[{"data":1,"prerenderedAt":189},["ShallowReactive",2],{"project-24433464":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":34,"aspects":35,"strip":40,"post_text":42,"what":43,"said":66,"maker_in_thread":112,"shape":120,"grid":122,"attempts":149,"same_week":150,"maker_shows":180,"maker_hidden":181,"reader_version":182,"card":183},"24433464","zoomstock","2M+ free stock photos in novel T-SNE based search engine","title","Show HN: 2M+ free stock photos in novel T-SNE based search engine","https:\u002F\u002Fzoomstock.com","zoomstock.com","jacobn",1599753240,2020,62,12,953,3028,32,"notable","creative","stock photo search","web_app",[24,25,26,27,28],"stock photos","image search","t-sne","free stock images","visual search","curious",-0.095,{"positive":32,"neutral":33,"mixed":32,"critical":32},1,3,6,{"execution":36,"design":38,"idea":39},{"positive":37,"critical":32},0,{"positive":37,"critical":37},{"positive":37,"critical":37},[37,33,37,37,33,37,32,33,37,41,33,37],5,null,{"problem":44,"audience":42,"pricing":51,"motivation":42,"time_to_build":42,"stack":57,"openness":64,"stage":65,"team_size":64},{"value":45,"source":46,"evidence":47},"Keyword stock photo search hides results and loses context when browsing related images","maker",[48],{"id":49,"by":11,"text":50},"24436934","You can absolutely search by keyword, and that's where you usually want to start. The difference lies in what you can do after that. Regular stock photo sites usually give you an…",{"value":52,"source":53,"evidence":54},"free","commenter",[55],{"id":56},"post",[58],{"value":59,"source":46,"evidence":60},"t-SNE",[61],{"id":62,"by":11,"text":63},"24433471","Hi! We created a new type of search engine for the top free stock photo sites. It works by taking all the images, using T-SNE to lay them out in a gigantic grid, then putting a…","unknown","launched",{"praise":67,"objections":79,"requests":91,"questions":92,"alternatives":107},[68],{"value":69,"source":53,"evidence":70},"Well done and useful for browsing images visually",[71,75],{"id":72,"by":73,"text":74},"24437355","throwaway888abc","This is great! Bookmarked, will use. I think, bug spotted: Show results only 3,567 results But the grid is shoving only aprox 300 photos when zoomed out. How can I see the rest…",{"id":76,"by":77,"text":78},"24439767","pilooch","Well done but be careful, TSNE has some drawbacks, well detailed here: https:\u002F\u002Fdistill.pub\u002F2016\u002Fmisread-tsne\u002F For building a more precise 2D embedding or image similarity search…",[80,87],{"value":81,"source":53,"evidence":82},"t-SNE distorts distances, so layout similarity is imprecise",[83,84],{"id":76,"by":77,"text":78},{"id":85,"by":11,"text":86},"24433747","By and large yes: T-SNE does it's best to achieve that - it's kind of tricky to take an N-dimensional space and flattening it to 2 dimensions. You inevitably get some tearing and…",{"value":88,"source":53,"evidence":89},"Zoomed-out view shows only a few hundred of the results",[90],{"id":72,"by":73,"text":74},[],[93,100],{"value":94,"source":53,"evidence":95},"Are visually similar images placed next to each other?",[96],{"id":97,"by":98,"text":99},"24433689","sharemywin","are visually similar images next to each other?",{"value":101,"source":53,"evidence":102},"Is t-SNE the best algorithm for this?",[103],{"id":104,"by":105,"text":106},"24433819","java-man","Is the t-SNE the best algorithm for this task?",[108],{"value":109,"source":53,"evidence":110},"Neural embeddings with FAISS or Annoy",[111],{"id":76,"by":77,"text":78},{"comments":41,"answered":113,"key":114},4,[115,116,117],{"id":62,"text":63},{"id":85,"text":86},{"id":118,"text":119},"24437803","You see the rest by zooming in (see the ?-button for all the ways to zoom).",{"commenters":34,"max_depth":113,"maker_replies":41,"first_comment_mins":32,"lifespan_hours":121},22,[123,125,127,128,131,133,135,137,142,143,146,147],[62,37,37,124,11,32],"project\u002Fpositive\u002Fidea",[97,37,33,126,98,37],"project\u002Fneutral\u002Fexecution",[85,37,37,126,11,32],[129,37,37,130,98,37],"24442644","other\u002Fneutral\u002Fnone",[72,37,33,132,73,37],"project\u002Fmixed\u002Fdesign",[118,37,37,134,11,32],"project\u002Fneutral\u002Fdesign",[76,32,32,136,77,37],"project\u002Fcritical\u002Fexecution",[138,139,33,140,141,37],"24436816",2,"project\u002Fneutral\u002Fidea","exolymph",[49,139,37,124,11,32],[144,139,41,145,141,37],"24438944","maker\u002Fpositive\u002Fnone",[104,33,33,126,105,37],[148,33,37,126,11,32],"24433848",[],[151,156,161,166,171,176],{"id":152,"name":153,"points":154,"category":155},"24442294","Octo",241,"infrastructure",{"id":157,"name":158,"points":159,"category":160},"24428206","Eiten",201,"web_saas",{"id":162,"name":163,"points":164,"category":165},"24493248","Cortex license plate reader",190,"ai",{"id":167,"name":168,"points":169,"category":170},"24485447","jazzit",162,"dev_tools",{"id":172,"name":173,"points":174,"category":175},"24476641","Lofimusic.app",136,"consumer_social",{"id":177,"name":178,"points":179,"category":175},"24478149","Lag",129,7,false,"v2",{"id":184,"name":5,"line":6,"ai":181,"cat":20,"year":13,"points":14,"comments":15,"rec":29,"s":185,"top":18,"kw":186,"maker":11,"domain":10,"tier":19,"attempts":37,"strip":187,"url":9,"mix":188,"praise":69,"objection":81,"sub":21},24433464,-0.09,[24,25,26,27,28],"030030130530",[32,33,32,32],1793991015874]