{"id":715,"date":"2026-10-29T16:32:08","date_gmt":"2026-10-29T13:32:08","guid":{"rendered":"https:\/\/teknolata.com\/?p=715"},"modified":"2026-10-29T16:32:08","modified_gmt":"2026-10-29T13:32:08","slug":"nvidia-rtx-spark-gucunu-tasiyan-dizustu-bilgisayarlar-neler-sunuyor","status":"publish","type":"post","link":"https:\/\/teknolata.com\/index.php\/2026\/10\/29\/nvidia-rtx-spark-gucunu-tasiyan-dizustu-bilgisayarlar-neler-sunuyor\/","title":{"rendered":"NVIDIA RTX Spark G\u00fcc\u00fcn\u00fc Ta\u015f\u0131yan Diz\u00fcst\u00fc Bilgisayarlar Neler Sunuyor?"},"content":{"rendered":"<p>NVIDIA, RTX Spark platformunu mobil i\u015f istasyonu ve yapay zek\u00e2 odakl\u0131 diz\u00fcst\u00fc bilgisayar pazar\u0131na sundu. ASUS, Dell, HP, Lenovo, Microsoft ve MSI taraf\u0131ndan haz\u0131rlanan modeller, Blackwell mimarili RTX GPU ile Grace CPU\u2019yu tek bir s\u00fcper \u00e7ip tasar\u0131m\u0131nda bir araya getiriyor. Platformun temel amac\u0131, yo\u011fun yapay zek\u00e2 i\u015f y\u00fcklerini, profesyonel i\u00e7erik \u00fcretimini ve y\u00fcksek performansl\u0131 oyun deneyimini daha ta\u015f\u0131nabilir bilgisayarlarda sunmak.<\/p>\n<p>\u00dcretici a\u00e7\u0131klamas\u0131na g\u00f6re RTX Spark sistemleri 128 GB\u2019a kadar birle\u015fik bellek ve 1 petaflop FP4 yapay zek\u00e2 performans\u0131 sa\u011flayabiliyor. Bu \u00f6zellikler, b\u00fcy\u00fck modellerin ve karma\u015f\u0131k projelerin cihaz \u00fczerinde \u00e7al\u0131\u015ft\u0131r\u0131lmas\u0131n\u0131 kolayla\u015ft\u0131rmay\u0131 hedefliyor. Ancak performans; se\u00e7ilen bilgisayar\u0131n donan\u0131m yap\u0131land\u0131rmas\u0131na, uygulama deste\u011fine, g\u00fc\u00e7 s\u0131n\u0131rlar\u0131na ve so\u011futma kapasitesine g\u00f6re de\u011fi\u015febilir. Dolay\u0131s\u0131yla duyurulan azami de\u011ferler her modelin ayn\u0131 sonucu verece\u011fi anlam\u0131na gelmiyor.<\/p>\n<h2>Blackwell ve Grace tek platformda bulu\u015fuyor<\/h2>\n<p>RTX Spark\u2019\u0131n \u00f6ne \u00e7\u0131kan \u00f6zelliklerinden biri, CPU ve GPU\u2019nun ortak bir bellek havuzunu kullanabilmesi. Geleneksel sistemlerde i\u015flemci ve ekran kart\u0131 belle\u011fi ayr\u0131 oldu\u011funda, baz\u0131 i\u015f y\u00fcklerinde verinin birimler aras\u0131nda ta\u015f\u0131nmas\u0131 performans\u0131 ve bellek kullan\u0131m\u0131n\u0131 s\u0131n\u0131rlayabiliyor. Birle\u015fik bellek yakla\u015f\u0131m\u0131, desteklenen uygulamalarda bu veriyi daha esnek bi\u00e7imde payla\u015fmaya imk\u00e2n tan\u0131yor. B\u00f6ylece b\u00fcy\u00fck dil modelleri, g\u00f6rsel \u00fcretim ara\u00e7lar\u0131 ve kapsaml\u0131 3D sahneler tek cihaz \u00fczerinde \u00e7al\u0131\u015ft\u0131r\u0131labiliyor.