如何搭建永不缺灵感的 AI 内容机器

如何构建一个永不枯竭的 AI 内容机器 June 16, 2026 · 18 min read · View source ↗ AI Reddit Marketing Claude 大多数创作者不是没了想法。 他们是没了系统。 每天他们打开一张空白页。 盯着它。 问 ChatGPT 要想法。 得到一个泛泛的东西。 重写它。 不管怎样发出去。 查 analytics。 毫无感觉。 明天重新开始。 那不是内容策略。 那是内容跑步机。 一台真正的内容机器长这样。 > 随机灵感 > > ↓ > > 随机帖子 > > ↓ > > 随机结果 > > ↓ > > 明天重新开始 对比： > 每天爬取爆款内容 > > ↓ > > 提取钩子和模式 > > ↓ > > 复利增长的创意数据库 > > ↓ > > AI 生成平台原生帖子 > > ↓ > > Kie 生成视觉和视频 > > ↓ > > Postiz 排期并发布 > > ↓ > > 表现反馈回系统 > > ↓ > > 明天有更好的想法 一个是苦役。一个是机器。 下面就是如何构建它的确切方法。 大多数创作者真正的问题 这不是一个创造力问题。 这是一个记忆和反馈循环问题。 你发一样东西。它表现好。你忘了为什么。 你发另一样东西。它翻车。你完全不知道哪里不一样。 你刷着找灵感，却没把任何东西存成有用格式。 你问 AI 要想法，却给它零关于以前什么奏效过的上下文。 所以每一次会话都从零开始。 一台真正的内容机器修好这个。 它捕捉什么奏效。存下它。从中学习。自动改进。 让这个运转起来的工具： → ScrapeCreators —— 从 35+ 平台拉取爆款公开数据 → Kie.ai —— 按需生成图片、视频和音频 → Postiz —— 跨每个平台排期并发布 （顺带，你可以在 nichetraffickit.com 用一键自动化做所有这些以及更多） 5 层。下面是它们如何连接。 第 1 层 —— 用 ScrapeCreators 捕捉爆款内容 机器需要燃料。 燃料是爆款内容——你的领域里、你的受众所用的平台上、已经在奏效的东西。 ScrapeCreators 给你一个 REST API 从 35+ 平台拉取公开数据。 没有手动刷。没有截图。只有直接喂进你系统的结构化数据。 你能拉什么： → TikTok 趋势 feed——现在什么在爆 → TikTok 主页视频——任何账号表现最好的视频 → TikTok 评论——受众真正在说什么 → Instagram 趋势 reels——什么格式在火 → Instagram 标签搜索——你精确领域里的内容 → YouTube 趋势 shorts——什么 60 秒点子奏效 → Reddit subreddit 帖子——大白话的受众痛点 → Twitter/X 用户推文——你领域账号的顶帖 → Pinterest 搜索——你领域的视觉内容 全部用一个 x-api-key header 认证。 下面是用 Python 写的捕捉循环： import requests import json from datetime import datetime SCRAPE_API_KEY = "your_api_key_here" # get at app.scrapecreators.com BASE_URL = "https://api.scrapecreators.com" HEADERS = { "x-api-key": SCRAPE_API_KEY, "Content-Type": "application/json" } def get_tiktok_trending(count=30): """Pull trending TikTok feed — highest signal for what's working""" response = requests.get( f"{BASE_URL}/v1/tiktok/get-trending-feed", headers=HEADERS, params={"count": count} ) return response.json() def get_tiktok_profile_videos(username, count=20): """Pull top videos from any competitor/inspiration account""" response = requests.get( f"{BASE_URL}/v3/tiktok/profile/videos", headers=HEADERS, params={"username": username, "count": count} ) return response.json() def get_tiktok_video_comments(video_url, count=50): """Pull comments — raw audience voice, pain points, reactions""" response = requests.get( f"{BASE_URL}/v1/tiktok/video/comments", headers=HEADERS, params={"video_url": video_url, "count": count} ) return response.json() def get_instagram_trending_reels(count=20): """Pull Instagram trending reels in your niche""" response = requests.get( f"{BASE_URL}/v1/instagram/reels/trending", headers=HEADERS, params={"count": count} ) return response.json() def get_reddit_pain_points(subreddit, sort="hot", limit=25): """Pull Reddit posts — unfiltered audience language""" response = requests.get( f"{BASE_URL}/v1/reddit/subreddit", headers=HEADERS, params={"subreddit": subreddit, "sort": sort, "limit": limit} ) return response.json() def get_youtube_trending_shorts(count=20): """Pull trending YouTube Shorts for short-form ideas""" response = requests.get( f"{BASE_URL}/v1/youtube/shorts/trending", headers=HEADERS, params={"count": count} ) return response.json() def run_daily_capture(niche_accounts, subreddits): """Run the full