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Tutorials News

Latest reporting, research and product updates filed under Tutorials.

434 picksNewest firstLatest article Oct 2, 2026, 8:00 AM

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OpenRouter：Announcements（RSS）AI score 62/100

LangChain vs CrewAI: Orchestration Compared to OpenRouter-Native Routing

You need more than one model in a workflow. One lower-cost model handles routine calls and a stronger model handles the difficult ones. Agent frameworks such as LangChain and CrewAI are one way to build that. Both provide an orchestration layer, and they organize it differently. LangChain’s runtime, LangGraph, gives you explicit state and control flow. CrewAI organizes work as role-based agents and event-driven flows. Both frameworks help when you’re building an agent that plans, keeps state, ca
LangChain：Blog（RSS）AI score 69/100

How to Build a Model Router in the Harness

Frontier LLMs continue to be expensive. As agents become ubiquitous and operate at massive scale, this is cost prohibitive. Luckily, most agents don't need frontier-level intelligence for every task. Model labs even say so: Anthropic's guide to choosing a model notes that "for many applications, starting with a faster, more cost-effective model like Claude Haiku 4.5 can be the optimal approach."Past a certain point, you hit diminishing returns: a more capable model adds little quality while cost
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Anthropic：Claude.dev 开发者博客（RSS）AI score 68/100

Getting started with Claude Code mods

Claude Code already lets you change a lot about how it behaves: settings, permission rules, slash commands, skills and a status line. Mods go further. Mods can rewrite or replace what Claude Code does, and can even draw custom UI. Under the hood, mods are hooks, and they ship inside plugins. Each one is a small JavaScript or TypeScript module that runs inside your session and sees every event as it happens. That makes mods a way to fit Claude Code to how you work. You can add a readout you check
Ethan Mollick：One Useful Thing（RSS）AI score 64/100

The Dot and the Swarm

I generally think I have done a good job anticipating the direction and pace of AI over the few years I have been writing this Substack, but I think I recently got something fairly large wrong. In the last year I have been posting about how I suspected that humans would have to approach working with agents as a manager, deciding how to delegate work to agents and specifying how those agents should be organized. I thought that getting agents to work effectively as a group would take careful const
OpenRouter：Announcements（RSS）AI score 68/100

Confidence Thresholds for Model Escalation Routing

Sending every request to your strongest model gets good answers at the highest price, because you pay frontier rates for requests a cheaper model would have answered correctly. Fixed routing rules, such as picking the model by keyword or task type, cost less but need rewriting as your traffic changes. Confidence-based escalation sits between the two. You ask the model to score its own answer, then route on that score. Answers with a high score stay on a cheap model. Answers with a low score go t
OpenRouter：Announcements（RSS）AI score 67/100

How to Gate Pull Requests on LLM Evals in CI

Changing one line in a support agent’s system prompt can ship an agent that tells customers the refund window is 30 days when your policy says 14. Nothing in a normal CI pipeline checks what the model says, so the build passes and the first person to see the wrong answer is a customer.Gating a pull request on a fixed eval set works the same way as gating on a failing unit test. You keep test cases in the repository, run them when a prompt changes, and block the merge when too many fail.In this g
OpenRouter：Announcements（RSS）AI score 67/100

Cost vs. Quality Tradeoff Framework for Agent Models

Picking a model for an agent by leaderboard rank pays frontier prices for tasks that a cheaper model may handle at the same accuracy. The question to answer is not which model scores highest, but which model is the cheapest one that is good enough for the task in front of you. This guide is a three-step framework for making that call. You set the quality bar the task needs, measure cost per quality point on your own examples, and pick the cheapest model that clears the bar with margin. Tl;dr Set
英国 AI Security Institute：Blog（网页）AI score 70/100

Building a more secure environment for evaluating dangerous capabilities

In August, we reported an incident in which AI agents, during a cyber evaluation, took sustained action against real people beyond the remit of their task. Our incident was one of several across the sector in which AI agents took actions during evaluations that their operators had not intended. Although the circumstances differed, these incidents highlight the need to ensure that there are robust security practices underpinning frontier AI development and research. In response, we paused our hig
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OpenRouter：Announcements（RSS）AI score 65/100

Building a Golden Eval Dataset from Production Traffic

You update a prompt, or a provider rolls out a new checkpoint under the same model ID, and something in production regresses. The last week of user complaints looks slightly different from the week before. A public benchmark like MMLU won’t catch that. It measures general capability across academic subjects, not how the model handles your product’s traffic. A golden eval dataset closes that gap. It’s a curated collection of production inputs paired with reviewed expected outputs, versioned in Gi
OpenRouter：Announcements（RSS）AI score 69/100

AI Agent Regression Testing After a Prompt or Model Change

A ~author/family-latest alias always resolves to the newest concrete model in a family. That’s convenient in production and a problem in a regression test, because the model can change between runs without any change in your repository. Our latest model resolution docs describe the mechanism and recommend a concrete model slug when you need a fixed version for reproducibility. This guide covers the locked case set and the behavioral contract, then the model-swap case in detail. Tl;dr Regression

OpenRouter：Announcements（RSS）AI score 62/100

How to Test Tool-Calling Accuracy in AI Agents

An agent can fail in two places when it uses a tool. It can choose the wrong tool, or it can choose the right one and send the wrong arguments. Those failures tell you different things. If an agent calls lookup_order instead of refund_order, the problem is tool selection. If it calls refund_order with the wrong order_id, it chose the right tool and passed the wrong arguments. This guide covers three ways to test tool-calling behavior. The first is a reference-free large language model (LLM) judg
METR：Blog（网页）AI score 72/100

Chris Painter's testimony to the U.S. Senate on AI agent incidents

On September 30, 2026, METR President Chris Painter testified before the U.S. Senate Committee on Homeland Security & Governmental Affairs’ Subcommittee on Disaster Management, District of Columbia, and Census, at a hearing titled “Rogue AI: Securing the Homeland Against AI Agent Attacks”. The written testimony is published in full below, and is also available as a PDF. Introduction Chairman Hawley, Ranking Member Kim, and members of the subcommittee, thank you for inviting me to testify today.

