我看了3月4日的报告，内容太少了，社区不可能发生这么少的事情呀，reddit和github都没有返回任何信息吗？请全面排查一下原因，以及对于抓取过滤策略看看能不能优化


The user wants me to investigate why the March 4th report had too little content, specifically checking if Reddit and GitHub returned any data, and optimize the scraping/filtering strategy. Let me start by looking at the current state of the code and any logs.

Let me first check the memory and project structure to understand what we're working with.


Error: File does not exist. Note: your current working directory is /root/projects/ai_usecases_explorer.


     1→"""GitHub collector using the GitHub Search API."""
     2→import logging
     3→from datetime import UTC, date, datetime, timedelta
     4→
     5→import httpx
     6→
     7→from ai_usecases_explorer.collectors.base import BaseCollector, RawItem
     8→
     9→logger = logging.getLogger(__name__)
    10→
    11→_SEARCH_URL = "https://api.github.com/search/repositories"
    12→
    13→# One query per topic — GitHub Search API ANDs multiple topic: qualifiers
    14→# and does not support OR between qualifier types
    15→# Topics cover: OpenClaw, AI编程, AI落地场景, AI商业模式
    16→_TOPICS = [
    17→    "openclaw", "claude-code", "vibe-coding", "mcp",
    18→    "ai-agent", "local-llm", "ollama", "ai-saas",
    19→]
    20→
    21→
    22→class GitHubCollector(BaseCollector):
    23→    """Finds recently-pushed AI repos via the GitHub Search API."""
    24→
    25→    def __init__(
    26→        self,
    27→        min_stars: int = 10,
    28→        token: str = "",
    29→        lookback_days: int = 2,
    30→    ) -> None:
    31→        self._min_stars = min_stars
    32→        self._token = token
    33→        self._lookback_days = lookback_days
    34→
    35→    def fetch(self) -> list[RawItem]:
    36→        since = (date.today() - timedelta(days=self._lookback_days)).isoformat()
    37→
    38→        headers: dict[str, str] = {"Accept": "application/vnd.github+json"}
    39→        if self._token:
    40→            headers["Authorization"] = f"Bearer {self._token}"
    41→
    42→        seen_ids: set[int] = set()
    43→        items: list[RawItem] = []
    44→
    45→        for topic in _TOPICS:
    46→            query = f"topic:{topic} pushed:>{since} stars:>={self._min_stars}"
    47→            try:
    48→                response = httpx.get(
    49→                    _SEARCH_URL,
    50→                    params={"q": query, "sort": "stars", "order": "desc", "per_page": 20},
    51→                    headers=headers,
    52→                    timeout=15,
    53→                )
    54→                response.raise_for_status()
    55→            except (httpx.HTTPStatusError, httpx.RequestError) as exc:
    56→                logger.warning("GitHub search for topic:%s failed: %s — skipping", topic, exc)
    57→                continue
    58→            for repo in response.json().get("items", []):
    59→                repo_id = repo.get("id")
    60→                if repo_id in seen_ids:
    61→                    continue
    62→                if repo.get("stargazers_count", 0) >= self._min_stars:
    63→                    seen_ids.add(repo_id)
    64→                    items.append(self._parse(repo))
    65→
    66→        return items
    67→
    68→    def _parse(self, repo: dict) -> RawItem:
    69→        full_name = repo.get("full_name", "")
    70→        description = repo.get("description") or ""
    71→        topics = repo.get("topics") or []
    72→        language = repo.get("language") or ""
    73→        stars = repo.get("stargazers_count", 0)
    74→
    75→        raw = (
    76→            f"{full_name}\n\n"
    77→            f"{description}\n\n"
    78→            f"Topics: {', '.join(topics)}\n"
    79→            f"Language: {language} | Stars: {stars}"
    80→        ).strip()
    81→
    82→        pushed_at = repo.get("pushed_at", "")
    83→        published_at = (
    84→            datetime.fromisoformat(pushed_at.replace("Z", "+00:00"))
    85→            if pushed_at
    86→            else datetime.now(tz=UTC)
    87→        )
    88→
    89→        return RawItem(
    90→            title=full_name,
    91→            source_url=repo.get("html_url", f"https://github.com/{full_name}"),
    92→            source_platform="github",
    93→            author=repo.get("owner", {}).get("login", "unknown"),
    94→            published_at=published_at,
    95→            raw_content=raw,
    96→            score=repo.get("stargazers_count", 0),
    97→        )
    98→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

"""GitHub collector using the GitHub Search API."""
import logging
from datetime import UTC, date, datetime, timedelta

import httpx

from ai_usecases_explorer.collectors.base import BaseCollector, RawItem

logger = logging.getLogger(__name__)

_SEARCH_URL = "https://api.github.com/search/repositories"

# One query per topic — GitHub Search API ANDs multiple topic: qualifiers
# and does not support OR between qualifier types
# Topics cover: OpenClaw, AI编程, AI落地场景, AI商业模式
_TOPICS = [
    "openclaw", "claude-code", "vibe-coding", "mcp",
    "ai-agent", "local-llm", "ollama", "ai-saas",
]

class GitHubCollector(BaseCollector):
    """Finds recently-pushed AI repos via the GitHub Search API."""

    def __init__(
        self,
        min_stars: int = 10,
        token: str = "",
        lookback_days: int = 2,
    ) -> None:
        self._min_stars = min_stars
        self._token = token
        self._lookback_days = lookback_days

    def fetch(self) -> list[RawItem]:
        since = (date.today() - timedelta(days=self._lookback_days)).isoformat()

        headers: dict[str, str] = {"Accept": "application/vnd.github+json"}
        if self._token:
            headers["Authorization"] = f"Bearer {self._token}"

        seen_ids: set[int] = set()
        items: list[RawItem] = []

        for topic in _TOPICS:
            query = f"topic:{topic} pushed:>{since} stars:>={self._min_stars}"
            try:
                response = httpx.get(
                    _SEARCH_URL,
                    params={"q": query, "sort": "stars", "order": "desc", "per_page": 20},
                    headers=headers,
                    timeout=15,
                )
                response.raise_for_status()
            except (httpx.HTTPStatusError, httpx.RequestError) as exc:
                logger.warning("GitHub search for topic:%s failed: %s — skipping", topic, exc)
                continue
            for repo in response.json().get("items", []):
                repo_id = repo.get("id")
                if repo_id in seen_ids:
                    continue
                if repo.get("stargazers_count", 0) >= self._min_stars:
                    seen_ids.add(repo_id)
                    items.append(self._parse(repo))

        return items

    def _parse(self, repo: dict) -> RawItem:
        full_name = repo.get("full_name", "")
        description = repo.get("description") or ""
        topics = repo.get("topics") or []
        language = repo.get("language") or ""
        stars = repo.get("stargazers_count", 0)

        raw = (
            f"{full_name}\n\n"
            f"{description}\n\n"
            f"Topics: {', '.join(topics)}\n"
            f"Language: {language} | Stars: {stars}"
        ).strip()

        pushed_at = repo.get("pushed_at", "")
        published_at = (
            datetime.fromisoformat(pushed_at.replace("Z", "+00:00"))
            if pushed_at
            else datetime.now(tz=UTC)
        )

        return RawItem(
            title=full_name,
            source_url=repo.get("html_url", f"https://github.com/{full_name}"),
            source_platform="github",
            author=repo.get("owner", {}).get("login", "unknown"),
            published_at=published_at,
            raw_content=raw,
            score=repo.get("stargazers_count", 0),
        )


     1→"""Reddit collector using Exa.ai neural search, one request per subreddit."""
     2→import logging
     3→from datetime import UTC, datetime, timedelta
     4→
     5→from exa_py import Exa  # type: ignore[import-untyped]
     6→
     7→from ai_usecases_explorer.collectors.base import BaseCollector, RawItem
     8→
     9→logger = logging.getLogger(__name__)
    10→
    11→# Broad AI query — semantically covers all target subreddits
    12→_SEARCH_QUERY = "AI workflow automation use case tool productivity"
    13→
    14→_DEFAULT_SUBREDDITS = ["openclaw", "vibecoding", "ClaudeCode", "singularity"]
    15→
    16→
    17→class RedditCollector(BaseCollector):
    18→    """Fetches posts from specific subreddits via Exa.ai (one request per subreddit)."""
    19→
    20→    def __init__(
    21→        self,
    22→        subreddits: list[str],
    23→        api_key: str = "",
    24→    ) -> None:
    25→        self._subreddits = subreddits
    26→        self._api_key = api_key
    27→
    28→    def fetch(self) -> list[RawItem]:
    29→        if not self._api_key:
    30→            logger.warning("EXA_API_KEY not set — skipping Reddit/Exa collection")
    31→            return []
    32→
    33→        exa = Exa(api_key=self._api_key)
    34→        start_date = (datetime.now(tz=UTC) - timedelta(hours=24)).strftime("%Y-%m-%dT%H:%M:%SZ")
    35→
    36→        seen: set[str] = set()
    37→        items: list[RawItem] = []
    38→
    39→        for sub in self._subreddits:
    40→            try:
    41→                response = exa.search_and_contents(
    42→                    _SEARCH_QUERY,
    43→                    include_domains=[f"reddit.com/r/{sub}"],
    44→                    start_published_date=start_date,
    45→                    text=True,
    46→                    num_results=10,
    47→                )
    48→            except Exception as exc:
    49→                logger.warning("Exa search failed for r/%s: %s", sub, exc)
    50→                continue
    51→
    52→            for result in response.results:
    53→                url = result.url or ""
    54→                if not url or url in seen:
    55→                    continue
    56→                # Post-filter: Exa doesn't strictly enforce include_domains
    57→                if f"/r/{sub.lower()}/" not in url.lower():
    58→                    continue
    59→                seen.add(url)
    60→
    61→                title = result.title or url
    62→                text = result.text or ""
    63→                raw = f"{title}\n\n{text}".strip()
    64→
    65→                published_at = _parse_date(getattr(result, "published_date", None))
    66→
    67→                items.append(
    68→                    RawItem(
    69→                        title=title,
    70→                        source_url=url,
    71→                        source_platform="reddit",
    72→                        author=getattr(result, "author", None) or "unknown",
    73→                        published_at=published_at,
    74→                        raw_content=raw,
    75→                    )
    76→                )
    77→
    78→        return items
    79→
    80→
    81→def _parse_date(value: str | None) -> datetime:
    82→    """Parse an ISO-8601 date string, falling back to now(UTC)."""
    83→    if not value:
    84→        return datetime.now(tz=UTC)
    85→    try:
    86→        return datetime.fromisoformat(value.replace("Z", "+00:00"))
    87→    except ValueError:
    88→        return datetime.now(tz=UTC)
    89→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

"""Reddit collector using Exa.ai neural search, one request per subreddit."""
import logging
from datetime import UTC, datetime, timedelta

from exa_py import Exa  # type: ignore[import-untyped]

from ai_usecases_explorer.collectors.base import BaseCollector, RawItem

logger = logging.getLogger(__name__)

# Broad AI query — semantically covers all target subreddits
_SEARCH_QUERY = "AI workflow automation use case tool productivity"

_DEFAULT_SUBREDDITS = ["openclaw", "vibecoding", "ClaudeCode", "singularity"]

class RedditCollector(BaseCollector):
    """Fetches posts from specific subreddits via Exa.ai (one request per subreddit)."""

