# AI-assisted coding The use of AI models and agents to assist software-development tasks such as suggesting, explaining, reviewing, testing, and modifying code. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Zed launches Delta public beta for collaborative code review with developers and coding agents DevFeed: [Zed launches Delta public beta for collaborative code review with developers and coding agents]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-16T19:45:30Z Content type: article Language: en Sources: [The New Stack]() Topics: [GitHub](), [AI-assisted coding](), [pull-requests](), [Developer Platform](), [Tooling]() Tags: [ai](), [ai-agents](), [beta](), [coding-agents](), [collaboration](), [developer](), [developer-tools](), [github](), [pull-requests](), [review](), [software-development]() ### AI overview Zed has announced the public beta of Delta, a collaborative environment where developers and coding agents work, review, and revise code together in shared threads rather than pull requests. The article presents Delta as a response to the volume of agent-generated code and limitations of the pull-request model. ### Source excerpt Something of a consensus has emerged from the developer fraternity in 2026 -- GitHub, a platform built substantively for human The post "Everyone's in a race to replace GitHub": Zed launches Delta because agents made pull requests obsolete appeared first on The New Stack. ## Android Bench 2.0 introduces long-horizon tasks, agentic evaluation, and continuous scoring DevFeed: [Android Bench 2.0 introduces long-horizon tasks, agentic evaluation, and continuous scoring]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-09-16T15:58:00Z Content type: release Language: en Sources: [Android Developers Blog]() Topics: [Android](), [Benchmark](), [AI-assisted coding](), [LLM evaluation / benchmarking](), [upgrade](), [cross-platform]() Tags: [agent](), [agentic](), [agentic-android-development](), [ai-models](), [android](), [benchmark](), [evaluation]() ### AI overview Android Bench 2.0 updates Android developer benchmarking for large language models and agents. It introduces multi-day long-horizon tasks, agentic evaluation, and continuous scoring based on functionality, visual fidelity, regressions, and other objective criteria. ### Source excerpt Posted by Matthew McCullough, VP, Product Management, Android Developer When we first launched Android Bench, we built a rigorous foundation for evaluating how large language models (LLMs) assist developers with real-world Android tasks. As AI models and agents rapidly evolve, we've been updating our methodology, such as aligning our benchmark framework with the Harbor framework. Today we're releasing the first set of long-horizon tasks (LHT), which are tasks of great complexity that take an engineer multiple days or even a week to complete. We are also introducing agentic evaluation, starting with agents from corresponding model providers. This addition brings us to Android Bench 2.0--a major upgrade designed to evaluate AI models and agents against the scale, ambiguity, and complex multi-step problem solving that you tackle every day. The Android Bench 2.0 leaderboard From incremental fixes to long-horizon tasks The first iteration of Android Bench, along with similar early AI coding benchmarks, focused on incremental changes to existing repositories, in many cases limited to bug fixes or smaller feature requests. This was a reflection of the capabilities of AI assistance at the time, as well as how you were using it. To continue helping you find the models and coding agents best suited to your development workflow, we have raised the bar of our evaluations to match the work you delegate to AI. Android Bench 2.0 mirrors these ambitious challenges with LHTs that include upgrading dependencies, adding new features, building apps from scratch, or converting a cross-platform app to Android. Complex tasks require a more nuanced evaluation and scoring On multi-day engineering tasks, binary pass or fail grading doesn't capture the full picture. For example, an agent might refactor 40 screens to Jetpack Compose, set up database tables, and pass 90% of requirements, but fail a single edge-case assertion. Binary scoring rates this run as 0%, obscuring the model's architectur ## Rider and ReSharper 2026.2.2 Are Out! DevFeed: [Rider and ReSharper 2026.2.2 Are Out!]