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Corgi – on-demand, 1-on-1 tutoring

Hacker News

Corgi – on-demand, 1-on-1 tutoring

Hey all — I’m Sameer, a software engineer and longtime math/CS tutor who’s just launched an on-demand tutoring platform called Corgi. The product is a mobile app (currently iOS only) that allows users to request and receive tutoring help whenever they want it, for as little or as long as they need. Here’s how it works: 1. User selects a subject, adds a brief summary of what they need help with, and submits their request 2. All tutors who are online and qualified in that subject receive a notification that someone needs help 3. The first tutor to accept the request gets matched with the user, and both parties are placed in a in-app video chat session 4. The user works with the tutor for as long as they need 5. When the session is complete, the user is charged for the time they used (currently billed at $1/minute, rounded to the nearest minute) and the tutor receives 80% of the student fee As a starting point, the only subject we’re offering is SAT math help, but we plan to expand to other subjects in the near future. I built Corgi because 1) tutoring is a lifelong passion of mine, and 2) I haven’t found a quality, on-demand tutoring service that currently exists, but I believe it’s something that could really help students. The idea here isn’t to try and replace the traditional tutoring model (i.e. working with the same tutor on a consistent basis over a long period of time) but to supplement existing resources with an office hours-like service that can provide immediate help (e.g. at the exact moment when someone is struggling with homework, or is studying for an imminent exam). Eventually, I’d like to offer this platform directly to schools or districts, so that students can use it irrespective of ability to pay. There is at least one major player in this space doing something similar ( https://paper.co ) but from my own research they seem to have a lot of issues (e.g. students getting matched with tutors who aren’t qualified in their subjects, tutors working with many students simultaneously despite the company claiming the tutoring is all 1-on-1) and districts who previously signed contracts with them don’t seem keen on continuing their relationships ( https://www.chalkbeat.org/2023/9/6/23861330/online-tutoring-... ). I’ve also been a tutor on some of the existing traditional tutoring platforms (e.g. Wyzant) and have found the experience frustrating (mainly due to unfairly low compensation combined with a struggle to match with students). My goal with Corgi is to build something that provides real value and a positive experience for both the tutors and students who use it. I’ve spent the last week or so recruiting an initial batch of tutors, and am now kicking off the process of finding students to help test it out. I’m also building out some additional features to help make the actual tutoring sessions feel a little smoother (e.g. browser-based sessions to allow usage from any device, virtual whiteboards for easier collaboration). I’d appreciate any and all feedback the HN community has to share, positive or negative!

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
93%93% 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: model, user · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, video, users · Missing: mobile apps, personal, entrepreneurs
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
41%41% 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
10%10% 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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