No More Fomo

No More FOMO

Daily AI intelligence briefing: Twitter KOLs + AI lab blogs + tech podcasts + HackerNews.

IMPORTANT: Primary digest is always in English. After generating the English version, automatically translate to Chinese and save as YYYY-MM-DD-zh.md (skip with --en-only). Both versions get HTML rendering. Speaker names, model names, and technical terms stay in original form in both versions.

When to Use

• User asks about today's AI papers, news, or trending research
• Morning routine check-in on new releases
• Scheduled daily cron trigger

When NOT to use: Searching for a specific paper (use WebFetch or arxiv directly).

Prerequisites

• xreach (npm i -g xreach-cli) — Twitter/X data. Requires auth: xreach auth
• curl — RSS feeds and HN API (standard on all systems)
• Jina Reader — free, no auth needed (https://r.jina.ai/URL)
• baoyu-youtube-transcript (optional) — podcast transcript download with chapters and speaker detection. Path: ~/.claude/plugins/ljg-skills/.agents/skills/baoyu-youtube-transcript. If not installed, falls back to yt-dlp.
• bun (optional) — runtime for youtube-transcript scripts. Required only if youtube-transcript is installed.
• yt-dlp — podcast YouTube subtitles (fallback if youtube-transcript unavailable)

Sources

Twitter — Tier 1: KOLs (Default, always fetched)

Twitter — Tier 2: More Companies & Tools (on-demand via --full)

Users can also add any handle via arguments: /no-more-fomo @someone

AI Lab Blogs

Tech Podcasts (RSS)

Transcript retrieval (Phase 2 — automatic, no flag needed):

Phase 2 automatically processes new podcast episodes. Primary method uses youtube-transcript:

# Step 1: Find YouTube URL for the episode
# Preferred: extract youtube.com URL from RSS <enclosure> or <link>
# Fallback: search YouTube
yt-dlp --flat-playlist "ytsearch1:PODCAST_NAME EPISODE_TITLE" --print url

# Step 2: Download transcript with chapters and speaker detection
bun ~/.claude/plugins/ljg-skills/.agents/skills/baoyu-youtube-transcript/scripts/main.ts VIDEO_URL \
  --chapters --speakers \
  --languages en,zh \
  --output-dir ~/no-more-fomo/.cache/pods

Fallback chain (if youtube-transcript fails or is not installed):

1. yt-dlp --write-auto-sub --sub-lang en --skip-download — auto-generated subtitles
2. curl -s "https://r.jina.ai/POST_URL" — Substack transcript (Latent Space, Dwarkesh)
3. Keep basic episode entry (title + description only)

Substack-based podcasts (Latent Space, Dwarkesh) embed full transcripts in posts and can be fetched directly via Jina Reader as a fallback.

arxiv Papers (Topic-filtered)

Default topics (customizable via config):

arxiv API — fetch papers from last 24h matching user topics:

curl -s "https://export.arxiv.org/api/query?search_query=cat:cs.AI+AND+abs:AI+agent&sortBy=submittedDate&sortOrder=descending&max_results=10"

HuggingFace Daily Papers — community-upvoted trending papers (no topic filter, but quality signal):

curl -s "https://huggingface.co/api/daily_papers?limit=20"

Filter: upvotes >= 3 (configurable via min_hf_upvotes).

Merge logic: Deduplicate by arxiv ID across both sources. If a paper appears in both arxiv topic search AND HF daily, mark it as high-signal.

HackerNews

Two parallel searches via HN Algolia API, filtered to last 24h:

1. ai agent — agent-specific stories
2. LLM OR GPT OR Claude OR Gemini — broader AI coverage

User Config (Optional)

Users can customize sources by creating ~/.no-more-fomo/config.yaml. The skill merges this with defaults — users only specify what they want to change.

Before fetching, always check if config exists:

cat ~/.no-more-fomo/config.yaml 2>/dev/null

Config format:

