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fix: canonicalize the instructor radar record to its moved repository
GitHub moved jxnl/instructor to 567-labs/instructor; the radar entry and the generated radar.json/README tables now use the canonical URL so the site's external-link gate stays green.
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‎catalog/projects/README.md‎

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| Project | Category | Status | AI familiarity | Why it matters | When not to use / Risk | Reviewed |
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| --- | --- | --- | --- | --- | --- | --- |
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| [fastapi](https://github.com/fastapi/fastapi)<br><sub>fastapi/fastapi · MIT</sub> | Web &amp; APIs | `stable` | AI: high | Production-standard ASGI framework with automatic OpenAPI docs, Pydantic validation, and dependency injection. | For non-HTTP services, or teams standardized on Django's bundled ORM/admin stack.&lt;br&gt;**Risk:** Ensure background tasks handle errors properly and use async endpoints responsibly; blocking calls inside async routes degrade the whole service. | 2026-09-02 |
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| [instructor](https://github.com/jxnl/instructor)<br><sub>jxnl/instructor · MIT</sub> | AI Tools | `stable` | AI: medium | Production standard for extracting structured JSON from LLMs using Pydantic models with retry validation. | When your provider already enforces structured outputs natively and you need nothing beyond it.&lt;br&gt;**Risk:** Requires API keys for the target LLM providers; retry loops add latency and token cost. | 2026-09-02 |
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| [instructor](https://github.com/567-labs/instructor)<br><sub>567-labs/instructor · MIT</sub> | AI Tools | `stable` | AI: medium | Production standard for extracting structured JSON from LLMs using Pydantic models with retry validation. | When your provider already enforces structured outputs natively and you need nothing beyond it.&lt;br&gt;**Risk:** Requires API keys for the target LLM providers; retry loops add latency and token cost. | 2026-09-02 |
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| [marimo](https://github.com/marimo-team/marimo)<br><sub>marimo-team/marimo · Apache-2.0</sub> | Interactive Notebooks | `rising` | AI: low | Reactive, pure-Python notebook stored as standard executable .py files with deterministic state execution. | When your workflow depends on Jupyter-only extensions, or kernel-state debugging is central to your process.&lt;br&gt;**Risk:** Requires a modern browser environment and replaces the Jupyter workflow rather than extending it. | 2026-09-02 |
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| [polars](https://github.com/pola-rs/polars)<br><sub>pola-rs/polars · MIT</sub> | Data &amp; Pipelines | `stable` | AI: medium | High-performance DataFrame library built in Rust on Apache Arrow with lazy query optimization. | When your pipeline leans on the pandas ecosystem (accessors, narrow libraries) or you need index-heavy semantics.&lt;br&gt;**Risk:** API differs from pandas and memory layout is columnar; budget migration time rather than assuming drop-in parity. | 2026-09-02 |
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| [pydantic-ai](https://github.com/pydantic/pydantic-ai)<br><sub>pydantic/pydantic-ai · MIT</sub> | AI Agents | `rising` | AI: low | Model-agnostic agent framework prioritizing type-safe structured outputs, dependency injection, and testability. | When you need a stable long-lived API surface today, or heavy multi-agent orchestration features.&lt;br&gt;**Risk:** Rapidly evolving API surface; pin minor versions and re-run your evals on every upgrade. | 2026-09-02 |

‎catalog/projects/README_cn.md‎

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| 项目 | 类别 | 状态 | AI 熟悉度 | 推荐理由 | 何时不用 / 风险 | 审核日期 |
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| --- | --- | --- | --- | --- | --- | --- |
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| [fastapi](https://github.com/fastapi/fastapi)<br><sub>fastapi/fastapi · MIT</sub> | Web 与 API | `stable` | AI 熟悉度：高 | 生产级 ASGI 框架，自带 OpenAPI 文档、Pydantic 校验与依赖注入。 | 非 HTTP 服务，或已围绕 Django 自带 ORM 与 admin 体系标准化的 团队。&lt;br&gt;**风险：**需正确处理后台任务异常、审慎使用 async 端点；在 async 路由中执行阻塞 调用会拖垮整个服务。 | 2026-09-02 |
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| [instructor](https://github.com/jxnl/instructor)<br><sub>jxnl/instructor · MIT</sub> | AI 工具 | `stable` | AI 熟悉度：中 | 从 LLM 提取结构化 JSON 的事实标准，基于 Pydantic 模型并带重试 校验。 | 所用提供商已原生强制结构化输出、且不需要更多能力时。&lt;br&gt;**风险：**需要目标 LLM 提供商的 API Key；重试循环会增加延迟与 token 成本。 | 2026-09-02 |
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| [instructor](https://github.com/567-labs/instructor)<br><sub>567-labs/instructor · MIT</sub> | AI 工具 | `stable` | AI 熟悉度：中 | 从 LLM 提取结构化 JSON 的事实标准，基于 Pydantic 模型并带重试 校验。 | 所用提供商已原生强制结构化输出、且不需要更多能力时。&lt;br&gt;**风险：**需要目标 LLM 提供商的 API Key；重试循环会增加延迟与 token 成本。 | 2026-09-02 |
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| [marimo](https://github.com/marimo-team/marimo)<br><sub>marimo-team/marimo · Apache-2.0</sub> | 交互式笔记本 | `rising` | AI 熟悉度：低 | 响应式纯 Python 笔记本，以标准可执行 .py 文件保存，状态执行具有 确定性。 | 工作流依赖 Jupyter 专属扩展，或内核状态调试是核心流程时。&lt;br&gt;**风险：**需要现代浏览器环境；它是 Jupyter 工作流的替代而非扩展。 | 2026-09-02 |
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| [polars](https://github.com/pola-rs/polars)<br><sub>pola-rs/polars · MIT</sub> | 数据与管线 | `stable` | AI 熟悉度：中 | 高性能 DataFrame 库，基于 Rust 与 Apache Arrow，支持惰性查询 优化。 | 管线依赖 pandas 生态（accessor、小众库）或需要重度索引语义时。&lt;br&gt;**风险：**API 与 pandas 不同、内存为列式布局；迁移需要预留时间，不能当作 直接替换。 | 2026-09-02 |
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| [pydantic-ai](https://github.com/pydantic/pydantic-ai)<br><sub>pydantic/pydantic-ai · MIT</sub> | AI Agent | `rising` | AI 熟悉度：低 | 模型无关的 Agent 框架，优先保证类型安全的结构化输出、依赖注入与 可测试性。 | 需要当下就稳定的长周期 API，或重度多 Agent 编排能力时。&lt;br&gt;**风险：**API 演进迅速；固定小版本，每次升级后重跑评测。 | 2026-09-02 |

‎catalog/projects/instructor.yml‎

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id: instructor
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repo: jxnl/instructor
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url: https://github.com/jxnl/instructor
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repo: 567-labs/instructor # repository moved from jxnl/instructor; same project
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url: https://github.com/567-labs/instructor
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category: ai-tools
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status: stable
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first_seen: 2026-09-02

‎radar.json‎

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},
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{
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"id": "instructor",
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"repo": "jxnl/instructor",
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"url": "https://github.com/jxnl/instructor",
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"repo": "567-labs/instructor",
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"url": "https://github.com/567-labs/instructor",
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"category": "ai-tools",
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"status": "stable",
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"first_seen": "2026-09-02",

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