<\/p>\n<p>NVIDIA\u2019n\u0131n aktard\u0131\u011f\u0131 tasar\u0131m hedefleri aras\u0131nda 14 mm\u2019ye kadar incelik ve yakla\u015f\u0131k 1,4 kg\u2019a kadar d\u00fc\u015f\u00fck a\u011f\u0131rl\u0131k bulunuyor. Bu de\u011ferler platformun t\u00fcm diz\u00fcst\u00fc bilgisayarlar\u0131 i\u00e7in ortak bir \u00f6l\u00e7\u00fc de\u011fil; \u00fcr\u00fcn\u00fcn kal\u0131nl\u0131\u011f\u0131 ve a\u011f\u0131rl\u0131\u011f\u0131 \u00fcreticiye ve modele g\u00f6re farkl\u0131l\u0131k g\u00f6sterebilir. Ta\u015f\u0131nabilirlik ile uzun s\u00fcreli y\u00fcksek performans aras\u0131ndaki dengeyi belirleyen unsurlar aras\u0131nda pil kapasitesi, so\u011futma sistemi ve cihaz\u0131n \u00e7al\u0131\u015fma g\u00fc\u00e7 s\u0131n\u0131rlar\u0131 da yer al\u0131yor.<\/p>\n<h2>Geli\u015ftiricilerden oyunculara farkl\u0131 kullan\u0131m alanlar\u0131<\/h2>\n<ul>\n<li><strong>Yapay zek\u00e2 geli\u015ftiricileri:<\/strong> CUDA ekosisteminden yararlanan geli\u015ftiriciler, desteklenen modelleri ve ajanlar\u0131 yerel donan\u0131mda \u00e7al\u0131\u015ft\u0131r\u0131p test edebilir. Yerel i\u015flem, geli\u015ftirme s\u0131ras\u0131nda bulut hizmetlerine ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 azaltabilir; fakat modelin b\u00fcy\u00fckl\u00fc\u011f\u00fc ve kulland\u0131\u011f\u0131 yaz\u0131l\u0131m altyap\u0131s\u0131 gerekli bellek ve i\u015flem g\u00fcc\u00fcn\u00fc belirler.<\/li>\n<li><strong>Video ve 3D \u00fcreticileri:<\/strong> Donan\u0131m destekli video i\u015fleme \u00f6zellikleri, profesyonel d\u00fczenleme i\u015f ak\u0131\u015flar\u0131n\u0131 h\u0131zland\u0131rmay\u0131 ama\u00e7l\u0131yor. Duyuruda yerel 4:2:2 ham video formatlar\u0131n\u0131n i\u015flenmesi ve 90 GB boyutundaki 3D sahnelerle \u00e7al\u0131\u015fma \u00f6rnekleri veriliyor. Ger\u00e7ek kullan\u0131mda format uyumlulu\u011fu, kullan\u0131lan d\u00fczenleme yaz\u0131l\u0131m\u0131 ve proje ayarlar\u0131 \u00f6nem ta\u015f\u0131yor.<\/li>\n<li><strong>Oyuncular:<\/strong> G-SYNC, Reflex, \u0131\u015f\u0131n izleme ve DLSS gibi NVIDIA teknolojileri desteklenen oyunlarda kullan\u0131labiliyor. Bu \u00f6zelliklerin t\u00fcm\u00fc her oyunda ayn\u0131 \u015fekilde uygulanmayabilir; kare h\u0131z\u0131 ve g\u00f6r\u00fcnt\u00fc kalitesi, oyuna ve grafik ayarlar\u0131na ba\u011fl\u0131d\u0131r.<\/li>\n<\/ul>\n<h2>Nemotron 3.5 Transcribe ile \u00e7ok dilli ses i\u015fleme<\/h2>\n<p>NVIDIA, donan\u0131m duyurusunun yan\u0131 s\u0131ra a\u00e7\u0131k a\u011f\u0131rl\u0131kl\u0131 <strong>Nemotron 3.5 Transcribe<\/strong> modelini de tan\u0131tt\u0131. Model; konu\u015fmay\u0131 yaz\u0131ya d\u00f6kme, konu\u015fmac\u0131lar\u0131 birbirinden ay\u0131rma ve \u00e7eviri gibi ses i\u015fleme g\u00f6revlerini tek bir sistemde birle\u015ftirmeyi hedefliyor. NVIDIA\u2019n\u0131n payla\u015ft\u0131\u011f\u0131 bilgilere g\u00f6re model 4,3 milyar aktif parametreye sahip ve 40 dili destekliyor. Bu diller aras\u0131nda yedi Hint dili de bulunuyor.