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GapQuery is an app ecosystem intelligence platform for developers and micro SaaS founders. It scans 11 major app ecosystems โ Shopify, WordPress, QuickBooks, Atlassian, Xero, Slack, Monday, GitHub, Freshworks, Zendesk, and Zoho โ covering 35,600+ apps to surface market gaps, pricing opportunities, and missing integrations. Connect GapQuery to Claude Code via MCP and use 17 AI-powered tools to discover underserved categories, spot overpriced apps, identify developer whitespace, and analyze cross-ecosystem patterns. Save opportunities to your pipeline and run deep research across market validation, competition, revenue, technical feasibility, keywords, and go-to-market strategy.
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GapQuery's answer:
GapQuery connects directly to your AI coding environment via MCP. Instead of browsing dashboards, you query 11 app ecosystems and 35,600+ apps through natural language, discovering market gaps, pricing opportunities, and missing integrations right where you code. It's market research that meets you in your terminal.
GapQuery's answer:
Most market research tools focus on consumer app stores or require expensive subscriptions. GapQuery is purpose built for B2B app ecosystems like Shopify, QuickBooks, and Atlassian, the platforms where micro SaaS businesses actually get built. It's a one time purchase starting at $29, not a recurring fee, and it integrates directly into Claude Code so insights turn into action immediately.
GapQuery's answer:
Solo developers, indie hackers, and micro SaaS founders who want to build apps for established platforms like Shopify, WordPress, or QuickBooks and want data to validate their ideas before writing code.
GapQuery's answer:
Laravel 12, Livewire 4, MySQL 8, Python (scrapers), Tailwind CSS, and Anthropic's Model Context Protocol (MCP) for AI tool integration.
GapQuery's answer:
GapQuery started as a personal tool. I was building micro SaaS apps and kept manually searching app stores to figure out what was missing. I realized the same gap analysis I was doing by hand could be automated: scrape the ecosystems, normalize the data, and let AI surface the patterns. What began as a spreadsheet became a database of 35,600+ apps across 11 ecosystems.
i like reddit very much
Based on our record, Reddit seems to be more popular. It has been mentiond 3301 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
From urllib.parse import urlparse Def normalize_gh(r): return { "title": r["name"], "url": r["url"], "source": "github", "score": r["stars_this_period"], "desc": r.get("description", ""), "date": r["trending_date"], "lang": r.get("language"), } Def normalize_hn(p): return { "title": p["title"].replace("Show HN: ", ""), "url":... - Source: dev.to / 3 months ago
@tool Def search_reddit(keywords: str, max_results: int = 20) -> list[dict]: """Fallback: search Reddit directly via PRAW.""" reddit = praw.Reddit( client_id=os.environ["REDDIT_CLIENT_ID"], client_secret=os.environ["REDDIT_CLIENT_SECRET"], user_agent="doug-agent/1.0", ) candidates = [] for submission in reddit.subreddit("all").search(keywords, sort="new",... - Source: dev.to / 3 months ago
Import requests Import time Def fetch_subreddit_posts(subreddit, sort="hot", limit=25): url = f"https://www.reddit.com/r/{subreddit}/{sort}.json" params = { "limit": limit, "raw_json": 1, # Prevents HTML encoding in responses } headers = { "User-Agent": "PythonScraper/1.0 (research project)" } response = requests.get(url, params=params, headers=headers) if... - Source: dev.to / 4 months ago
From sessionkeeper import SessionKeeper Async with SessionKeeper("reddit") as sk: page = await sk.get_authenticated_page("https://reddit.com") # You're logged in. Do your automation. await page.goto("https://reddit.com/r/blender/submit"). - Source: dev.to / 5 months ago
It's completely free, and takes just moments to set up - you just need to create an account, and set up keywords for the service to track. When your keywords are mentioned on Reddit, Hackernews, or Lobste.rs, you'll get a tidy little email in your inbox. - Source: dev.to / over 1 year ago
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BigIdeasDB - Explore a database of niche specific problems shared by users across the internet and discover profitable curated solutions tailored for each.
YouTube - Our mission is to give everyone a voice and show them the world.
Google - Google Search, also referred to as Google Web Search or simply Google, is a web search engine developed by Google. It is the most used search engine on the World Wide Web