daily capture across all platforms""" captured = { "date": datetime.now().isoformat(), "tiktok_trending": get_tiktok_trending(30), "instagram_reels": get_instagram_trending_reels(20), "youtube_shorts": get_youtube_trending_shorts(20), "competitor_videos": [], "reddit_posts": [] } # Pull from competitor/niche accounts for account in niche_accounts: videos = get_tiktok_profile_videos(account, count=10) captured["competitor_videos"].extend( videos.get("videos", []) ) # Pull Reddit pain points for sub in subreddits: posts = get_reddit_pain_points(sub) captured["reddit_posts"].extend( posts.get("posts", []) ) # Save to your content database with open(f"capture_{datetime.now().strftime('%Y%m%d')}.json", "w") as f: json.dump(captured, f, indent=2) print(f"Captured: {len(captured['competitor_videos'])} videos, " f"{len(captured['reddit_posts'])} Reddit posts") return captured # Run it daily if __name__ == "__main__": run_daily_capture( niche_accounts=["account1", "account2", "account3"], subreddits=["entrepreneurship", "SideProject", "AItools"] ) 每天早晨跑这个。 你醒来时有一个新鲜的 JSON 文件，装满每个平台昨晚表现的东西。 那就是你的原材料。 第 2 层 —— 用 AI 提取模式 原始数据不是想法。 你有 200 个爆款视频。 你需要的是：它们为什么奏效。 把捕捉到的数据喂给 Claude。提取模式。 import anthropic import json client = anthropic.Anthropic() # uses ANTHROPIC_API_KEY env var def extract_content_patterns(captured_data): """ Feed viral content data to Claude. Extract hooks, formats, emotions, templates. """ # Format the data for analysis video_sample = [] for video in captured_data.get("tiktok_trending", {}).get("videos", [])[:20]: video_sample.append({ "description": video.get("desc", ""), "views": video.get("stats", {}).get("playCount", 0), "likes": video.get("stats", {}).get("diggCount", 0), "comments": video.get("stats", {}).get("commentCount", 0), "shares": video.get("stats", {}).get("shareCount", 0), }) reddit_sample = [] for post in captured_data.get("reddit_posts", [])[:15]: reddit_sample.append({ "title": post.get("title", ""), "upvotes": post.get("score", 0), "comments": post.get("num_comments", 0), }) prompt = f""" You are a viral content strategist analyzing social media performance data. Here is today's captured viral content data: TIKTOK TRENDING VIDEOS: {json.dumps(video_sample, indent=2)} REDDIT POSTS (audience pain points): {json.dumps(reddit_sample, indent=2)} Analyze this data and extract: 1. TOP 5 HOOKS - The opening line or text overlay pattern - Why it triggers a stop-scroll reaction - Emotion triggered: curiosity / fear / FOMO / relief / identity 2. TOP 5 CONTENT FORMATS - Structure of the content (step-by-step / story / comparison / list) - What makes this format work on this platform 3. TOP 5 AUDIENCE PAIN POINTS - Real problems from Reddit and video comments - Exact language the audience uses (their words, not marketing words) 4. 10 ORIGINAL CONTENT IDEAS - Based on what's working + the pain points - Each idea: [Hook] + [Format] + [Platform] + [Why it will work] Output as structured JSON only. No preamble. JSON structure: {{ "hooks": [ {{"hook": "...", "emotion": "...", "why_it_works": "..."}} ], "formats": [ {{"format": "...", "structure": "...", "why_it_works": "..."