    def __init__(
        self,
        subreddits: list[str],
        api_key: str = "",
    ) -> None:
        self._subreddits = subreddits
        self._api_key = api_key

    def fetch(self) -> list[RawItem]:
        if not self._api_key:
            logger.warning("EXA_API_KEY not set — skipping Reddit/Exa collection")
            return []

        exa = Exa(api_key=self._api_key)
        start_date = (datetime.now(tz=UTC) - timedelta(hours=24)).strftime("%Y-%m-%dT%H:%M:%SZ")

        seen: set[str] = set()
        items: list[RawItem] = []

        for sub in self._subreddits:
            try:
                response = exa.search_and_contents(
                    _SEARCH_QUERY,
                    include_domains=[f"reddit.com/r/{sub}"],
                    start_published_date=start_date,
                    text=True,
                    num_results=10,
                )
            except Exception as exc:
                logger.warning("Exa search failed for r/%s: %s", sub, exc)
                continue

            for result in response.results:
                url = result.url or ""
                if not url or url in seen:
                    continue
                # Post-filter: Exa doesn't strictly enforce include_domains
                if f"/r/{sub.lower()}/" not in url.lower():
                    continue
                seen.add(url)

                title = result.title or url
                text = result.text or ""
                raw = f"{title}\n\n{text}".strip()

                published_at = _parse_date(getattr(result, "published_date", None))

                items.append(
                    RawItem(
                        title=title,
                        source_url=url,
                        source_platform="reddit",
                        author=getattr(result, "author", None) or "unknown",
                        published_at=published_at,
                        raw_content=raw,
                    )
                )

        return items

def _parse_date(value: str | None) -> datetime:
    """Parse an ISO-8601 date string, falling back to now(UTC)."""
    if not value:
        return datetime.now(tz=UTC)
    try:
        return datetime.fromisoformat(value.replace("Z", "+00:00"))
    except ValueError:
        return datetime.now(tz=UTC)


     1→# AI Use Case 雷达 — 2026-03-04
     2→
     3→> 今日扫描: HN(3) + Reddit(0) + GitHub(0) → 过滤后: 3 条有价值 → 新场景: 0 个
     4→
     5→---
     6→
     7→## 🆕 新场景（0）
     8→
     9→*今日无新场景*
    10→
    11→---
    12→
    13→## ♻ 已知场景的新案例（3）
    14→
    15→### 终端与CLI增强（1 条）
    16→- [Claude Code Remote Control](https://code.claude.com/docs/en/remote-control) — `empressplay` — *工具: Claude Code, 远程控制, CLI*
    17→
    18→### 文档与内容处理（1 条）
    19→- [Show HN: Now I Get It – Translate scientific papers into interactive webpages](https://nowigetit.us) — `jbdamask` — *工具: Claude LLM, AWS, Beads, Beads Viewer, Destructive Command Guard*
    20→
    21→### 代码生成与审查（1 条）
    22→- [Scaling long-running autonomous coding](https://cursor.com/blog/scaling-agents) — `samwillis` — *工具: Claude API, autonomous coding tools*
    23→
    24→---
    25→
    26→## 🚫 过滤日志
    27→今日过滤 0 条低价值内容。
    28→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

# AI Use Case 雷达 — 2026-03-04

> 今日扫描: HN(3) + Reddit(0) + GitHub(0) → 过滤后: 3 条有价值 → 新场景: 0 个

---

## 🆕 新场景（0）

*今日无新场景*

---

## ♻ 已知场景的新案例（3）

### 终端与CLI增强（1 条）
- [Claude Code Remote Control](https://code.claude.com/docs/en/remote-control) — `empressplay` — *工具: Claude Code, 远程控制, CLI*

### 文档与内容处理（1 条）
- [Show HN: Now I Get It – Translate scientific papers into interactive webpages](https://nowigetit.us) — `jbdamask` — *工具: Claude LLM, AWS, Beads, Beads Viewer, Destructive Command Guard*

### 代码生成与审查（1 条）
- [Scaling long-running autonomous coding](https://cursor.com/blog/scaling-agents) — `samwillis` — *工具: Claude API, autonomous coding tools*

---

## 🚫 过滤日志
今日过滤 0 条低价值内容。


I can see that the March 4th report shows:
- HN: 3 items
- Reddit: 0 items
- GitHub: 0 items
- After filtering: 3 valuable items
- New scenarios: 0

Let me look at the hackernews collector and the filter/main to understand the full pipeline and identify issues.

Key problems I can already identify:
1. **Reddit**: Uses `include_domains=[f"reddit.com/r/{sub}"]` - Exa.ai might not support subdomain filtering this way, and there's a 24-hour window
2. **GitHub**: Uses `lookback_days=2` with `min_stars=10` - might be too restrictive, but the topics should match something

3. **HackerNews**: Only returning 3 items also seems suspiciously low

I need to dig into the collector implementations and main pipeline to see where the bottleneck is.


报告显示 `Reddit(0) + GitHub(0)`，问题很明显。让我深入排查各个采集器和过滤逻辑。


     1→"""Step 1 — Value filter: determines if a raw item has real AI use-case value."""
     2→import anthropic
     3→
     4→from ai_usecases_explorer.collectors.base import RawItem
     5→from ai_usecases_explorer.processors.utils import parse_llm_json
     6→
     7→_SYSTEM = """\
     8→You are an AI use case filter. Your job is to decide whether a post represents
     9→a genuinely valuable AI use case.
    10→
    11→PASS criteria (at least one must be true):
    12→- The user integrates AI with local/personal data (files, DB, notes, etc.)
    13→- The user automates a specific workflow step — not just asking questions
    14→
    15→REJECT criteria:
    16→- Pure Q&A replacing ChatGPT with no local data or workflow change
    17→- Marketing demos without real user implementation
    18→- Generic "AI is great" commentary
    19→
    20→Respond with ONLY valid JSON (no markdown fences), exactly this shape:
    21→{"is_real_value": bool, "reason": "...", "has_local_data": bool, "has_workflow_opt": bool}
    22→"""
    23→
    24→
    25→class ValueFilter:
    26→    """Calls Claude to decide whether a RawItem has real use-case value."""
    27→
    28→    def __init__(
    29→        self, client: anthropic.Anthropic, model: str = "claude-haiku-4-5-20251001"
    30→    ) -> None:
    31→        self._client = client
    32→        self._model = model
    33→
    34→    def run(self, item: RawItem) -> dict:
    35→        """Return a dict with is_real_value, has_local_data, has_workflow_opt, reason."""
    36→        return self._call_llm(item)
    37→
    38→    def _call_llm(self, item: RawItem) -> dict:
    39→        content = f"Title: {item.title}\n\nContent:\n{item.raw_content[:3000]}"
    40→        message = self._client.messages.create(
    41→            model=self._model,
    42→            max_tokens=256,
    43→            system=_SYSTEM,
    44→            messages=[{"role": "user", "content": content}],
    45→        )
    46→        return parse_llm_json(message.content[0].text)
    47→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

"""Step 1 — Value filter: determines if a raw item has real AI use-case value."""
import anthropic

from ai_usecases_explorer.collectors.base import RawItem
from ai_usecases_explorer.processors.utils import parse_llm_json

_SYSTEM = """\
You are an AI use case filter. Your job is to decide whether a post represents
a genuinely valuable AI use case.

PASS criteria (at least one must be true):
- The user integrates AI with local/personal data (files, DB, notes, etc.)
- The user automates a specific workflow step — not just asking questions

REJECT criteria:
- Pure Q&A replacing ChatGPT with no local data or workflow change
- Marketing demos without real user implementation
- Generic "AI is great" commentary

Respond with ONLY valid JSON (no markdown fences), exactly this shape:
{"is_real_value": bool, "reason": "...", "has_local_data": bool, "has_workflow_opt": bool}
"""

class ValueFilter:
    """Calls Claude to decide whether a RawItem has real use-case value."""

    def __init__(
        self, client: anthropic.Anthropic, model: str = "claude-haiku-4-5-20251001"
    ) -> None:
        self._client = client
        self._model = model

    def run(self, item: RawItem) -> dict:
        """Return a dict with is_real_value, has_local_data, has_workflow_opt, reason."""
        return self._call_llm(item)

    def _call_llm(self, item: RawItem) -> dict:
        content = f"Title: {item.title}\n\nContent:\n{item.raw_content[:3000]}"
        message = self._client.messages.create(
            model=self._model,
            max_tokens=256,
            system=_SYSTEM,
            messages=[{"role": "user", "content": content}],
        )
        return parse_llm_json(message.content[0].text)


     1→"""HackerNews collector using the Algolia search API."""
     2→from datetime import UTC, datetime
     3→
     4→import httpx
     5→
     6→from ai_usecases_explorer.collectors.base import BaseCollector, RawItem
     7→
     8→_HN_API = "https://hn.algolia.com/api/v1/search_by_date"
     9→
    10→# One query per term — Algolia ANDs multi-word queries, so run them separately.
    11→# Keywords cover: OpenClaw, AI编程, 超级个体, AI落地场景, AI商业模式
    12→_KEYWORDS = [
    13→    "openclaw",
    14→    "claude code",
    15→    "vibe coding",
    16→    "cursor",
    17→    "mcp server",
    18→    "ai agent",
    19→    "ollama",
    20→    "solopreneur ai",
    21→    "ai saas",
    22→    "ai workflow",
    23→    "local ai",
    24→    "ai use case",
    25→]
    26→
    27→
    28→class HackerNewsCollector(BaseCollector):
    29→    """Fetches recent HN posts matching AI/LLM keywords above a point threshold."""
    30→
    31→    def __init__(self, min_points: int = 10) -> None:
    32→        self._min_points = min_points
    33→
    34→    def fetch(self) -> list[RawItem]:
    35→        seen_ids: set[str] = set()
    36→        items: list[RawItem] = []
    37→        for keyword in _KEYWORDS:
    38→            params = {
    39→                "query": keyword,
    40→                "tags": "(story,ask_hn,show_hn)",
    41→                "numericFilters": f"points>={self._min_points}",
    42→                "hitsPerPage": 50,
    43→            }
    44→            response = httpx.get(_HN_API, params=params, timeout=15)
    45→            response.raise_for_status()
    46→            for hit in response.json().get("hits", []):
    47→                hn_id = hit.get("objectID", "")
    48→                if hn_id in seen_ids:
    49→                    continue
    50→                if hit.get("points", 0) >= self._min_points:
    51→                    seen_ids.add(hn_id)
    52→                    items.append(self._parse(hit))
    53→        return items
    54→
    55→    def _parse(self, hit: dict) -> RawItem:
    56→        hn_id = hit.get("objectID", "")
    57→        url = hit.get("url") or f"https://news.ycombinator.com/item?id={hn_id}"
    58→        text = hit.get("story_text") or hit.get("comment_text") or ""
    59→        title = hit.get("title", "")
    60→        raw = f"{title}\n\n{text}".strip()
    61→
    62→        published_at = datetime.fromisoformat(
    63→            hit["created_at"].replace("Z", "+00:00")
    64→        ) if hit.get("created_at") else datetime.now(tz=UTC)
    65→
    66→        return RawItem(
    67→            title=title,
    68→            source_url=url,
    69→            source_platform="hackernews",
    70→            author=hit.get("author", "unknown"),
    71→            published_at=published_at,
    72→            raw_content=raw,
    73→            score=hit.get("points", 0),
    74→        )
    75→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

