惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Blog — PlanetScale
Blog — PlanetScale
小众软件
小众软件
F
Fortinet All Blogs
博客园 - 叶小钗
博客园_首页
D
DataBreaches.Net
Apple Machine Learning Research
Apple Machine Learning Research
U
Unit 42
爱范儿
爱范儿
aimingoo的专栏
aimingoo的专栏
博客园 - Franky
Martin Fowler
Martin Fowler
酷 壳 – CoolShell
酷 壳 – CoolShell
The Cloudflare Blog
A
About on SuperTechFans
Google DeepMind News
Google DeepMind News
Microsoft Security Blog
Microsoft Security Blog
IT之家
IT之家
M
MIT News - Artificial intelligence
有赞技术团队
有赞技术团队
博客园 - 【当耐特】
S
SegmentFault 最新的问题
Hugging Face - Blog
Hugging Face - Blog

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
I scraped Chrome Web Store reviews to find abandoned exte...
TuanAnhNguyen · 2026-06-15 · via DEV Community
Cover image for I scraped Chrome Web Store reviews to find abandoned extensions that still have 100k+ users

TuanAnhNguyen

I've shipped 4 Chrome extensions and 2 VS Code extensions. The advice that always sounds smart — "find a popular extension the dev abandoned, rebuild it better" — is miserable in practice. You open the Web Store, see 100k users and a 4.4 rating, think you found gold, then burn a weekend reading reviews only to realize half the complaints are unfixable traps (sync died, login broke, backend gone).

So I built a small pipeline to do the boring part automatically.

The method

  1. Scrape public Chrome Web Store metadata — users, rating, last-updated date.
  2. Filter: 20k–300k users, 18+ months without an update, rating 3.3–4.4 (good enough to prove demand, bad enough to prove pain).
  3. Pull up to 50 recent reviews per candidate via public CWS data.
  4. Score each one: score = log10(users)10 + months_stale0.5 + feature_request_count2 - trap_count1.5 The key part is trap_count — I subtract points for complaints about sync/login/server issues, because those are unfixable without inheriting someone else's dead backend. High "demand" with high trap count is a mirage.

One example

Extension Manager — 100k users, 4.4★, last updated ~25 months ago. Looks healthy until you read the 1–2★ reviews:

  • "The site-specific rules feature simply does not work… the core feature advertised is broken."
  • "It won't save any changes made… extensions are re-enabled automatically."
  • A user even posted an RCE report: the dev parses JSON with a Function(str)() fallback — executing arbitrary code from untrusted input.

That's not "build a clone." That's "fix the rules engine, kill the eval, add local backup, ship something 100k people already want."

The counterintuitive part

The highest-scoring extension in my list (200k users, abandoned ~4 years) is actually the worst business opportunity — it's a simple toggle utility whose users will never pay, and the original asks for camera/mic permissions (adware-grade). Raw download counts would put it at the top of your build list. Revenue potential buries it.

That gap between "looks like an opportunity" and "is actually monetizable" is the whole reason I started scoring monetization separately.

What I did with it

I analyzed 30 of these — 14 deep-dives and 16 honest "avoid this" verdicts — with demand, the gap, build difficulty, monetization reality, and why nobody rebuilt it yet. Packaged it with the raw CSV here if it's useful to anyone: https://tuanspark85.gumroad.com/l/wnnxyq (there's a free Top-3 preview too).

Happy to answer questions about the scraping pipeline in the comments — what tripped me up was the CWS review endpoint and pagination.