}} ], "pain_points": [ {{"pain": "...", "exact_language": "...", "frequency": "..."}} ], "content_ideas": [ {{ "hook": "...", "format": "...", "platform": "...", "why_it_will_work": "...", "estimated_emotion": "..." }} ] }} """ response = client.messages.create( model="claude-sonnet-4-6", max_tokens=2000, messages=[{"role": "user", "content": prompt}] ) # Parse the JSON response raw = response.content[0].text return json.loads(raw) # Example output: # { # "hooks": [ # { # "hook": "I replaced my entire content team with 3 files", # "emotion": "curiosity + FOMO", # "why_it_works": "identity challenge + impossible-sounding claim" # } # ], # "pain_points": [ # { # "pain": "Running out of ideas after 2 weeks", # "exact_language": "I never know what to post anymore", # "frequency": "high" # } # ] # } 每天你得到一份新鲜分析。 新钩子。新格式。你受众此刻正在表达的新痛点。 不是常青猜测。是实时模式。 第 3 层 —— 构建创意记忆 这是"永不枯竭"的部分。 大多数人问 AI 要一次想法。 拿到一个列表。 用一些。 忘掉剩下的。 机器存储一切，并随时间复利增长。 你的创意记忆有 6 个存储： → 钩子库——每一个奏效的钩子，按情绪和平台打标签 → 赢家格式——持续表现的结构 → 受众语言——你受众用的确切措辞，来自 Reddit 和评论 → 内容想法积压——生成好的、等着被生产的想法 → 帖子表现日志——你发布过什么、表现如何 → 你的声音规则——什么听起来像你、什么不像 import json from datetime import datetime from pathlib import Path class ContentBrain: """ The memory layer of your content machine. Stores and retrieves patterns, ideas, and performance data. Grows smarter every week. """ def __init__(self, db_path="content_brain.json"): self.db_path = Path(db_path) self.brain = self._load() def _load(self): if self.db_path.exists(): with open(self.db_path) as f: return json.load(f) return { "hooks": [], "formats": [], "pain_points": [], "ideas_backlog": [], "performance_log": [], "voice_rules": { "always": [], "never": [], "examples": [] } } def _save(self): with open(self.db_path, "w") as f: json.dump(self.brain, f, indent=2) def add_patterns(self, extracted_patterns): """Add freshly extracted patterns from today's capture""" today = datetime.now().isoformat() for hook in extracted_patterns.get("hooks", []): hook["added"] = today hook["used_count"] = 0 self.brain["hooks"].append(hook) for pain in extracted_patterns.get("pain_points", []): pain["added"] = today self.brain["pain_points"].append(pain) for idea in extracted_patterns.get("content_ideas", []): idea["added"] = today idea["status"] = "pending" self.brain["ideas_backlog"].append(idea) self._save() print(f"Added {len(extracted_patterns.get('hooks', []))} hooks, " f"{len(extracted_patterns.get('content_ideas', []))} ideas") def get_next_ideas(self, platform=None, count=5): """Pull the next unused ideas from the backlog""" pending = [i for i in self.brain["ideas_backlog"] if i["status"] == "pending"] if platform: pending = [i for i in pending if i.get("platform") == platform] return pending[:count] def mark_used(self, idea_hook, performance=None): """Mark an idea as used and optionally log performance""" for idea in self.brain["ideas_backlog"]: if idea["hook"] == idea_hook: idea["status"] = "used" idea["used_date"] = datetime.now().isoformat() if performance: idea["performance"] = performance break if performance: self.brain["performance_log"].append({ "hook": idea_hook, "performance": performance, "date": datetime.now().isoformat() }) self._save() def get_top_hooks(self, emotion=None, limit=10): """Get the best hooks from the library""" hooks = self.brain["hooks"] if emotion: hooks = [h for h in hooks if h.get("emotion") == emotion] return hooks[:limit] def get_backlog_size(self): pending = [i for i in self.brain["ideas_backlog"] if i["status"] == "pending"] return len(pending) def weekly_summary(self): """Summary of what the brain knows""" return { "total_hooks": len(self.brain["hooks"]), "ideas_pending": self.get_backlog_size(), "ideas_used": len([i for i in self.brain["ideas_backlog"] if i["status"] == "used"]), "pain_points_tracked": len(self.brain["pain_points"]), "posts_logged": len(self.brain["performance_log"]) } # Usage brain = ContentBrain() print(brain.weekly_summary()) # After 30 days: # { # "total_hooks": 147, # "ideas_pending": 83, # "ideas_used": 64, # "pain_points_tracked": 210, # "posts_logged": 64 # } 30 天后你有 150+ 钩子。 90 天后积压里有 400+ 想法。 机器永远不会到达零。 第 4 层 —— 用 AI + Kie.ai 生成内容 大脑有了想法。 现在你需要生产真正的内容。 文本帖子：Claude 原生生成。视觉、图片、视频：Kie.ai 处理。 下面是完整的生成管线： import anthropic import requests import json client = anthropic.Anthropic() KIE_API_KEY = "your_kie_api_key" # kie.ai KIE_BASE_URL = "https://api.kie.ai" # ── TEXT GENERATION ─────────────────────────────────── def generate_platform_post(idea, platform, voice_rules): """ Generate a platform-native post from an idea. Uses the voice rules from the Content Brain. """ platform_guides = { "twitter": "Short, punchy, lowercase, no hashtags, line breaks between every thought. Max 280 chars or thread format.", "linkedin": "Professional but human, personal narrative, first-person story with lesson, 1300-2000 chars.", "instagram": "Visual-first, carousel format, bold hook on slide 1, max 30 words per slide, save CTA at end.", "tiktok": "45-60 second video script, hook in first 2 seconds, show don't tell, natural spoken language.", "reddit": "Helpful, no-sell energy, actual useful content, community voice, upvote-worthy." } prompt = f""" You are a viral content writer for {platform}. The idea: Hook: {idea['hook']} Format: {idea['format']} Pain point: {idea.get('pain_point', 'not specified')} Emotion to trigger: {idea.get('emotion', 'curiosity')} Platform rules for {platform}: {platform_guides.get(platform, 'Write naturally for this platform.')} Voice rules (MUST follow): Always: {', '.join(voice_rules.get('always', []))} Never: {', '.join(voice_rules.get('never', []))} Write the complete post now. Ready to publish. No preamble. """ response = client.messages.create( model="claude-sonnet-4-6", max_tokens=1000, messages=[{"role": "user", "content": prompt}] ) return response.content[0].text # ── VISUAL GENERATION (KIE.AI) ──────────────────────── def generate_image_kie(prompt_text, style="photorealistic"): """ Generate an image using Kie.ai. Returns the image URL. """ response = requests.post( f"{KIE_BASE_URL}/v1/images/generate", headers={ "Authorization": f"Bearer {KIE_API_KEY}", "Content-Type": "application/json" }, json={ "prompt": prompt_text, "style": style, "width": 1080, "height": 