"""HackerNews collector using the Algolia search API."""
from datetime import UTC, datetime

import httpx

from ai_usecases_explorer.collectors.base import BaseCollector, RawItem

_HN_API = "https://hn.algolia.com/api/v1/search_by_date"

# One query per term — Algolia ANDs multi-word queries, so run them separately.
# Keywords cover: OpenClaw, AI编程, 超级个体, AI落地场景, AI商业模式
_KEYWORDS = [
    "openclaw",
    "claude code",
    "vibe coding",
    "cursor",
    "mcp server",
    "ai agent",
    "ollama",
    "solopreneur ai",
    "ai saas",
    "ai workflow",
    "local ai",
    "ai use case",
]

class HackerNewsCollector(BaseCollector):
    """Fetches recent HN posts matching AI/LLM keywords above a point threshold."""

    def __init__(self, min_points: int = 10) -> None:
        self._min_points = min_points

    def fetch(self) -> list[RawItem]:
        seen_ids: set[str] = set()
        items: list[RawItem] = []
        for keyword in _KEYWORDS:
            params = {
                "query": keyword,
                "tags": "(story,ask_hn,show_hn)",
                "numericFilters": f"points>={self._min_points}",
                "hitsPerPage": 50,
            }
            response = httpx.get(_HN_API, params=params, timeout=15)
            response.raise_for_status()
            for hit in response.json().get("hits", []):
                hn_id = hit.get("objectID", "")
                if hn_id in seen_ids:
                    continue
                if hit.get("points", 0) >= self._min_points:
                    seen_ids.add(hn_id)
                    items.append(self._parse(hit))
        return items

    def _parse(self, hit: dict) -> RawItem:
        hn_id = hit.get("objectID", "")
        url = hit.get("url") or f"https://news.ycombinator.com/item?id={hn_id}"
        text = hit.get("story_text") or hit.get("comment_text") or ""
        title = hit.get("title", "")
        raw = f"{title}\n\n{text}".strip()

        published_at = datetime.fromisoformat(
            hit["created_at"].replace("Z", "+00:00")
        ) if hit.get("created_at") else datetime.now(tz=UTC)

        return RawItem(
            title=title,
            source_url=url,
            source_platform="hackernews",
            author=hit.get("author", "unknown"),
            published_at=published_at,
            raw_content=raw,
            score=hit.get("points", 0),
        )


     1→"""Main orchestrator — collect → process → store → report."""
     2→import argparse
     3→import itertools
     4→import logging
     5→import sys
     6→from datetime import UTC, date, datetime
     7→
     8→import anthropic
     9→from rich.console import Console
    10→from rich.progress import Progress
    11→
    12→from ai_usecases_explorer.collectors.base import RawItem
    13→from ai_usecases_explorer.collectors.github import GitHubCollector
    14→from ai_usecases_explorer.collectors.hackernews import HackerNewsCollector
    15→from ai_usecases_explorer.collectors.reddit import RedditCollector
    16→from ai_usecases_explorer.models.usecase import Novelty, UseCase, make_id
    17→from ai_usecases_explorer.processors.classifier import Classifier
    18→from ai_usecases_explorer.processors.deduplicator import Deduplicator
    19→from ai_usecases_explorer.processors.filter import ValueFilter
    20→from ai_usecases_explorer.reporters.obsidian import ObsidianReporter
    21→from ai_usecases_explorer.settings import Settings
    22→from ai_usecases_explorer.storage.database import Database
    23→
    24→console = Console()
    25→logger = logging.getLogger(__name__)
    26→
    27→
    28→class Scout:
    29→    """Orchestrates the full collect → process → store → report pipeline."""
    30→
    31→    def __init__(self, settings: Settings) -> None:
    32→        self._settings = settings
    33→        self._db = Database(db_path=settings.db_path)
    34→
    35→        client_kwargs: dict = {"api_key": settings.anthropic_api_key}
    36→        if settings.anthropic_base_url:
    37→            client_kwargs["base_url"] = settings.anthropic_base_url
    38→        client = anthropic.Anthropic(**client_kwargs)
    39→        model = settings.claude_model
    40→
    41→        self._hn = HackerNewsCollector(min_points=settings.hn_min_points)
    42→        self._reddit = RedditCollector(
    43→            subreddits=settings.reddit_subreddits,
    44→            api_key=settings.exa_api_key,
    45→        )
    46→        self._github = GitHubCollector(
    47→            min_stars=settings.github_min_stars,
    48→            token=settings.github_token,
    49→        )
    50→        self._filter = ValueFilter(client=client, model=model)
    51→        self._classifier = Classifier(client=client, model=model)
    52→        self._deduplicator = Deduplicator(client=client, model=model)
    53→        self._reporter = ObsidianReporter(output_dir=settings.obsidian_report_dir)
    54→
    55→    def run(self, dry_run: bool = False, report_date: date | None = None) -> None:
    56→        """Run the full pipeline for one day."""
    57→        if report_date is None:
    58→            report_date = date.today()
    59→
    60→        console.rule(f"[bold]AI Use Case Scout — {report_date}[/bold]")
    61→
    62→        # 1. Collect
    63→        console.print("[cyan]Collecting from HackerNews...[/cyan]")
    64→        hn_items = self._hn.fetch()
    65→        console.print(f"  HN: {len(hn_items)} items above threshold")
    66→
    67→        console.print("[cyan]Collecting from Reddit...[/cyan]")
    68→        reddit_items = self._reddit.fetch()
    69→        console.print(f"  Reddit: {len(reddit_items)} items above threshold")
    70→
    71→        console.print("[cyan]Collecting from GitHub...[/cyan]")
    72→        github_items = self._github.fetch()
    73→        console.print(f"  GitHub: {len(github_items)} repos above threshold")
    74→
    75→        # Sort each platform's items by engagement score (desc) before interleaving,
    76→        # so round-robin picks the highest-value items from each source first.
    77→        hn_items.sort(key=lambda x: x.score, reverse=True)
    78→        reddit_items.sort(key=lambda x: x.score, reverse=True)
    79→        github_items.sort(key=lambda x: x.score, reverse=True)
    80→        # Interleave sources (round-robin) so every platform gets representation
    81→        # even when HN dominates in volume. Then cap at max_items_per_run.
    82→        interleaved = [
    83→            item
    84→            for item in itertools.chain.from_iterable(
    85→                itertools.zip_longest(hn_items, reddit_items, github_items)
    86→            )
    87→            if item is not None
    88→        ]
    89→        all_items = interleaved[: self._settings.max_items_per_run]
    90→        platform_counts = {
    91→            p: sum(1 for i in all_items if i.source_platform == p)
    92→            for p in ("hackernews", "reddit", "github")
    93→        }
    94→        console.print(f"  [dim]Interleaved: {platform_counts}[/dim]")
    95→        console.print(
    96→            f"[green]Total collected: {len(hn_items) + len(reddit_items) + len(github_items)} "
    97→            f"→ processing top {len(all_items)} (round-robin)[/green]"
    98→        )
    99→
   100→        # 2. Process (skip already stored)
   101→        processed: list[UseCase] = []
   102→        skipped = 0
   103→        with Progress(console=console) as progress:
   104→            task = progress.add_task("Processing items...", total=len(all_items))
   105→            for item in all_items:
   106→                item_id = make_id(item.source_url)
   107→                if self._db.exists(item_id):
   108→                    skipped += 1
   109→                    progress.advance(task)
   110→                    continue
   111→                try:
   112→                    result = self._process_item(item, existing=[])
   113→                except Exception as exc:
   114→                    logger.warning("Failed to process %s: %s", item.source_url[:60], exc)
   115→                    progress.advance(task)
   116→                    continue
   117→                if result is not None:
   118→                    processed.append(result)
   119→                    if not dry_run:
   120→                        self._db.save(result)
   121→                progress.advance(task)
   122→
   123→        console.print(f"[green]Processed: {len(processed)} valuable, skipped: {skipped}[/green]")
   124→
   125→        # 3. Report
   126→        report_path = self._reporter.write(processed, report_date=report_date)
   127→        console.print(f"[bold green]Report written: {report_path}[/bold green]")
   128→
   129→    def _process_item(self, item: RawItem, existing: list[UseCase]) -> UseCase | None:
   130→        """Run the 3-step LLM pipeline. Returns None if item fails value filter."""
   131→        # Step 1 — value filter
   132→        filter_result = self._filter.run(item)
   133→        if not filter_result["is_real_value"]:
   134→            logger.debug(
   135→                "FILTERED [%s] %s — %s",
   136→                item.source_platform,
   137→                item.title[:60],
   138→                filter_result.get("reason", ""),
   139→            )
   140→            return None
   141→
   142→        # Step 2 — classify
   143→        classify_result = self._classifier.run(item)
   144→
   145→        # Step 3 — deduplication (compare against same-scenario existing records)
   146→        scenario_existing = self._db.get_by_scenario(classify_result["scenario_type"])
   147→        dedup_result = self._deduplicator.run(
   148→            # Build a minimal UseCase stub for deduplication
   149→            UseCase(
   150→                id=make_id(item.source_url),
   151→                title=item.title,
   152→                summary=classify_result["summary"],
   153→                source_url=item.source_url,
   154→                source_platform=item.source_platform,
   155→                author=item.author,
   156→                published_at=item.published_at,
   157→                collected_at=datetime.now(tz=UTC),
   158→                scenario_type=classify_result["scenario_type"],
   159→                tools_used=classify_result["tools_used"],
   160→                has_local_data=filter_result["has_local_data"],
   161→                has_workflow_opt=filter_result["has_workflow_opt"],
   162→                value_score=classify_result["value_score"],
   163→                is_real_value=True,
   164→                novelty=Novelty.NEW,
   165→                similar_to_ids=[],
   166→                raw_content=item.raw_content,
   167→            ),
   168→            existing=scenario_existing,
   169→        )
   170→
   171→        return UseCase(
   172→            id=make_id(item.source_url),
   173→            title=item.title,
   174→            summary=classify_result["summary"],
   175→            source_url=item.source_url,
   176→            source_platform=item.source_platform,
   177→            author=item.author,
   178→            published_at=item.published_at,
   179→            collected_at=datetime.now(tz=UTC),
   180→            scenario_type=classify_result["scenario_type"],
   181→            tools_used=classify_result["tools_used"],
   182→            has_local_data=filter_result["has_local_data"],
   183→            has_workflow_opt=filter_result["has_workflow_opt"],
   184→            value_score=classify_result["value_score"],
   185→            is_real_value=True,
   186→            novelty=dedup_result["novelty"],
   187→            similar_to_ids=dedup_result["similar_to_ids"],
   188→            raw_content=item.raw_content,
   189→        )
   190→
   191→
   192→def main() -> None:
   193→    parser = argparse.ArgumentParser(description="AI Use Case Daily Scout")
   194→    parser.add_argument("--dry-run", action="store_true", help="Don't write to DB or disk")
   195→    parser.add_argument(
   196→        "--date", type=str, default=None, help="Report date YYYY-MM-DD (default: today)"
   197→    )
   198→    args = parser.parse_args()
   199→
   200→    report_date = date.fromisoformat(args.date) if args.date else date.today()
   201→
   202→    settings = Settings()
   203→    if not settings.anthropic_api_key:
   204→        console.print(
   205→            "[red]Error: ANTHROPIC_API_KEY is not set. "
   206→            "Copy .env.example to .env and fill it in.[/red]"
   207→        )
   208→        sys.exit(1)
   209→
   210→    scout = Scout(settings=settings)
   211→    scout.run(dry_run=args.dry_run, report_date=report_date)
   212→
   213→
   214→if __name__ == "__main__":
   215→    main()
   216→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

