gi

gig-finding app for freelancers

Hacker News

gig-finding app for freelancers

My (revised) MVP - the concept is to put all job-finding activities in one app: job search/listings, emails/replies, resume/portfolio pages. Automate the searches, have an email editor that sends out themeable HTML emails, know which recipients are interested, and point them to a portfolio site of sorts. Briefly,the main features: * Aggregates jobs via job board APIs based on skills (searches twice a day) * Includes Twitter job search, displays linked content in-app * Email editor/sender (can be scheduled), with HTML templates * Email analytics (track clicks and views) * Preserves email and job listing associations * Landing pages (resume, dashboard-type info sheet), with LinkedIn imports Might have missed a couple more features somewhere, but that's about the gist of it. I've been trying it out myself (time to land a gig now that the app is "done"), do give it a spin and let me know how things go. http://impressar.io

Visit product View on Hacker NewsMay 9, 2012

Actual performance

8points

3comments

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: email, apis · Missing: mac, agents, macos

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% 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

23%23% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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