() Original publisher: [Read original article]() Author: Alexander Kurakin Published: 2026-09-16T14:12:44Z Content type: release Language: en Sources: [The JetBrains Blog]() Topics: [rider](), [resharper](), [AI-assisted coding](), [test-coverage]() Tags: [2026](), [ai-agent-hooks](), [ai-agents](), [code-coverage](), [coverage](), [hooks](), [jetbrains](), [macos](), [mcp](), [net](), [net-tools](), [releases](), [resharper](), [rider](), [update]() ### AI overview JetBrains releases Rider, ReSharper, and .NET tools 2026.2.2, adding an AI Agent Setup widget in Rider, more targeted hook feedback for coding agents, TUnit coverage support, and fixes including a macOS freezing issue. ### Source excerpt We've released version 2026.2.2 of ReSharper, Rider, and .NET tools. You can install this update from inside the tools themselves, through the Toolbox App, or on our website. Here's what's new in this update. Rider 2026.2.2 AI Agent Setup widget We've recently added a wide range of features to help you work with coding agents [...] ## How to Review AI-Generated Python Code Efficiently DevFeed: [How to Review AI-Generated Python Code Efficiently]() Original publisher: [Read original article]() Author: Leodanis Pozo Ramos Published: 2026-09-16T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [AI-assisted coding](), [Code review](), [Python](), [Pytest](), [coding]() Tags: [agents](), [ai](), [ai-coding-agents](), [code](), [coding-agents](), [how-to](), [python](), [review]() ### AI overview This tutorial presents a five-step workflow for reviewing AI-generated Python code. It recommends clarifying the code's intent, running automated checks such as ruff, mypy, bandit, and pytest, inspecting higher-risk areas, and confirming problems by running the code before fixing them. ### Source excerpt Learn an efficient workflow to review AI-generated code in Python: run ruff, mypy, bandit, and pytest, then catch the bugs agents get wrong. ## sem-ai 0.4.0: smaller responses, safer access, and pre-flight checks DevFeed: [sem-ai 0.4.0: smaller responses, safer access, and pre-flight checks]() Original publisher: [Read original article]() Author: Pete Miloravac Published: 2026-09-16T12:01:04Z Content type: release Language: en Sources: [Semaphore Engineering]() Topics: [AI-assisted coding](), [Claude Code](), [Command-line interface](), [Authorization](), [API](), [configuration]() Tags: [ai-coding-agents](), [api](), [audio](), [claude-code](), [command-line](), [configuration](), [permissions](), [pipelines](), [product-news](), [release](), [video]() ### AI overview Semaphore's sem-ai 0.4.0 release adds more compact workflow and pipeline responses, context switching across organizations and credentials, and support for managing pre-flight checks. The update is aimed at reducing context usage and enabling more targeted permissions for AI coding agents. ### Source excerpt AI coding agents work best when they receive the right information without unnecessary noise. They also need clearly defined permissions and reliable safeguards for the changes they make. sem-ai 0.4.0 improves all three areas. The release introduces more compact pipeline responses, flexible context switching, and support for Semaphore pre-flight checks. Watch Nick demonstrate the new [...] The post sem-ai 0.4.0: smaller responses, safer access, and pre-flight checks appeared first on Semaphore. ## How sandbox boundaries affect AI coding agent evaluations DevFeed: [How sandbox boundaries affect AI coding agent evaluations]() Original publisher: [Read original article]() Author: Waldek Mastykarz Published: 2026-09-16T09:09:51Z Content type: opinion Language: en Sources: [Developer Blogs]() Topics: [AI-assisted coding](), [Testing](), [Artificial Intelligence](), [coding]() Tags: [agent-experience](), [ai](), [ai-coding](), [ax](), [coding](), [developers](), [eval](), [evaluation](), [measurement](), [sandbox]() ### AI overview The article explains that an AI coding agent evaluation can produce misleading scores when the agent retrieves answers from its prompt, environment, or other accessible resources. It argues that evaluators should define the capability being tested and set information boundaries accordingly, including restricting access to evidence when measuring internal model knowledge. ### Source excerpt Your AI coding agent passed the eval. But did the model know the answer, or did it find it somewhere on your machine? A correct answer can still invalidate your measurement. The post Your AI coding agent evaluation is only as good as its sandbox appeared first on Microsoft for Developers. ## Rise of the Knowledge Engineer DevFeed: [Rise of the Knowledge Engineer]() Original publisher: [Read original article]() Author: David Isquick Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [Artificial Intelligence](), [AI-assisted coding](), [Users]() Tags: [agents](), [ai](), [ai-trends](), [api](), [developers](), [documentation](), [knowledge-base](), [operational](), [source-of-truth](), [support](), [workflows]() ### AI overview The article examines the rise of the knowledge engineer as companies increasingly serve AI agents that read documentation, compare products, solve support problems, and perform tasks. It argues that organizations need operating systems and processes to keep knowledge accurate, accessible, consistent, and retrievable, because incorrect information can