#Tutorial/How-to

AIAWS published a reference architecture for running multiple teams on one Amazon SageMaker HyperPod EKS cluster, with each team isolated in its own Kubernetes namespace. The design combines AWS IAM Identity Center for authentication, per-team SageMaker AI domains, HyperPod Task Governance for fair resource allocation, and namespace-level cost allocation for per-team spend visibility.

AIAnthropic says the fastest way to use its new credits is through Claude Managed Agents. Developers can build an agent with the claude-api skill in Claude Code by running /claude-api managed-agents-onboard.

AIFish Audio's Drama 3 voice model lets users direct tone, emotion, and pacing in plain language, and can shift emotion mid-sentence. The poster, who found it remarkably effective in testing, says the control over delivery is unlike anything previously seen. A preview is available through the API as drama-3-preview.

AITessl's Code Review defines review standards as skills in the repository, called lenses, routed to files by a repo-owned profile file. Because these team-visible standards are portable, lessons from review can feed back into code generation and maintenance, not only the next review.

AIGuizang (@op7418) says a Grok bot paired with a foldable phone lets him chat on one screen while the bot publishes content on the other. Background post: he set Grok to produce a daily morning AI news video on a schedule, running content collection, code writing, and video rendering entirely on Grok's cloud virtual machine rather than his local computer. He shares the full prompt so others can run the same workflow with their own Grok bot.

AIReplit and Databricks integration, now generally available with native Lakebase support, lets enterprise teams build apps from plain-language prompts using Replit Agent and deploy them as Databricks Apps. Deployed apps inherit automatic user authentication and Unity Catalog access controls, and Replit Agent auto-provisions a managed Lakebase Postgres database for operational data. Lakebase keeps app-written data inside the Databricks perimeter instead of a separate external database.

AIElevenLabs explains how to build meeting transcription products using its Scribe v2 and Scribe v2 Realtime models through its API. Real-time transcription suits live captions and in-meeting bots, while batch transcription suits post-meeting notes and records, with Scribe v2 Realtime reporting 150 ms latency and supporting up to 50 key terms for prompting.

AIThe author suggests installing a coding agent such as Pi or DeepSeek Harness in a cloud computer and allocating unused Code plan quota to it. Grok Bot could then use those tokens to write code and render videos, so leftover quota is not wasted.

AILangChain has added three engineering upgrades to Skills in its Deep Agents framework: tool-binding Skills, pinned Skills, and mid-thread reloading. Tool-binding lets a SKILL.md declare tools via metadata.include_tools, so tools are injected only when the Skill is read, and pinned Skills inject full instructions before the next model call, skipping a round trip. Setting skills_metadata to None rescans the Skills library mid-thread without restarting, at the cost of invalidating the cache.

AINVIDIA AI highlights how H-Company optimizes vision-language model (VLM) serving for computer use agents using NVIDIA Dynamo. The post is a broadcast announcement with a link, and no performance figures or technical details are given in the text.

AIGuizang (@op7418) says Muse, Dots, and other agents with cloud computers can run his setup. He used a Grok bot on a cloud virtual machine to collect content, write code, and render a daily AI news video on a schedule, without his local computer.

AIThe author set up a Grok bot to produce a daily morning AI news video on a schedule, running content collection, code writing, and video rendering entirely on Grok's cloud virtual machine without local computers. The author says the results are quite good and shares the full prompt so others can run the same workflow with their own Grok bot.

AIEvery moved its Slack-based Every Agent from its own OpenClaw fleet to Anthropic's Claude Managed Agents, letting the team focus on building a shared coworker. The newsletter, written by engineer Paridhi Agarwal, covers the gains and trade-offs of stopping self-run agent infrastructure.

AIJohns Hopkins astrophysicist Brice Ménard, working as an Anthropic researcher, used Claude Science to produce the first complete map of the sky in ultraviolet light. Claude orchestrated agents to merge GALEX, Swift, and FIMS/SPEAR data, then predicted roughly a third of the sky that no UV telescope had observed, using relationships to visible, infrared, and radio data. Hidden test regions were reconstructed to within about 10% of real measurements, and each pixel is labeled measured or predicted with uncertainty estimates.

