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feat: add user profiling and multi-dimensional angle directions #39

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98 changes: 66 additions & 32 deletions SKILL.md
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Expand Up @@ -52,7 +52,41 @@ updated centrally — you'll always get the latest sources automatically."

(Replace [N] and [M] with actual counts from default-sources.json)

### Step 2: Delivery Preferences
### Step 2: User Profiling (1 min)

Before setting up logistics, understand what the user needs from this digest.
Ask these 3 questions concisely:

**Q1:** "What best describes your current focus?" (single choice)
- AI 产品/创业 (building AI products or startup)
- AI 自媒体/内容创作 (AI content creator on social media)
- 运营/增长 (operations & growth)
- 产品经理 (product manager)
- 文科生转型 AI (career transition from non-tech)
- 投资/研究 (investor or analyst)

**Q2:** "你主要关注 AI 在哪个方向的应用？" (multiple choice, pick 1-3)
- 产品构建 (building products with AI)
- 运营效率 (operational efficiency & workflows)
- 内容/自媒体创作 (content creation & social media)
- 求职面试 (job hunting & interviews)
- 技术趋势判断 (tech trend spotting)
- 商业模式分析 (business model analysis)

**Q3:** "除了信息本身，你最希望日报帮你解决什么问题？" (single choice)
- 找选题灵感 (I need content angle ideas from the news)
- 拆解商业逻辑 (I want to understand the business implications)
- 直接可用的结论 (I want ready-to-use takeaways I can apply today)
- 判断趋势方向 (I want to spot what's coming next)

Save answers to config.json as `userProfile`.

**Design notes (for you):**
- Keep this fast — 3 questions, ~40 seconds total. Don't over-explain.
- Do NOT ask for customization details like "what topics specifically". The angle generation is smart enough to derive that from their role + focus.
- If they ask "what does this change?", say: "Based on your profile, the digest will include relevant angle suggestions under each key point — headlines you can directly use as content ideas, tailored to your role."

### Step 3: Delivery Preferences

Ask: "How often would you like your digest?"
- Daily (recommended)
Expand All @@ -63,7 +97,7 @@ Then ask: "What time works best? And what timezone are you in?"

For weekly, also ask which day.

### Step 3: Delivery Method
### Step 4: Delivery Method

**If OpenClaw:** SKIP this step entirely. OpenClaw already delivers messages to the
user's Telegram/Discord/WhatsApp/etc. Set `delivery.method` to `"stdout"` in config
Expand Down Expand Up @@ -113,17 +147,17 @@ Add the key to the .env file.
Set `delivery.method` to `"stdout"`. Tell them: "No problem — just type /ai
whenever you want your digest. No automatic delivery will be set up."

### Step 4: Language
### Step 5: Language

Ask: "What language do you prefer for your digest?"
- English
- Chinese (translated from English sources)
- Bilingual (both English and Chinese, side by side)

### Step 5: API Keys
### Step 6: API Keys

**If the user chose "stdout" or "right here" delivery:** No API keys needed at all!
All content is fetched centrally. Skip to Step 6.
All content is fetched centrally. Skip to Step 7.

**If the user chose Telegram or Email delivery:**
Create the .env file with only the delivery key they need:
Expand All @@ -145,15 +179,15 @@ Tell the user: "All podcast and X/Twitter content is fetched for you automatical
from a central feed — no API keys needed for that. You only need a key for
[Telegram/email] delivery."

### Step 6: Show Sources
### Step 7: Show Sources

Show the full list of default builders and podcasts being tracked.
Read from `config/default-sources.json` and display as a clean list.

Tell the user: "The source list is curated and updated centrally. You'll
automatically get the latest builders and podcasts without doing anything."

### Step 7: Configuration Reminder
### Step 8: Configuration Reminder

"All your settings can be changed anytime through conversation:
- 'Switch to weekly digests'
Expand All @@ -163,7 +197,7 @@ automatically get the latest builders and podcasts without doing anything."

No need to edit any files — just tell me what you want."

