与「Recall」相关的搜索结果

阿里把团队内部用了两年的官方 AI Code Review Skills 开源了，采用 “确定性工程 pipeline + AI Agent” 的混合架构，专门解决通用 Agent 做代码审查时 “漏审、定位漂移、质量不稳” 的老问题。 40.5K ✨ 开源项目 OpenCodeReview： # 核心设计：确定性工程 pipeline × Agent 各司其职 确定性工程负责硬约束： · 精确文件选择：用代码决定哪些文件必须审、哪些要过滤，不依赖模型自觉； · 智能文件捆绑：把相关文件合成一个审查单元（例如 message_en.properties 和 message_zh.properties 捆绑），每个单元以上下文隔离的 sub-agent 运行，分治策略让超大变更集也稳，且天然支持并发（默认 8 个文件 worker）； · 细粒度规则匹配：内置约 54 个按语言/文件类型的规则文档（Java、Go、TS/JS、Python、Rust、SQL/XML mapper、properties 等），用模板引擎而非自然语言把规则匹配到文件特征上，从源头消除信息噪声； · 外部定位与反思模块：评论的“落点”和“内容”分别由独立的 re-location 和 reflection 模块系统性校正，这正对“位置漂移”痛点。 Agent 负责动态决策： · 深度优化的场景 prompt（内部分为 plan → grouping → main → memory_compression → re_location → review_filter 多个任务模板，可在 internal/config/template/prompts/ 看到）； · 从海量生产环境的 tool-call 轨迹（调用频率分布、单工具重复率、新工具对调用链的影响）反向蒸馏出的专用工具集，包括全文件读取、代码搜索、其他变更文件查阅等，比通用 agent 工具箱更小更稳。 # 能力面与生态集成 功能上覆盖：workspace/分支区间/单 commit 审查、断点恢复（ocr session）、全文件 scan（无 git 历史也能审计陌生代码库）、本地 Session Viewer 网页查看与回放、SARIF/JSON 输出、OpenTelemetry 可观测性、MCP Server 扩展。 作为 “Skills 生态” 级项目，它的形态相当完整：既提供 npm 全局 CLI，也提供可移植的 Agent Skill（skills/open-code-review/SKILL.md，带标准 frontmatter，可直接被兼容 skill 的 agent 加载），还有面向 Claude Code、Codex、Cursor、Kimi Code、OpenCode 等平台的插件，每种都封装成斜杠命令或可调用 skill。LLM 侧兼容 OpenAI、Anthropic、AWS Bedrock 三类协议，并可直接复用 Claude Code 的 ANTHROPIC_* 环境变量。 其中一个设计很巧妙：Delegation 模式（ocr delegate preview/rule）。此时 OCR 只做自己擅长的确定性部分（文件选择和规则解析）审查本身交给宿主 coding agent 的 LLM 执行，用户无需给 OCR 配任何 API key。这实际上是把“harness 能力”与“模型能力”彻底解耦。 # 工程质量：超出平均水准的部分 · 安全有正式的 Assurance Case（ASSURANCE_CASE.md）：完整的威胁模型、四条信任边界、T1–T7 威胁逐条给出缓解措施，并按 Saltzer & Schroeder 设计原则和 OWASP Top 10 做了映射。细节经得起推敲：所有外部进程调用只限 git 且子命令硬编码、--end-of-options 防 flag 注入；Agent 读文件路径经 pathutil.WithinBase() 在符号链接解析前后双重校验；本地 Viewer 有 Host 白名单防 DNS rebinding + 严格 CSP。这类文档在一般开源项目里非常罕见。 · 贡献规范近乎严苛（AGENTS.md）：使用 AI 必须在 issue/PR 中披露工具与模型、必须逐行理解 AI 生成的代码、禁止“AI 生成→反复修复→再修复”的循环、禁止把 commit 署名给 AI。源码强制英文（CI 有 english-check，连全角标点都查）、90% 测试覆盖率门槛、-race 与 govulncheck 每次 push 都跑、SPDX 头与 LF 行尾强制。 # Benchmark：数据情况 官方基准 AACR-Bench（已在 Hugging Face 开放）规模不小：50 个流行开源仓库、200 个真实 PR、10 种语言、80+ 资深工程师交叉验证出 1505 条标注问题。结论是同模型对比 Claude Code：Precision 和 F1 显著更高、token 消耗约为 1/9、速度更快。 需要指出两点：其一，Recall 低于通用 agent，README 自己承认这是“以精度换噪声”的刻意权衡，如果你最怕漏问题而非误报，可能不适合；其二，该基准由阿里自建，虽开放了数据集供社区复核，但独立第三方的复现结论目前还少，可以把它当作“有披露的、方向可信的参考”。

Introducing Headlong, an open source microharness for persistent agents: self-guided agents that think continuously. Most agent harnesses are reactive: you send a task, the agent completes it, and then it sits frozen until the next request. Cron jobs and heartbeats wake it up to run a checklist and put it back to sleep. A Headlong agent is never asleep. It keeps generating thoughts about whatever it decides is interesting, in a self-guided loop inspired by human inner monologue. Your message doesn't start a session. It's one more observation that lands in the agent's thought stream, and the agent decides if and when to reply. Headlong is built on the idea of persistent agency: continuous inner thought generation between external interactions. The agent sets its own interests and priorities, comes up with its own projects, and sometimes pings you unprompted with progress. To keep our prototype as simple and small as possible, we implemented Headlong as a microharness: a complete agent harness in under 10K lines of Bash, organized as a handful of small executables. It includes a loop that generates the next thought, shellm (a recursive language model written in Bash), a trajectory stored as a DAG of jsonl files, and context as a projection of that trajectory. We've been running one Headlong agent internally at Laude for several weeks. The whole team talks to it over Slack and Telegram, and every conversation lands in its single stream of thought. It works in its own fork of Headlong and we've pulled over 50 of its commits into main. One night, with nobody talking to it, it went back to check whether a recall process it had built was actually wired into its mind, found that it wasn't, diagnosed and fixed the bug, and verified the fix end to end. 48 minutes, no human asked for the fix or was in the loop at any point. Every step is a timestamped line in its log. Things broke too, and we wrote those up. Background thinking costs us $1 to $2 an hour, our agent stopped its own service three times by accident, and self-delegation died on day one. Details in the post. One line installs everything and starts an agent. Use a dedicated sandbox and spend-capped API key; it runs real shell commands and thinks around the clock. Headlong is research software, be careful! curl -fsSL | bash Launch post: Repo: Headlong is a @LaudeInstitute / MIT collaboration.

"You wanna be fast like Isiah, and you wanna shoot like Bird... rebound like Barkley. Pass like Magic. Be dominant like Shaq... but man, I just wanted to be like Mike." @alleniverson recalled the first time he played against his idol, Michael Jordan in his 2016 @Hoophall enshrinement speech 🥹

Congrats on an incredible career and retirement @russwest44 much respect. Definitely didn’t always get along on the court as I recall 😎 but always respected the way you competed and played the game. Real talk! Hell of a career... salute 🫡

Claude Code's auto memory never leaves Claude Code. Open Codex and your agent starts from zero. Walrus Memory ties memory to your account, not the tool. Write it in Claude Code, recall it in Codex, Cursor, or your own SDK. Two commands to install. Here's how ↓

Walk in with matcha. Listen to Bieber. Stare down camera. Over-the-head shot. Pray under basket. Jalen Brunson is LOCKED IN on his pregame routine... watch him recall every moment from arrivals to tipoff from memory 🔒 (via @nyknicks)