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🔥 huangxd- / danmu_api - 一个人人都能部署的基于 js 的弹幕 API 服务器，支持爱优腾芒哔咪人韩巴狐乐西埋帆弹幕直接获取，兼容弹弹play的搜

GitHub热门项目 | 一个人人都能部署的基于 js 的弹幕 API 服务器，支持爱优腾芒哔咪人韩巴狐乐西埋帆弹幕直接获取，兼容弹弹play的搜索、详情查询和弹幕获取接口规范，并提供日志记录，支持vercel/netlify/edgeone/cloudflare/docker/hf等部署方式，不用提前下载弹幕，没有nas或小鸡也能一键部署。 | Stars: 2,789 | 13 stars today | 语言: JavaScript

2026-07-20 原文 →
AI 资讯

Copilot's Organization-Level Custom Agents Need a Diff Review Before Rollout

Visual Studio 2026's July update added organization-level custom agents for GitHub Copilot. An organization owner can now define a custom agent that every repository in the org automatically detects and offers in the agent selector. This is powerful and introduces a rollout risk: a single agent definition affects every developer in the organization simultaneously. The rollout problem When an org-level agent is published, every developer working in any repo in that org sees it in their agent selector. If the agent definition contains an incorrect instruction, a broken tool reference, or an overly permissive scope, it affects all developers at once. There is no canary by default. The agent definition review checklist Before publishing an org-level custom agent, review its definition against this checklist. 1. Instruction review # agent-definition.yml (example structure) name : org-code-reviewer description : Reviews PRs for security and style instructions : | Review each file change. Flag: - SQL injection in string concatenation - Missing input validation on public endpoints - Hardcoded credentials Do not modify files. Only comment. tools : - read_file - search_code - post_comment scope : organization Check: [ ] Instructions are specific enough to be testable ("flag SQL injection in string concatenation" not "review for security") [ ] Instructions do not conflict with existing repository-level AGENTS.md files [ ] The agent does not claim capabilities it does not have (e.g., "run tests" when no tools entry supports it) 2. Tool surface audit # Extract all tool references from the agent definition rg -n 'tools:' agent-definition.yml -A 20 | grep '^\s*-' For each tool: What resource does it access? (filesystem, network, repository API) Is it read-only or read-write? Does it require elevated permissions beyond the developer's own role? A post_comment tool can create noise across every PR. A write_file tool can modify code in every repo. Audit accordingly. 3. Canary test Be

2026-07-20 原文 →
AI 资讯

Measure Copilot Cost per Retained Change, Not Accepted Suggestion

An accepted AI suggestion is an event, not a durable outcome. If the code is rewritten tomorrow, acceptance rate still calls it a success. For an adoption review, I would connect three timestamps: suggestion : accepted_at : 2026-07-19T09:00:00Z repository : api task_type : test change : retained_lines_24h : 31 rewritten_lines_24h : 9 reverted_at : null review : human_minutes : 12 incident_link : null Then report a funnel rather than one flattering percentage: shown → accepted → merged → retained_24h → retained_14d A useful unit is cost per retained task : (tool cost + review labor + rework labor) / retained tasks “Retained” needs a written contract. For example: the change remains merged after 14 days, passes required checks, and has not caused a linked rollback. Line survival alone is weak because formatting and refactoring can change lines without rejecting the solution. Segment the result by task type and repository. Boilerplate tests and unfamiliar security changes should not be blended into one portfolio average. Also publish counter-metrics: review time, escaped defects, rollback rate, and developer-reported interruption. GitHub documents available fields and limitations in its Copilot metrics API . Those product metrics can be inputs, but the retained-task join belongs to the adopting organization and should be versioned like any other analytics contract. My pilot gate would be simple: expand only if retained-task cost beats the existing workflow without worsening rollback rate. Otherwise, change the workflow before buying more seats. Record the baseline before enabling the tool, and keep one comparable task cohort outside the rollout; without that counterfactual, a rising retention rate may only reflect easier work entering the sample. What retention window would make an accepted change meaningful for your team?

2026-07-20 原文 →
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