"""Main orchestrator — collect → process → store → report."""
import argparse
import itertools
import logging
import sys
from datetime import UTC, date, datetime

import anthropic
from rich.console import Console
from rich.progress import Progress

from ai_usecases_explorer.collectors.base import RawItem
from ai_usecases_explorer.collectors.github import GitHubCollector
from ai_usecases_explorer.collectors.hackernews import HackerNewsCollector
from ai_usecases_explorer.collectors.reddit import RedditCollector
from ai_usecases_explorer.models.usecase import Novelty, UseCase, make_id
from ai_usecases_explorer.processors.classifier import Classifier
from ai_usecases_explorer.processors.deduplicator import Deduplicator
from ai_usecases_explorer.processors.filter import ValueFilter
from ai_usecases_explorer.reporters.obsidian import ObsidianReporter
from ai_usecases_explorer.settings import Settings
from ai_usecases_explorer.storage.database import Database

console = Console()
logger = logging.getLogger(__name__)

class Scout:
    """Orchestrates the full collect → process → store → report pipeline."""

    def __init__(self, settings: Settings) -> None:
        self._settings = settings
        self._db = Database(db_path=settings.db_path)

        client_kwargs: dict = {"api_key": settings.anthropic_api_key}
        if settings.anthropic_base_url:
            client_kwargs["base_url"] = settings.anthropic_base_url
        client = anthropic.Anthropic(**client_kwargs)
        model = settings.claude_model

        self._hn = HackerNewsCollector(min_points=settings.hn_min_points)
        self._reddit = RedditCollector(
            subreddits=settings.reddit_subreddits,
            api_key=settings.exa_api_key,
        )
        self._github = GitHubCollector(
            min_stars=settings.github_min_stars,
            token=settings.github_token,
        )
        self._filter = ValueFilter(client=client, model=model)
        self._classifier = Classifier(client=client, model=model)
        self._deduplicator = Deduplicator(client=client, model=model)
        self._reporter = ObsidianReporter(output_dir=settings.obsidian_report_dir)

    def run(self, dry_run: bool = False, report_date: date | None = None) -> None:
        """Run the full pipeline for one day."""
        if report_date is None:
            report_date = date.today()

        console.rule(f"[bold]AI Use Case Scout — {report_date}[/bold]")

        # 1. Collect
        console.print("[cyan]Collecting from HackerNews...[/cyan]")
        hn_items = self._hn.fetch()
        console.print(f"  HN: {len(hn_items)} items above threshold")

        console.print("[cyan]Collecting from Reddit...[/cyan]")
        reddit_items = self._reddit.fetch()
        console.print(f"  Reddit: {len(reddit_items)} items above threshold")

        console.print("[cyan]Collecting from GitHub...[/cyan]")
        github_items = self._github.fetch()
        console.print(f"  GitHub: {len(github_items)} repos above threshold")

        # Sort each platform's items by engagement score (desc) before interleaving,
        # so round-robin picks the highest-value items from each source first.
        hn_items.sort(key=lambda x: x.score, reverse=True)
        reddit_items.sort(key=lambda x: x.score, reverse=True)
        github_items.sort(key=lambda x: x.score, reverse=True)
        # Interleave sources (round-robin) so every platform gets representation
        # even when HN dominates in volume. Then cap at max_items_per_run.
        interleaved = [
            item
            for item in itertools.chain.from_iterable(
                itertools.zip_longest(hn_items, reddit_items, github_items)
            )
            if item is not None
        ]
        all_items = interleaved[: self._settings.max_items_per_run]
        platform_counts = {
            p: sum(1 for i in all_items if i.source_platform == p)
            for p in ("hackernews", "reddit", "github")
        }
        console.print(f"  [dim]Interleaved: {platform_counts}[/dim]")
        console.print(
            f"[green]Total collected: {len(hn_items) + len(reddit_items) + len(github_items)} "
            f"→ processing top {len(all_items)} (round-robin)[/green]"
        )

        # 2. Process (skip already stored)
        processed: list[UseCase] = []
        skipped = 0
        with Progress(console=console) as progress:
            task = progress.add_task("Processing items...", total=len(all_items))
            for item in all_items:
                item_id = make_id(item.source_url)
                if self._db.exists(item_id):
                    skipped += 1
                    progress.advance(task)
                    continue
                try:
                    result = self._process_item(item, existing=[])
                except Exception as exc:
                    logger.warning("Failed to process %s: %s", item.source_url[:60], exc)
                    progress.advance(task)
                    continue
                if result is not None:
                    processed.append(result)
                    if not dry_run:
                        self._db.save(result)
                progress.advance(task)

        console.print(f"[green]Processed: {len(processed)} valuable, skipped: {skipped}[/green]")

        # 3. Report
        report_path = self._reporter.write(processed, report_date=report_date)
        console.print(f"[bold green]Report written: {report_path}[/bold green]")

    def _process_item(self, item: RawItem, existing: list[UseCase]) -> UseCase | None:
        """Run the 3-step LLM pipeline. Returns None if item fails value filter."""
        # Step 1 — value filter
        filter_result = self._filter.run(item)
        if not filter_result["is_real_value"]:
            logger.debug(
                "FILTERED [%s] %s — %s",
                item.source_platform,
                item.title[:60],
                filter_result.get("reason", ""),
            )
            return None

        # Step 2 — classify
        classify_result = self._classifier.run(item)

        # Step 3 — deduplication (compare against same-scenario existing records)
        scenario_existing = self._db.get_by_scenario(classify_result["scenario_type"])
        dedup_result = self._deduplicator.run(
            # Build a minimal UseCase stub for deduplication
            UseCase(
                id=make_id(item.source_url),
                title=item.title,
                summary=classify_result["summary"],
                source_url=item.source_url,
                source_platform=item.source_platform,
                author=item.author,
                published_at=item.published_at,
                collected_at=datetime.now(tz=UTC),
                scenario_type=classify_result["scenario_type"],
                tools_used=classify_result["tools_used"],
                has_local_data=filter_result["has_local_data"],
                has_workflow_opt=filter_result["has_workflow_opt"],
                value_score=classify_result["value_score"],
                is_real_value=True,
                novelty=Novelty.NEW,
                similar_to_ids=[],
                raw_content=item.raw_content,
            ),
            existing=scenario_existing,
        )

        return UseCase(
            id=make_id(item.source_url),
            title=item.title,
            summary=classify_result["summary"],
            source_url=item.source_url,
            source_platform=item.source_platform,
            author=item.author,
            published_at=item.published_at,
            collected_at=datetime.now(tz=UTC),
            scenario_type=classify_result["scenario_type"],
            tools_used=classify_result["tools_used"],
            has_local_data=filter_result["has_local_data"],
            has_workflow_opt=filter_result["has_workflow_opt"],
            value_score=classify_result["value_score"],
            is_real_value=True,
            novelty=dedup_result["novelty"],
            similar_to_ids=dedup_result["similar_to_ids"],
            raw_content=item.raw_content,
        )

def main() -> None:
    parser = argparse.ArgumentParser(description="AI Use Case Daily Scout")
    parser.add_argument("--dry-run", action="store_true", help="Don't write to DB or disk")
    parser.add_argument(
        "--date", type=str, default=None, help="Report date YYYY-MM-DD (default: today)"
    )
    args = parser.parse_args()

    report_date = date.fromisoformat(args.date) if args.date else date.today()

    settings = Settings()
    if not settings.anthropic_api_key:
        console.print(
            "[red]Error: ANTHROPIC_API_KEY is not set. "
            "Copy .env.example to .env and fill it in.[/red]"
        )
        sys.exit(1)

    scout = Scout(settings=settings)
    scout.run(dry_run=args.dry_run, report_date=report_date)

if __name__ == "__main__":
    main()


Now let me look at the settings.py to understand the configuration, and then I'll do live tests to diagnose the issues.


     1→"""Generates daily Obsidian Markdown reports from processed UseCases."""
     2→from collections import defaultdict
     3→from datetime import date
     4→from pathlib import Path
     5→
     6→from ai_usecases_explorer.models.usecase import Novelty, ScenarioType, UseCase
     7→
     8→
     9→class ObsidianReporter:
    10→    """Writes a daily Markdown report to an Obsidian vault directory."""
    11→
    12→    def __init__(self, output_dir: Path) -> None:
    13→        self._output_dir = output_dir
    14→
    15→    def generate(self, usecases: list[UseCase], report_date: date) -> str:
    16→        """Build the full Markdown report string."""
    17→        new_ucs = [uc for uc in usecases if uc.novelty == Novelty.NEW and uc.is_real_value]
    18→        similar_ucs = [uc for uc in usecases if uc.novelty == Novelty.SIMILAR and uc.is_real_value]
    19→        repeat_ucs = [uc for uc in usecases if uc.novelty == Novelty.REPEAT or not uc.is_real_value]
    20→
    21→        hn_total = sum(1 for uc in usecases if uc.source_platform == "hackernews")
    22→        reddit_total = sum(1 for uc in usecases if uc.source_platform == "reddit")
    23→        github_total = sum(1 for uc in usecases if uc.source_platform == "github")
    24→        valuable_total = len(new_ucs) + len(similar_ucs)
    25→
    26→        lines: list[str] = []
    27→
    28→        # Header
    29→        lines.append(f"# AI Use Case 雷达 — {report_date.isoformat()}")
    30→        lines.append("")
    31→        lines.append(
    32→            f"> 今日扫描: HN({hn_total}) + Reddit({reddit_total}) + GitHub({github_total}) "
    33→            f"→ 过滤后: {valuable_total} 条有价值 → 新场景: {len(new_ucs)} 个"
    34→        )
    35→        lines.append("")
    36→        lines.append("---")
    37→        lines.append("")
    38→
    39→        # New use cases
    40→        lines.append(f"## 🆕 新场景（{len(new_ucs)}）")
    41→        lines.append("")
    42→        if new_ucs:
    43→            for uc in new_ucs:
    44→                lines.append(f"### {uc.scenario_type.value} · {uc.title}")
    45→                platform_label = "Reddit r/" + uc.source_url.split("/r/")[1].split("/")[0] \
    46→                    if "reddit.com" in uc.source_url else "HackerNews"
    47→                lines.append(
    48→                    f"**来源**: {platform_label} · `{uc.author}` · [原链接]({uc.source_url})"
    49→                )
    50→                lines.append("")
    51→                lines.append(uc.summary)
    52→                lines.append("")
    53→                lines.append(f"**工具**: {', '.join(uc.tools_used)}")
    54→                lines.append("")
    55→                lines.append("---")
    56→                lines.append("")
    57→        else:
    58→            lines.append("*今日无新场景*")
    59→            lines.append("")
    60→            lines.append("---")
    61→            lines.append("")
    62→
    63→        # Similar use cases grouped by scenario
    64→        lines.append(f"## ♻ 已知场景的新案例（{len(similar_ucs)}）")
    65→        lines.append("")
    66→        if similar_ucs:
    67→            by_scenario: dict[ScenarioType, list[UseCase]] = defaultdict(list)
    68→            for uc in similar_ucs:
    69→                by_scenario[uc.scenario_type].append(uc)
    70→            for scenario, items in by_scenario.items():
    71→                lines.append(f"### {scenario.value}（{len(items)} 条）")
    72→                for uc in items:
    73→                    tools_str = ", ".join(uc.tools_used) if uc.tools_used else "N/A"
    74→                    lines.append(
    75→                        f"- [{uc.title}]({uc.source_url}) — `{uc.author}` — *工具: {tools_str}*"
    76→                    )
    77→                lines.append("")
    78→        else:
    79→            lines.append("*今日无此类内容*")
    80→            lines.append("")
    81→
    82→        lines.append("---")
    83→        lines.append("")
    84→
    85→        # Filter log
    86→        lines.append("## 🚫 过滤日志")
    87→        lines.append(f"今日过滤 {len(repeat_ucs)} 条低价值内容。")
    88→        lines.append("")
    89→
    90→        return "\n".join(lines)
    91→
    92→    def write(self, usecases: list[UseCase], report_date: date) -> Path:
    93→        """Write the report to YYYY-MM-DD.md in the output directory."""
    94→        self._output_dir.mkdir(parents=True, exist_ok=True)
    95→        path = self._output_dir / f"{report_date.isoformat()}.md"
    96→        content = self.generate(usecases, report_date=report_date)
    97→        path.write_text(content, encoding="utf-8")
    98→        return path
    99→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

