[{"data":1,"prerenderedAt":333},["ShallowReactive",2],{"project-49776523":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":36,"aspects":37,"strip":43,"post_text":45,"what":46,"said":76,"maker_in_thread":185,"shape":196,"grid":201,"attempts":295,"same_week":296,"maker_shows":35,"maker_hidden":325,"reader_version":326,"card":327},"49776523","Tiny Brains","A competition for small neural networks that play strategy games","title","Show HN: A competition for small neural networks that play strategy games","https:\u002F\u002Ftinybrains.dev","tinybrains.dev","codetiger",1789916350,2026,116,41,584,4366,14,"notable","ai","neural network competition platform","web_app",[24,25,26,27,28],"neural network","game ai","strategy games","model competition","small neural networks","curious",0.092,{"positive":32,"neutral":33,"mixed":34,"critical":35},5,9,1,2,17,{"idea":38,"docs":40,"execution":41,"performance":42},{"positive":35,"critical":39},0,{"positive":39,"critical":35},{"positive":39,"critical":39},{"positive":39,"critical":39},[39,39,44,39,32,44,39,39,39,39,39,44,39,39,44,44,34,39,44,39,39,44,32,32,39,32,44,39,44,44,39,39,32,39,39,34,39,39,39,39],3,null,{"problem":47,"audience":54,"pricing":45,"motivation":63,"time_to_build":45,"stack":67,"openness":74,"stage":75,"team_size":74},{"value":48,"source":49,"evidence":50},"Gives people a place to build small neural networks that play strategy games and compete.","maker",[51],{"id":52,"by":11,"text":53},"49776645","15yrs back I participated in \"Google Ants AI Challenge 2011\", an ai programming competition, hosted by the University of Waterloo, and I ranked #127 (#1 in my country). The…",{"value":55,"source":56,"evidence":57},"Developers and hobbyists who like building game-playing neural networks.","inferred",[58,59],{"id":52,"by":11,"text":53},{"id":60,"by":61,"text":62},"49782695","willmarch","Pretty neat! I'm considering entering some models. How long will you be running these competitions?",{"value":64,"source":49,"evidence":65},"Bring back the fun of the Google Ants AI Challenge and enjoy optimizing neural networks.",[66],{"id":52,"by":11,"text":53},[68],{"value":69,"source":49,"evidence":70},"ONNX model upload",[71],{"id":72,"by":11,"text":73},"49784679","Where do you see a hard requirement? I have added support for ONNX model upload for now and would love to extend support for other formats. How you build the model is totally…","unknown","beta",{"praise":77,"objections":102,"requests":121,"questions":131,"alternatives":145},[78,90],{"value":79,"source":80,"evidence":81},"Nostalgic link to the Google Ants AI Challenge.","commenter",[82,86],{"id":83,"by":84,"text":85},"49785946","Muthaalagan","From competing with the world to building a place for the world to compete—what a full-circle moment. Love the challenge: how much strategy can a tiny neural network learn?…",{"id":87,"by":88,"text":89},"49781965","AnotherGoodName","Nice. I was 72nd. Working in AI research today and still making ai for games as a hobby (tfmbot.com is an ai i’m working on for my favourite board game terraforming mars).",{"value":91,"source":80,"evidence":92},"Fun concept with small network size focus.",[93,97,101],{"id":94,"by":95,"text":96},"49783917","DylanMerigaud","Great idea to focus on small, efficient neural networks.",{"id":98,"by":99,"text":100},"49777779","adityamishra241","This looks fun. How small are the networks you're aiming for?",{"id":60,"by":61,"text":62},[103,114],{"value":104,"source":80,"evidence":105},"Documentation was ambiguous and AI-sounding, especially weight class rules.",[106,110],{"id":107,"by":108,"text":109},"49781691","lostdog","Cool idea! It would help to delete all the text on the page, and write it without AI. For example, \"model and manifest bytes together pick the class; every version also plays on…",{"id":111,"by":112,"text":113},"49786050","vova_hn2","> Your class is measured, not chosen > model and manifest bytes together pick the class What? How hard is it to write something like \"your weight class is determined by the total…",{"value":115,"source":80,"evidence":116},"Hard PyTorch requirement made entry difficult.",[117],{"id":118,"by":119,"text":120},"49783648","cookiengineer","OMG! Just yesterday I published my reworked GoNEAT library that implements HyperNEAT combined with phased search and backpropagation [1]. But it's kind of impossible to enter for…",[122,127],{"value":123,"source":80,"evidence":124},"Support more model formats beyond ONNX and a gym-style interface.",[125,126],{"id":118,"by":119,"text":120},{"id":72,"by":11,"text":73},{"value":128,"source":49,"evidence":129},"Add more games.",[130],{"id":52,"by":11,"text":53},[132,139],{"value":133,"source":80,"evidence":134},"How small can networks get and still play well?",[135,138],{"id":136,"by":84,"text":137},"49785909","Interesting—how small can a neural network get and still make good strategic decisions? Curious whether these