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Crawlspace – A centralized web crawling platform built on Cloudflare

Hacker News

Crawlspace – A centralized web crawling platform built on Cloudflare

Crawlspace is a centralized web crawling platform that benefits crawler developers AND website owners. Developers can affordably crawl tens of millions of pages per month, scrape with LLMs, and save data in attached storage. Website owners are shielded by a platform-wide TTL cache that absorbs redundant bot traffic. AI bots are running rampant on the open web. Many recent HN stories[1][2][3][4] describe how web crawlers have run amok and hammer websites with DDoS-like traffic. They often do this with blatant disregard of website owners' wishes (e.g. ignoring robots.txt, 429s, Retry-After headers, etc) because they face no repercussions for deploying poorly-behaved crawlers (and are not incentivized to improve them). The knee-jerk reaction to fix this problem is to give more tools to website owners. Maintaining denylists of IP addresses and user agents, implementing honeypots and tarpits, etc are tactics that website owners use to combat the problem. However, this ends up resulting in and endless arms race between web crawlers and website owners, as they each try to employ new mechanisms of one-upping each other. Crawlspace takes a different approach _by providing a convenient and affordable platform to web crawler developers_. By funneling web crawling traffic through a centralized platform, we can control neat things like making crawlers well-behaved by default, implementing proper caching, and more — all the tedium that that developers don't want to (and therefore, don't) do themselves. Music streaming services like Spotify used convenience and affordability to curb music piracy; we're following the same playbook to curb rampant bot traffic on the internet. In about 50 lines of code, you can deploy a performant and polite web crawler on Cloudflare's network. Every crawler gets its own queue, SQLite database, vector database, and S3-compatible bucket, which allows you to query your crawl as it's crawling with either SQL statements or a RAG chat interface. We've stitched together 10+ Cloudflare products including Queues, Durable Objects, Browser Rendering, Workers AI, D1, R2, and Vectorize. Please let us know what you think! Happy to answer any questions. [1] https://news.ycombinator.com/item?id=42549624 [2] https://news.ycombinator.com/item?id=42660377 [3] https://news.ycombinator.com/item?id=42725147 [4] https://news.ycombinator.com/item?id=42750420

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Indie HackersFits the IH revenue-focused audience · Strong signals: including, compatible · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor
88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
81%81% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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