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LaunchPad – Job aggregator I built overnight after Amazon layoffs

Hacker News

LaunchPad – Job aggregator I built overnight after Amazon layoffs

Hi HN, After hearing about Amazon's layoffs, I spent an evening building LaunchPad ( https://launchpad-kappa-ashy.vercel.app ) - a simple job search aggregator. : Job hunting means checking LinkedIn, Indeed, Glassdoor, AngelList, Wellfound, Naukri, and dozens of other platforms. It's exhausting and inefficient. : A single interface aggregating jobs from multiple ATS platforms via Adzuna API. Real-time listings, autocomplete search, save favorites, no signup required. : React frontend, Adzuna API for data, deployed on Vercel. ' : • Debounced search with autocomplete to reduce API calls • Local storage for saved jobs (no backend needed) • Mobile-first responsive design • Direct deep-linking to application pages ' : • Rate limiting on free API tier - debating whether to upgrade or implement request queuing • Some ATS platforms don't play nice with deep links, redirecting to homepage instead • Deciding on filter implementation (remote, salary, location) without overcomplicating the UI • Balancing freshness of data vs API costs : Layoffs are stressful enough. If I could make job searching even slightly less painful, it seemed worth the effort. It's completely free and always will be. Would love feedback from the HN community on: • Technical approach (better ways to handle API limits?) • UX improvements • Features you'd actually use vs feature bloat • Any edge cases I'm missing

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
69%69% 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: single · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface, efficient · Missing: plus, intuitive, reviews
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
42%42% 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
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% 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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