15:[[48567759,{"by":"himata4113","descendants":219,"id":48567759,"kids":[48568309,48569244,48568950,48569360,48568360,48568374,48568791,48568549,48570287,48568750,48568603,48570584,48570434,48570018,48568442,48568555,48569529,48568669,48570245,48568483,48569041,48568640,48570411,48568769,48569488,48569786,48568599,48568342,48570358,48568361,48568606,48569099,48569233,48568449,48568997,48569123,48569094,48568956,48570477,48568524],"score":401,"time":1781687520,"title":"GLM-5.2 is the new leading open weights model on Artificial Analysis","type":"story","url":"https://artificialanalysis.ai/articles/glm-5-2-is-the-new-leading-open-weights-model-on-the-artificial-analysis-intelligence-index"}],[48550693,{"by":"paulmooreparks","descendants":160,"id":48550693,"kids":[48551299,48551052,48550898,48551405,48551146,48552353,48559436,48556923,48550931,48554173,48551008,48554754,48551117,48551627,48550932,48550952,48554705,48557825,48551049,48551696,48551618,48559035],"score":486,"time":1781585196,"title":"The time the x86 emulator team found code so bad they fixed it during emulation","type":"story","url":"https://devblogs.microsoft.com/oldnewthing/20260615-00/?p=112419"}],[48553224,{"by":"itsmarcelg","descendants":1579,"id":48553224,"kids":[48558036,48558059,48554123,48561928,48553836,48560418,48554711,48554513,48559625,48554106,48558715,48554047,48555886,48554804,48558555,48553270,48564504,48553610,48558953,48556988,48569072,48570851,48558410,48564991,48553825,48554417,48553777,48560532,48559650,48555384,48560515,48559947,48560125,48557112,48554438,48567422,48554200,48557994,48554400,48554468,48560071,48553930,48568004,48568929,48559782,48553793,48554540,48554435,48563997,48556033,48555946,48554389,48558758,48556608,48559566,48553607,48563257,48566990,48566828,48566517,48559539,48553263,48563059,48562420,48567118,48559347,48565283,48559500,48562536,48554524,48567904,48554467,48553854,48554818,48556982,48557042,48554399,48554469,48558579,48560422,48565252,48556110,48559463,48556573,48555028,48557562,48560224,48559584,48554556,48556156,48563004,48558533,48554699,48558310,48560049,48567066,48563131,48553786,48565712,48558481,48556452,48564704,48565677,48554405,48556016,48557823,48564443,48560167,48562084,48555832,48556111,48563640,48561581,48554402,48565318,48558646,48555940,48555261,48556667,48559375,48559526,48554920,48561583,48553768,48566309,48559939,48563673,48562927,48560864,48565423,48558332,48558280,48554229,48560291,48556671,48565403,48558017,48559113,48560078,48558686,48555109,48554391,48560252,48563307,48558847,48554112,48554752,48554470,48559903,48560055,48558576,48558767,48565040,48564462,48565516,48556820,48554879,48565201,48559381,48553693,48554465,48558455,48553967,48564177,48558089,48554493,48561562,48554664,48563505,48554779,48556960,48559551,48559724,48559668,48559577,48559187,48558638,48558535,48558383,48563924,48554496,48556002,48565005,48555573,48554066,48554727,48554537,48554453,48555789,48564243,48560375,48557822,48559689,48560624,48558719,48562933,48561214,48555761,48554816,48554692,48565114,48554948,48555143,48562003,48554807,48559956,48558825,48558321,48555665,48554461,48556359,48570053,48570040,48566928,48553764,48568973,48554561,48560196,48555179,48561688,48565420,48559604,48560852,48558765,48556211,48561489,48559748,48564033,48564030,48564315,48554408,48553831,48557020,48554582,48555281,48564634,48565533],"score":1078,"time":1781606664,"title":"SpaceX to buy Cursor for $60B","type":"story","url":"https://www.reuters.com/legal/transactional/spacex-buy-anysphere-60-billion-2026-06-16/"}],[48542100,{"by":"cloudking","descendants":539,"id":48542100,"kids":[48545304,48544680,48544206,48544883,48544416,48544369,48544749,48544681,48543466,48546231,48551176,48555297,48545835,48548103,48544634,48545568,48547933,48545412,48543610,48545498,48549139,48544377,48558962,48545466,48544630,48547289,48543919,48545391,48543967,48544186,48551679,48553715,48544149,48555494,48553857,48545424,48551616,48550177,48545505,48548651,48549279,48545803,48552563,48547305,48544062,48545537,48551246,48544766,48547891,48544741,48544503,48544767,48544860,48545153,48544406,48548395,48545709,48543460,48551811,48555190,48544068,48545892,48554343,48547902,48552970,48545654,48545141,48553039,48545371,48550688,48547166,48545608,48548038,48549630,48544300,48550028,48552090,48547177,48556634,48546