Improve and get automatic feedback on drawn sketches

Hacker News

As I was struggling to draw people that actually looked like people, I created a website that compares a hand drawn portrait of a person to a photo. Instead of rapidly looking back and forth at the end to see how I did, I ended up making some software that automatically puts the two pictures together and blends them back and forth. This way you can see small deviations in your drawing and areas to improve. Instead of asking for feedback every time, you can see your own mistakes and learn to make the proportions, shading and edges as close to the original as possible. The site is below with a few examples you can look at to see how it works. http://drawingapps.ca/portrait-vs-photo-comparison http://drawingapps.ca/portrait-vs-photo-comparison/alGoreN5R... http://drawingapps.ca/portrait-vs-photo-comparison/brie http://drawingapps.ca/portrait-vs-photo-comparison/brie2ICPW... Let me know what you think.

Visit product View on Hacker NewsNov 26, 2017

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

76%76% 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: apps · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 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: apps, way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · 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 · Missing: arr, mrr, revenue

18%18% 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

Similar products

AgentVoice – Get feedback from AI to improve your MCP29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AgentVoice – Get feedback from AI to improve your MCP

Hacker News1

Automatic Parallelization for Haskell74%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatic Parallelization for Haskell

Hacker News1

Automatic CloudWatch Alarms52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatic CloudWatch Alarms

Hacker News1

APReF: An Automatic Parallelizer of Recursive Functions for Haskell67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

APReF: An Automatic Parallelizer of Recursive Functions for Haskell

Hacker News2

Automatic Installer for ArchLinux ARM51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatic Installer for ArchLinux ARM

Hacker News1

AIlight: Automatic Highlighting52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AIlight: Automatic Highlighting

Hacker News1

Galene-stt: automatic captioning for the Galene videconferencing system58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Galene-stt: automatic captioning for the Galene videconferencing system

Hacker News2

Smig – Automatic SurrealDB Migrations36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Smig – Automatic SurrealDB Migrations

Hacker News1

Automatic initializer methods for Objective-C39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatic initializer methods for Objective-C

Hacker News3

Automatic feedback on your LinkedIn profile31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatic feedback on your LinkedIn profile

Hacker News2

Browse all products Analyze your launch