[{"data":1,"prerenderedAt":110},["ShallowReactive",2],{"maker-asavinov":3},[4,30,57,73,95],{"id":5,"name":6,"line":7,"ai":8,"cat":9,"year":10,"points":11,"comments":11,"rec":12,"s":13,"top":14,"kw":15,"maker":21,"domain":22,"tier":23,"attempts":24,"strip":25,"url":26,"mix":27,"praise":-1,"objection":29,"sub":16},14351461,"Data Commandr","As Easy as a Spreadsheet, as Powerful as SQL",false,"data",2017,5,"quiet",null,94,[16,17,18,19,20],"data wrangling","spreadsheet","column formulas","data integration","data transformation","asavinov","conceptoriented.com","shown",0,"30300","http:\u002F\u002Fconceptoriented.com",[24,28,24,24],2,"Server error prevents loading",{"id":31,"name":32,"line":33,"ai":8,"cat":9,"year":34,"points":35,"comments":36,"rec":37,"s":38,"top":39,"kw":40,"maker":21,"domain":46,"tier":47,"attempts":24,"strip":48,"url":49,"mix":50,"praise":54,"objection":55,"sub":56},16161673,"Bistro","A light-weight column-oriented data processing engine",2018,95,29,"mixed",-0.13,22,[41,42,43,44,45],"columnar data processing","dataframe","java data engine","column store","linking tables","github.com","notable","10303530003510001130105013305","https:\u002F\u002Fgithub.com\u002Fasavinov\u002Fbistro",[51,52,53,52],4,6,1,"Ideas around linking and column operations are interesting","Not clear what problem it solves or why use it over SQL or pandas","data processing engine",{"id":58,"name":59,"line":60,"ai":8,"cat":9,"year":34,"points":61,"comments":11,"rec":12,"s":13,"top":62,"kw":63,"maker":21,"domain":46,"tier":23,"attempts":24,"strip":68,"url":69,"mix":70,"praise":-1,"objection":71,"sub":72},17304969,"Bistro Streams","A light-weight column-oriented stream analytics server",26,50,[64,65,66,67],"stream processing","column-oriented","edge computing","iot analytics","03031","https:\u002F\u002Fgithub.com\u002Fasavinov\u002Fbistro\u002Fblob\u002Fmaster\u002Fserver",[24,28,24,53],"Hard to tell what it is beyond 'like Kafka Streams but different'; docs and examples are sparse.","stream analytics",{"id":74,"name":75,"line":76,"ai":8,"cat":77,"year":34,"points":78,"comments":79,"rec":80,"s":81,"top":82,"kw":83,"maker":21,"domain":46,"tier":47,"attempts":24,"strip":89,"url":90,"mix":91,"praise":92,"objection":93,"sub":94},18591065,"Lambdo","Feature engineering and machine learning together","ai",73,24,"curious",0.17,28,[84,85,86,87,88],"feature engineering","machine learning pipeline","model training","time series analysis","python","000000533303003000000000","https:\u002F\u002Fgithub.com\u002Fasavinov\u002Flambdo",[53,11,24,24],"Feature engineering deployment tooling is needed","Unclear why feature engineering and ML should be treated the same","ml workflow engine",{"id":96,"name":97,"line":98,"ai":8,"cat":99,"year":100,"points":101,"comments":53,"rec":12,"s":13,"top":102,"kw":103,"maker":21,"domain":46,"tier":23,"attempts":24,"strip":107,"url":108,"mix":109,"praise":-1,"objection":-1,"sub":104},29099042,"Intelligent Trading Bot","Intelligent Trading Bot using machine learning and feature engineering","finance_crypto",2021,13,69,[104,105,84,106],"trading bot","machine learning","crypto trading","5","https:\u002F\u002Fgithub.com\u002Fasavinov\u002Fintelligent-trading-bot",[53,24,24,24],1794036752395]