Added logic to the 4 languages so it can search each language at a time

browser-use-service/main.py

@@ -711,11 +711,17 @@ async def _quick_search(page, context, query: str) -> tuple:
         if '/login' in current_url.lower():
             logger.warning("Quick search redirected to login for '%s'", query[:40])
             return page, []
-        await page.wait_for_timeout(random.randint(4000, 6000))
+        await page.wait_for_timeout(random.randint(3000, 7000))
-        await page.evaluate("window.scrollBy(0, 600)")
+        await page.evaluate(f"window.scrollBy(0, {random.randint(400, 900)})")
-        await page.wait_for_timeout(random.randint(2000, 4000))
+        await page.wait_for_timeout(random.randint(2000, 5000))
-        await page.evaluate("window.scrollBy(0, 600)")
+        if random.random() < 0.35:
-        await page.wait_for_timeout(random.randint(2000, 3000))
+            await page.evaluate(f"window.scrollBy(0, -{random.randint(100, 400)})")
+            await page.wait_for_timeout(random.randint(1500, 3500))
+        await page.evaluate(f"window.scrollBy(0, {random.randint(400, 900)})")
+        await page.wait_for_timeout(random.randint(2000, 5000))
+        if random.random() < 0.25:
+            await page.evaluate("window.scrollTo(0, 0)")
+            await page.wait_for_timeout(random.randint(1000, 3000))
         raw_articles = await _get_article_elements(page)
         posts = _extract_posts_from_elements(raw_articles, url) if raw_articles else []
         raw = await page.evaluate('document.body.innerText')
@@ -865,7 +871,7 @@ def _parse_fb_date(block: list[str]) -> str:
     return datetime.now().strftime('%Y-%m-%d')
 
 
-def _is_within_days(date_str: str, max_days: int = 3) -> bool:
+def _is_within_days(date_str: str, max_days: int = 2) -> bool:
     """Check if date is within max_days from now. Empty/unparseable = keep."""
     if not date_str:
         return True
@@ -1442,6 +1448,7 @@ async def _scrape_with_firefox(profile_path: str, force: bool, query: str | None
             context = await pw.firefox.launch_persistent_context(
                 user_data_dir=profile_dir,
                 headless=True,
+                viewport=random.choice(VIEWPORTS),
                 firefox_user_prefs={
                     "dom.webdriver.enabled": False,
                     "dom.webdriver.timeout": 0,
@@ -1503,7 +1510,9 @@ async def _scrape_with_firefox(profile_path: str, force: bool, query: str | None
                 if not posts:
                     continue
                 if i > 0:
-                    await page.wait_for_timeout(random.uniform(3000, 7000))
+                    await page.wait_for_timeout(random.uniform(5000, 12000))
+                    if random.random() < 0.2:
+                        await random_idle(page)
                     continue
                 if random.random() < 0.4:
                     await page.evaluate(f"window.scrollBy(0, {random.randint(-300, 300)})")
@@ -1532,8 +1541,8 @@ async def _scrape_with_firefox(profile_path: str, force: bool, query: str | None
                     seen.add(key)
                     deduped.append(p)
 
-            # Filter to last 3 days only
+            # Filter to last 2 days only
-            deduped = [p for p in deduped if _is_within_days(p.get('date', ''), 7)]
+            deduped = [p for p in deduped if _is_within_days(p.get('date', ''), 2)]
 
             leads = deduped[:20]
             if leads:
@@ -1589,6 +1598,7 @@ async def _scrape_with_chromium(profile_path: str, browser: str, force: bool = F
             launch_kwargs = dict(
                 user_data_dir=profile_dir,
                 headless=True,
+                viewport=random.choice(VIEWPORTS),
                 args=CHROME_LAUNCH_ARGS,
             )
             if channel:
@@ -1649,7 +1659,9 @@ async def _scrape_with_chromium(profile_path: str, browser: str, force: bool = F
                 if not posts:
                     continue
                 if i > 0:
-                    await page.wait_for_timeout(random.uniform(3000, 7000))
+                    await page.wait_for_timeout(random.uniform(5000, 12000))
+                    if random.random() < 0.2:
+                        await random_idle(page)
                     continue
                 if random.random() < 0.4:
                     await page.evaluate(f"window.scrollBy(0, {random.randint(-300, 300)})")
@@ -1678,7 +1690,7 @@ async def _scrape_with_chromium(profile_path: str, browser: str, force: bool = F
                     seen.add(key)
                     deduped.append(p)
 
-            deduped = [p for p in deduped if _is_within_days(p.get('date', ''), 7)]
+            deduped = [p for p in deduped if _is_within_days(p.get('date', ''), 2)]
             leads = deduped[:20]
             if leads:
                 leads = await classify_leads(leads, tutoring=tutoring)
@@ -1734,7 +1746,7 @@ async def _scrape_with_agent(force: bool = False) -> dict:
    - The post text content
    - The post URL (if visible)
    - The post date
-5. ONLY include posts from the last 3 days
+5. ONLY include posts from the last 2 days
 6. Collect as many posts as you can (aim for 5-10 per search)
 
