AI

AI for analyzing developer skill level

Hacker News

AI for analyzing developer skill level

Rankode is AI that uses GitHub or existing code to generate a report analyzing programmer skills without coding tests. Initially, we needed to hire a junior Python developer quickly for our A.I. legal startup. When we got 150 applicants who all basically looked the same on paper we had no idea who to even reach out to. So we looked for quick solutions to our problem and were surprised to see there wasn’t really anything that didn’t involve a ton of set up or taking up valuable time from our Engineers to help in the process. So we put a couple people on our team to the task of using our ML background to solve this problem. After training our Python models on thousands of lines of code today we are launching Rankode for use by developers, hiring teams, and educators. With Rankode just paste in the link to a public repository or 500 lines of code. Rankode's ML models analyze code across 6 categories: Sample volume - Quantity of code submitted (we require 500 lines for accuracy) Original code vs. boilerplate - How much of the submitted code is originally written by the developer? Syntax use - Is the code fluid and understandable, or is it a mumbo-jumbo of sticky and twisty lines of code? Domain reflection - How much of the concepts and terminology specific to the application domain are reflected in the code? Solution clarity - To what extent the written code is understandable as a description of the idea of solving a problem. Modular structure - How well the code’s structure is thought out, designed, and organised on multiple levels. Even though we first developed Rankode for hiring we’ve been talking to people and finding a few other good use cases like for ongoing performance management (especially for remote teams), and also for educators. New developers, self-taught devs, or those changing careers see our report as being a useful way to demonstrate to recruiters their skills when they’re applying. Attaching their Rankode report makes it easy for recruiters (especially from non-technical backgrounds) to easily see their skill level and it devs stand out in a sea of newbies. Since Rankode only analyzes code the results are free from bias and fairer. We think everything you need to know about a developer’s hard skills is in their code. For our launch, we are giving a free report to anyone who signs up for our newsletter but HN members can email us a hello@rankode.ai and we can get yours without you needing to sign up. Thanks for any questions or feedback!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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, new, models · 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 · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
40%40% 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, lua, existing · Missing: https docs, excited, just released
39%39% 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: training · 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.

Correct prediction on native model

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