1920, # vertical for social media "quality": "high" } ) data = response.json() return data.get("image_url") def generate_video_kie(script, voice="natural", duration=60): """ Generate a short video from a script using Kie.ai. Returns the video URL. """ response = requests.post( f"{KIE_BASE_URL}/v1/videos/generate", headers={ "Authorization": f"Bearer {KIE_API_KEY}", "Content-Type": "application/json" }, json={ "script": script, "voice": voice, "duration_seconds": duration, "format": "mp4", "aspect_ratio": "9:16" } ) data = response.json() return data.get("video_url") def generate_carousel_visuals(slides, brand_colors=None): """ Generate carousel slide images for Instagram/LinkedIn. One image per slide. """ image_urls = [] for i, slide in enumerate(slides): visual_prompt = f""" Create a clean, bold social media carousel slide. Text overlay: "{slide['text']}" Slide number: {i + 1} of {len(slides)} Style: minimal, high contrast, professional Background: dark with light text OR light with dark text, alternating Font style: bold, sans-serif, easy to read at mobile size {"Brand colors: " + str(brand_colors) if brand_colors else ""} No stock photo feel. Editorial, modern, scroll-stopping. """ url = generate_image_kie(visual_prompt, style="graphic design") image_urls.append(url) print(f"Generated slide {i+1}: {url}") return image_urls # ── FULL PRODUCTION PIPELINE ────────────────────────── def produce_content_batch(brain, platforms=None, ideas_per_platform=2): """ Pull ideas from brain, generate content for each platform. Returns batch ready for scheduling. """ if platforms is None: platforms = ["twitter", "linkedin", "instagram", "tiktok"] voice_rules = brain.brain["voice_rules"] batch = [] for platform in platforms: ideas = brain.get_next_ideas(platform=platform, count=ideas_per_platform) for idea in ideas: print(f"\nGenerating {platform} post: {idea['hook'][:50]}...") post_text = generate_platform_post(idea, platform, voice_rules) produced = { "platform": platform, "idea": idea, "text": post_text, "media": None } # Generate visuals for Instagram carousels if platform == "instagram" and "carousel" in idea.get("format", "").lower(): slides = [ {"text": idea["hook"]}, {"text": "Here is why this matters"}, {"text": "Step 1: Start with this"}, {"text": "Step 2: Then do this"}, {"text": "Step 3: Finally this"}, {"text": "Save this. Follow for more."} ] produced["media"] = generate_carousel_visuals(slides) # Generate video for TikTok elif platform == "tiktok": produced["media"] = generate_video_kie( script=post_text, duration=60 ) batch.append(produced) brain.mark_used(idea["hook"]) return batch # Run it brain = ContentBrain() batch = produce_content_batch(brain, platforms=["twitter", "linkedin", "instagram"]) print(f"\nProduced {len(batch)} pieces of content") 一次运行就产出跨每个平台整整一周的内容。 文本来自 Claude。视觉和视频来自 Kie.ai。一切准备好排期。 第 5 层 —— 用 Postiz 排期 + 把表现反馈回来 批次被生产出来了。 现在它需要在正确时间、到正确平台、自动发出。 然后结果需要回到大脑里。 那个反馈循环是让整个系统复利的东西。 