454,48550430,48544640,48552812,48548209,48544094,48549377,48553274,48545347,48550265,48546219,48547107,48544520,48553253,48546316,48544025,48545861,48544717,48552621,48545296,48550613,48546139,48548522,48545002,48544213,48551693,48552183,48547215,48547481,48549542,48553114,48544759,48546193,48548346,48547900,48544118,48543981,48544782,48552778,48545826,48546084,48545272,48543859,48545897,48545333,48546432,48550673,48548070,48549081,48548711,48548588,48547987,48546567,48545759,48545154,48543938,48551701,48554946,48555376,48548950,48548149,48545343,48567299,48565559,48565486,48557225,48549091,48549815,48565042,48550614,48556624,48545592,48547278,48549965,48556351,48554406,48549533,48556789,48548084,48555270,48563678,48552070,48555464,48556043,48553886,48543953,48548206,48544398,48545573,48554328,48553649,48546442,48554308,48556055,48550965,48543413,48556535,48555906,48550709,48551139,48545760,48544338,48543785],"score":1268,"text":"Has anyone here fully swapped Claude/GPT for a local model as their main coding tool, not just for side experiments? If so, please share your setup and performance (e.g tok/s)","time":1781534813,"title":"Ask HN: Has anyone replaced Claude/GPT with a local model for daily coding?","type":"story"}],[48502347,{"by":"nekofneko","descendants":240,"id":48502347,"kids":[48503581,48503595,48503148,48507538,48503161,48502638,48502859,48504348,48502661,48505949,48537804,48507096,48505405,48505456,48503463,48507923,48506499,48504565,48504011,48503345,48503855,48503278,48514054,48503528,48538566,48503625,48503169,48503638],"score":455,"time":1781260970,"title":"Kimi K2.7-Code: open-source coding model with better token efficiency","type":"story","url":"https://huggingface.co/moonshotai/Kimi-K2.7-Code"}],[48507020,{"by":"kkm","descendants":126,"id":48507020,"kids":[48508209,48507763,48507679,48510866,48508234,48508310,48507773,48515485,48508105,48508826,48508352,48509656,48517592,48516038,48516327,48513463,48509434,48507775,48510811,48510921,48511085,48516145,48507483,48510104,48509488,48508141,48508774,48508360,48510208,48520223,48517856,48508487,48517669,48538396,48508556,48518767,48508028,48508377,48511296,48511006,48516866,48512502,48512318,48517593],"score":501,"time":1781285695,"title":"How to setup a local coding agent on macOS","type":"story","url":"https://ikyle.me/blog/2026/how-to-setup-a-local-coding-agent-on-macos"}],[48527700,{"by":"yegg","descendants":544,"id":48527700,"kids":[48528775,48528462,48528641,48530017,48528231,48532200,48529401,48530622,48528933,48528240,48532882,48533362,48528762,48528892,48531135,48529668,48528857,48535150,48538450,48532768,48528888,48531633,48532217,48531465,48535857,48530042,48528516,48530817,48529433,48529400,48529596,48528268,48528308,48528774,48536024,48535723,48529285,48531779,48528609,48538258,48528806,48529153,48528753,48528809,48528440,48529134,48528708,48533709,48532189,48530093,48531726,48534511,48533130,48529141,48531339,48529516,48533416,48529423,48538207,48530861,48544128,48531237,48528987,48528275,48528330],"score":508,"time":1781448269,"title":"Not everyone is using AI for everything","type":"story","url":"https://gabrielweinberg.com/p/people-are-consuming-ai-like-they"}],[48467387,{"by":"mcchen51","descendants":2,"id":48467387,"kids":[48467406,48499758],"score":7,"time":1781037510,"title":"DeepSeek is 17% of token volume, Anthropic is 65% of spend (Vercel gateway data)","type":"story","url":"https://vercel.com/blog/ai-gateway-production-index-june-2026"}],[48440448,{"by":"yogthos","descendants":225,"id":48440448,"kids":[48441125,48441517,48442028,48440796,48440890,48441083,48444827,48442257,48446602,48442300,48444125,48440728,48440993,48441295,48440698,48441029,48441059,48440963,48441110,48448217,48442680,48444429,48442681,48445738,48443785,48443923,48441013,48441583,48440992,48442933,48446495,48444566,48441659,48440847,48442228,48443720,48441711,48486373,48444990,48441706,48444872,48440775,48441535,48443260,48441049,48443732,48442790,48443508,48441067,48440839,48442167],"score":397,"time":1780882770,"title":"DeepSeek V4 Pro beats GPT-5.5 Pro on