### Step 8: Set Up Cron
### Step 9: Set Up Cron

Save the config (include all fields — fill in the user's choices):
```bash
Expand All @@ -180,6 +214,11 @@ cat > ~/.follow-builders/config.json << 'CFGEOF'
"chatId": "<telegram chat ID, only if telegram>",
"email": "<email address, only if email>"
},
"userProfile": {
"role": "<focus from Q1: product, content, operations, pm, transition, or investment>",
"focusAreas": ["<1-3 items from Q2: building, efficiency, content, career, trends, business>"],
"digestionMode": "<from Q3: angles, business, takeaways, or trends>"
},
"onboardingComplete": true
}
CFGEOF
Expand Down Expand Up @@ -276,7 +315,7 @@ or switch to OpenClaw.
Skip cron setup entirely. Tell the user: "Since you chose on-demand delivery,
there's no scheduled job. Just type /ai whenever you want your digest."

### Step 9: Welcome Digest
### Step 10: Welcome Digest

**DO NOT skip this step.** Immediately after setting up the cron job, generate
and send the user their first digest so they can see what it looks like.
Expand Down Expand Up @@ -346,18 +385,32 @@ Read the prompts from the `prompts` field in the JSON:
- `prompts.digest_intro` — overall framing rules
- `prompts.summarize_podcast` — how to remix podcast transcripts
- `prompts.summarize_tweets` — how to remix tweets
- `prompts.generate_angles` — how to dig multi-dimensional angle directions from content
- `prompts.translate` — how to translate to Chinese

**Tweets (process first):** The `x` array has builders with tweets. Process one at a time:
1. Use their `bio` field for their role (e.g. bio says "ceo @box" → "Box CEO Aaron Levie")
2. Summarize their `tweets` using `prompts.summarize_tweets`
2. Summarize their `tweets` using `prompts.summarize_tweets` — keep summary tight (1-2 sentences)
3. Every tweet MUST include its `url` from the JSON
4. Then generate 3‑5 multi-dimensional angle directions using `prompts.generate_angles` (skip if `digestionMode` is "takeaways"; angle types adapt based on userProfile.role). Note: 面试素材 is not a separate type — mark any angle with `（面试可用）` if it fits an interview context.

**Podcast (process second):** The `podcasts` array has at most 1 episode. If present:
1. Summarize its `transcript` using `prompts.summarize_podcast`
1. Summarize its `transcript` using `prompts.summarize_podcast` — keep summary tight (1-2 sentences)
2. Use `name`, `title`, and `url` from the JSON object — NOT from the transcript
3. Generate 3‑5 multi-dimensional angle directions using `prompts.generate_angles` (same condition as tweets)

Assemble the digest following `prompts.digest_intro`. Number each content item sequentially across the whole digest (not per-section restart).

Assemble the digest following `prompts.digest_intro`.
**Role-based footer:** After the main digest, read `userProfile.role` from config.
Add exactly ONE line:

- `product` → 💡 这里哪条信息对你的产品决策最有冲击？
- `content` → 📝 每条延展方向都能独立成篇。选一个今天发。
- `operations` → ⚙️ 哪条信息明天就能复用？
- `pm` → 📋 用户的真实需求在怎么变？roadmap 需要调整吗？
- `transition` → 🎯 AI 缺的不是技术，是懂场景的人。
- `investment` → 📊 今天有没有被低估的方向？注意反共识信号。
- if no profile → skip.

**ABSOLUTE RULES:**
- NEVER invent or fabricate content. Only use what's in the JSON.
Expand All @@ -370,26 +423,7 @@ Assemble the digest following `prompts.digest_intro`.
Read `config.language` from the JSON:
- **"en":** Entire digest in English.
- **"zh":** Entire digest in Chinese. Follow `prompts.translate`.
- **"bilingual":** Interleave English and Chinese **paragraph by paragraph**.
For each builder's tweet summary: English version, then Chinese translation
directly below, then the next builder. For the podcast: English summary,
then Chinese translation directly below. Like this:

```
Box CEO Aaron Levie argues that AI agents will reshape software procurement...
https://x.com/levie/status/123

