Hacker News Digest

Hacker News Digest

Surface Hacker News threads on any topic and extract high-signal comments from the technical community. Uses HN's public Algolia search API — no API keys needed.

No API keys required — HN has a public search API.

Output directory: ~/Desktop/{slug}_hn_{YYYY-MM-DD}/

What It Extracts

• Top threads by topic (sorted by points or recency)
• Post type: Show HN, Ask HN, news, job
• Top comments (sorted by score)
• Key opinions and technical perspectives
• Sentiment breakdown (positive/skeptical/critical)
• Links to further reading shared in comments

Data Sources

Pipeline

Step 1: Search via Algolia API (Free, No Auth)

# Search all HN content
curl "https://hn.algolia.com/api/v1/search?query={TOPIC}&tags=story&hitsPerPage=20" \
  | node -e "
    const d = JSON.parse(require('fs').readFileSync('/dev/stdin','utf8'));
    d.hits.forEach(h => console.log(h.points, h.title, h.objectID));
  "

# Search only Show HN posts
curl "https://hn.algolia.com/api/v1/search?query={TOPIC}&tags=show_hn&hitsPerPage=10"

# Search only Ask HN posts
curl "https://hn.algolia.com/api/v1/search?query={TOPIC}&tags=ask_hn&hitsPerPage=10"

# Recent discussions (past 30 days)
curl "https://hn.algolia.com/api/v1/search_by_date?query={TOPIC}&tags=story&numericFilters=created_at_i>$(date -d '30 days ago' +%s)"

Step 2: Get Full Thread + Comments

# Fetch item data from Firebase API
STORY_ID="{HN_STORY_ID}"
curl "https://hacker-news.firebaseio.com/v0/item/${STORY_ID}.json" | node -e "
  const item = JSON.parse(require('fs').readFileSync('/dev/stdin','utf8'));
  console.log(JSON.stringify({ title: item.title, score: item.score, kids: item.kids?.slice(0,20) }));
"

# Or — for unusual threads where the API returns truncated content — drive the page:
composio execute BROWSER_TOOL_CREATE_TASK -d '{
  "task":     "Capture the full thread text, including all top-level and nested comments. Stop once the page is fully loaded and the comment tree is visible.",
  "startUrl": "https://news.ycombinator.com/item?id={STORY_ID}"
}'

Step 3: Extract Top Comments

For each comment ID from kids array:

curl "https://hacker-news.firebaseio.com/v0/item/{COMMENT_ID}.json"

Sort by score, take top 10-15.

Step 4: Cluster by Signal Type

Group comments into:

• Positive signals — praise, endorsement, "this is great"
• Skeptical signals — technical concerns, "but what about X"
• Critical signals — fundamental objections, "this doesn't work because"
• Resource links — URLs to papers, tools, alternatives shared in comments
• Competitive mentions — other tools mentioned positively

Output Format

---
topic: {TOPIC}
total_threads: {N}
date_range: past year
researched_at: {ISO date}
---

## Top Threads

### 1. {Post Title} ({N} points, {M} comments)
**Type**: Show HN | Ask HN | News
**URL**: https://news.ycombinator.com/item?id={ID}
**Posted**: {date}

**Top Comments**:
> "{comment text}"  — {score} points

> "{comment text}" — {score} points

**Signal**: {positive/skeptical/critical}
**Key Takeaway**: {1 sentence summary of community opinion}

---

### 2. {Post Title}
...

## Community Sentiment Summary

| Sentiment | Count | Key Quote |
|-----------|-------|-----------|
| Positive | {N} | "..." |
| Skeptical | {N} | "..." |
| Critical | {N} | "..." |

## Frequently Mentioned Alternatives
- {Tool/resource}: mentioned {N} times

## Key Technical Concerns Raised
- {Concern 1}: mentioned in {N} threads
- {Concern 2}: ...

Key Use Cases

1. Technical community pulse — what do engineers actually think about a topic
2. Show HN benchmarking — what gets traction when launching on HN
3. Competitive intelligence — which tools get praised vs criticized
4. Content research — what angles resonate with technical readers
5. Problem validation — do engineers recognize the problem you're solving