models can adapt to unfamiliar opponents.",{"id":98,"by":99,"text":100},{"value":140,"source":80,"evidence":141},"How are networks evaluated, by game performance or size and speed?",[142],{"id":143,"by":99,"text":144},"49783558","This looks fun. How do you evaluate the networks — is it purely based on game performance, or are there other metrics like size and inference speed too?",[146,150,157,164,171,178],{"value":147,"source":80,"evidence":148},"Google Ants AI Challenge",[149],{"id":52,"by":11,"text":53},{"value":151,"source":80,"evidence":152},"Core War",[153],{"id":154,"by":155,"text":156},"49781900","lokar","See also: https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCore_War",{"value":158,"source":80,"evidence":159},"Screeps",[160],{"id":161,"by":162,"text":163},"49782014","cheschire","Also: https:\u002F\u002Fscreeps.com\u002F",{"value":165,"source":80,"evidence":166},"MIT Battlecode",[167],{"id":168,"by":169,"text":170},"49793081","RomanKornev","There is also MIT Battlecode - same idea, but for human teams https:\u002F\u002Fbattlecode.org\u002F",{"value":172,"source":80,"evidence":173},"MechMania",[174],{"id":175,"by":176,"text":177},"49782982","Qworg","Reminds me of MechMania at UIUC - exciting!",{"value":179,"source":80,"evidence":180},"AI Wars",[181],{"id":182,"by":183,"text":184},"49794136","b800h","Around 1997 there was something awfully similar to this doing the rounds, called AI Wars. IIRC it was code rather than weights, but it's a funny parallel. 30 years! God I'm old.",{"comments":186,"answered":187,"key":188},13,6,[189,190,193],{"id":52,"text":53},{"id":191,"text":192},"49782124","Thanks for sharing. I remember #1 xathis had a score, big leap ahead of others. The difference in techniques in top 100 was almost the same.",{"id":194,"text":195},"49788128","Unfortunately the competition site is mostly down and couldn't find much about the old competition other than the participants blog articles. Do a search on \"Google Ants AI…",{"commenters":197,"max_depth":198,"maker_replies":186,"first_comment_mins":199,"lifespan_hours":200},23,4,11,70,[202,204,208,210,212,214,218,219,222,225,228,230,232,234,236,238,239,241,243,245,247,249,253,258,259,261,263,266,268,269,272,274,276,277,278,282,284,288,291,292,293],[52,39,39,203,11,34],"maker\u002Fneutral\u002Fidea",[205,39,39,206,207,39],"49778565","other\u002Fneutral\u002Fnone","atmanactive",[87,39,44,209,88,39],"project\u002Fneutral\u002Fnone",[191,39,39,211,11,34],"maker\u002Fneutral\u002Fnone",[83,39,32,213,84,39],"project\u002Fpositive\u002Fidea",[215,39,44,216,217,39],"49786651","maker\u002Fneutral\u002Fdocs","nickledave",[194,39,39,203,11,34],[220,39,39,221,217,39],"49815248","commenter\u002Fneutral\u002Fnone",[223,34,39,206,224,39],"49787162","euroderf",[226,34,39,206,227,39],"49790686","TeMPOraL",[229,34,39,221,224,39],"49792515",[118,35,44,231,119,39],"project\u002Fmixed\u002Fexecution",[72,35,39,233,11,34],"project\u002Fneutral\u002Fexecution",[235,35,39,233,11,34],"49784705",[182,44,44,237,183,39],"project\u002Fneutral\u002Fidea",[136,198,44,237,84,39],[107,32,34,240,108,39],"project\u002Fcritical\u002Fdocs",[242,32,39,216,11,34],"49782143",[143,187,44,244,99,39],"project\u002Fneutral\u002Fperformance",[246,187,39,233,11,34],"49784648",[248,187,39,233,11,34],"49784653",[250,251,44,237,252,39],"49787965",7,"WanderZil",[254,255,32,256,257,39],"49785425",8,"project\u002Fpositive\u002Fnone","awfm9",[60,33,32,256,61,39],[260,33,39,209,11,34],"49782705",[262,33,32,256,61,39],"49784774",[264,265,44,237,99,39],"49797229",10,[267,265,39,203,11,34],"49798929",[98,199,44,244,99,39],[270,199,44,244,271,39],"49781157","sitzkrieg",[273,199,39,244,11,34],"49782588",[168,275,39,206,169,39],12,[94,186,32,213,95,39],[175,18,39,206,176,39],[279,280,39,206,281,39],"49792147",15,"alex7o",[111,283,34,240,112,39],16,[285,283,39,286,287,39],"49786449","commenter\u002Fneutral\u002Fdocs","FrustratedMonky",[289,283,39,290,11,34],"49796978","maker\u002Fpositive\u002Fdocs",[154,36,39,206,155,39],[161,36,39,206,162,39],[294,36,39,211,11,34],"49782154",[],[297,302,306,311,316,320],{"id":298,"name":299,"points":300,"category":301},"49823738","Bastardica",862,"creative",{"id":303,"name":304,"points":305,"category":20},"49783133","Mini-AGI",277,{"id":307,"name":308,"points":309,"category":310},"49740105","Setup",251,"productivity",{"id":312,"name":313,"points":314,"category":315},"49801329","Drop",193,"dev_tools",{"id":317,"name":318,"points":319,"category":315},"49800574","JevBench",154,{"id":321,"name":322,"points":323,"category":324},"49746346","Snapdrop",112,"infrastructure",false,"v2",{"id":328,"name":5,"line":6,"ai":325,"cat":20,"year":13,"points":14,"comments":15,"rec":29,"s":329,"top":18,"kw":330,"maker":11,"domain":10,"tier":19,"attempts":39,"strip":331,"url":9,"mix":332,"praise":79,"objection":104,"sub":21},49776523,0.09,[24,25,26,27,28],"0030530000030033103003550530330050010000",[32,33,34,35],1794036710485]