spread through generated code, support responses, and automated workflows. ### Source excerpt Agents are one of the largest audiences for company knowledge, accounting for 66% of measured web traffic across documentation powered by Mintlify. The emerging role responsible for keeping that knowledge accurate and retrievable is the knowledge engineer. ## Building the new GitHub Copilot Inline Suggestions Model: Part One DevFeed: [Building the new GitHub Copilot Inline Suggestions Model: Part One]() Original publisher: [Read original article]() Author: Julia Gong, Ben Liggett, Ulugbek Abdullaev Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [GitHub Copilot](), [AI-assisted coding](), [coding](), [Development](), [GitHub](), [Software Engineering]() Tags: [agent](), [ai-assisted-coding](), [blog](), [coding](), [github](), [github-copilot](), [quality](), [vs-code](), [workflows]() ### AI overview GitHub Copilot unified completion-style ghost text, nearby next edit suggestions, and long-distance edits into a single "3-in-1" model. The article explains that training, evaluation, and editor design evolved together, and that the unified model can improve suggestion selection and cache additional edits for faster subsequent interactions. ### Source excerpt Explore how GitHub Copilot unified completion, next edit, and long-distance suggestions into one model for a faster, more cohesive coding experience. Read the full article ## Meta lets Claude and Codex configure WhatsApp Business via MCP DevFeed: [Meta lets Claude and Codex configure WhatsApp Business via MCP]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-15T22:08:56Z Content type: news Language: en Sources: [The New Stack]() Topics: [Meta](), [Model Context Protocol](), [AI-assisted coding](), [Claude](), [codex](), [API]() Tags: [ai](), [ai-agents](), [ai-coding-agents](), [api](), [claude](), [codex](), [developer-tools](), [mcp](), [mcp-server](), [meta](), [model-context-protocol](), [product](), [updates]() ### AI overview Meta announced a WhatsApp Business Tools MCP server that lets developers connect Claude or Codex to selected businesses they administer. The agents can help configure WhatsApp Business accounts, verify phone numbers, manage templates, and perform related API tasks, with access scoped to the businesses the developer selects. ### Source excerpt Any business worth its salt in 2026 needs to be embracing the right tools to reach its customers, and few The post Meta lets Claude and Codex configure WhatsApp Business via MCP appeared first on The New Stack. ## Bolt.new tests Forge, offering more coding-model usage in exchange for anonymized developer sessions DevFeed: [Bolt.new tests Forge, offering more coding-model usage in exchange for anonymized developer sessions]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-15T18:47:23Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI-assisted coding](), [Training AI Models](), [Open Source Models & Datasets](), [Agent Harness]() Tags: [agent](), [agent-harness](), [ai](), [ai-development](), [ai-engineering](), [ai-models](), [coding-agents](), [data](), [developer-tools](), [developers](), [training]() ### AI overview Bolt.new is testing Forge, a research preview for individual Pro subscribers that offers up to 50 times more usage of open-weight coding models in exchange for opting in to share anonymized coding sessions. The sessions may include prompts, source code, fix traces, and conversations with the coding agent, and will support an Arcee AI project to train a trillion-parameter-class open-weight model. ### Source excerpt Bolt.new, StackBlitz's browser-based AI development platform, is testing a new trade with developers: more coding-model usage in exchange for training The post Bolt is giving developers 50x more compute. But there's a catch. appeared first on The New Stack. ## Let coding agents review PRs without giving them unrestricted merge access DevFeed: [Let coding agents review PRs without giving them unrestricted merge access]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T15:29:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [AI-assisted coding](), [Code review](), [GitHub](), [Authorization](), [Pull Request](), [pull-requests]() Tags: [ai-code-review](), [authorization](), [code-review](), [coding-agents](), [github](), [reviews]() ### AI overview This guide explains how to let coding agents review GitHub pull requests without granting unrestricted merge access. It recommends separate review and merge credentials, explicit merge assignments governed by WorkOS Airlock, required checks and reviews, and reapproval when the reviewed commit changes. ### Source excerpt Set GitHub permissions for AI code review, govern merges with Airlock, and prevent agents from merging code that changed after approval. ## Red Hat Developer Hub: Preventing compliance violations with AI coding agents DevFeed: [Red Hat Developer Hub: Preventing compliance violations with AI coding agents]() Original publisher: [Read original article]() Author: Evan Shortiss, Ben Wilcock Published: 2026-09-15T13:17:13Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [internal developer portal](), [AI-assisted coding](), [Backstage](), [Claude Code](), [Command-line interface](), [Model Context Protocol](), [Security]() Tags: [ai-coding-agents](), [ai-inference](), [backstage](), [catalog](), [claude-code](), [cli](), [developer](), [developer-productivity](), [developer-tools](), [mcp](), [platform-engineering](), [security]() ### AI overview This tutorial demonstrates how an AI coding agent connected to Red Hat Developer Hub can query a Backstage software catalog, TechDocs, and software templates before making architectural decisions. Using a simulated insurance-company catalog, the demo shows how this context can help avoid PCI-DSS compliance violations, resolve governance conflicts, and create a traceable decision record. ### Source excerpt AI coding agents can generate entire services, reason about architectures, and scaffold applications in minutes. But ask one to build a service for your organization and you'll quickly notice the gap: it doesn't know your rules. It doesn't know which messaging broker your compliance team mandates, which services already exist in adjacent domains, or which project template is the golden path for your team. The post Red Hat Developer Hub: Preventing compliance violations with AI coding agents appeared first on Red Hat Developer. ## How to Run Codex in an Ubuntu Virtual Machine on Proxmox DevFeed: [How to Run Codex in an Ubuntu Virtual Machine on Proxmox]() Original publisher: [Read original article]() Author: Travis Henderson Published: 2026-09-15T12:00:11Z Content type: tutorial Language: en Sources: [Atomic Object]() Topics: [AI-assisted coding](), [codex](), [Linux](), [Ubuntu](), [Proxmox](), [OpenSSH](), [Authorization]() Tags: [ai-agent](), [ai-for-developers](), [codex](), [coding-agents](), [development](), [linux](), [openssh](), [permissions](), [proxmox](), [sandbox](), [ubuntu]() ### AI overview This guide explains how to isolate Codex in a headless Ubuntu virtual machine running on Proxmox, using a separate Linux account and OpenSSH. The setup limits the files, dependencies, and network access available to the agent while keeping administrator tasks separate, but it does not eliminate risks from credentials, network access, or mistakes pushed to GitHub. ### Source excerpt The more useful coding agents become, the more access they tend to need. Installing dependencies, running commands, changing files, and generally more autonomy are part of the appeal. They're also the point where I start thinking about what else the coding agent can reach, especially while these tools are changing so quickly. My answer is [...] The post Your Coding Agent Should be in Jail. Here's Why and How. appeared first on Atomic Spin. ## Why Coding Agents Still Need Human Code Reviews DevFeed: [Why Coding Agents Still Need Human Code Reviews]() Original publisher: [Read original article]() Author: Anton Zaides Published: 2026-09-15T06:01:00Z Content type: opinion Language: en Sources: [Manager.dev]() Topics: [AI-assisted coding](), [Code review](), [engineering-culture](), [Claude](), [Software Engineering]() Tags: [claude](), [code-review](), [code-reviews](), [coding-agents](), [software-engineering]() ### AI overview The article argues that coding-agent workflows still require human code reviews. It describes how making reviews optional caused them to drop sharply, allowing passable implementations to become templates that spread bugs, performance problems, race conditions, and confusion among agents. ### Source excerpt When my most experienced engineer got tired of our shitty code ## Introducing Encore x E2B DevFeed: [Introducing Encore x E2B]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-09-15T00:00:00Z Content type: release Language: en Sources: [Encore Updates]() Topics: [AI-assisted coding](), [Cloud](), [backends](), [SDKs](), [Databases](), [Deployment](), [API](), [Code]() Tags: [agents](), [api](), [backend](), [cloud](), [code](), [coding](), [coding-agents](), [database](), [deployment](), [pull-request](), [sandbox](), [sdk]() ### AI overview Encore and E2B can provide coding agents with isolated sandboxes containing complete local backends. The integration lets agents build and verify end-to-end changes against infrastructure such as databases, queues, and APIs, while supporting deployment in the user's AWS or GCP account. ### Source excerpt Give coding agents an isolated sandbox with a complete backend inside it -- and keep both in your own cloud. ## Rails Hyperdrive: supercharged agentic development for Rails DevFeed: [Rails Hyperdrive: supercharged agentic development for Rails]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Evil