# ~/.no-more-fomo/config.yaml

twitter:
  add:                          # Extra accounts to follow
    - handle: "@elonmusk"
      count: 15
    - handle: "@sama"
      count: 15
  remove:                       # Accounts to skip from defaults
    - "@ylecun"
    - "@hardmaru"

papers:
  topics:                       # arxiv topic searches (default: ai agent + llm)
    - query: "AI agent"
      categories: ["cs.AI", "cs.CL"]
    - query: "large language model"
      categories: ["cs.CL", "cs.AI"]
    # Add your own:
    # - query: "world model"
    #   categories: ["cs.AI", "cs.CV"]
    # - query: "diffusion transformer"
    #   categories: ["cs.CV"]
  hf_daily: true                # Also pull HuggingFace trending papers
  min_hf_upvotes: 3             # Quality threshold for HF papers

podcasts:
  add:
    - name: "Lex Fridman"
      rss: "https://lexfridman.com/feed/podcast/"
      transcript: youtube       # youtube | substack | none
    - name: "80000 Hours"
      rss: "https://feeds.feedburner.com/80000HoursPodcast"
      transcript: none
  remove:
    - "Training Data"           # Match by podcast name
  depth: full                   # full | none (default: full)
                                #   full = TLDR + chapters + speaker quotes
                                #   none = title + description only (skip Phase 2 podcasts)
  max_episodes: 3               # Max episodes per podcast to deep-process (default: 3)
  cache_dir: ~/no-more-fomo/.cache/pods  # Transcript cache directory

blogs:
  add:
    - name: "Meta AI"
      url: "https://ai.meta.com/blog/"
  remove: []

hn:
  extra_queries:                # Additional HN search terms
    - "robotics"
    - "computer vision"

discovery:
  enabled: true                 # Enable s.jina.ai discovery layer (default: true)
  max_per_topic: 3              # Max discoveries per topic (default: 3)

topic_search:
  enabled: true                 # Enable xreach search supplementation (default: true)
  min_mentions: 2               # Entity must be mentioned N+ times to trigger search (default: 2)
  max_topics: 5                 # Max topics to search (default: 5)

language: zh                    # zh | en — output language (default: zh)

Merge rules:

• add items are appended to defaults
• remove items are excluded from defaults (match by handle or name)
• If no config file exists, use all defaults as-is
• Unspecified sections keep their defaults

Process

1. Fetch All Sources (PARALLEL)

Launch ALL fetches in parallel. Use separate Bash tool calls. First read ~/.no-more-fomo/config.yaml if it exists, then merge with defaults to determine the final source list.

CRITICAL — Filter at fetch time to minimize context usage. Pipe xreach output through jq to keep only relevant tweets. This reduces ~30KB/account to ~2KB/account.

xreach JSON structure (IMPORTANT — verified):

• Top-level keys: items, cursor, hasMore (NOT data.items)
• Each item: {id, text, createdAt, likeCount, retweetCount, isQuote, isRetweet, isReply, user, media, ...}
• NO entities.urls field — URLs appear as t.co links within text
• id — tweet ID string, used to construct tweet URL: https://x.com/HANDLE/status/ID
• createdAt — format: "Tue Mar 24 16:56:24 +0000 2026"

# Template for each account — extracts id for tweet links, filters to last 24h:
CUTOFF=$(date -u -v-24H +"%Y-%m-%dT%H:%M:%S" 2>/dev/null || date -u -d "24 hours ago" +"%Y-%m-%dT%H:%M:%S")
xreach tweets @HANDLE --json -n N | jq --arg cutoff "$CUTOFF" '[.items[] | select(.isRetweet==false or .isQuote==true) | select(.createdAt | strptime("%a %b %d %H:%M:%S %z %Y") | strftime("%Y-%m-%dT%H:%M:%S") > $cutoff) | {id,text: .text[:300],createdAt,likeCount,retweetCount,isQuote}]'

Constructing tweet links: For every tweet used in the digest, construct the link as [tweet](https://x.com/HANDLE/status/ID). This is the primary reference link for Twitter-sourced items.

Twitter — Tier 1 (always, batch accounts to reduce tool calls):

# Set 24h cutoff ONCE before all batches (macOS + Linux compatible)
CUTOFF=$(date -u -v-24H +"%Y-%m-%dT%H:%M:%S" 2>/dev/null || date -u -d "24 hours ago" +"%Y-%m-%dT%H:%M:%S")

# Batch 1: High volume
xreach tweets @_akhaliq --json -n 50 | jq --arg cutoff "$CUTOFF" '[.items[] | select(.isRetweet==false or .isQuote==true) | select(.createdAt | strptime("%a %b %d %H:%M:%S %z %Y") | strftime("%Y-%m-%dT%H:%M:%S") > $cutoff) | {id,text: .text[:300],createdAt,likeCount,retweetCount,isQuote}]'
xreach tweets @dotey --json -n 30 | jq --arg cutoff "$CUTOFF" '[.items[] | select(.isRetweet==false or .isQuote==true) | select(.createdAt | strptime("%a %b %d %H:%M:%S %z %Y") | strftime("%Y-%m-%dT%H:%M:%S") > $cutoff) | {id,text: .text[:300],createdAt,likeCount,retweetCount,isQuote}]'

