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Tierlist.fyi – Company Reviews designed to be compared

Hacker News

Tierlist.fyi – Company Reviews designed to be compared

Hello HN, This is my first show HN, so thanks for being here. Tierlist.fyi is a crowdsourced company reviews site that uses the tier list format (the very first to my knowledge). Users can create and submit tier lists of tech companies based on one of the 5 metrics we currently have (Overall, Work Life Balance, Interview Difficulty, Prestige, Comp & Benefits). We then aggregate the results and display a “master” tier list. The impetus for this website was my struggles evaluating companies I was applying to during my internship search. Comparing salary was simple, but other stuff such as work life balance or prestige (what looks better on your resume?) was much harder. Surprisingly, I learned the most about the companies I was applying to from the tier list posts on blind. Coming from being a player of LoL and smash, I always knew tier lists were incredibly helpful if you have several similar options and you need to pick a winner, so when I had this problem I figured it was worth a shot. Tier lists are not a novel idea, but I have only ever seen curated tier lists before. Don’t get me wrong, the data collected from this format may still contain inaccuracies and biases, but it will eliminate a lot of ambiguity. It’s often hard to compare similar 5-star reviews because each rating is done in a vacuum. The delta could be entirely due to margin of error. On the other hand, creating a tier list necessitates comparison. When two companies are in different tiers, that leaves no room for interpretation. Please have a look at the website, and let me know what you think! Link: https://tierlist.fyi/

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86%86% 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, users · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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41%41% 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: users, way · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, new · Missing: mac, agents, macos
25%25% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: margin · Missing: arr, mrr, revenue
17%17% 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.

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