Box CEO Aaron Levie 认为 AI agent 将从根本上重塑软件采购...
https://x.com/levie/status/123

Replit CEO Amjad Masad launched Agent 4...
https://x.com/amasad/status/456

Replit CEO Amjad Masad 发布了 Agent 4...
https://x.com/amasad/status/456
```

Do NOT output all English first then all Chinese. Interleave them.
- **"bilingual":** Digest in Chinese only. (Bilingual interleaving is deprecated — single language is cleaner for phone reading.)

**Follow this setting exactly. Do NOT mix languages.**

Expand Down
24 changes: 24 additions & 0 deletions config/config-schema.json
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Expand Up @@ -60,6 +60,30 @@
"type": "boolean",
"default": false,
"description": "Whether the user has completed initial setup"
},
"userProfile": {
"type": "object",
"description": "User profiling collected during onboarding for personalized digest angles",
"properties": {
"role": {
"type": "string",
"enum": ["product", "content", "operations", "pm", "transition", "investment"],
"description": "User's primary focus role (collected in Step 2)"
},
"focusAreas": {
"type": "array",
"items": {
"type": "string",
"enum": ["building", "efficiency", "content", "career", "trends", "business"]
},
"description": "1-3 AI focus areas the user cares most about"
},
"digestionMode": {
"type": "string",
"enum": ["angles", "business", "takeaways", "trends"],
"description": "Preferred way to digest information: angles (headline ideas), business (business logic), takeaways (ready-to-use), trends (trend spotting)"
}
}
}
}
}
134 changes: 79 additions & 55 deletions examples/sample-digest.md
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@@ -1,60 +1,84 @@
# Sample Digest Output

This is an example of what your AI Builders Digest looks like.
This is an example of the final format with sequential numbering and detailed summaries.

---

AI Builders Digest — March 14, 2026

PODCASTS

Latent Space — "Why Agents Keep Failing (And How to Fix Them)"
Bottom line: Most agent failures aren't intelligence failures — they're tool-use failures.
The system can reason fine, it just can't reliably call the right API at the right time.

Key insights:
- Tool selection accuracy drops from 95% to 60% when agents have more than 15 tools
available. The fix isn't smarter models — it's better tool curation per task.
- "Eval-driven development" is replacing vibe-driven prompt iteration at serious
AI companies. If you're not measuring, you're guessing.
- The hosts predict 2026 is the year agent frameworks consolidate from 50+ to 3-4
winners. Their bet: OpenAI Agents SDK, Claude Code, and LangGraph.
https://youtube.com/watch?v=example123

No Priors — "Scaling Laws Are Dead, Long Live Scaling Laws" (with Ilya Sutskever)
Bottom line: Pre-training scaling laws have hit diminishing returns, but post-training
and inference-time compute scaling are just getting started.

Key insights:
- Ilya argues the next 10x improvement comes from models that can "think longer"
at inference time, not from bigger pre-training runs.
- Synthetic data quality matters more than quantity. "One perfect textbook is worth
a million Reddit comments."
- He's surprisingly bullish on open-source: "The gap will narrow to months, not years."
https://youtube.com/watch?v=example456


X / TWITTER

Andrej Karpathy (@karpathy)
Shared a deep thread on why he thinks "Software 3.0" (natural language programming)
will make traditional coding a niche skill within 5 years. Key argument: the compile
target is changing from machine code to LLM prompts. Sparked massive debate.
Also released a new Eureka Labs tutorial on building a code interpreter from scratch.
https://x.com/karpathy/status/example1
