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From Google to E2y.to: Recreating Go Links for Everyone

Hacker News

From Google to E2y.to: Recreating Go Links for Everyone

Hey HN, After 15 years at Google, one of the tools I missed the most upon leaving was Go Links. For those unfamiliar, Go Links is an internal URL shortening and redirection system at Google, allowing employees to create short, memorable links for easy access to frequently used resources. The efficiency of Go Links had become a crucial part of my workflow, and its absence was immediately felt. Navigating through numerous long URLs and locating essential documents became cumbersome. Determined to regain this convenience, I set out to develop a solution that not only replicated Go Links but also made it accessible to everyone. Introducing e2y.to e2y.to is my version of Go Links, designed to bring the same productivity and ease of use to a broader audience. Here are some key features: Domain Focus and Data Isolation: Designed primarily for Google Workspace domains, ensuring data is isolated per domain to maintain security and privacy. Access Control: All access control is managed by the target service, preserving the security and permissions of the linked content. Programmability: Supports regular expressions with group substitutions, enabling dynamic and customizable link creation. Custom Hosting: Can be hosted on a separate domain or subdomain, allowing use without the need for an e2y account. Free Usage: All accounts can create up to 5 links without any subscription, making it easy to start and experience the benefits. Developing e2y.to has been a rewarding experience, and I'm thrilled to share it with the community. If you’ve ever used Go Links or are looking for a way to streamline your workflow with short, memorable URLs, give e2y.to a try. I’d love to hear your feedback! Thanks for reading!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created
97%97% 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: google · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way, para · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
8%8% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward · Missing: web3, chat, crypto
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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