[{"data":1,"prerenderedAt":194},["ShallowReactive",2],{"project-26518411":3},{"id":4,"name":5,"line":6,"line_source":7,"title":8,"url":9,"domain":10,"maker":11,"posted_at":12,"year":13,"points":14,"comments":15,"rank_in_year":16,"shown_in_year":17,"top":18,"tier":19,"category":20,"subcategory":21,"format":22,"keywords":23,"reception":29,"sentiment":30,"stance_mix":31,"scored_comments":35,"aspects":36,"strip":41,"post_text":42,"what":43,"said":82,"maker_in_thread":125,"shape":134,"grid":137,"attempts":156,"same_week":157,"maker_shows":33,"maker_hidden":187,"reader_version":188,"card":189},"26518411","GrewAI","Predict Mailchimp subject line open rate with AI","title","Show HN: Predict Mailchimp subject line open rate with AI","https:\u002F\u002Fwww.grewai.com","grewai.com","ihmissuti",1616190164,2021,7,10,2231,2533,89,"shown","ai","email marketing prediction","web_app",[24,25,26,27,28],"mailchimp","open rate prediction","subject line","email marketing","machine learning","curious",0.25,{"positive":32,"neutral":33,"mixed":34,"critical":34},2,3,0,5,{"idea":37,"performance":39,"pricing":40},{"positive":38,"critical":34},1,{"positive":34,"critical":34},{"positive":38,"critical":34},[33,34,33,34,35,34,35,34,33,34],null,{"problem":44,"audience":42,"pricing":50,"motivation":58,"time_to_build":42,"stack":65,"openness":80,"stage":81,"team_size":80},{"value":45,"source":46,"evidence":47},"Predicting how well a Mailchimp subject line will get opened before sending","inferred",[48],{"id":49},"post",{"value":51,"source":52,"evidence":53},"Affordable; maker did not state a price","commenter",[54],{"id":55,"by":56,"text":57},"26518628","apinamk","Good call to make it actually affordable.",{"value":59,"source":60,"evidence":61},"Maker sells custom UI and algorithm work to enterprises and wanted an affordable product","maker",[62],{"id":63,"by":11,"text":64},"26518656","Yea thanks! I've been selling basically same stuff to enterprises (customized UI's and algorithms) but I didn't want to overprice this one.",[66,72,76],{"value":67,"source":60,"evidence":68},"Deep-learning neural network",[69],{"id":70,"by":11,"text":71},"26518675","In short there's a stack of prediction models that analyze the data: deep-learning neural network, keyword-based machine learning model, and shortly also I'll include a BERT NLP…",{"value":73,"source":60,"evidence":74},"Keyword-based machine learning model",[75],{"id":70,"by":11,"text":71},{"value":77,"source":60,"evidence":78},"BERT NLP analyzer (planned)",[79],{"id":70,"by":11,"text":71},"unknown","launched",{"praise":83,"objections":99,"requests":100,"questions":115,"alternatives":124},[84,95],{"value":85,"source":52,"evidence":86},"Cool, neat tool",[87,91],{"id":88,"by":89,"text":90},"26518568","codeforfun","Whats the prediction accuracy? Neat tool anyway!",{"id":92,"by":93,"text":94},"26518642","icebin_icebin","Cool! How does the AI work? How does it make it's predictions?",{"value":96,"source":52,"evidence":97},"Good to be affordable",[98],{"id":55,"by":56,"text":57},[],[101,108],{"value":102,"source":52,"evidence":103},"Support Twitter",[104],{"id":105,"by":106,"text":107},"26518501","thefedoration","Very cool. You should make this for twitter.",{"value":109,"source":52,"evidence":110},"Support other channels such as ads",[111],{"id":112,"by":113,"text":114},"26518535","vuijws","Is this doable to other channels?",[116,120],{"value":117,"source":52,"evidence":118},"How the AI makes predictions",[119],{"id":92,"by":93,"text":94},{"value":121,"source":52,"evidence":122},"Prediction accuracy",[123],{"id":88,"by":89,"text":90},[],{"comments":35,"answered":33,"key":126},[127,128,131],{"id":70,"text":71},{"id":129,"text":130},"26518575","Thanks! Depends on the data (size and quality). We've been able to get 85% accuracy if the data is decent.",{"id":132,"text":133},"26518578","It's coming ;)",{"commenters":135,"max_depth":32,"maker_replies":35,"first_comment_mins":136,"lifespan_hours":38},6,11,[138,140,141,143,145,147,148,150,152,154],[92,34,33,139,93,34],"project\u002Fneutral\u002Fidea",[70,34,34,139,11,38],[88,38,33,142,89,34],"project\u002Fneutral\u002Fperformance",[129,38,34,144,11,38],"project\u002Fpositive\u002Fperformance",[105,32,35,146,106,34],"project\u002Fpositive\u002Fidea",[132,32,34,139,11,38],[55,33,35,149,56,34],"project\u002Fpositive\u002Fpricing",[63,33,34,151,11,38],"project\u002Fneutral\u002Fpricing",[112,153,33,139,113,34],4,[155,153,34,139,11,38],"26518594",[],[158,163,168,173,177,182],{"id":159,"name":160,"points":161,"category":162},"26568014","Practical Go Lessons",919,"languages",{"id":164,"name":165,"points":166,"category":167},"26503572","Sorted CSS Colors",559,"dev_tools",{"id":169,"name":170,"points":171,"category":172},"26537734","compiler.org",219,"learning_science",{"id":174,"name":175,"points":176,"category":167},"26554451","PyWebIO",200,{"id":178,"name":179,"points":180,"category":181},"26503715","LitiHolo",180,"hardware",{"id":183,"name":184,"points":185,"category":186},"26573930","Arc",175,"data",false,"v2",{"id":190,"name":5,"line":6,"ai":187,"cat":20,"year":13,"points":14,"comments":15,"rec":29,"s":30,"top":18,"kw":191,"maker":11,"domain":10,"tier":19,"attempts":34,"strip":192,"url":9,"mix":193,"praise":85,"objection":-1,"sub":21},26518411,[24,25,26,27,28],"3030505030",[32,33,34,34],1794036604690]