# Batch 2: KOLs
for h in karpathy bcherny oran_ge trq212 swyx emollick; do echo "---$h---"; xreach tweets @$h --json -n 20 | jq -c --arg cutoff "$CUTOFF" "[.items[] | select(.isRetweet==false or .isQuote==true) | select(.createdAt | strptime(\"%a %b %d %H:%M:%S %z %Y\") | strftime(\"%Y-%m-%dT%H:%M:%S\") > \$cutoff) | {id,text: .text[:300],createdAt,likeCount,isQuote}]"; done

# Batch 3: More KOLs
for h in drjimfan simonw hardmaru ylecun; do echo "---$h---"; xreach tweets @$h --json -n 20 | jq -c --arg cutoff "$CUTOFF" "[.items[] | select(.isRetweet==false or .isQuote==true) | select(.createdAt | strptime(\"%a %b %d %H:%M:%S %z %Y\") | strftime(\"%Y-%m-%dT%H:%M:%S\") > \$cutoff) | {id,text: .text[:300],createdAt,likeCount,isQuote}]"; done

# Batch 4: Company accounts (may hit rate limits — retry after 10s if needed)
for h in cursor_ai AnthropicAI OpenAI GoogleDeepMind; do echo "---$h---"; xreach tweets @$h --json -n 15 | jq -c --arg cutoff "$CUTOFF" "[.items[] | select(.isRetweet==false or .isQuote==true) | select(.createdAt | strptime(\"%a %b %d %H:%M:%S %z %Y\") | strftime(\"%Y-%m-%dT%H:%M:%S\") > \$cutoff) | {id,text: .text[:300],createdAt,likeCount,isQuote}]"; done

Twitter — Tier 2 (only with --full flag):

for h in xai WindsurfAI cognition replit huggingface llama_index; do echo "---$h---"; xreach tweets @$h --json -n 15 | jq -c --arg cutoff "$CUTOFF" "[.items[] | select(.isRetweet==false or .isQuote==true) | select(.createdAt | strptime(\"%a %b %d %H:%M:%S %z %Y\") | strftime(\"%Y-%m-%dT%H:%M:%S\") > \$cutoff) | {id,text: .text[:300],createdAt,likeCount,isQuote}]"; done
# + any extra @handles from arguments, same jq filter

Rate limiting: xreach may return "Rate limit exceeded" instead of JSON after ~15 accounts in quick succession. If this happens, wait 10 seconds and retry. Company accounts (Tier 1 Batch 4) are most likely to be affected.

AI Lab Blogs (one call per source):

curl -s "https://r.jina.ai/https://deepmind.google/blog/rss.xml"
curl -s "https://r.jina.ai/https://www.anthropic.com/news"
curl -s "https://r.jina.ai/https://openai.com/blog/rss.xml"

Podcasts (one call per feed):

curl -s "https://r.jina.ai/https://rss.art19.com/no-priors-ai"
curl -s "https://r.jina.ai/https://api.substack.com/feed/podcast/1084089.rss"
curl -s "https://r.jina.ai/https://apple.dwarkesh-podcast.workers.dev/feed.rss"
curl -s "https://r.jina.ai/https://feeds.megaphone.fm/trainingdata"

arxiv Papers (one call per topic from config, default: ai agent + llm):

# For each topic, build query from categories + keywords
curl -s "https://export.arxiv.org/api/query?search_query=(cat:cs.AI+OR+cat:cs.CL)+AND+abs:%22AI+agent%22&sortBy=submittedDate&sortOrder=descending&max_results=10"
curl -s "https://export.arxiv.org/api/query?search_query=(cat:cs.CL+OR+cat:cs.AI)+AND+abs:%22large+language+model%22&sortBy=submittedDate&sortOrder=descending&max_results=10"

HuggingFace Daily Papers:

curl -s "https://huggingface.co/api/daily_papers?limit=20"

Filter to upvotes >= 3 (or min_hf_upvotes from config). Extract: title, arxiv ID, upvotes.