"""Generates daily Obsidian Markdown reports from processed UseCases."""
from collections import defaultdict
from datetime import date
from pathlib import Path

from ai_usecases_explorer.models.usecase import Novelty, ScenarioType, UseCase

class ObsidianReporter:
    """Writes a daily Markdown report to an Obsidian vault directory."""

    def __init__(self, output_dir: Path) -> None:
        self._output_dir = output_dir

    def generate(self, usecases: list[UseCase], report_date: date) -> str:
        """Build the full Markdown report string."""
        new_ucs = [uc for uc in usecases if uc.novelty == Novelty.NEW and uc.is_real_value]
        similar_ucs = [uc for uc in usecases if uc.novelty == Novelty.SIMILAR and uc.is_real_value]
        repeat_ucs = [uc for uc in usecases if uc.novelty == Novelty.REPEAT or not uc.is_real_value]

        hn_total = sum(1 for uc in usecases if uc.source_platform == "hackernews")
        reddit_total = sum(1 for uc in usecases if uc.source_platform == "reddit")
        github_total = sum(1 for uc in usecases if uc.source_platform == "github")
        valuable_total = len(new_ucs) + len(similar_ucs)

        lines: list[str] = []

        # Header
        lines.append(f"# AI Use Case 雷达 — {report_date.isoformat()}")
        lines.append("")
        lines.append(
            f"> 今日扫描: HN({hn_total}) + Reddit({reddit_total}) + GitHub({github_total}) "
            f"→ 过滤后: {valuable_total} 条有价值 → 新场景: {len(new_ucs)} 个"
        )
        lines.append("")
        lines.append("---")
        lines.append("")

        # New use cases
        lines.append(f"## 🆕 新场景（{len(new_ucs)}）")
        lines.append("")
        if new_ucs:
            for uc in new_ucs:
                lines.append(f"### {uc.scenario_type.value} · {uc.title}")
                platform_label = "Reddit r/" + uc.source_url.split("/r/")[1].split("/")[0] \
                    if "reddit.com" in uc.source_url else "HackerNews"
                lines.append(
                    f"**来源**: {platform_label} · `{uc.author}` · [原链接]({uc.source_url})"
                )
                lines.append("")
                lines.append(uc.summary)
                lines.append("")
                lines.append(f"**工具**: {', '.join(uc.tools_used)}")
                lines.append("")
                lines.append("---")
                lines.append("")
        else:
            lines.append("*今日无新场景*")
            lines.append("")
            lines.append("---")
            lines.append("")

        # Similar use cases grouped by scenario
        lines.append(f"## ♻ 已知场景的新案例（{len(similar_ucs)}）")
        lines.append("")
        if similar_ucs:
            by_scenario: dict[ScenarioType, list[UseCase]] = defaultdict(list)
            for uc in similar_ucs:
                by_scenario[uc.scenario_type].append(uc)
            for scenario, items in by_scenario.items():
                lines.append(f"### {scenario.value}（{len(items)} 条）")
                for uc in items:
                    tools_str = ", ".join(uc.tools_used) if uc.tools_used else "N/A"
                    lines.append(
                        f"- [{uc.title}]({uc.source_url}) — `{uc.author}` — *工具: {tools_str}*"
                    )
                lines.append("")
        else:
            lines.append("*今日无此类内容*")
            lines.append("")

        lines.append("---")
        lines.append("")

        # Filter log
        lines.append("## 🚫 过滤日志")
        lines.append(f"今日过滤 {len(repeat_ucs)} 条低价值内容。")
        lines.append("")

        return "\n".join(lines)

    def write(self, usecases: list[UseCase], report_date: date) -> Path:
        """Write the report to YYYY-MM-DD.md in the output directory."""
        self._output_dir.mkdir(parents=True, exist_ok=True)
        path = self._output_dir / f"{report_date.isoformat()}.md"
        content = self.generate(usecases, report_date=report_date)
        path.write_text(content, encoding="utf-8")
        return path


     1→"""Application settings loaded from environment / .env file."""
     2→from pathlib import Path
     3→
     4→from pydantic import Field
     5→from pydantic_settings import BaseSettings, SettingsConfigDict
     6→
     7→
     8→class Settings(BaseSettings):
     9→    """All configuration is read from environment variables or a .env file."""
    10→
    11→    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
    12→
    13→    anthropic_api_key: str = Field(default="", alias="ANTHROPIC_API_KEY")
    14→    anthropic_base_url: str = Field(default="", alias="ANTHROPIC_BASE_URL")
    15→    claude_model: str = Field(default="claude-haiku-4-5-20251001", alias="CLAUDE_MODEL")
    16→
    17→    db_path: Path = Field(default=Path("data/usecases.db"), alias="DB_PATH")
    18→    obsidian_report_dir: Path = Field(
    19→        default=Path("/root/vault/obsidian_vault/obsidian/obsidian/auto_report"),
    20→        alias="OBSIDIAN_REPORT_DIR",
    21→    )
    22→
    23→    hn_min_points: int = Field(default=10, alias="HN_MIN_POINTS")
    24→
    25→    exa_api_key: str = Field(default="", alias="EXA_API_KEY")
    26→    reddit_subreddits: list[str] = Field(
    27→        default=["openclaw", "vibecoding", "ClaudeCode", "singularity"],
    28→        alias="REDDIT_SUBREDDITS",
    29→    )
    30→
    31→    github_token: str = Field(default="", alias="GITHUB_TOKEN")
    32→    github_min_stars: int = Field(default=10, alias="GITHUB_MIN_STARS")
    33→
    34→    # Cap LLM calls per run to keep runtime under ~5 minutes
    35→    max_items_per_run: int = Field(default=80, alias="MAX_ITEMS_PER_RUN")
    36→
    37→    deep_dive_report_dir: Path = Field(
    38→        default=Path("/root/vault/obsidian_vault/obsidian/obsidian/auto_report/deep-dive"),
    39→        alias="DEEP_DIVE_REPORT_DIR",
    40→    )
    41→    deep_dive_model: str = Field(
    42→        default="claude-sonnet-4-6",
    43→        alias="DEEP_DIVE_MODEL",
    44→    )
    45→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

"""Application settings loaded from environment / .env file."""
from pathlib import Path

from pydantic import Field
from pydantic_settings import BaseSettings, SettingsConfigDict

class Settings(BaseSettings):
    """All configuration is read from environment variables or a .env file."""

    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")

    anthropic_api_key: str = Field(default="", alias="ANTHROPIC_API_KEY")
    anthropic_base_url: str = Field(default="", alias="ANTHROPIC_BASE_URL")
    claude_model: str = Field(default="claude-haiku-4-5-20251001", alias="CLAUDE_MODEL")

    db_path: Path = Field(default=Path("data/usecases.db"), alias="DB_PATH")
    obsidian_report_dir: Path = Field(
        default=Path("/root/vault/obsidian_vault/obsidian/obsidian/auto_report"),
        alias="OBSIDIAN_REPORT_DIR",
    )

    hn_min_points: int = Field(default=10, alias="HN_MIN_POINTS")

    exa_api_key: str = Field(default="", alias="EXA_API_KEY")
    reddit_subreddits: list[str] = Field(
        default=["openclaw", "vibecoding", "ClaudeCode", "singularity"],
        alias="REDDIT_SUBREDDITS",
    )

    github_token: str = Field(default="", alias="GITHUB_TOKEN")
    github_min_stars: int = Field(default=10, alias="GITHUB_MIN_STARS")

    # Cap LLM calls per run to keep runtime under ~5 minutes
    max_items_per_run: int = Field(default=80, alias="MAX_ITEMS_PER_RUN")

    deep_dive_report_dir: Path = Field(
        default=Path("/root/vault/obsidian_vault/obsidian/obsidian/auto_report/deep-dive"),
        alias="DEEP_DIVE_REPORT_DIR",
    )
    deep_dive_model: str = Field(
        default="claude-sonnet-4-6",
        alias="DEEP_DIVE_MODEL",
    )


Now let me do some live tests to understand why Reddit and GitHub returned 0 results. I'll need to check:

1. Whether the EXA_API_KEY is set
2. Whether the GitHub token is set
3. Test the Reddit Exa.ai query live
4. Test the GitHub API query live

Let me check the .env file first, then do direct tests.


现在来实际测试一下各个采集器，诊断根本原因：


Bash

cat .env 2>/dev/null | grep -v "KEY\|TOKEN\|SECRET" | head -20 && echo "---" && ls -la .env 2>/dev/null || echo "no .env file"

Check .env file (masked secrets)


ANTHROPIC_BASE_URL=https://anyrouter.top
CLAUDE_MODEL=claude-haiku-4-5-20251001
OBSIDIAN_REPORT_DIR=/root/vault/obsidian_vault/obsidian/obsidian/auto_report
DB_PATH=data/usecases.db
HN_MIN_POINTS=10
REDDIT_MIN_SCORE=20

DEEP_DIVE_MODEL=claude-haiku-4-5-20251001
---
-rw-r--r-- 1 root root 381 Feb 28 19:31 .env


source venv/bin/activate && python -c "
from ai_usecases_explorer.settings import Settings
s = Settings()
print('EXA_API_KEY set:', bool(s.exa_api_key))
print('GITHUB_TOKEN set:', bool(s.github_token))
print('HN min_points:', s.hn_min_points)
print('GitHub min_stars:', s.github_min_stars)
print('Reddit subreddits:', s.reddit_subreddits)
"

Check actual settings values


EXA_API_KEY set: True
GITHUB_TOKEN set: False
HN min_points: 10
GitHub min_stars: 10
Reddit subreddits: ['openclaw', 'vibecoding', 'ClaudeCode', 'singularity']


source venv/bin/activate && python -c "
import httpx
# Test GitHub API directly for one topic
topic = 'ai-agent'
url = 'https://api.github.com/search/repositories'
from datetime import date, timedelta
since = (date.today() - timedelta(days=2)).isoformat()
query = f'topic:{topic} pushed:>{since} stars:>=10'
print(f'Query: {query}')
resp = httpx.get(url, params={'q': query, 'sort': 'stars', 'order': 'desc', 'per_page': 5}, headers={'Accept': 'application/vnd.github+json'}, timeout=15)
print(f'Status: {resp.status_code}')
data = resp.json()
print(f'Total count: {data.get(\"total_count\", 0)}')
print(f'Items returned: {len(data.get(\"items\", []))}')
if data.get(\"items\"):
    for item in data[\"items\"][:3]:
        print(f'  - {item[\"full_name\"]} (stars={item[\"stargazers_count\"]}, pushed={item[\"pushed_at\"][:10]})')
elif data.get(\"message\"):
    print(f'Error: {data[\"message\"]}')
" 2>&1

Test GitHub API query directly


Query: topic:ai-agent pushed:>2026-03-02 stars:>=10
Status: 200
Total count: 117
Items returned: 5
  - zhayujie/chatgpt-on-wechat (stars=41820, pushed=2026-03-03)
  - CherryHQ/cherry-studio (stars=40645, pushed=2026-03-04)
  - CopilotKit/CopilotKit (stars=29150, pushed=2026-03-04)


So GitHub API works and returns results! The issue must be something else. Let me check the actual GitHubCollector to see why it returned 0 results on the actual run.

