Arbington.com – 1,200 online courses from todays top instructors

Hacker News

Hi everybody! Today I launched arbington.com, a new learning website that's affordable and treats the content creators better than other leading platforms (as in we share much more profit than our competitors and communicate with them regularly). I've taught somewhere around a half million people how to code over the last 10 years on various platforms, and I think it's time to support our content creators better! I personally coded about 98% of this website using Django, Postgres and Tailwind CSS and we spent the last 4-5 months talking with over 200 instructors to get this off the ground, and ended up launching today with just over 1,200 available courses. Now the hard part: I'm looking for feedback, namely UX feedback (I know the design is still a bit rough around the edges, it's a never ending work in progress). Is there anything feels "off" or flows in an awkward direction? Any free tips or tricks from the community that could help us grow in the right direction would be absolutely amazing! We did a little bit of user testing already and have some good feedback coming in, but I thought: hey the best place to ask for feedback is probably from people in a similar field - Hacker News! Or if people have questions about the tech stack, how it works, etc. I'm always happy to talk tech with people!

Visit product View on Hacker NewsSep 9, 2021

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

84%84% 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: user, new, using · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal, month, way · Missing: mobile apps, ios, entrepreneurs

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · 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 · Strong signals: profit · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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