HackerNews:

YESTERDAY=$(python3 -c "import time; print(int(time.time()) - 86400)")
curl -s "https://hn.algolia.com/api/v1/search?query=ai+agent&tags=story&numericFilters=created_at_i%3E$YESTERDAY&hitsPerPage=20"

YESTERDAY=$(python3 -c "import time; print(int(time.time()) - 86400)")
curl -s "https://hn.algolia.com/api/v1/search?query=LLM+OR+GPT+OR+Claude+OR+Gemini&tags=story&numericFilters=created_at_i%3E$YESTERDAY&hitsPerPage=15"

2. Parse & Extract

xreach JSON structure is documented above in Step 1. Key reminders:

• Items are at .items[] (NOT .data.items[])
• There is NO entities.urls field — URLs are t.co links inside text
• Use id field to construct tweet links: https://x.com/HANDLE/status/ID

URL extraction from tweet text: Tweets contain t.co shortened URLs. To get real URLs:

1. Regex extract https://t.co/\w+ from text
2. Resolve via curl -sI URL | grep -i location
3. Or: if the tweet mentions a known paper/repo, search for the URL directly

Every digest item MUST have at least one clickable link. For Twitter-sourced items without embedded URLs, use the tweet link [tweet](https://x.com/HANDLE/status/ID) as the minimum reference.

3. Filter

Twitter:

• Filter to last 24h
• Include original tweets and quote tweets with commentary
• Exclude pure retweets (isRetweet: true without isQuote: true) and reply threads
• Quality: likeCount > 100 for @_akhaliq, > 50 for others (lower on weekends)
• Dedup vs previous day: If ~/no-more-fomo/YYYY-MM-(DD-1).md exists, scan it for titles. Skip any item whose title/name already appeared in the previous digest.

Blogs: Only posts from last 7 days. Skip non-technical posts (hiring, events).

Podcasts: Only episodes from last 7 days. Extract: title, date, description (200 chars), link.

HN: points > 20. Deduplicate against Twitter (same URL = merge, note both sources).

4. Enrich (IMPORTANT — this is what makes the digest useful)

After filtering, collect all unique URLs from tweets, blogs, and HN. Then enrich in parallel:

arxiv papers — fetch abstract:

curl -s "https://r.jina.ai/https://arxiv.org/abs/PAPER_ID" | head -80

Extract: title, authors (first 3), abstract (first 2 sentences). This is the primary value of the digest — users need to know what the paper actually does, not just its title.

GitHub repos — fetch description:

curl -s "https://api.github.com/repos/OWNER/REPO" | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('description',''), '|', d.get('stargazers_count',0), 'stars')"

HuggingFace models — fetch model card summary:

curl -s "https://r.jina.ai/https://huggingface.co/MODEL_ID" | head -40

Blog posts (from Lab Updates) — already fetched in Step 1, extract first paragraph as summary.

Podcast transcripts — basic metadata extracted in Phase 1 (title, date, description, link). Full transcript processing (TLDR, chapters, speaker quotes) happens in Phase 2 below. Phase 1 writes a placeholder: > ⏳ 深度摘要生成中...

Parallelism: Launch all enrichment fetches in parallel. Typically 5-15 URLs to enrich.

What "enriched" looks like:

5. Categorize

Section titles follow language config. The table below shows both versions:

6. Format & Output

The template below uses zh section titles (default). When language: en, use the English equivalents from the Categorize table above.

# No More FOMO Digest — YYYY-MM-DD

## 今日要点
1. **[Title]** — [1 sentence summary] | [link](URL)
2. **[Title]** — [1 sentence summary] | [link](URL)
3. **[Title]** — [1 sentence summary] | [link](URL)

## 模型与发布
- **[Name]** — [what it is, key capability, how it compares to previous] | [HF](URL) [paper](URL) | @source | Likes: N

## 工具与演示
- **[Name]** — [what it does, why it matters, key differentiator] (N stars) | [github](URL) | @source | Likes: N

## AI Agents
- **[Title]** — [what it does, architecture insight if available, why notable] | [link](URL) | @source | Likes: N

## 实验室动态
- **[DeepMind]** [Title] — [1-paragraph summary of the blog post] | [link](URL)
- **[Anthropic]** [Title] — [1-paragraph summary] | [link](URL)
- **[OpenAI]** [Title] — [1-paragraph summary] | [link](URL)

## 播客 (Last 7 Days)
- **[Show Name]** [Episode Title] — [guest name & role] | [link](URL)
  > ⏳ 深度摘要生成中...

## HN 讨论
- **[Title]** — [points] pts, [comments] comments | [url](URL) | [HN](URL)
  > [1-2 sentences: what it is + why HN cares]

## 行业动态
- **[Topic]** — [what happened, who's involved, why it matters] | @source | Likes: N

---

## HF 热门论文
Community-upvoted papers from [HuggingFace Daily Papers](https://huggingface.co/papers). Sorted by upvotes, no topic filter — shows what the broader ML community finds interesting today.