Why it matters: The post shows how an astrophysicist used Claude Science agents to merge UV surveys and predict missing sky regions, with a validation step that makes the method reusable.

AILangChain built Restock, a sample office-supply agent that runs in Slack on Managed Deep Agents and pays through Stripe's Link wallet. The agent searches products, builds a cart, and pays over the Machine Payments Protocol, with the user approving the purchase in Slack and the payment in Link. The post uses a pens order at $22.18 to show the flow from request to confirmed order.

Why it matters: The post walks through how an agent handles search, budget limits, Slack review, and Link approval, showing where each control sits outside the model.

AIStanford's CS 329Z: Engineering AI Agents course has published slides for lectures 4 and 5, following the earlier release of lectures 1–3. Lecture 4 covers tool use and is taught by Diyi Yang, while lecture 5 covers frameworks and orchestration, taught by Diyi Yang, Michael Ryan, and John Yang.

AIThariq, of Anthropic, says the most common failure in AI coding is users working outside their domain who cannot write precise prompts and plans. As a result, they spend many turns iterating imprecisely. The quoted post contrasts this with programmers' advantage in vibe coding.

AIUnsloth Studio binds remote-code approval to a fingerprint of the scanned code, so changed code requires fresh consent before it runs. It also blocks weight files that Hugging Face has flagged for malware in the path the selected loader would deserialize. The article describes these checks as one layer among several, alongside package-content scans and OS sandboxes, and notes that the scanner is not a sandbox and cannot catch every evasion.

AIGoogle Developers Blog introduces AQuA, an ambient quality agent that runs in a customer's Google Cloud project and samples production sessions to find recurring agent failures. In a 32-session travel-concierge sweep, it verified six issues and traced two of them to specific prompt lines, and a replay after the fixes raised full-session passes from 5/32 to 13/32. The post notes that verification and diagnosis are model-based, and that the tool proposes edits without applying them.

Why it matters: The post walks through a concrete production workflow, from sweep and verification to a code-anchored fix and replay, that shows how to diagnose silent agent failures.

AIA Hugging Face blog author used the ML-Intern agent in HuggingChat to build six small models by writing detailed prompts that specify datasets, base models, baselines, smoke tests, and spending limits. The projects include a citrus disease vision-language model, a Huggy character LoRA, a camera-angle LoRA, a doodle-to-object LoRA, a 0.8B prompt rewriter, and a 4-step distilled Agate model, with total compute cost of about USD 103. Each project's prompts and public models are linked from the post.

Why it matters: The author shows how prompt structure, baselines, smoke tests, and budget caps shape an agent-driven training workflow, with per-project costs given.

AIAdaption Labs has launched Invent-a-dataset, which creates post-training datasets from a natural language description alone. A technical report compares the Invent API against frontier proprietary and open-weight LLMs used for data generation. Sara Hooker's post links to the launch page and the report's technical details.

AIask your Grok @Bot to monitor X for user feedback on your products! send feature requests to your issue tracker, bug reports to a cursor cloud agent or project to triage and fix the loop is complete

AIThe post says users can ask the Grok bot to find all feedback about what they are building, summarize it, and propose a plan to address it, without needing an X account or connector. The background post notes that Grok Bot can now search, read, and monitor X.

AIvLLM's bounded sliding-window attention (SWA) replay reruns only each request's last 128 tokens after a prefix hit, instead of replaying 40 × 128 tokens. With CUDA graphs, prefill compute drops 30–40% for layers 21–39.

AIThree weeks after launch, vLLM's serving of DeepSeek-V4.1-Flash runs 1.9× faster at low concurrency and delivers 5.3× the throughput at 150 TPS per user on SemiAnalysis AgentX. The post offers interactive figures explaining how these gains were achieved.

AIOpenAI says developers can now build richer, more interactive ChatGPT plugins using plugin extensions. The extensions support launching plugins from the sidebar, @ mentions, and file viewers, with a walkthrough shared alongside the post.

AIAnthropic's ClaudeDevs account recommends compatible browser drivers from Browser Use, Browserbase, E2B, and Daytona for computer-use tooling. Developers can also write their own driver based on the example drivers in the quickstart. Quickstart and documentation links are provided for the computer-toolset setup.

