Changelog, dev startup, README, AgentHub 0.4.0, model catalog, and landing site (#7)

Branch-length batch covering tooling, the model layer, the Web App and the public
surfaces. Highlights:

- Changelog: a per-release `changelog/<version>/` tree, grouped by the surface each
  change touches, with a root CHANGELOG.md holding one line per release.
- Dev startup: `scripts/dev-prebuild.mjs` serializes the skills+core prebuild behind a
  lock and keeps `pnpm install` current; `pnpm dev` runs server+web together.
- AgentHub 0.3.3 -> 0.4.0: OmniMessage complete payloads carry one opaque `fidelity`
  object in place of item-level `signature`/`phase`, threaded verbatim through Trace,
  replay and resume; malformed classification adapted to the new error types.
- Model layer: a model is always referenced by an explicit `(provider, model_id)` pair.
  The provider is never inferred, guessed or defaulted -- both the catalog inference and
  the unique-match config resolution are gone, and CLI, SDK, server routes and
  run_subagent all require the complete pair. Catalog gains the Qwen Token Plan, Qwen
  Pay-As-You-Go and Fireworks AI gateways, plus an expanded OpenRouter group.
- Web App: catalog preset sync and per-group speed test on the Models page, positional
  slash commands, a markdown renderer, skill-library update reminders, and a vertically
  centred draft page whose upward menus size themselves to the room available.
- Public surfaces: restructured READMEs, the penguin.ooo landing site and blog, refreshed
  benchmark results for both suites, and the demo videos playing on the landing page.

Includes the fixes from a full review of the branch: 23 confirmed findings, among them a
provider-inference bug that could send one vendor's API key to another vendor's endpoint,
and an Escape handler that destroyed the composer's contents unrecoverably.

Verified on the branch head: pnpm test (1127 passing, 7 packages), pnpm typecheck and
pnpm format:check clean, Playwright e2e 14/14.
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Yaowei Zheng authored and GitHub committed 2026-07-21 17:43:31 +08:00
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---
title: 携手 AMD 开发者计划:免费领取 $50 Fireworks 额度,直连 PenguinHarness
date: 2026-07-20
category: news
excerpt: 我们很高兴与 AMD AI Developer Program 合作,为大家带来 Fireworks 的免费兑换码——按本文申请 $50 Credits,再在 PenguinHarness 里三步用起来。
---
我们很高兴与 **AMD AI Developer Program**(AMD 开发者计划)合作,为大家带来 Fireworks 的免费兑换码:加入计划并通过审核,即可获得可兑换 **$50 Fireworks AI Credits** 的 Coupon Code。PenguinHarness 内置 Fireworks AI 网关分组——OpenAI 协议、预置 base URL 与五个模型,额度到手即刻可用。
> 页面内容、Credits 金额与有效期可能调整,请以申请时的页面及审批邮件为准。
## 第一步:加入 AMD AI Developer Program
访问官方入口:<https://developer.amd.com/ai-developer-program/>,在 **Join the AMD AI Developer Program** 区域:
- 没有 AMD ADP 账户:点击 **Create Account**,填写个人信息创建账户;
- 已有账户:点击 **Log In**,按页面提示登录。
![AMD AI Developer Program 加入页面](https://github.com/user-attachments/assets/47a3055b-9a95-40a1-80c6-e3bac7a9ac49)
## 第二步:在 Member Perks 申请 Cloud Credits
注册并登录后:
1. 点击顶部导航栏中的 **Member Perks**;
2. 找到 **Cloud Credit Options**;
3. 点击底部的 **Request Cloud Credits**。
![Member Perks 中的 Cloud Credit Options](https://github.com/user-attachments/assets/773f8cf1-d72f-4aa3-83b8-f31fc7c9ed9e)
## 第三步:填写申请表
进入申请表后填写个人信息:
- **Product Needed** 处选择 **Fireworks AI**;
- **Profile** 处提供至少一个公开资料用于账户验证:LinkedIn、GitHub、Portfolio、公司 / 学校主页、WeChat、WeCom 等均可。
![申请表:Product Needed 选择 Fireworks AI](https://github.com/user-attachments/assets/12a49136-0956-4f29-9d47-f0e473615075)
填完页面中其他带 `*` 的必填项,检查邮箱、身份、产品选项与公开资料链接无误后提交。
## 第四步:等待审核
AMD 会验证账户与申请资料,通常需要 **2–3 个工作日**;实际时间可能因申请量、资料完整度或节假日而变化。
## 第五步:收到兑换码并兑换
审核通过后,AMD 会向申请邮箱发送包含唯一 **Coupon Code** 的邮件,可兑换 **$50 Fireworks AI Credits**。请妥善保存,不要公开、转发或提交到代码仓库。
![审批邮件中的 Coupon Code](https://github.com/user-attachments/assets/c53129c7-2c87-4510-a4e3-31f52598dc24)
兑换与创建 API key:
1. 打开 <https://fireworks.ai/> 并登录;
2. 点击 **Redeem Promo**,输入邮件中的 Coupon Code,兑换 $50 Credits;
3. 点击 **Create API Key**,生成 Fireworks API key。
![在 Fireworks 控制台兑换并创建 API key](https://github.com/user-attachments/assets/051a1e69-db7f-4867-b899-89981df15142)
## 在 PenguinHarness 中用起来
拿到 API key 后,三步接入:
**1. 安装并启动**
```bash
curl -fsSL https://penguin.ooo/install.sh | sh
penguin web # 打开 http://127.0.0.1:7364(首次登录:admin / penguin-2026)
```
**2. 配置 Fireworks 模型**
进入「模型仓库」页,找到 **Fireworks AI** 分组,点击「统一配置 key」粘贴刚创建的 API key。分组预置了五个模型——GLM 5.2、Kimi K2.7 Code、DeepSeek V4 Pro、MiniMax M3、DeepSeek V4 Flash——base URL 与价格已填好,任选一个设为默认即可;也可以点组头的「测速」,实测各模型的 TTFT 与 TPS 再决定。
**3. 开始使用**
回到对话页,把第一个任务交给 Agent——例如「分析 data.csv,输出各季度销售额汇总」。
## 参考链接
- [AMD AI Developer Program](https://developer.amd.com/ai-developer-program/)
- [AMD 官方 Cloud Credits 申请视频演示](https://www.youtube.com/watch?v=masSW53JkTY)
- 申请步骤与截图整理自 WhatGhost 的申请指南([中文](https://github.com/WhatGhost/whatghost_Notebooks/blob/main/other/AMD_AI_Developer_Program_Credits_%E7%94%B3%E8%AF%B7%E6%8C%87%E5%8D%97.md) / [English](https://github.com/WhatGhost/whatghost_Notebooks/blob/main/other/AMD_AI_Developer_Program_Credits_Application_Guide_EN.md)),感谢原作者
- [PenguinHarness 模型配置文档](https://penguin.ooo/docs/models)