<\/p>\n<p>Nemotron 3.5 Transcribe\u2019\u0131n BF16, FP8 ve NVFP4 bi\u00e7imlerinde Hugging Face \u00fczerinden eri\u015filebilir oldu\u011fu belirtiliyor. Farkl\u0131 say\u0131sal bi\u00e7imler, modelin bellek kullan\u0131m\u0131 ve \u00e7al\u0131\u015fma h\u0131z\u0131 \u00fczerinde etkili olabilir; uygun se\u00e7enek kullan\u0131lan donan\u0131ma ve uygulamaya g\u00f6re de\u011fi\u015fir. Modelin Hugging Face Open ASR s\u0131ralamas\u0131nda lider oldu\u011fu bilgisi de NVIDIA\u2019n\u0131n duyurusunda aktar\u0131lan bir sonu\u00e7tur. Bu t\u00fcr s\u0131ralamalar, de\u011ferlendirme veri k\u00fcmelerine ve \u00f6l\u00e7\u00fcm y\u00f6ntemlerine ba\u011fl\u0131 oldu\u011fundan, farkl\u0131 dillerdeki ve ger\u00e7ek kullan\u0131m ko\u015fullar\u0131ndaki performans ayr\u0131ca incelenmelidir.<\/p>\n<h2>LFM3 modelleriyle cihaz \u00fczerinde \u00e7al\u0131\u015fan ajanlar<\/h2>\n<p>Liquid AI taraf\u0131ndan geli\u015ftirilen <strong>LFM3 (Liquid Foundation Models)<\/strong> ailesinin de RTX Spark \u00fczerinde yerle\u015fik olarak desteklendi\u011fi duyuruldu. 1,7 milyar parametreli temel modelin metin, g\u00f6r\u00fcnt\u00fc, ses ve karar mekanizmalar\u0131n\u0131 ayn\u0131 model ailesinde bir araya getirmesi ama\u00e7lan\u0131yor. Bu yetenekler, bir ajan\u0131n yaln\u0131zca metin komutlar\u0131na de\u011fil, desteklenen g\u00f6rsel ve ses girdilerine de yan\u0131t verebilmesine olanak tan\u0131yabilir.<\/p>\n<p>NVIDIA\u2019n\u0131n \u00f6rneklerine g\u00f6re yerel ajanlar e-posta tasla\u011f\u0131 haz\u0131rlama, takvim y\u00f6netimi, ekrandaki bilgileri okuma ve el yaz\u0131s\u0131yla yaz\u0131lm\u0131\u015f form\u00fclleri \u00e7\u00f6zme gibi g\u00f6revlerde kullan\u0131labiliyor. llama.cpp ve vLLM gibi yaz\u0131l\u0131m altyap\u0131lar\u0131yla cihaz \u00fczerinde \u00e7al\u0131\u015ft\u0131r\u0131labilmeleri, verinin buluta g\u00f6nderilmeden i\u015flenmesine imk\u00e2n sa\u011flayabilir. Bununla birlikte, gizlilik ve g\u00fcvenlik yaln\u0131zca modelin yerel \u00e7al\u0131\u015fmas\u0131na ba\u011fl\u0131 de\u011fildir; uygulama izinleri, i\u015fletim sistemi ayarlar\u0131 ve kullan\u0131lan verilerin nas\u0131l sakland\u0131\u011f\u0131 da dikkate al\u0131nmal\u0131d\u0131r.<\/p>\n<h2>NVIDIA App \u00fczerinden GPU kaynaklar\u0131n\u0131 y\u00f6netme<\/h2>\n<p>NVIDIA App\u2019e, arka plandaki yapay zek\u00e2 ajanlar\u0131 ile \u00f6n planda \u00e7al\u0131\u015fan oyun veya \u00fcretim yaz\u0131l\u0131mlar\u0131 aras\u0131nda GPU kaynaklar\u0131n\u0131 d\u00fczenlemeye y\u00f6nelik bir se\u00e7enek eklendi. Kullan\u0131c\u0131lar bu ayara <strong>Sistem &gt; Geli\u015fmi\u015f<\/strong> b\u00f6l\u00fcm\u00fcnden eri\u015febiliyor. \u00d6zellik, Windows donan\u0131m h\u0131zland\u0131rmal\u0131 GPU zamanlamas\u0131ndan yararlanarak kaynak da\u011f\u0131l\u0131m\u0131n\u0131n kullan\u0131m \u00f6nceliklerine g\u00f6re belirlenmesini sa\u011fl\u0131yor.