Wait - looking at the lookback_days parameter:

```python
def __init__(
    self,
    min_stars: int = 10,
    token: str = "",
    lookback_days: int = 2,
) -> None:
```

And the actual usage in `main.py`:
```python
self._github = GitHubCollector(
    min_stars=settings.github_min_stars,
    token=settings.github_token,

```

So `lookback_days` defaults to 2 days, which seems reasonable. The API is returning results now, so maybe the collector was actually working but all items got filtered out downstream? Looking at the report output, it shows `GitHub(0)` which is counting items where source_platform=="github" after filtering. Let me trace through the actual report to understand what's happening with the filtering logic.

The reporter is counting items from the processed `usecases` list that's passed to it, not the raw collected items. So `HN(3) + Reddit(0) + GitHub(0)` means only 3 HN items survived the filter, while Reddit and GitHub contributed nothing valuable. If Reddit collected zero items initially from the API, then it makes sense it would show zero in the final report.

Let me check whether the Exa API is actually returning Reddit results, or if they're all getting filtered out. I'm wondering if the domain filtering syntax might be the issue—Exa might not support the `reddit.com/r/subreddit` path-based filtering the way the code is using it.


source venv/bin/activate && python -c "
from ai_usecases_explorer.settings import Settings
from exa_py import Exa
from datetime import UTC, datetime, timedelta

s = Settings()
exa = Exa(api_key=s.exa_api_key)
start_date = (datetime.now(tz=UTC) - timedelta(hours=48)).strftime('%Y-%m-%dT%H:%M:%SZ')

# Test 1: include_domains with path (current approach)
print('=== Test 1: include_domains=[reddit.com/r/ClaudeCode] ===')
try:
    resp = exa.search_and_contents(
        'AI workflow automation use case tool productivity',
        include_domains=['reddit.com/r/ClaudeCode'],
        start_published_date=start_date,
        text=True,
        num_results=5,
    )
    print(f'Results: {len(resp.results)}')
    for r in resp.results[:3]:
        print(f'  URL: {r.url}')
except Exception as e:
    print(f'ERROR: {e}')
" 2>&1

Test Reddit Exa search with path-based domain filter


=== Test 1: include_domains=[reddit.com/r/ClaudeCode] ===
Results: 0


source venv/bin/activate && python -c "
from ai_usecases_explorer.settings import Settings
from exa_py import Exa
from datetime import UTC, datetime, timedelta

s = Settings()
exa = Exa(api_key=s.exa_api_key)
start_date = (datetime.now(tz=UTC) - timedelta(hours=48)).strftime('%Y-%m-%dT%H:%M:%SZ')

# Test 2: just include_domains=['reddit.com'] with query mentioning subreddit
print('=== Test 2: include_domains=[reddit.com], subreddit in query ===')
try:
    resp = exa.search_and_contents(
        'site:reddit.com/r/ClaudeCode AI agent workflow tool',
        include_domains=['reddit.com'],
        start_published_date=start_date,
        text=True,
        num_results=10,
    )
    print(f'Results: {len(resp.results)}')
    for r in resp.results[:5]:
        print(f'  URL: {r.url}')
except Exception as e:
    print(f'ERROR: {e}')

# Test 3: Exa Neural search with direct query
print()
print('=== Test 3: include_domains=[reddit.com] broad AI query, last 48h ===')
try:
    resp = exa.search_and_contents(
        'AI tool automation workflow use case',
        include_domains=['reddit.com'],
        start_published_date=start_date,
        text=True,
        num_results=10,
    )
    print(f'Results: {len(resp.results)}')
    for r in resp.results[:5]:
        print(f'  URL: {r.url[:80]}')
except Exception as e:
    print(f'ERROR: {e}')
" 2>&1

Test Reddit Exa with domain-only filter and different queries


=== Test 2: include_domains=[reddit.com], subreddit in query ===
Results: 0

=== Test 3: include_domains=[reddit.com] broad AI query, last 48h ===
Results: 5
  URL: https://www.reddit.com/r/automation/comments/1huuoaa/best_starting_point_for_lea
  URL: https://www.reddit.com/r/n8n/comments/1m7wv4a/whats_the_most_impactful_daily_aut
  URL: https://www.reddit.com/r/ProductManagement/comments/1iw0zip/useful_ai_agents_or_
  URL: https://www.reddit.com/r/nocode/comments/1ek02fe/curated_list_of_ai_workflow_aut
  URL: https://www.reddit.com/r/automation/comments/1iuwnuh/anyone_having_success_with_


Now I can see the issue with Reddit. The broad AI query using `include_domains=['reddit.com']` without a specific subreddit gets 5 results, but they're from different subreddits (r/automation, r/n8n, r/ProductManagement, r/nocode, etc.), not from our target subreddits.

Let me now try with `category="news"` or by specifying the subreddit in a different way. Also, let me test what happens with a longer time window.

Key findings so far:
1. `include_domains=['reddit.com/r/ClaudeCode']` returns 0 - path-based filtering doesn't work in Exa

2. Adding the subreddit to the query also returns 0 - that approach doesn't work either
3. Broad Reddit searches return results but from random subreddits, not the ones I'm targeting

So the real issue is that Exa doesn't support path-based domain filtering. The workaround would be to search Reddit broadly and then filter the results by URL afterward to keep only posts from my target subreddits.

But the current post-processing logic rejects anything that doesn't match the target subreddit exactly, so when the domain filter returns nothing, I end up with zero results. I need to either accept non-target subreddits or change how the filtering works.

Now let me look at the Hacker News collector. It's using the search API with date-based ordering but without explicit time filters, just relying on the API's default sorting. Setting up the query parameters...

Counting the HN results in the report shows only 3 items made it through, but I'm wondering how many were actually collected before filtering. Let me trace through the reporter logic to see the total count. I'm looking at how the `processed` list gets passed to the reporter, and realizing the GitHub count of 0 makes sense now—if all 30 items were either already in the database or filtered out by the ValueFilter, none would make it into that final processed list. Let me test the GitHub collector directly to confirm where the bottleneck is.


source venv/bin/activate && python -c "
from ai_usecases_explorer.collectors.github import GitHubCollector
from ai_usecases_explorer.settings import Settings

s = Settings()
collector = GitHubCollector(min_stars=s.github_min_stars, token=s.github_token, lookback_days=2)
items = collector.fetch()
print(f'GitHub raw items collected: {len(items)}')
for item in items[:5]:
    print(f'  - {item.title} (score={item.score}, platform={item.source_platform})')
" 2>&1

Test GitHub collector directly


GitHub raw items collected: 122
  - openclaw/openclaw (score=255072, platform=github)
  - zhayujie/chatgpt-on-wechat (score=41822, platform=github)
  - CherryHQ/cherry-studio (score=40649, platform=github)
  - 1Panel-dev/1Panel (score=33934, platform=github)
  - VoltAgent/awesome-openclaw-skills (score=26590, platform=github)


GitHub is returning 122 items! So why did the report show 0? The issue is that these items all got either:
1. Already in the DB and were skipped
2. Failed the ValueFilter

Let me check the DB to see if these items were already stored. Let me also look at the existing DB items.

Wait, looking at the `openclaw/openclaw` repo - the score is 255072! That's suspicious for a repo called "openclaw". Let me investigate...

Actually, looking more carefully, the issue is that there's a topic `openclaw` in the _TOPICS list. But GitHub returns repos with that topic, not just AI-related repos.

The "openclaw" topic is pulling in unrelated repos with massive star counts, and I'm wondering if that 255072 score is even legitimate for a real GitHub repo. Let me verify what URL this is coming from and check whether all 122 items were filtered out because they already existed in the database from a previous run.


source venv/bin/activate && python -c "
from ai_usecases_explorer.collectors.github import GitHubCollector
from ai_usecases_explorer.settings import Settings
from ai_usecases_explorer.storage.database import Database
from ai_usecases_explorer.models.usecase import make_id

s = Settings()
collector = GitHubCollector(min_stars=s.github_min_stars, token=s.github_token, lookback_days=2)
items = collector.fetch()
db = Database(db_path=s.db_path)

already_in_db = 0
new_items = 0
for item in items:
    item_id = make_id(item.source_url)
    if db.exists(item_id):
        already_in_db += 1
    else:
        new_items += 1

print(f'Total collected: {len(items)}')
print(f'Already in DB: {already_in_db}')
print(f'New (not in DB): {new_items}')
" 2>&1

Check how many GitHub items are already in DB


Total collected: 122
Already in DB: 32
New (not in DB): 90


source venv/bin/activate && python -c "
# Check what actually happened: test value filter on a sample of GitHub items
from ai_usecases_explorer.collectors.github import GitHubCollector
from ai_usecases_explorer.settings import Settings
from ai_usecases_explorer.storage.database import Database
from ai_usecases_explorer.models.usecase import make_id
import anthropic

s = Settings()
collector = GitHubCollector(min_stars=s.github_min_stars, token=s.github_token, lookback_days=2)
items = collector.fetch()
db = Database(db_path=s.db_path)

# Get new items only
new_items = [item for item in items if not db.exists(make_id(item.source_url))]
print(f'New GitHub items: {len(new_items)}')
print('Sample of new items:')
for item in new_items[:10]:
    print(f'  - {item.title} (stars={item.score})')
    print(f'    raw: {item.raw_content[:120]}')
    print()
" 2>&1

Sample new GitHub items to understand quality


New GitHub items: 90
Sample of new items:
  - CherryHQ/cherry-studio (stars=40656)
    raw: CherryHQ/cherry-studio

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access t

  - 1Panel-dev/1Panel (stars=33938)
    raw: 1Panel-dev/1Panel

🔥 Take full control of your VPS with 1Panel. Deploy OpenClaw in one click.

Topics: 1panel, agent, cl

  - VoltAgent/awesome-openclaw-skills (stars=26653)
    raw: VoltAgent/awesome-openclaw-skills

The awesome collection of OpenClaw skills. 5,400+ skills filtered and categorized fro

  - zeroclaw-labs/zeroclaw (stars=21783)
    raw: zeroclaw-labs/zeroclaw

Fast, small, and fully autonomous AI assistant infrastructure — deploy anywhere, swap anything 🦀

  - hesamsheikh/awesome-openclaw-usecases (stars=17339)
    raw: hesamsheikh/awesome-openclaw-usecases

A community collection of OpenClaw use cases for making life easier.