precision","type":"story","url":"https://runtimewire.com/article/deepseek-v4-pro-beats-gpt-5-5-pro-on-precision"}],[48420827,{"by":"Ekami","descendants":766,"id":48420827,"kids":[48421063,48421820,48428522,48422401,48420937,48428675,48422553,48421848,48421846,48423298,48421229,48430560,48428224,48421155,48428629,48426862,48422058,48423572,48421128,48421180,48424011,48431654,48421433,48421499,48421766,48422229,48420851,48422395,48420961,48421262,48420935,48422397,48421234,48421647,48421708,48421669,48421501,48428882,48422694,48421466,48422755,48434991,48429015,48424287,48421339,48421430,48433939,48428377,48421662,48421781,48421203,48421516,48421559,48420857,48421444,48431337,48421512,48421103,48423520,48421562,48428795,48484872,48422842,48422665,48421235,48428902,48475208,48421971,48421427,48424028,48421384,48422539,48421407,48422106,48420852,48421194,48421169,48421326,48421716,48504197,48428625,48421144,48421142,48421972,48421423,48424938,48421424,48429328,48425978,48425700,48428700,48428440,48429208,48421570,48421404,48421978,48423626,48428403,48447986,48421318,48427491,48422615,48421583,48423483,48430561,48421673,48423869,48421232,48426227,48424493,48438410,48441322,48421838,48421700,48437974,48429721,48423404,48421496,48424836,48421735,48422815,48423690,48428720,48423533,48423274,48421878,48422528,48421137,48429545,48422211,48429871,48429633,48421623,48429968,48421974,48422610,48429440,48429844,48422134,48428491,48424243,48433774,48428351,48422526,48424000,48423061,48421428,48428028,48442794,48430530,48428564,48430250,48422761,48421627,48431240,48422028,48428543,48420863,48435304,48421492,48421363,48425374,48429490,48422008,48425344,48433484,48428230,48443568,48421612,48444906,48424049,48451955,48421982,48422703,48422707,48424558,48422664,48466388,48434870,48424171,48433450,48424340,48428493,48428380,48421380,48428657,48423434,48422017,48421320,48430833,48431124,48429449,48442784,48429004,48422007,48434338,48430128,48421954,48430592,48424183,48429997,48421795,48422579,48437867,48421219,48428047,48430347,48438544,48422421,48428733,48422265,48423439,48421653,48422544,48423586,48421998,48429272,48421272,48421237,48421486,48429124,48429841,48420982,48421594,48421688,48421634,48425818,48426334,48430011,48422071,48423201,48426121,48424160,48421510,48426343,48445498,48428650,48428348,48422127,48423525,48425373,48422595,48434358,48422289,48431722,48504119,48422714,48421633,48428312,48428613,48422024,48429572,48423496,48425281,48421875,48422232,48422814,48424537,48422118,48424802,48426017,48428982,48431308,48424194,48430522,48442059,48422090,48421885,48425160,48430163,48421374,48421421,48427737,48421883,48443536,48430916,48421790,48422804,48428603,48424070,48442718,48434663,48507515,48423680,48430774,48424617,48421498,48428931,48422124,48421150,48422019,48421687,48430701,48434164,48421804,48422435,48430026,48423445,48433352,48421606,48431330,48427130,48423878,48429402,48428715,48432415,48421095,48423801,48427057,48421805,48424025,48429249,48430948,48422107,48426725,48424638,48430504,48422679,48489902,48486342,48428478,48429978,48430681,48428770,48420840,48427784,48429931,48475726,48421574,48421359,48422042,48423559,48430337,48423420,48422269,48431929,48425721,48420934,48421709,48421239,48421811,48428744,48423765,48421409],"score":463,"text":"Genuine question.

Over the past six months, there hasn’t been a single day where I’ve checked the HN Best RSS feed without seeing a post about how AI “writes bad code,” “introduces bugs,” “creates technical debt,” or something along those lines.

I’ll probably make a lot of enemies by saying this, but do people realize that code is just a means to an end?

Users don’t care whether the code was written by AI or by hand, or which framework you used. They care that the product works.

I say this as someone who has spent more than 20 years honing their craft as a software engineer.

Let’s face it: by the time I manually ship version 1.0 of a product, the AI-assisted version could have been deployed 10x faster. By then, enough real-world feedback would have surfaced to identify the major issues, and tools like Claude Code would make it possible to fix and ship version 2.0 at an incredible pace.

At some point, execution speed starts to matter more than the elegance of the code.","time":1780713111,"title":"Ask HN: Why is the HN crowd so 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HN: What is your (AI) dev tech stack / 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user:	zftnb666
created:	June 1, 2026
karma:	3
about:	DeepSeek V4 Flash $0.20/M tokens – 1M context <https://api-hub.cc>