<\/p>\n<p>\u00d6rne\u011fin kullan\u0131c\u0131, oyun oynarken \u00f6n plandaki uygulamaya \u00f6ncelik vermeyi veya arka planda derleme yapan bir ajana daha fazla GPU kayna\u011f\u0131 ay\u0131rmay\u0131 tercih edebilir. Bu ayar\u0131n etkisi, ayn\u0131 anda \u00e7al\u0131\u015fan uygulamalara ve ilgili yaz\u0131l\u0131mlar\u0131n kaynak y\u00f6netimine ne \u00f6l\u00e7\u00fcde uyum sa\u011flad\u0131\u011f\u0131na g\u00f6re de\u011fi\u015febilir. Dolay\u0131s\u0131yla se\u00e7enek, her ko\u015fulda belirli bir kare h\u0131z\u0131 ya da i\u015flem s\u00fcresi garantisi vermiyor; kaynak kullan\u0131m\u0131n\u0131 tercih do\u011frultusunda y\u00f6netmeye yard\u0131mc\u0131 oluyor.<\/p>\n<h2>Phantom Blade Zero ve DLSS 4.5<\/h2>\n<p>Oyun taraf\u0131nda \u00f6ne \u00e7\u0131kan duyurulardan biri, S-GAME taraf\u0131ndan geli\u015ftirilen aksiyon rol yapma oyunu <strong>Phantom Blade Zero<\/strong> i\u00e7in \u0131\u015f\u0131n izleme ve DLSS 4.5 deste\u011finin sunulmas\u0131. DLSS teknolojileri, desteklenen oyunlarda g\u00f6r\u00fcnt\u00fc olu\u015fturma i\u015f y\u00fck\u00fcn\u00fc azaltmay\u0131 ve performans\u0131 y\u00fckseltmeyi ama\u00e7l\u0131yor. Sonu\u00e7lar oyunun uygulamas\u0131na, ekran kart\u0131na, \u00e7\u00f6z\u00fcn\u00fcrl\u00fc\u011fe ve se\u00e7ilen g\u00f6r\u00fcnt\u00fc kalitesi ayarlar\u0131na ba\u011fl\u0131 oldu\u011fundan, her sistemde ayn\u0131 kare h\u0131z\u0131n\u0131 beklemek do\u011fru olmaz.<\/p>\n<p>Duyuruda yer verilen <strong>DLSS Dynamic Multi Frame Generation<\/strong>, hedeflenen kare h\u0131z\u0131na g\u00f6re kare \u00fcretim \u00e7arpan\u0131n\u0131 dinamik olarak ayarlayabiliyor. NVIDIA, bu teknolojiyle kare \u00fcretiminin alt\u0131 kata kadar art\u0131r\u0131labilece\u011fini belirtiyor. Bu ifade, teknolojinin desteklenen ko\u015fullardaki azami art\u0131\u015f\u0131n\u0131 anlat\u0131yor; temel performans, gecikme ve g\u00f6r\u00fcnt\u00fc kalitesi oyuna ve donan\u0131m yap\u0131land\u0131rmas\u0131na g\u00f6re farkl\u0131l\u0131k g\u00f6sterebilir.<\/p>\n<h2>RTX Remix 1.6 ve klasik oyunlar\u0131n yenilenmesi<\/h2>\n<p>NVIDIA\u2019n\u0131n RTX Remix platformu 1.6 s\u00fcr\u00fcm\u00fcne y\u00fckseltildi. G\u00fcncellemenin, Minecraft Java Edition 1.21.1 i\u00e7in tam yol izleme deste\u011fi sa\u011flad\u0131\u011f\u0131 ve modlama ara\u00e7lar\u0131n\u0131 geni\u015fletti\u011fi duyuruldu. Tam yol izleme, \u0131\u015f\u0131\u011f\u0131n sahnedeki yans\u0131malar\u0131n\u0131 ve ayd\u0131nlatma davran\u0131\u015f\u0131n\u0131 daha kapsaml\u0131 bi\u00e7imde hesaplayan bir grafik tekni\u011fidir; buna kar\u015f\u0131l\u0131k y\u00fcksek i\u015flem g\u00fcc\u00fc gerektirebilir.