Topics: awe

  - RightNow-AI/openfang (stars=9718)
    raw: RightNow-AI/openfang

Open-source Agent Operating System

Topics: agent-framework, ai-agents, llm, mcp, open-source, ope

  - mcp-use/mcp-use (stars=9360)
    raw: mcp-use/mcp-use

The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.

Topi

  - MemTensor/MemOS (stars=6108)
    raw: MemTensor/MemOS

AI memory OS for LLM and Agent systems(moltbot,clawdbot,openclaw), enabling persistent Skill memory for

  - volcengine/OpenViking (stars=4550)
    raw: volcengine/OpenViking

OpenViking is an open-source context database designed specifically for AI Agents(such as opencla

  - BlockRunAI/ClawRouter (stars=3983)
    raw: BlockRunAI/ClawRouter

The agent-native LLM router for OpenClaw. 41+ models, <1ms routing, USDC payments on Base & Solan


I see the issue more clearly now. The GitHub data contains quality items (CherryHQ/cherry-studio, mcp-use/mcp-use, etc.) that should pass the value filter. But the report shows GitHub(0).

Wait - I need to re-read the reporter code. The report header says `GitHub(0)` after filtering, but there are 90 new items from GitHub. This means all 90 were filtered out by ValueFilter. But these items look like they should pass...

Let me re-read the reporter code:
```python
hn_total = sum(1 for uc in usecases if uc.source_platform == "hackernews")reddit_total = sum(1 for uc in usecases if uc.source_platform == "reddit")

```

The `usecases` list in the reporter is ONLY items that passed the ValueFilter. So if GitHub shows 0, that means either the ValueFilter rejected all GitHub items, or GitHub items weren't even processed due to the max_items_per_run cap.

Looking at main.py, I'm examining how items are interleaved from the different sources...

Examining interleaving logic...

With `max_items_per_run=80` and HN returning many items while Reddit returns 0 and GitHub returns 122, the interleaving would actually include GitHub items. But I need to trace when the DB check happens—it's during the processing loop, not before interleaving. So HN, Reddit, and GitHub are fetched as raw items first, then interleaved and capped at 80, then each item is checked against the DB. If HN returns 200+ items and Reddit returns 0, the zip_longest interleaving of those sources would alternate between HN and GitHub items until one source runs out.

Actually, let me think through the zip_longest logic more carefully. Each tuple contains one item from each source, so with 200 HN items, 0 Reddit items, and 122 GitHub items, the first 122 tuples would be (hn_item, None, github_item), and the remaining 78 tuples would be (hn_item, None, None). After filtering out the None values and flattening, I'd get roughly alternating HN and GitHub items for the first 244 positions, then just HN items. Capped at 80, that should give me around 40 HN and 40 GitHub items, so GitHub should definitely be included in the final cap.

But the log only showed 3 items being processed, which makes me wonder if `max_items_per_run` was set to something much lower than expected.

Looking at the environment variables, there's no `MAX_ITEMS_PER_RUN` defined, so it should be using the default of 80. The `EXA_API_KEY` isn't visible in the output but the settings confirmed it's configured.

The report shows `HN(3) + Reddit(0) + GitHub(0)` with 3 items passing the value filter, which initially seemed like aggressive filtering, but I'm realizing I may have misread it. Let me trace through the reporter logic to understand how it's actually counting the sources and calculating what makes it into the final report. I'm realizing the core issue: the report shows `GitHub(0)` in the final output, meaning zero GitHub items passed the ValueFilter, even though 90 new ones were collected. I need to figure out whether those 90 items were actually processed through the filter or if something else prevented them from being evaluated. The timing might be relevant too—when exactly did the cron job run this morning, and were those GitHub repos actually new at that point?

Looking at the processing logic, if the LLM API calls were failing due to proxy issues or rate limits, items would get logged as warnings and skipped entirely. That could explain why GitHub items disappeared from the pipeline. Let me check what the actual HN results look like to understand how aggressive the filter really is.


source venv/bin/activate && python -c "
# Check HN collector actual count
from ai_usecases_explorer.collectors.hackernews import HackerNewsCollector
from ai_usecases_explorer.settings import Settings
from ai_usecases_explorer.storage.database import Database
from ai_usecases_explorer.models.usecase import make_id

s = Settings()
hn = HackerNewsCollector(min_points=s.hn_min_points)
items = hn.fetch()
db = Database(db_path=s.db_path)

new = [item for item in items if not db.exists(make_id(item.source_url))]
print(f'HN raw collected: {len(items)}')
print(f'HN new (not in DB): {len(new)}')
print('Sample new HN items:')
for item in new[:5]:
    print(f'  - {item.title[:80]} (pts={item.score})')
" 2>&1

Check HN raw item count and DB status


HN raw collected: 441
HN new (not in DB): 424
Sample new HN items:
  - Show HN: Mozilla.ai introduces Clawbolt, an AI Assistant for the trades (pts=11)
  - OpenClaw Exposure Watchboard (pts=49)
  - Show HN: I turned Claude Code into a personal assistant (pts=11)
  - OpenClaw surpasses React to become the most-starred software project on GitHub (pts=288)
  - Setting up OpenClaw on a cloud VM (pts=84)


Interesting! HN has 441 items collected, 424 new. But the report shows only 3 passed the filter. This suggests the ValueFilter is being extremely aggressive.

Let me look at the ValueFilter prompt again:
```
PASS criteria (at least one must be true):
- The user integrates AI with local/personal data (files, DB, notes, etc.)
- The user automates a specific workflow step — not just asking questions

REJECT criteria:
- Pure Q&A replacing ChatGPT with no local data or workflow change
- Marketing demos without real user implementation
- Generic "AI is great" commentary
```

The filter's designed for user-story posts about personal AI use cases, but GitHub repos and HN stories about AI tools themselves—like Claude Code features or new tool announcements—probably don't explicitly mention local data integration or workflow automation, so they're getting rejected. And with the 80-item-per-run cap, I'm hitting limits across sources: 424 new HN items, 90 new GitHub items, and 0 from Reddit, which means the interleaving strategy is only processing a fraction of what's available.

After round-robin distribution, I'd expect roughly 40 HN and 40 GitHub items in the top 80, but all the GitHub items are failing the filter—that's a red flag. Let me directly test the ValueFilter against an actual GitHub item to see what's happening.


source venv/bin/activate && python -c "
# Test ValueFilter on GitHub items
from ai_usecases_explorer.collectors.github import GitHubCollector
from ai_usecases_explorer.processors.filter import ValueFilter
from ai_usecases_explorer.settings import Settings
import anthropic

s = Settings()
collector = GitHubCollector(min_stars=s.github_min_stars, token=s.github_token, lookback_days=2)
items = collector.fetch()[:5]

client_kwargs = {'api_key': s.anthropic_api_key}
if s.anthropic_base_url:
    client_kwargs['base_url'] = s.anthropic_base_url
client = anthropic.Anthropic(**client_kwargs)
vf = ValueFilter(client=client, model=s.claude_model)

for item in items[:3]:
    print(f'=== {item.title} ===')
    print(f'raw: {item.raw_content[:200]}')
    result = vf.run(item)
    print(f'Filter result: {result}')
    print()
" 2>&1

Test ValueFilter on GitHub items directly


=== openclaw/openclaw ===
raw: openclaw/openclaw

Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

Topics: ai, assistant, crustacean, molty, openclaw, own-your-data, personal
Language: TypeScript | Stars:
Filter result: {'is_real_value': True, 'reason': "OpenClaw is a personal AI assistant framework that emphasizes 'own-your-data' and cross-platform deployment. This represents genuine infrastructure for integrating AI with local/personal data and systems, enabling workflow automation across any OS/platform. It's a tool enabling real implementation, not a marketing demo or generic commentary.", 'has_local_data': True, 'has_workflow_opt': True}

=== zhayujie/chatgpt-on-wechat ===
raw: zhayujie/chatgpt-on-wechat

CowAgent是基于大模型的超级AI助理，能主动思考和任务规划、访问操作系统和外部资源、创造和执行Skills、拥有长期记忆并不断成长。同时支持飞书、钉钉、企业微信应用、微信公众号、网页等接入，可选择OpenAI/Claude/Gemini/DeepSeek/ Qwen/GLM/Kimi/LinkAI，能处理文本、语音、图片和文件，可快速搭
Filter result: {'is_real_value': True, 'reason': 'This is a legitimate AI integration framework that connects LLMs to multiple communication platforms (WeChat, Feishu, DingTalk, enterprise WeChat) and enables workflow automation through skills, external resource access, and OS integration. It represents a genuine implementation enabling users to build practical AI agents across real communication channels and business tools, not pure Q&A.', 'has_local_data': True, 'has_workflow_opt': True}

=== CherryHQ/cherry-studio ===
raw: CherryHQ/cherry-studio

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

Topics: ai-agent, claude-code, code-agent, codex, openclaw, ope
Filter result: {'is_real_value': False, 'reason': "This is a product announcement/repository showcase without evidence of a specific user implementing it to solve a real problem. It describes tool capabilities (chat, agents, assistants) but doesn't demonstrate integration with local data, a concrete workflow automation, or actual implementation results.", 'has_local_data': False, 'has_workflow_opt': False}


Now I understand the problem better. The ValueFilter is designed to detect user-story type posts (where a user describes how THEY are using AI), but GitHub repos are tool announcements. The filter incorrectly rejects CherryHQ/cherry-studio saying "this is a product announcement/repository showcase without evidence of a specific user implementing it to solve a real problem."

But `openclaw/openclaw` and `chatgpt-on-wechat` pass because they have more descriptive keywords like "own-your-data" or more detailed descriptions.

The core issues are:

**Issue 1: Reddit - Broken Exa.ai domain filter**

The path-based domain filtering isn't working with Exa.ai—specifying `reddit.com/r/ClaudeCode` returns nothing because Exa doesn't support that level of granularity. The post-filter then rejects everything that doesn't match the subreddit pattern. I need to either use broader domain filtering with the subreddit in the search query itself, or run multiple queries per subreddit.

**Issue 2: GitHub - ValueFilter too strict for tool repos**
The filter expects user-story narratives but GitHub repos are technical announcements, not personal use cases. CherryHQ/cherry-studio gets rejected while others pass because they have more descriptive language around ownership or implementation details.

**Issue 3: HN - 424 new items but only 3 pass**
The 0.7% pass rate seems suspiciously low. Looking at what actually passed—a tool announcement, a scientific paper translation use case, and a Cursor blog post about autonomous coding—they all have clear value propositions. The ValueFilter might be too restrictive for HN's style of content, or there's something about how the filter evaluates these items that's causing most to get rejected.

Actually, I'm realizing the real issue: the HN `search_by_date` function returns all historical posts without a date filter, not just today's items. So those 424 "new" items are likely old posts that were never stored in the database before. The points filter (`points>=10`) helps, but without a proper date boundary, we're re-processing tons of historical content that will keep failing the criteria. That explains why we're seeing so many items but such a tiny pass rate—we're not actually filtering by recency at the API level. The `search_by_date` endpoint returns all-time results sorted by date when no date filter is applied, so the HN collector is pulling historical items we haven't processed yet rather than fresh ones. With the 80-item-per-run cap, only the most relevant items get processed through the LLM, which explains why just 3 passed the filter today.

The core issue is that we're fetching too much historical data instead of focusing on today's items. I need to add a 24-48 hour date filter for HN, fix the Reddit domain filter, relax the ValueFilter for GitHub repos since it's designed for user stories not tool announcements, and reconsider whether the filter prompt's criteria are too strict for what we're actually trying to surface.

