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land that gig by making a great first impression. Feedback appreciated.

Hacker News

land that gig by making a great first impression. Feedback appreciated.

http://impressar.io Another 'scratch your own itch' MVP, bear with me. I'm a long-time telecommuter, and my gig-hunting success depends on two critical elements: an email cover letter that is visually appealing, and a corresponding portfolio landing page with content tailored to that specific email. Last year I decided to automate this process, and after months of lost weekends and late nights fine-tuning it for public consumption, I'm launching my MVP, Impressario. Briefly, it solves these pain points: * Collect and keep track of job listings in one place * Maintain a set of email templates for specific job titles/requirements * Be able to create and send out HTML emails (with images) * Host and manage different versions of a portfolio (with custom domains) * Track sent emails to gauge response Thoughts / comments / feedback appreciated.

Visit productView on Hacker NewsJan 27, 2012
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Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
57%57% predicted probability of success on AppSumo, 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 · Missing: supports, reddit linkedin, podcasting
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
48%48% 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, visual · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
42%42% 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
16%16% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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