<\/p>\n<p>RTX Remix 1.6 kapsam\u0131nda DLSS 5 i\u00e7in 3D-Guided Neural Rendering \u00f6zellikleri, ComfyUI entegrasyonu ve ajan destekli modlama ara\u00e7lar\u0131 da tan\u0131t\u0131ld\u0131. NVIDIA, bu olanaklar\u0131n Portal with RTX ve Half-Life 2 RTX gibi projelerde kullan\u0131labildi\u011fini belirtiyor. S\u00f6z konusu \u00f6zellikler, eski oyunlar\u0131n g\u00f6rsel olarak yenilenmesini ve modlama s\u00fcrecinin baz\u0131 ad\u0131mlar\u0131n\u0131n kolayla\u015fmas\u0131n\u0131 hedefliyor; sonu\u00e7lar kullan\u0131lan i\u00e7eriklere, modlara ve uyumlu donan\u0131ma ba\u011fl\u0131.<\/p>\n<h2>Fiyat ve sat\u0131\u015f bilgileri<\/h2>\n<p>ASUS, Dell, HP, Lenovo, Microsoft ve MSI imzal\u0131 RTX Spark diz\u00fcst\u00fc bilgisayarlar\u0131n k\u00fcresel pazarda sat\u0131\u015fa \u00e7\u0131kt\u0131\u011f\u0131 ve ba\u015flang\u0131\u00e7 fiyat\u0131n\u0131n 2.599 dolar oldu\u011fu bildirildi. Baz\u0131 modellerde dokunmatik ekran, kalem deste\u011fi ve 2\u2019si 1 arada d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilir tasar\u0131m se\u00e7enekleri bulunuyor. Ba\u015flang\u0131\u00e7 fiyat\u0131 temel yap\u0131land\u0131rmay\u0131 ifade edebilece\u011finden, bellek, depolama alan\u0131, ekran \u00f6zellikleri ve \u00fcreticiye g\u00f6re sat\u0131\u015f fiyatlar\u0131 de\u011fi\u015febilir. \u00dclkeye g\u00f6re vergiler, stok durumu ve yerel sat\u0131\u015f ko\u015fullar\u0131 da nihai tutar\u0131 etkileyebilir.<\/p>\n<p>Kurumsal ara\u015ft\u0131rmac\u0131lar i\u00e7in tasarlanan <strong>DGX Spark<\/strong> masa\u00fcst\u00fc platformunun da yetkili i\u015f ortaklar\u0131 \u00fczerinden tedarik edilmeye ba\u015fland\u0131\u011f\u0131 a\u00e7\u0131kland\u0131. GB10 Grace Blackwell tabanl\u0131 bu sistemin 64 GB birle\u015fik belle\u011fe sahip oldu\u011fu belirtiliyor. DGX Spark, diz\u00fcst\u00fc bilgisayarlardan farkl\u0131 olarak masa\u00fcst\u00fc kullan\u0131m\u0131na y\u00f6nelik bir se\u00e7enek sunuyor ve \u00f6zellikle yerel yapay zek\u00e2 geli\u015ftirme ile ara\u015ft\u0131rma i\u015f ak\u0131\u015flar\u0131na odaklan\u0131yor.<\/p>\n<p>Genel olarak RTX Spark ailesi, y\u00fcksek bellek kapasitesini, Blackwell GPU teknolojisini ve yerel yapay zek\u00e2 yaz\u0131l\u0131mlar\u0131n\u0131 ta\u015f\u0131nabilir sistemlerde bir araya getirmeyi ama\u00e7l\u0131yor. Platformun pratik de\u011feri, yaln\u0131zca duyurulan azami performans de\u011ferlerine de\u011fil; se\u00e7ilen modele, uygulama deste\u011fine, enerji t\u00fcketimine ve kullan\u0131c\u0131n\u0131n i\u015f ak\u0131\u015f\u0131na ba\u011fl\u0131 olacak. Bu nedenle sat\u0131n alma karar\u0131 verirken \u00fcr\u00fcn\u00fcn ger\u00e7ek yap\u0131land\u0131rmas\u0131n\u0131, yaz\u0131l\u0131m uyumlulu\u011funu ve ba\u011f\u0131ms\u0131z performans incelemelerini birlikte de\u011ferlendirmek \u00f6nem ta\u015f\u0131yor.