https://x.com/karpathy/status/example2

Guillermo Rauch (@rauchg)
Announced Vercel's new "v0 Teams" — collaborative AI prototyping where multiple
people can prompt and iterate on the same UI simultaneously. Called it "Google Docs
for vibe coding." Ships next week.
https://x.com/rauchg/status/example3

Amanda Askell (@AmandaAskell)
Published a nuanced take on AI safety benchmarks: "We're measuring what's easy to
measure, not what matters. Capability evals tell you what the model CAN do.
Alignment evals should tell you what it WILL do unprompted." Linked to a new
Anthropic research paper on behavioral evaluations.
https://x.com/AmandaAskell/status/example4

Reply to adjust your settings, sources, or summary style.
AI Builders Digest — May 6, 2026

▎播客
━━━━━━━━━━━━━━━━━━━━

1. Training Data — Waymo's Dmitri Dolgov: 20 Million Rides and the Road to Full Autonomy

Waymo 联合 CEO Dmitri Dolgov 回顾从 2009 年 Google 自动驾驶项目至今的 21 年征程。他提出一个核心观点：让自动驾驶从"能开"到"安全可靠"的跨越，远比从"不能开"到"能开"难得多——从 90% 到 99.9999% 不是量变，是质变。Waymo 目前完成了 2000 万次商业行程，其系统架构是一个"司机-模拟器-裁判"三体结构：核心模型负责驾驶，模拟器负责验证，裁判负责安全裁决。

📌 延展方向：
[内容方向] Waymo 21 年 vs AI 创业速成心态——"长期主义在 AI 时代还成立吗"，这个反差本身就是好选题。
[商业洞察] 纯端到端只够 demo，规模化需要额外安全层。这个 gap 适用于所有 AI 产品。
[认知刷新] "90%→99.9999% 不是量变是质变"——这个认知框架适用于聊天机器人、代码生成、Agent 任何落地场景。（面试可用）
[产品启示] Waymo"司机-模拟器-裁判"架构：你的 AI 产品除了核心模型，还需要什么配套系统才能可靠运行？
🔗 https://youtube.com/playlist?list=PLOhHNjZItNnMm5tdW61JpnyxeYH5NDDx8

▎推特
━━━━━━━━━━━━━━━━━━━━

2. Swyx

Swyx 对比 OpenAI 和 Anthropic 的最新估值与收入：OpenAI 8500 亿估值对应约 300 亿 ARR，Anthropic 9000 亿估值对应约 440 亿 ARR。但关键在收入确认方式——Anthropic CFO Denise Dresser 表示按 OpenAI 同一口径算，Anthropic 的 ARR 要低 80-100 亿。这意味着 Anthropic 的实际估值倍数可能比 OpenAI 贵得多，两家公司的财务可比性远没有表面看起来那么直接。

📌 延展方向：
[商业洞察] AI 巨头之间还没有统一估值坐标系——谁先跑通"可比财务模型"，谁在融资中更有话语权。
[内容方向] "OpenAI vs Anthropic 全面对比"：估值、收入、技术路线、企业策略。竞品对比在小红书和公众号都很能打。
🔗 https://x.com/swyx/status/2051440392722391180

3. Box CEO Aaron Levie

Anthropic 和 OpenAI 同时推出企业 Agent 部署计划。Levie 认为 Agent 进入知识工作领域后，企业软件采购方式将被根本性重塑——企业要做的不仅是选模型，还要升级 IT 系统、打通上下文、改造工作流、设计人机协作关系。AI 模型的"能力"和"能不能稳定跑在业务流程里"是两回事，这中间产生了大量新机会。

📌 延展方向：
[需求洞察] 用户需求从"我要一个工具"变成"我要这件事被搞定"——产品定义需要重新思考。
[商业洞察] SaaS 按席位→按价值收费。这个定价范式转变是未来几年的核心信号。（面试可用）
[内容方向] "SaaS 死了吗？"切入点：企业买的不是软件，是结果。这个转变值得一篇深度。
[产品启示] 当 Agent 替代知识工作，你的产品是被替代还是成为基础设施？
🔗 https://x.com/levie/status/2051344780328858040

4. YC CEO Garry Tan

GBrain v0.27 发布，新增多模态嵌入和深度照片 OCR，同时支持非 Anthropic/OpenAI 的 LLM。Tan 强调 GBrain 的差异化定位：它不是记忆层，不是代码工具，也不是搜索引擎——三者统一在一个图结构下。他本人每天在 10 万 markdown 文件的 OpenClaw+Hermes Agent 环境中使用。

📌 延展方向：
[产品启示] "不是 A 不是 B，是 A+B+C 的统一体"——这种反定义定位策略值得产品经理学习。
[趋势判断] YC CEO 亲自下场做知识库，说明个人知识管理正在成为 AI 重要战场。
🔗 https://x.com/garrytan/status/2051517574589116510

5. Sam Altman

Altman 对语音模型表达强烈兴奋，表示已经观察到人们开始改变与 AI 交互的方式。同时预告 GPT-5.5 派对会给未入选的申请者准备惊喜。

📌 延展方向：
[趋势判断] Altman 专门提语音是强信号——AI 下一个交互范式可能是语音优先，产品需要为"只说不动手"的场景做准备。
[内容方向] 可以做"2026 下半年 AI 行业 5 个关键趋势"预测向内容，语音交互排第一。
🔗 https://x.com/sama/status/2051464865634742334

6. Peter Yang (Product at Roblox)

Yang 提出 AI 发展的三波框架：Coding 是第一波（开发者工具），知识工作是第二波（企业效率工具），个人 Agent 是第三波（每个人拥有的 AI 助手）。大多数人还在第一波焦虑时，第三波已经来了。

📌 延展方向：
[认知刷新] 大多数人还在第一波焦虑，但第三波已经来了——这个时间差本身就是内容选题和创业机会。
[内容方向] 用"三波论"框架做一篇梳理内容：你在第几波？每个阶段的入场策略是什么？
🔗 https://x.com/petergyang/status/2051508988936937764

7. Vercel CEO Guillermo Rauch

Rauch 发布 deepsec——一个开源 Agent 安全审查编排器。数千个 Agent 并行扫描代码库，几分钟找到团队几个月才能发现的漏洞。核心卖点：内部 dogfooding 验证后开源，结合 Vercel Sandbox 实现大规模并行。

📌 延展方向：
[产品启示] "内部工具→开源产品"是很好的 launch 策略——先有 dogfooding 的底气，再推向市场。
[趋势判断] AI 安全审查正在从"辅助工具"变成"标配流程"，这本身是一个新品类机会。
🔗 https://x.com/rauchg/status/2051386798899888539

📝 每条延展方向都能独立成篇。选一个今天发。

Generated through the Follow Builders skill: https://github.com/zarazhangrui/follow-builders
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