For the fixes, I'm adding a timestamp filter to HN to get only recent items, correcting the Reddit search to use site-specific queries, keeping the GitHub collector as-is but adjusting how the filter evaluates repos, and updating the ValueFilter to recognize that GitHub repositories and HN stories about tools are inherently valuable even without explicit workflow descriptions.

Implementing the HN date filter... I'm realizing the Exa neural search might not handle the `site:` syntax the way I expected, and the `include_domains` filter alone isn't returning results either. Let me try a different approach—maybe I should remove the domain restriction entirely and instead search with the subreddit name in the query itself, then filter the results by URL afterward to ensure they're from the right subreddit. subreddit URLs. Instead, I should use `include_domains=['reddit.com']` and cast a wider net across multiple AI-related topics to get better coverage. This means accepting results from any relevant AI subreddit rather than limiting to our specific target list.

For the ValueFilter issue, I'm realizing the current criteria are too user-story-focused. GitHub repos need different evaluation—I should be asking whether the project enables real AI use cases and serves practical automation needs. The filter prompt assumes a "user" doing something, but for repos the creator is the one building the tool, so I need to either make the filter context-aware of the source platform or reframe what "user" means in each context.

Looking at the actual PASS and REJECT criteria, repos like "CherryHQ/cherry-studio" should pass because they do enable workflow automation, but the filter is incorrectly rejecting them under the "marketing demos" criterion. The cleanest solution is to add a third PASS criterion specifically for tools and libraries that directly enable AI workflows, and clarify in the system prompt that GitHub repos should be evaluated on whether the tool itself powers real automation.

I also need to fix the HN date filter since it's currently returning all-time results instead of recent ones.

So my plan is to update the HN query to filter by timestamp for the last 24 hours, fix the Reddit domain filtering to work properly with subreddit queries, add that third PASS criterion to the filter, and add a config setting for Reddit's lookback period. Before implementing, I should check if any existing tests would break and maybe test the HN date filter to see what volume of results we'd actually get.

For Reddit specifically, the test results show that using `include_domains=['reddit.com']` with a site-specific query string returns nothing, but just the domain filter alone gets results from multiple subreddits. I'm thinking the better approach is to run separate queries for each target subreddit, include the subreddit name directly in the search text, keep the domain filter simple, and either remove or relax the strict URL post-filter to accept posts from related subreddits too. Let me test this approach to see if it works better.


source venv/bin/activate && python -c "
# Test: HN with date filter - how many items from last 24h?
import httpx, time
from ai_usecases_explorer.collectors.hackernews import _KEYWORDS

created_since = int(time.time()) - 86400  # last 24h
seen_ids = set()
total = 0

for keyword in _KEYWORDS:
    params = {
        'query': keyword,
        'tags': '(story,ask_hn,show_hn)',
        'numericFilters': f'points>=10,created_at_i>{created_since}',
        'hitsPerPage': 50,
    }
    resp = httpx.get('https://hn.algolia.com/api/v1/search_by_date', params=params, timeout=15)
    hits = resp.json().get('hits', [])
    new_hits = [h for h in hits if h.get('objectID') not in seen_ids]
    for h in new_hits:
        seen_ids.add(h.get('objectID'))
    total += len(new_hits)
    if new_hits:
        print(f'  [{keyword}]: {len(new_hits)} new items')

print(f'Total HN items (last 24h, 10+ pts): {total}')
" 2>&1

Test HN with 24h date filter to check item count


  [openclaw]: 1 new items
  [claude code]: 3 new items
  [vibe coding]: 1 new items
  [ai agent]: 3 new items
  [ollama]: 1 new items
  [ai saas]: 1 new items
Total HN items (last 24h, 10+ pts): 10


source venv/bin/activate && python -c "
# Test Reddit with broader approach - various queries for our communities
from ai_usecases_explorer.settings import Settings
from exa_py import Exa
from datetime import UTC, datetime, timedelta

s = Settings()
exa = Exa(api_key=s.exa_api_key)
start_date = (datetime.now(tz=UTC) - timedelta(hours=48)).strftime('%Y-%m-%dT%H:%M:%SZ')

# Test multiple queries without subreddit-specific domain filter
queries = [
    'AI tool workflow automation use case productivity agent',
    'Claude Code vibe coding LLM productivity developer',
    'AI singularity future technology automation impact',
]

seen = set()
for q in queries:
    resp = exa.search_and_contents(
        q,
        include_domains=['reddit.com'],
        start_published_date=start_date,
        text=True,
        num_results=10,
    )
    print(f'Query [{q[:50]}...]: {len(resp.results)} results')
    for r in resp.results:
        if r.url not in seen:
            seen.add(r.url)
            print(f'  - {r.url[:70]}')
print(f'Total unique Reddit results: {len(seen)}')
" 2>&1

Test Reddit with broader topic-based queries


Query [AI tool workflow automation use case productivity ...]: 3 results
  - https://www.reddit.com/r/AI_Agents/comments/1ijnzrd/what_ai_agents_do_
  - https://www.reddit.com/r/ProductManagement/comments/1iw0zip/useful_ai_
  - https://www.reddit.com/r/automation/comments/1huuoaa/best_starting_poi
Query [Claude Code vibe coding LLM productivity developer...]: 0 results
Query [AI singularity future technology automation impact...]: 5 results
  - https://www.reddit.com/r/singularity/comments/1clgzaf/ai_is_likely_to_
  - https://www.reddit.com/r/singularity/comments/1k1z4a2/does_anyone_stil
  - https://www.reddit.com/r/PLC/comments/124v78o/impacts_of_ai_and_machin
  - https://www.reddit.com/r/singularity/comments/19203xj/one_thing_i_dont
  - https://www.reddit.com/r/singularity/comments/1dv7hp0/what_happened_to
Total unique Reddit results: 8


source venv/bin/activate && python -c "
# Test with even wider time window for Exa
from ai_usecases_explorer.settings import Settings
from exa_py import Exa
from datetime import UTC, datetime, timedelta

s = Settings()
exa = Exa(api_key=s.exa_api_key)
# Try 7 days instead of 24h
start_date = (datetime.now(tz=UTC) - timedelta(days=7)).strftime('%Y-%m-%dT%H:%M:%SZ')

resp = exa.search_and_contents(
    'AI agent workflow automation tool use case productivity',
    include_domains=['reddit.com'],
    start_published_date=start_date,
    text=True,
    num_results=15,
)
print(f'Reddit results (7 days): {len(resp.results)}')
for r in resp.results:
    print(f'  - {r.url[:80]}')
    print(f'    published: {getattr(r, \"published_date\", \"unknown\")}')
" 2>&1

Test Reddit Exa with 7-day window


Reddit results (7 days): 2
  - https://www.reddit.com/r/ProductManagement/comments/1iw0zip/useful_ai_agents_or_
    published: None
  - https://www.reddit.com/r/automation/comments/1huuoaa/best_starting_point_for_lea
    published: None


Interesting! When we use a 7-day window, we get even FEWER results (2 vs 8). This suggests Exa.ai doesn't have good Reddit indexing coverage for recent posts. The Exa `published_date` is also `None` for these results, which means Exa doesn't have reliable date metadata for Reddit posts.

The issue is that Exa.ai doesn't index Reddit well in the short term (last 24h/7 days). Reddit content takes longer to get indexed by Exa.

Some options:
1. **Extend the search window further** (30 days?) and accept that we might get older content

2. **Remove the start_published_date filter entirely for Reddit** and let relevance ranking take over
3. **Switch to the Reddit API directly** with PRAW or the JSON endpoint
4. **Use more targeted keywords** that are more likely to surface relevant AI subreddit posts

I'm realizing the `start_published_date` filter might be the culprit here—since Exa doesn't have reliable date metadata for Reddit posts, the filter could be excluding results where the published_date is missing. Let me test removing that constraint entirely to see what we actually get.


source venv/bin/activate && python -c "
# Test WITHOUT date filter - Exa might not know Reddit published dates
from ai_usecases_explorer.settings import Settings
from exa_py import Exa

s = Settings()
exa = Exa(api_key=s.exa_api_key)

resp = exa.search_and_contents(
    'AI agent workflow automation tool use case productivity',
    include_domains=['reddit.com'],
    text=True,
    num_results=15,
)
print(f'Reddit results (NO date filter): {len(resp.results)}')
for r in resp.results:
    print(f'  r/{r.url.split(\"/r/\")[1].split(\"/\")[0] if \"/r/\" in r.url else \"?\"}: {r.url[:70]}')
    print(f'    published: {getattr(r, \"published_date\", None)}, title: {(r.title or \"\")[:60]}')
" 2>&1

Test Reddit Exa without date filter


Reddit results (NO date filter): 13
  r/AI_Agents: https://www.reddit.com/r/AI_Agents/comments/1ijnzrd/what_ai_agents_do_
    published: None, title: What AI Agents Do You Use Daily? : r/AI_Agents - Reddit
  r/ProductManagement: https://www.reddit.com/r/ProductManagement/comments/1iw0zip/useful_ai_
    published: None, title: Useful AI agents or workflow automations for product manager
  r/MarketingAutomation: https://www.reddit.com/r/MarketingAutomation/comments/1q4aid4/a_practi
    published: None, title: A practical AI agent workflow for marketing ops that won’t b
  r/n8n_on_server: https://www.reddit.com/r/n8n_on_server/comments/1r1xuqw/build_a_powerf
    published: None, title: Build a Powerful AI Agent with n8n Workflow Automation : r/n
  r/lovable: https://www.reddit.com/r/lovable/comments/1rb1tha/i_built_an_awesome_c
    published: None, title: I built an awesome CRM and Project Management tool on Lovabl
  r/n8n: https://www.reddit.com/r/n8n/comments/1r78er1/built_an_ai_voice_agent_
    published: None, title: Built an AI Voice Agent System That Calls & Qualifies Leads
  r/buildinpublic: https://www.reddit.com/r/buildinpublic/comments/1ov37zk/launched_kadab
    published: None, title: Launched Kadabra today - AI agent that turns plain text into
  r/ChatGPTEnterprise: https://www.reddit.com/r/ChatGPTEnterprise/comments/1m2om4w/openai_lau
    published: None, title: 🚨 OpenAI Launches ChatGPT Agent — A Major Upgrade in AI Work
  r/ClaudeAI: https://www.reddit.com/r/ClaudeAI/comments/1r68pb1/claude_code_toolkit
    published: None, title: Claude Code Toolkit — agents, skills, and rules for any proj
  r/AISEOInsider: https://www.reddit.com/r/AISEOInsider/comments/1r62o15/the_openclaw_mu
    published: None, title: The OpenClaw Multi-Agent System That Works While You Do Othe
  r/n8n: https://www.reddit.com/r/n8n/comments/1oo3x04/the_ai_agent_node_is_a_g
    published: None, title: The AI Agent node is a game-changer! I built a "What to Wear
  r/AISEOInsider: https://www.reddit.com/r/AISEOInsider/comments/1r56d36/openclaw_workfl
    published: None, title: OpenClaw Workflow Automation: A Simple Setup That Removes Ma
  r/ClaudeCode: https://www.reddit.com/r/ClaudeCode/comments/1ok16z6/introducing_hepha
    published: None, title: Introducing Hephaestus: AI workflows that build themselves a