses resource contention between rollout generation and policy training in large-scale RL workloads such as RLHF and GRPO, high-throughput communication across hundreds of accelerators, and the cross-node all-to-all overhead introduced by Expert Parallelism.

  4. AWS Machine Learning Blog34

    NarrateAI delivers production-grade LLM quality assurance on Amazon Bedrock

    NarrateAI uses five techniques on Amazon Bedrock to achieve about 99% numerical accuracy while supporting real-time streaming responses: adaptive pipeline orchestration, cross-account multi-model failover, real-time streaming evaluation, a composite evaluation framework, and data-accuracy validation. Each addresses failure modes such as hallucinated metrics, API throttling, validation delays, and subjective language. Built on Amazon Bedrock AgentCore, the solution serves more than 4,000 AWS executives.

9/25Fri