Martians]() Topics: [Ruby on Rails](), [AI-assisted coding](), [Model Context Protocol]() Tags: [agent-experience](), [ai](), [ai-coding-agents](), [claude-code](), [mcp](), [open-source](), [rails](), [ruby-on-rails](), [ruby-on-rails-consulting]() ### AI overview This tutorial introduces Rails Hyperdrive, a development-only Rails engine that gives AI coding agents live answers from a booted Rails application and lets gems provide agent guidance through Gemfile-gated knowledge. It covers installation, MCP server setup, skills, sub-agents, slash commands, upgrades, and integration for gem authors. ### Source excerpt Introducing Rails Hyperdrive: a development-only Rails engine that gives AI coding agents live answers from your booted app and lets any gem in your bundle ship guidance gated to your Gemfile. Convention over configuration, applied to agent knowledge. ## AI's best coding agent fails 60% of the time -- and the data backs it up DevFeed: [AI's best coding agent fails 60% of the time -- and the data backs it up]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-14T22:22:27Z Content type: news Language: en Sources: [The New Stack]() Topics: [Benchmark](), [AI-assisted coding](), [Fable](), [Claude Code](), [codex](), [Command-line interface](), [anthropic]() Tags: [ai](), [ai-engineering](), [ai-models](), [benchmark](), [benchmarks](), [claude-code](), [cli](), [codex](), [coding](), [fable](), [performance](), [software-testing]() ### AI overview Real-SWE evaluates coding agents on private company codebases and reports substantially lower success rates than public-repository benchmarks. Claude Fable 5.1, running through Claude Code, led the comparison with a 38.8% score, while the tested systems often failed most attempts. ### Source excerpt Claude Fable 5.1 just won a new coding benchmark despite failing more than six out of 10 times. Its 38.8% The post AI's best coding agent fails 60% of the time -- and the data backs it up appeared first on The New Stack. ## Quiz: Agentic Engineering in Python: From Vibes to Evidence DevFeed: [Quiz: Agentic Engineering in Python: From Vibes to Evidence]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-14T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python](), [AI-assisted coding](), [Code]() Tags: [ai-coding](), [ai-coding-agents](), [code](), [coding](), [coding-agents](), [engineering](), [how-to](), [python]() ### AI overview An interactive eight-question quiz about agentic engineering in Python, covering bounded task delegation to AI coding agents, agent and human review loops, and the RECAP method for evaluating candidate diffs. ### Source excerpt Test your understanding of agentic engineering in Python, from bounded tasks and review loops to the evidence that makes a diff safe to keep. ## What's New for C++ Developers in Visual Studio 2026 (18.7 - 18.10) DevFeed: [What's New for C++ Developers in Visual Studio 2026 (18.7 - 18.10)]() Original publisher: [Read original article]() Author: Augustin Popa Published: 2026-09-14T06:38:16Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [Visual Studio 2026](), [C++](), [GitHub Copilot](), [MSVC](), [AI-assisted coding](), [CMake](), [Git](), [Submodules]() Tags: [2026](), [build-optimization](), [c-plus-plus](), [cmake](), [copilot](), [git](), [modernization](), [msvc](), [submodules](), [testing](), [tools](), [visual-studio](), [visual-studio-2026]() ### AI overview This Microsoft C++ Team Blog post recaps Visual Studio 2026 versions 18.7 through 18.10 for C++ developers. It covers C++ reliability fixes, GitHub Copilot modernization for upgrading MSBuild and CMake projects to newer MSVC Build Tools, improved code navigation, discoverable MSVC Build Tools installations, Git worktrees and submodules in the IDE, and debugging improvements. ### Source excerpt Over the past few months, we have continued shipping monthly Visual Studio 2026 releases, delivering improvements across all stages of the development cycle. In this blog post, we'll recap everything that changed from version 18.7 through 18.10 (released this month) that is relevant for C++ developers. This includes new tools for maintaining and navigating C++ [...] The post What's New for C++ Developers in Visual Studio 2026 (18.7 - 18.10) appeared first on C++ Team Blog. ## GitHub Copilot's Project HydraFusion Promises Frontier Level Performance Through Multi-Model Routing DevFeed: [GitHub Copilot's Project HydraFusion Promises Frontier Level Performance Through Multi-Model Routing]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-13T06:06:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [GitHub Copilot](), [Model Routing](), [AI-assisted coding](), [Orchestration]() Tags: [agentic-coding](), [ai](), [ai-ml-data-engineering](), [benchmarks](), [development](), [frontier-model](), [github-copilot](), [github-hydrafusion](), [ml-data-engineering](), [model-routing](), [news](), [orchestration]() ### AI