I

I made a platform to help modernise candidate outreach

Hacker News

I made a platform to help modernise candidate outreach

You are probably familiar with this: "Hey x, I have a great opportunity at company y and I think you would be a fantastic fit. This company is [all the buzzwords] + [insert rocket emojis]",... right? I usually get bombarded by standard LinkedIn message such as the one from above, all with a slight twist but essentially the same, not a lot of info, I have to do a lot of research around the company, culture etc., I'm sure you know the drill. One thing that I always appreciated the most is when I was sent a candidate info pack. It made my life so much more easier, because I could get a high level sense of the company, right off the bat - however it's a pain to put these together and to be frank, it's a tad outdated to say the least as there's no interactivity to it. After talking with a few recruiters, I came up with the idea that is now Role.so. It's my take on trying to improve the generic candidate outreach methods. What is Role.so? It's a no-code candidate pack builder (think micro-sites/landing pages) that can be personalised at scale and offer an interactive way for companies and talent partners to present a role and attract candidates. It's a win-win for both parties. For candidates: A comprehensive way to learn more about a company and a role, while also reducing friction for them to take action, be that continuing the chat or simply declining. For companies and talent partners: valuable data (reason for declining is a big one!), a way to present their company culture, benefits, interview process etc. but almost above all is the fact that candidates are basically self-filtering themselves - that's a lot of time and money saved. There is a demo of course on the site or you can go directly here: https://role.so/demo but don't miss the video on the homepage - it was probably one of the most fun part of building this project :) I could probably speak lengths about this project as I'm quite passioned about it, however I tried to pack everything on the site. So, instead of repeating what's already there, I would just be happy to answer any questions in the comments. Any thoughts or feedback on this would be highly appreciated. Thank you! P.S. If anyone is interested in the stack it's: Next.js with server components (was a good time to deep dive on this), Tailwind CSS for everything UI, Supabase as BaaS, SimpleAnalytics for the stats, Stripe for everything payments and Vercel for hosting.

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Product HuntOn track for Day 1 leaderboard · Strong signals: stripe, activity, code · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
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.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
40%40% 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, host, builder · Missing: plus, intuitive, reviews
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
12%12% 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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