AIMicrosoft's Foundry blog guide advises keeping existing Azure Document Intelligence workloads that meet production requirements. It recommends evaluating Azure Content Understanding for high-variation, unstructured, reasoning, RAG, or multimodal document scenarios, and for new cloud OCR or layout workloads.

AIAnthropic's Lydia Hallie says API-billed users can set Haiku 5.5's autocompact window to 100K to remain in the cheaper token pricing tier. The setting is saved per model, so it applies only to Haiku, including subagents, and is configured with /model haiku followed by /autocompact 100k. Per the background post, prompts under 100K tokens cost $0.10/$0.50 per million tokens with $0.01 cache reads, versus $0.50/$2.50 with $0.05 cache reads above 100K.

AIWorth reading — teach according to each student's aptitude! Sherpa: Teaching LLMs to Teach Adaptively

AIUnsloth says users can train their own Decision Model on as little as 2.5GB of VRAM using small models such as Laya. Training is done through a UI, with a video tutorial and guide linked from the post and the project's GitHub repository.

AIGoogle DevRel's James O'Reilly compares two ways Antigravity exposes local MCP tools from Agent Plugins to the model. Eager loading registers each tool as a top-level function with its full schema in every turn's system prompt, which speeds calls but consumes fixed tokens. Lazy loading, the plugin default, exposes tools through a proxy call_mcp_tool and reads schemas on demand, saving baseline context at the cost of an extra discovery step.

AIDatabricks announced general availability of on-behalf-of-user (OBO) authorization for Databricks Apps, letting apps act with the signed-in user's identity so Unity Catalog enforces that user's row filters and column masks. Developers can request narrow API scopes such as sql:restricted-query, which allows only read-only SQL queries, while apps keep a dedicated service principal for app-owned operations.

AIUnsloth now lets users train their own decision models locally on just 3GB of VRAM by fine-tuning Qwen, Gemma, and Llama with a Clef head. The post says this raises accuracy from 30% to as high as 78%, and the tool is available through Unsloth Desktop.

AIUnsloth released an open-source method to fine-tune LLMs into decision models that run locally, lifting Qwen3.5 0.8B's aggregate accuracy from 20.7% to 74.3% across three decision benchmarks. The team used a Clef head with LoRA (r=64) for one epoch on just 4GB VRAM, with the approach applicable to models such as Qwen3.8 and Gemma 4. A guide and notebooks are available on the Unsloth documentation site and GitHub.

AINVIDIA describes using digital twins and AI agents to validate changes to AI factory infrastructure, which combines GPUs, CPUs, switches, DPUs, and SuperNICs with schedulers, orchestration services, security controls, and a fast-changing software stack. The source frames the challenge as confirming that hardware, software, and policies work together for target workloads before deployment. The available excerpt does not give further detail on specific tools or results.

AIAWS Machine Learning Blog argues that the traditional hours-saved ROI model, built for rule-based RPA, misses most of the value of agentic automation. It proposes an Agentic Value Model covering time savings, exception handling, decision quality, and change resilience, with value counted only when tied to a defined P&L mechanism and owner.

AIQlik built Qlik Answers, a natural-language assistant that returns sourced answers from knowledge bases, analytics apps, glossaries, and documents, using Amazon Bedrock for model access. The system routes each question through specialist agents and retrieval on Amazon OpenSearch Service, with Amazon Bedrock Guardrails applied to every request and response. Qlik serves more than 40,000 customers across regions, using Amazon SageMaker AI as an in-Region fallback when models are not yet available on Bedrock.

AIThe AWS Machine Learning Blog describes an automated remediation workflow that acts on AWS DevOps Agent investigation results. Amazon EventBridge triggers a Lambda durable function that uses Amazon Bedrock to propose fixes from an allowlist of tools, running read-only actions autonomously and pausing for human approval before infrastructure changes. The post demonstrates the flow with a Lambda function whose 3-second timeout is raised to 30 seconds after a single approval.

AIAWS ran a six-week program pairing non-engineering professionals with mentors and tools like Amazon Bedrock AgentCore and the Strands Agents SDK to build working AI prototypes. Four participants with no engineering background built WealthWise, a multi-agent financial advisory tool with five agents on Amazon Nova models, which won first place. The article says participants who completed the phased program retained three times more practical skills than those in two-day intensive formats.