 When done, return the data as a JSON list with keys: content, author, url, date.""",
@@ -1763,8 +1775,8 @@ When done, return the data as a JSON list with keys: content, author, url, date.
                 seen.add(key)
                 deduped.append(p)
 
-        # Filter to last 3 days only
+        # Filter to last 2 days only
-        deduped = [p for p in deduped if _is_within_days(p.get('date', ''), 7)]
+        deduped = [p for p in deduped if _is_within_days(p.get('date', ''), 2)]
 
         leads = deduped[:20]
         if leads:

data/ai/ai.md

@@ -40,12 +40,19 @@ The scraper lives at `browser-use-service/main.py` port 3008.
    - 6-7 non-English quick searches (Afrikaans, isiXhosa, isiZulu — 2 queries each per category)
    - Total: ~14 searches per scrape, completed in 2-4 minutes
 4. **Quick searches** — load page, double-scroll, extract visible posts (~12-18s each)
-5. **Date filter** — only posts within 7 days are considered
+5. **Date filter** — only posts within **2 days** are considered. Anything older is discarded. Fresh leads only.
-6. **2-pass classification (dead-accurate)**:
+6. **Stealth mechanics**:
+   - Random viewport dimensions (1280×800 to 1920×1080) — never the same size twice
+   - Variable delays between searches (5-12 seconds) with mouse idle actions mixed in
+   - Human-like scroll patterns: scroll down, pause, sometimes scroll back up, sometimes return to top
+   - Canvas/WebGL/audio fingerprint spoofing via injected init scripts
+   - Random decoy page visits (e.g., Facebook Groups) between searches
+   - Profile directory is temp-copied and cleaned up after each scrape
+   - Detection signal monitoring (checkpoint, login pages, security challenges)
+7. **2-pass classification (dead-accurate)**:
    - **Pass 1 (AI)**: Ollama classifies each post as LEAD or NOT using a strict prompt per category. This is the primary filter and most accurate.
    - **Pass 2 (Keyword)**: Only posts matching BOTH a target term AND a request term are kept. Requires multi-word phrases — standalone words like "need", "want", "help" are NOT used as they cause false positives. Aggressive reject list catches service offers, self-promotions, portfolio posts, learning-requests, and existing-site issues.
    - **No loose fill**: Unlike the old approach, there is NO third pass that accepts posts matching EITHER term. Every returned lead has passed both AI and/or strict keyword validation. If fewer than 5 posts pass, that means only genuine leads are returned — no noise to pad the count.
-7. **Anti-detection** — random delays, human-like scrolling, user-agent rotation, proxy support
 8. **Scrape timing** — 3-6 minutes for a complete run. Returns 5-10 leads with high confidence.
 
 ### Lead Categories

rust-ai/src/AI.md

@@ -40,12 +40,19 @@ The scraper lives at `browser-use-service/main.py` port 3008.
    - 6-7 non-English quick searches (Afrikaans, isiXhosa, isiZulu — 2 queries each per category)
    - Total: ~14 searches per scrape, completed in 2-4 minutes
 4. **Quick searches** — load page, double-scroll, extract visible posts (~12-18s each)
-5. **Date filter** — only posts within 7 days are considered
+5. **Date filter** — only posts within **2 days** are considered. Anything older is discarded. Fresh leads only.
-6. **2-pass classification (dead-accurate)**:
+6. **Stealth mechanics**:
+   - Random viewport dimensions (1280×800 to 1920×1080) — never the same size twice
+   - Variable delays between searches (5-12 seconds) with mouse idle actions mixed in
+   - Human-like scroll patterns: scroll down, pause, sometimes scroll back up, sometimes return to top
+   - Canvas/WebGL/audio fingerprint spoofing via injected init scripts
+   - Random decoy page visits (e.g., Facebook Groups) between searches
+   - Profile directory is temp-copied and cleaned up after each scrape
+   - Detection signal monitoring (checkpoint, login pages, security challenges)
+7. **2-pass classification (dead-accurate)**:
    - **Pass 1 (AI)**: Ollama classifies each post as LEAD or NOT using a strict prompt per category. This is the primary filter and most accurate.
    - **Pass 2 (Keyword)**: Only posts matching BOTH a target term AND a request term are kept. Requires multi-word phrases — standalone words like "need", "want", "help" are NOT used as they cause false positives. Aggressive reject list catches service offers, self-promotions, portfolio posts, learning-requests, and existing-site issues.
    - **No loose fill**: Unlike the old approach, there is NO third pass that accepts posts matching EITHER term. Every returned lead has passed both AI and/or strict keyword validation. If fewer than 5 posts pass, that means only genuine leads are returned — no noise to pad the count.
-7. **Anti-detection** — random delays, human-like scrolling, user-agent rotation, proxy support
 8. **Scrape timing** — 3-6 minutes for a complete run. Returns 5-10 leads with high confidence.
 
 ### Lead Categories
@@ -76,7 +83,7 @@ Each lead returned includes:
 - `author` — poster's name (may include location in name)
 - `content` — extracted post text
 - `url` — direct link to the post
-- `date` — when posted (filtered within 7 days)
+- `date` — when posted (filtered within 2 days)
 - `category` — "website" or "tutor"
 
 Target is 5-10 dead-accurate leads per scrape. Quality over quantity — no loose padding.