import requests from datetime import datetime, timedelta import json POSTIZ_API_KEY = "your_postiz_api_key" # app.postiz.com POSTIZ_BASE = "https://api.postiz.com" POSTING_SCHEDULE = { "twitter": {"times": ["08:00", "12:00", "17:00"], "days": "daily"}, "linkedin": {"times": ["07:30", "12:00"], "days": "weekdays"}, "instagram": {"times": ["11:00", "19:00"], "days": "daily"}, "tiktok": {"times": ["08:00", "13:00", "20:00"], "days": "daily"}, } def get_next_slot(platform): """Calculate the next available posting time for a platform""" schedule = POSTING_SCHEDULE.get(platform, {}) times = schedule.get("times", ["09:00"]) now = datetime.now() for time_str in times: hour, minute = map(int, time_str.split(":")) slot = now.replace(hour=hour, minute=minute, second=0) if slot > now: return slot.isoformat() # Next day first slot tomorrow = now + timedelta(days=1) first_time = times[0] hour, minute = map(int, first_time.split(":")) return tomorrow.replace(hour=hour, minute=minute, second=0).isoformat() def schedule_post_postiz(content_item, integration_id): """ Schedule a single post via Postiz API. Handles text + media upload. """ headers = { "Authorization": f"Bearer {POSTIZ_API_KEY}", "Content-Type": "application/json" } scheduled_at = get_next_slot(content_item["platform"]) payload = { "content": content_item["text"], "scheduledAt": scheduled_at, "integrationId": integration_id, "status": "schedule" } # Attach media if generated if content_item.get("media"): if isinstance(content_item["media"], list): # Multiple images (carousel) payload["media"] = [{"url": url} for url in content_item["media"]] else: # Single video or image payload["media"] = [{"url": content_item["media"]}] response = requests.post( f"{POSTIZ_BASE}/posts", headers=headers, json=payload ) result = response.json() print(f"Scheduled {content_item['platform']} post for {scheduled_at}") print(f"Post ID: {result.get('id')}") return result def schedule_full_batch(content_batch, platform_integrations): """ Schedule an entire week's content batch via Postiz. platform_integrations: dict mapping platform name to Postiz integration ID """ scheduled = [] for item in content_batch: platform = item["platform"] integration_id = platform_integrations.get(platform) if not integration_id: print(f"No integration ID for {platform} — skipping") continue result = schedule_post_postiz(item, integration_id) scheduled.append({ "platform": platform, "hook": item["idea"]["hook"], "scheduled_at": result.get("scheduledAt"), "post_id": result.get("id") }) print(f"\nScheduled {len(scheduled)} posts across {len(set(s['platform'] for s in scheduled))} platforms") return scheduled # ── FEEDBACK LOOP ───────────────────────────────────── def fetch_post_performance(post_id): """Fetch performance metrics after a post has gone live""" response = requests.get( f"{POSTIZ_BASE}/posts/{post_id}/analytics", headers={"Authorization": f"Bearer {POSTIZ_API_KEY}"} ) return response.json() def update_brain_with_performance(brain, scheduled_posts, days_old=3): """ After posts have run for 3 days, pull performance back into the brain. This is what makes the system learn. """ cutoff = datetime.now() - timedelta(days=days_old) updated = 0 for post in scheduled_posts: scheduled_time = datetime.fromisoformat(post.get("scheduled_at", "")) if