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Sarvam AI（网页）AI score 74/100

Building AI Agents: A First-Principles Guide

An agent is a language model running in a loop, with tools it can call and instructions that tell it how to behave. This guide builds up from that idea to a working support agent.ResearchSeptember 29, 2026·25 min readAn agent is a language model running in a loop, with tools it can call and instructions that tell it how to behave. Everything else - skills, memory, domain knowledge - is a way of putting the right text in front of the model at the right time.How to read thisThis guide goes top to

vLLM 官方博客（RSS）AI score 62/100

Taking vLLM Apart: A Practical Guide to Disaggregated Serving

A single vllm serve process does three jobs that get in each other's way: processing prompts (prefill), generating tokens (decode) and a pile of CPU work around them. Disaggregated serving in vLLM separates the different stages of LLM inference. Splitting prefill from decode stops long prompts stalling everyone else's output, as long as the KV cache moves between them fast. Moving tokenization and parsing to a CPU-only frontend (/render, /derender) takes that work off your GPU nodes and leaves t
Databricks：Blog（RSS）AI score 65/100

How Databricks rolls out frontier models to 12,000 employees on Day 1

Providing our employees access to frontier AI capabilities is a top priority at Databricks, and consequently, it is important to us for them to use new models instantly when they become available. At the same time, it is nontrivial to give more than 12,000 people rapid access to a new model because:Models that are marketed as frontier often aren’t. For example, Opus 5.0 was more expensive and ranked lower on both quantitative and qualitative quality scores among our engineers compared with Opus

GitHub BlogAI score 71/100

How we found 24 Android vulnerabilities using our open source AI security agent

With the rise of AI in the security space, our team created the GitHub Security Lab Taskflow Agent as a way for security researchers to easily automate, package, and share the AI prompts and workflows that they find effective for their work. In this blog post, I’ll share how I created auditing taskflows to find vulnerabilities in Android applications. While new models are getting better at understanding code, custom taskflow prompts let security researchers guide them—splitting research into inc
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Anthropic：Claude.dev 开发者博客（RSS）AI score 67/100

Automating eval design and hillclimbing with Claude

Evaluations provide a signal on how your app or skill is performing on specific tasks. But designing evaluations, and improving performance on them without fooling yourself, is hard. We've added guidance for both to the claude-api skill. With the skill, you can run /claude-api build-eval to build an evaluation inside your codebase, and run /claude-api hillclimb to improve your application against it, one change at a time, with a held-out set of examples to catch overfitting. In this article, we

Hacker News：AI 热帖AI score 86/100

Prompting Claude Opus 5.5

This guide covers the prompting patterns specific to Claude Opus 5.5. For the model's capabilities and API changes, see What's new in Claude Opus 5.5. For techniques that apply across all current Claude models, see Prompting best practices. Claude Opus 5.5 generates output tokens more than 30 percent faster than Claude Opus 5 and tends to finish the same task with fewer tokens. Existing Claude Opus 5 prompts should perform well without changes, and the patterns in Prompting Claude Opus 5 remain

Tomer Tunguz 博客（VC 分析）AI score 63/100

How GPU Prices Can Double While AI Gets Cheaper

GPU prices have doubled in the last six months, from $4.40 to $8.08 per GPU-hour. But AI prices are falling. How can that be? It is not a simple answer. Higher GPU costs are likely to remain while the industry races to build out new data centers. Every component of the buildout is increasing in cost, from concrete to copper to credit.1 Above all, electricity remains the limiting factor, which Oracle is experiencing : last week it invoked force majeure on its New Mexico campus after the natural-g
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GitHub BlogAI score 63/100

GitHub Copilot app for Beginners: How to build custom workflows with canvases

Most tools give you a fixed set of screens and ask you to fit your work into them. But what if you could start with the workflow you want instead and have the interface take shape around it? That’s the idea behind canvases in the GitHub Copilot app. A canvas, also called a canvas extension, is a customizable interface that you and the agent share. It can be a kanban board, an issue triage board, a release checklist, a dashboard, a form, or even a spreadsheet: a UI shaped to how you work. Because
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GitHub BlogAI score 64/100

Improving site performance by shipping more CSS

The Primer Design System powers many of the experiences you see on GitHub today. From buttons to banners to breadcrumbs, these foundational components are required to be accessible, flexible, and performant across a wide variety of scenarios. Back in 2023, the number of components on certain pages began to explode. This led to several performance-related challenges with our existing CSS-in-JS solution: Initial page loads took longer due to styles being initialized on the client Server-side rende
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X：Nathan Lambert (@natolambert)AI score 85/100

I never got to comment on Nvidia-HF because I was OOO, but something that struck me is how HuggingFa...

Nathan Lambert 评价 Nvidia 收购 HuggingFace，认为 HuggingFace 影响 AI 讨论方向的能力对 Nvidia 值得每年付出约 100 亿美元。他认为 Nvidia 比三大云厂商更适合做买方，HuggingFace 应摆脱盈利单位定位，去争取下一代 1 亿 AI 开发者；作者曾在 HuggingFace 工作并于去年预言过这一收购。
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