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I Built an AI job search assistant that works

Hacker News

I Built an AI job search assistant that works

Hey HN, I'm Serdar, and after getting laid off, I found myself drowning in job applications. LinkedIn, Dice, Otta and company websites were exhausting and demoralizing. That's why my friend and I created Wobo (wobo.ai) – an AI recruiter that automates the entire process. But Wobo goes beyond just matching keywords on your resume. We use LLMs and a series of specialized questions to create a detailed "persona" of you, capturing not just your skills and experience, but also your personality and how you approach different situations. Here's how Wobo works: Gets to know you: We analyze your responses to reveal your work style, how you handle challenges, and what motivates you. We find jobs that fit YOU: We go beyond basic skills matching to find jobs that really fit your personality and preferences. We even use a "similarity score" to see how well a job matches your profile. If it's not a good fit, we won't apply Application questions: Wobo uses your "persona" to answer those personality-revealing questions in a way that showcases your best self. We've been developing Wobo for over two years and have 14,000+ users. One user recently told us that Wobo gave him back the time to spend with his daughter – that's exactly the kind of impact we're hoping to make. Think of Wobo as your personal job search assistant, working tirelessly in the background so you can focus on what matters most. Have questions about Wobo? Ask me anything! I'm happy to answer them.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
93%93% 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 · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, users, way · Missing: mobile apps, ios, entrepreneurs
63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released
49%49% 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 · Missing: arr, mrr, revenue
11%11% 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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