scheduled_time < cutoff: perf = fetch_post_performance(post["post_id"]) brain.mark_used( idea_hook=post["hook"], performance={ "views": perf.get("impressions", 0), "likes": perf.get("likes", 0), "shares": perf.get("shares", 0), "comments": perf.get("comments", 0), "platform": post["platform"] } ) updated += 1 print(f"Updated brain with performance for {updated} posts") # ── WEEKLY REPORT ───────────────────────────────────── def weekly_report(brain): """What the machine learned this week""" summary = brain.weekly_summary() print("\n━━━━━━━━━━━━━━━━━━") print("WEEKLY MACHINE REPORT") print("━━━━━━━━━━━━━━━━━━") print(f"Total hooks stored: {summary['total_hooks']}") print(f"Ideas in backlog: {summary['ideas_pending']}") print(f"Ideas used this week: {summary['ideas_used']}") print(f"Pain points tracked: {summary['pain_points_tracked']}") print(f"Posts with data: {summary['posts_logged']}") print("━━━━━━━━━━━━━━━━━━\n") 每周运行的循环： ScrapeCreators 在夜间捕捉新的爆款内容 Claude 从数据里提取新鲜模式 新钩子和想法落进 Content Brain Claude 从积压里生成平台原生帖子 Kie.ai 生成视觉和视频 Postiz 排期一切 3 天后，表现数据流回大脑 系统学习什么奏效，并在下周生成更好的想法 agent 工作流 你不是手动跑这个。 你设置 6 个轻量 agent 或 Claude Schedules，每个自动处理一层。 Trend Scout —— 每天早晨 6 点在你领域的平台上跑 ScrapeCreators。把结果倒进你的内容数据库。完成时触发 Pattern Analyst。 Pattern Analyst —— 接收新鲜数据。把它发给 Claude 提取。把新钩子、格式、痛点加进 Content Brain。把摘要发到你的 Slack 或邮件。 Idea Generator —— 检查积压大小。如果 pending 想法低于 20 个，自动跑生成。不用你碰就把队列补满。 Creative Agent —— 拿已批准的想法。调 Claude 做文本。调 Kie.ai 做视觉和视频。组装完整内容包。 Scheduler Agent —— 拿生产好的包。按平台用正确时机推到 Postiz。为反馈循环记录排期的帖子 ID。 Feedback Agent —— 每 3 天跑。从 Postiz analytics 拉表现。用奏效的东西更新 Content Brain。浮现本周表现最好的钩子。 唯一需要人的东西： 一次 10 分钟的每周审核。 批准积压里的想法。如果有什么听起来不对，调整声音规则。查看每周报告。 其他一切在你睡觉时运行。 前后对比 之前：你每天早上醒来面对一张空白页。 之后：你醒来有一份简报——这是昨天奏效的，这是这周的 20 个想法。 之前：你得手动刷 5 个平台找什么在热。 之后：ScrapeCreators 在早上 6 点以结构化 JSON 把它带给你。 之前：你问 AI 要想法却不给它任何上下文。 之后：Claude 在生成一个字之前，先从 200 个真实爆款帖子里提取模式。 之前：你忘了 3 个帖子之前什么奏效。 之后：Content Brain 追踪每个钩子、每个格式、每个表现数字——并且每周变聪明。 之前：视觉是瓶颈。你要么花钱要么跳过。 之后：Kie.ai 按需自动生成 carousel、视频和缩略图。 之前：发布不稳定。看你什么时候想起来。 之后：Postiz 在最优时间、跨每个平台、自动排期一切。 内容的未来不是 AI 写随机帖子。 是 AI 记住什么奏效、找到新模式、并每周复利你的品味。 大多数创作者每个早晨都从零开始。 这个系统不会。 从这里开始 你不需要在一个周末构建整个系统。 一层层构建。 第 1 天：设置捕捉 → 在 app.scrapecreators.com 拿 ScrapeCreators API key → 在你领域 3-5 个竞品账号和 2-3 个 subreddit 上跑每日捕捉脚本 → 看看回来什么 第 2 天：跑模式提取 → 把捕捉到的数据喂给 Claude → 看它提取的钩子和痛点 → 先手动把它们加进你的 Content Brain JSON 第 3 天：生成你的第一批 → 从大脑拿 5 个想法 → 每个生成平台原生文本 → 审核它们——如果有什么听起来不对，调整声音规则 第 4 天：排期它 → 在 app.postiz.com 连接 Postiz → 排期这批 → 这周完成 到第 4 周，系统大部分自己跑。 到第 3 个月，Content Brain 已经看过足够表现数据，能生成明显比你开始时更好的想法。 那就是复利效应。 机器在你睡觉时学习。 工具： → ScrapeCreators: docs.scrapecreators.com（35+ 平台，x-api-key 认证） → Kie.ai: kie.ai（图片、视频、音频生成）→ Postiz: postiz.com（排期 + analytics） → Claude: claude.ai（模式提取 + 内容生成） 如果这对你有用： → 转发，分享给你认识的每个创作者 → 关注 @sairahul1，获取更多像这样的系统 → 收藏这个——代码能跑，这个周末就跑起来 我写 AI、构建产品、以及在你睡觉时运转的系统。Tags: # X # AI # Reddit # Marketing # Claude # Growth # Instagram # Tiktok # Youtube # Content # Linkedin # Thread Related articles How I Built My AI Research Engine (FULL SYSTEM) Google shows you what exists, signal mining shows you what's missing. 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