overview GitHub's Project HydraFusion research preview for Copilot orchestrates multiple models at runtime for coding tasks. Its single, cascade, and critique execution patterns aim to balance task quality, latency, and estimated cost. ### Source excerpt GitHub's Project HydraFusion is a research preview for GitHub Copilot that enhances coding intelligence through runtime model orchestration. It dynamically assembles execution plans using models from various providers. The system employs three execution patterns based on task complexity. Evaluations indicate that it achieves high task quality while significantly reducing operational costs. By Olimpiu Pop ## Perplexity trusts GPT-6 Astra with end-to-end systems DevFeed: [Perplexity trusts GPT-6 Astra with end-to-end systems]() Original publisher: [Read original article]() Published: 2026-09-12T11:14:21.124044Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [Language models]() Tags: [api](), [code](), [gpt](), [monitor](), [production](), [search](), [systems](), [testing](), [workflow]() ### AI overview Perplexity describes using GPT-6 Astra to write code and communications, modify and monitor production software, and create end-to-end tests with simulated service responses. ### Source excerpt Perplexity uses Astra to write communications, change software, and monitor production systems, and checks in much less frequently than with earlier models. ## The Anatomy of Harness Engineering: How to Evaluate, Iterate, and Guard AI Coding Agents DevFeed: [The Anatomy of Harness Engineering: How to Evaluate, Iterate, and Guard AI Coding Agents]() Original publisher: [Read original article]() Author: Taylor Mullen; Christian Gunderman Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [AI-assisted coding](), [ai observability]() Tags: [ai-agents](), [ai-coding](), [benchmarks](), [engineering](), [evaluation]() ### AI overview The article recommends behavioral evaluations for AI coding agents: fast checks of discrete actions that complement broad end-to-end benchmarks. These evaluations help teams diagnose changes, iterate on prompts and tools, and prevent regressions during model upgrades. ### Source excerpt While end-to-end benchmarks like SWE-bench provide broad performance scores for AI agents, they are often expensive, slow, and lack the root-cause diagnostics needed to explain exactly where an agent's logic broke down. To solve this, developers should adopt behavioral evaluations--fast, local, unit-style tests that assert on discrete intermediate actions, such as verifying specific tool calls or file modifications rather than final string equality. By building these inexpensive micro-checks alongside macro benchmarks, engineering teams can confidently iterate on system prompts and upgrade models without the risk of regressions. ## Designing Documentation for LLMs and Coding Agents DevFeed: [Designing Documentation for LLMs and Coding Agents]() Original publisher: [Read original article]() Author: CommonsWare Published: 2026-09-11T22:55:25Z Content type: opinion Language: en Sources: [The CommonsBlog]() Topics: [Documentation](), [Large Language Model](), [AI-assisted coding](), [SDKs]() Tags: [ai](), [blog](), [coding-agents](), [documentation](), [llms](), [markdown](), [sdk](), [sdks](), [software-development](), [vibe-coding]() ### AI overview This opinion article argues that SDK and library documentation should be designed for both human developers and AI systems. It recommends text-based formats such as Markdown, smaller topic-focused sections, and strong indexing to make documentation more efficient for LLMs and coding agents, while retaining formats such as PDFs and videos for human consumption. ### Source excerpt Let's continue to explore how we can succeed with libraries and SDKs at a time when coding agents will be consuming what we create, as will ordinary developers ## Marketing ops as code: Automating events from planning to follow-up on GitHub DevFeed: [Marketing ops as code: Automating events from planning to follow-up on GitHub]() Original publisher: [Read original article]() Author: Tomoko Tanaka Published: 2026-09-11T18:26:10Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [AI-assisted coding]() Tags: [ai-ml](), [api](), [automation](), [cli](), [events](), [github](), [github-actions](), [github-copilot](), [github-issues]() ### AI overview A GitHub marketing lead describes using GitHub Copilot and GitHub Actions to automate event operations from a GitHub Issue, including setup, daily registrant processing, and post-event cleanup. ### Source excerpt If you can write down how you do your work, you can automate it. Here's what I did to support GitHub's APAC marketing team. The post Marketing ops as code: Automating events from planning to follow-up on GitHub appeared first on The GitHub Blog. [Next page]()