We

We tried to build a job board that isn't awful

Hacker News

We tried to build a job board that isn't awful

Hi HN, We’re definitely not the first to realise there’s something seriously wrong with how hiring and job-seeking works today. Zero-cost communication and LLMs have created so much noise that good candidates can’t get heard, and it becomes all too tempting to game the system with keywords and prompt-hacking. In fact we discovered that 70% of early stage AI startups don't post their jobs on LinkedIn. Instead, many founders hire exclusively within their network, which works at the start but doesn’t scale. We thought a lot about this problem, and pivoted through a few ideas including an AI voice agent recruiter. We even spent some time trying to be conventional tech recruiters to better understand the problem space. And in the end we built...a job board. But we think there are a few things that make ours different: - We decided to not put barriers between the user and the data. You can search, filter or browse however you like from the minute you sign up. Zero onboarding - We wanted to nail one niche, so we focused on surfacing opportunities at early-stage AI companies (over 30,000 jobs at 24,000 companies) - You can navigate it using keyboard shortcuts! - We built a voice agent, Nell, who conducts a technical recruiter call with you through your browser and immediately finds matches, the way a well-connected friend who knows you well would - When you tell us you’re interested in a role, we make a best effort to connect you to founders directly, along with your profile, so no cover letters, no pointless forms - We enriched jobs data with investor-grade intelligence - you can look at same data that VCs use to decide whether a startup is worth joining or not Give it a try and let us know what you think: https://teeming.ai

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Actual performance

32points
56comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, using · Missing: mac, agents, macos
92%92% 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 · Strong signals: created, including · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
59%59% 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: way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: exclusive · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
25%25% 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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