- **[Title]** (N upvotes) — [1-2 sentence summary] | [arxiv](URL) [HF](https://huggingface.co/papers/ID)

## arxiv: AI Agents
Recent papers matching `"AI agent" OR "LLM agent"` in cs.AI, cs.CL. Customize topics in `~/.no-more-fomo/config.yaml`.

- **[Title]** — [2-3 sentence summary from abstract] | [arxiv](URL) | categories

## arxiv: Large Language Models
Recent papers matching `"large language model"` in cs.CL, cs.AI.

- **[Title]** — [2-3 sentence summary from abstract] | [arxiv](URL) | categories

## 发现 (Beyond arxiv/HN)
来自全网搜索的技术内容，未被 KOL 推文或 HN 覆盖。

- **[类型]** [Title] — [summary in configured language] | [link](URL)

---
Sources: Tier1-KOLs(N) [Tier2-Companies(N)] Labs(N) Podcasts(N/深度N) HN(N) HF-Trending(N) arxiv(N) 社区补充(N) 发现(N)
Total: N items

Save to ~/no-more-fomo/YYYY-MM-DD.md (create directory if needed).

6.5. Save Phase 1 Digest & Extract Hot Topics

Save the digest file immediately: ~/no-more-fomo/YYYY-MM-DD.md

Before proceeding to Phase 2, scan all digest entries and extract high-frequency entities:

• Paper names mentioned by 2+ different sources
• Model names mentioned by 2+ KOLs
• Tool/repo names discussed in both Twitter and HN
• Collect 3-5 hot topics (pass to Phase 2 Topic Search)

Podcast entries at this point have the placeholder ⏳ 深度摘要生成中... — these will be filled in Phase 2.

7. Relevance Check

If user has CLAUDE.md or memory files with project keywords, tag matching items with [RELEVANT].

8. Summary to User

Print concise summary:

• Top 3 highlights with links
• Count per section
• New podcast episodes (always mention)
• Any [RELEVANT] items called out

Phase 2: Deep Layer (same session, after Phase 1)

Phase 2 runs in the same Claude Code session immediately after Phase 1 saves the digest. Skip Phase 2 entirely if --quick flag is set.

Phase 2 Step A: Parallel Network Requests

Launch ALL of the following as parallel Bash tool calls:

Podcast transcripts (if podcasts.depth is full, which is the default): For each new episode found in Phase 1 (up to max_episodes per podcast, default 3): For each episode, check cache first: if ~/no-more-fomo/.cache/pods/{channel}/{episode}/summary.md exists, skip download. Otherwise:

bun ~/.claude/plugins/ljg-skills/.agents/skills/baoyu-youtube-transcript/scripts/main.ts VIDEO_URL \
  --chapters --speakers --languages en,zh \
  --output-dir ~/no-more-fomo/.cache/pods

Topic Search (if topic_search.enabled, default true): For each hot topic extracted in step 6.5 (max 5):

xreach search "TOPIC_NAME" --type top -n 15 --json

Discovery (if discovery.enabled, default true): For each topic in papers.topics config (max 3):

curl -s "https://s.jina.ai/latest%20TOPIC%20research%202026"

Phase 2 Step B: AI Processing (Serial)

Podcast structured summaries — for each episode with downloaded transcript:

1. Read the generated .md file from ~/no-more-fomo/.cache/pods/{channel}/{title}/transcript.md
2. If --speakers was used, reference the speaker identification prompt at: ~/.claude/plugins/ljg-skills/.agents/skills/baoyu-youtube-transcript/prompts/speaker-transcript.md
3. Identify speakers (host vs guest) from video metadata (title, channel, description)
4. Generate structured summary in the configured language (default zh):

**TLDR:** [3 sentences: core thesis, most surprising insight, practical takeaway]

**章节:**
- *[Chapter Title]* — [1-2 sentence summary of this chapter]
- *[Chapter Title]* — [1-2 sentence summary]

**关键引用:**
> **[Speaker Name]:** "[Translated quote]" [HH:MM:SS]
> **[Speaker Name]:** "[Translated quote]" [HH:MM:SS]

5. Cache the summary to ~/no-more-fomo/.cache/pods/{channel}/{title}/summary.md

Speaker names stay in original form (English). Technical terms stay in original form. Select 2-3 quotes that are most insightful or surprising.