<\/p>\n<div class=\"newspaper-x-tags\"><strong>TAGS: <\/strong><span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/birlesik-bellek\/\" rel=\"tag\">birle\u015fik bellek<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/blackwell\/\" rel=\"tag\">Blackwell<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/dgx-spark\/\" rel=\"tag\">DGX Spark<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/dlss-4-5\/\" rel=\"tag\">DLSS 4.5<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/grace-cpu\/\" rel=\"tag\">Grace CPU<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/lfm3\/\" rel=\"tag\">LFM3<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/nemotron-3-5-transcribe\/\" rel=\"tag\">Nemotron 3.5 Transcribe<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/nvidia-app\/\" rel=\"tag\">NVIDIA App<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/nvidia-rtx-spark\/\" rel=\"tag\">NVIDIA RTX Spark<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/phantom-blade-zero\/\" rel=\"tag\">Phantom Blade Zero<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/rtx-dizustu-bilgisayar\/\" rel=\"tag\">RTX diz\u00fcst\u00fc bilgisayar<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/rtx-remix\/\" rel=\"tag\">RTX Remix<\/a><\/span><a href=\"https:\/\/teknolata.com\/index.php\/tag\/yapay-zeka\/\" rel=\"tag\">Yapay Zeka<\/a> <\/div>\n","protected":false},"excerpt":{"rendered":"<p>NVIDIA RTX Spark destekli diz\u00fcst\u00fc bilgisayarlar\u0131n performans, yapay zek\u00e2 ve ta\u015f\u0131nabilirlik a\u00e7\u0131s\u0131ndan sundu\u011fu yenilikleri ke\u015ffedin.<\/p>\n","protected":false},"author":1,"featured_media":716,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9,1],"tags":[912,909,917,530,910,914,913,915,797,532,911,916,29],"class_list":["post-715","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-teknoloji","category-tumu","tag-birlesik-bellek","tag-blackwell","tag-dgx-spark","tag-dlss-4-5","tag-grace-cpu","tag-lfm3","tag-nemotron-3-5-transcribe","tag-nvidia-app","tag-nvidia-rtx-spark","tag-phantom-blade-zero","tag-rtx-dizustu-bilgisayar","tag-rtx-remix","tag-yapay-zeka"],"_links":{"self":[{"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/posts\/715","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/comments?post=715"}],"version-history":[{"count":1,"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/posts\/715\/revisions"}],"predecessor-version":[{"id":717,"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/posts\/715\/revisions\/717"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/media\/716"}],"wp:attachment":[{"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/media?parent=715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/categories?post=715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/teknolata.com\/index.php\/wp-json\/wp\/v2\/tags?post=715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}