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Atlarch – Organize and manage all your interviews from a single place

Hacker News

Atlarch – Organize and manage all your interviews from a single place

Hello, Atlarch helps you organize and manage all your interview from a single place. It helps you track applications and interviews across different companies and gives you detailed statistics about your job search. The idea for this started quite a while back, during one of my early job searches. As anyone who has applied for jobs would quickly realize you have to apply for a lot of jobs before you get a response, and you have to go through a decent number of interviews before you get a final offer. It becomes quickly obvious job search is essentially a funnel, and once you start modelling it as a funnel, you can apply a lot of the same tools and techniques developed for funnel analysis and conversion to job search also. Most companies with a mature HR function essentially view hiring as a funnel and they have tools that help HR's manage the "hiring funnel" which give them an overview of the pipeline along with other features. These can then be used for planning and so on. Atlarch is tooling for you, i.e, interviewers, which mirrors the capabilities of HR management tools but focused on how your interview process is going across companies and various applications. Currently it supports ability to track applications, interviews across companies. Statistics on how you are doing in specific interview types, statistics on applications and companies such as response rate, offer rate and ability to filter companies based on status.

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created
89%89% 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 HuntUnlikely to reach the leaderboard · Strong signals: model, single · Missing: mac, agents, macos
46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
46%46% 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 · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% 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
15%15% 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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