Topic Search analysis — for each xreach search result set:

• Filter: likeCount > 200, exclude tweets from KOLs already in the digest (deduplicate by handle)
• If >80% overlap with existing KOL tweets, skip this topic
• Extract 2-3 external perspectives (disagreements, supplementary info, user feedback)
• Format as a > 社区热议: blockquote appended to the matching digest entry

Discovery filtering — for each s.jina.ai result set:

• Deduplicate: remove any URL already in the digest
• Filter: only keep technical content (papers, blog posts, conference pages — not news aggregators or SEO)
• Keep max 3 per topic, format as:

- **[类型]** [Title] — [1-2 sentence summary in configured language] | [link](URL)

Where 类型 is one of: 博客, 会议, 报告, 教程

Phase 2 Step C: Update Digest File

Read ~/no-more-fomo/YYYY-MM-DD.md and apply updates:

1. Podcasts: Find each ⏳ 深度摘要生成中... placeholder → replace with the structured summary (TLDR + chapters + quotes). If transcript failed, replace placeholder with basic description from RSS.

2. Topic Search: Find matching entries by title → append > 社区热议: blockquote after the entry.

3. Discovery: Find the --- line immediately above the Sources: line → insert before it:

## 发现 (Beyond arxiv/HN)
来自全网搜索的技术内容，未被 KOL 推文或 HN 覆盖。

[discovery entries here]

Only add this section if there are discovery results. If empty, skip.

4. Update Sources line to include Phase 2 counts:

Sources: Tier1-KOLs(N) [Tier2-Companies(N)] Labs(N) Podcasts(N/深度N) HN(N) HF-Trending(N) arxiv(N) 社区补充(N) 发现(N)

社区补充 and 发现 only appear if Phase 2 produced results for them.

Phase 2 Step D: Generate HTML

Skip this step if --no-save or --no-html flag is set.

Run the render script (uses bun, <1 second):

bun /path/to/no-more-fomo/scripts/render.js ~/no-more-fomo/YYYY-MM-DD.md

This automatically:

1. Reads template/digest.html and the .md file
2. Parses markdown sections → HTML fragments (with escaping, badges, links)
3. Replaces {{PLACEHOLDER}} markers → writes YYYY-MM-DD.html
4. Scans all .html files → regenerates index.html with date cards

HTML features:

• Three view layouts: newspaper (default), sidebar, grid — CSS-only toggle
• Light/dark theme with system preference detection
• 中/EN button navigates between YYYY-MM-DD.html ↔ YYYY-MM-DD-zh.html (page redirect, not just UI label swap)
• ← 归档 back link to index.html
• render.js supports both English AND Chinese section headings (Top Highlights/今日要点, Sources:/来源:, Total:/总计: etc.)

For --quick mode: Run this step at the end of Phase 1. The render script works with whatever content is in the .md file at that point.

Phase 2 Step E: Generate Chinese Translation

Skip this step if --no-save flag is set or if the digest is already in Chinese (language: zh).

After the English digest is saved, translate it to Chinese:

1. Read ~/no-more-fomo/YYYY-MM-DD.md (English version, already in memory)
2. Translate all content to Chinese:
   • Section titles → use the zh titles from Categorize table
   • Item descriptions and summaries → translate to Chinese
   • Speaker names, model names, tool names, arxiv IDs → keep original
   • Links → keep as-is
   • Engagement metrics (likes, points) → keep as-is
3. Write to ~/no-more-fomo/YYYY-MM-DD-zh.md
4. Run render script for the Chinese version:

bun /path/to/no-more-fomo/scripts/render.js ~/no-more-fomo/YYYY-MM-DD-zh.md

Default behavior: Always generate both English and Chinese versions. The English version is the primary (generated first), the Chinese version is a translation pass.

Skip translation with --en-only flag.

Arguments

Flag combinations:

Common Mistakes

Scheduling

Claude Code Cloud (recommended)

Go to preview.claude.ai/code → Scheduled → + New scheduled task:

Runs on cloud infra — no local machine needed.

Local fallback (crontab)

# Add to crontab
0 9 * * * claude -p "run /no-more-fomo and save the digest" --allowedTools "Bash,Read,Write,Glob" --output-format stream-json >> /tmp/no-more-fomo.log 2>&1

Fallback

• xreach fails: curl -s "https://r.jina.ai/https://twitter.com/HANDLE"
• HN API fails: curl -s "https://r.jina.ai/https://news.ycombinator.com"
• Podcast RSS fails: curl -s "https://r.jina.ai/PODCAST_WEBSITE"