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Glavno izlaganje bilo je prepuno agenata, programerskih alata, nosivih interfejsa i integracija proizvoda, ali svi ti slojevi zavise od dva smera razvoja modela: \u003Cb>Gemini Omni\u003C\u002Fb> i \u003Cb>Gemini 3.5\u003C\u002Fb>. Jedan gura multimodalno kreiranje i uređivanje. Drugi gura naprednu inteligenciju sa akcijom, posebno kroz Gemini 3.5 Flash.\u003C\u002Fp>\n\u003Cp>Ovaj članak je \u003Cb>krak računarskog sloja\u003C\u002Fb> unutar šireg \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\">arhitektonskog čvorišta Google I\u002FO 2026\u003C\u002Fa>. Strana sa programerskim alatima pokrivena je u članku \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\">Antigravity, AI Studio i Google DevTools\u003C\u002Fa>. Priča o uređajima i interfejsima pokrivena je u \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\">Android XR i pametne naočare\u003C\u002Fa>. Priča o proizvodima pokrivena je u \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\">Agentski proizvodi u pretrazi, Workspace-u i kupovini\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>Važno je izbeći posmatranje modela Gemini Omni i Gemini 3.5 kao jedne generičke najave „novog AI modela“. Oni rešavaju različite probleme sa proizvodima i infrastrukturom. Omni se bavi kreativnim generisanjem iz bilo kog unosa, počevši od videa. Gemini 3.5 se bavi brzom inteligencijom orijentisanom na akciju za agente, kodiranje i dugoročne tokove posla. Ta razlika je važna za programere, timove za proizvode i arhitekte platformi.\u003C\u002Fp>\n\u003Ch2>Računarski sloj iza glavnog izlaganja\u003C\u002Fh2>\n\u003Cp>Google-ova sopstvena kolekcija sa I\u002FO 2026 uokviruje događaj oko dva nova modela: Gemini Omni i Gemini 3.5. Gemini Omni je opisan kao korak napred u razumevanju sveta, multimodalnosti i uređivanju, sposoban da kreira izlaz iz unosa kroz tekst, slike, audio i video, počevši od videa. Gemini 3.5 Flash je predstavljen kao prvi model u porodici Gemini 3.5, kombinujući naprednu inteligenciju sa akcijom.\u003C\u002Fp>\n\u003Cul>\u003Cli>Gemini Omni se fokusira na multimodalno kreiranje i uređivanje, počevši od videa.\u003C\u002Fli>\u003Cli>Gemini 3.5 Flash se fokusira na brzinu na nivou akcije za agente, kodiranje i složene tokove posla.\u003C\u002Fli>\u003Cli>Podela modela odražava širu podelu Google-ove platforme: kreirajte bogatije medije, a zatim delujte brže kroz proizvode i alate.\u003C\u002Fli>\u003Cli>Prava strateška vrednost dolazi kada se ovi modeli povežu sa Antigravity, AI Studio, Search, Workspace, Android XR i uređajima koje pokreće Gemini.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Priča o modelima nije samo o većem kontekstu ili boljim demo snimcima. Reč je o specijalizaciji uloga: kreiranje sa jedne strane, akcija sa druge.\u003Ccite class=\"block mt-2 text-sm\">— Čitanje računarskog sloja\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Ch2>Gemini Omni: Kreiranje iz bilo kog unosa\u003C\u002Fh2>\n\u003Cp>Gemini Omni je Google-ov najdirektniji pokušaj da spoji rasuđivanje sa kreiranjem. Zvanično pozicioniranje je jasno: može da kreira bilo šta iz bilo kog unosa, počevši od videa. Model prihvata kombinacije teksta, slike, zvuka i videa, a zatim generiše visokokvalitetni video zasnovan na realnom znanju Gemini-ja. Takođe podržava konverzacijsko uređivanje videa, što će verovatno biti važnije od jednokratnog generisanja u stvarnim kreativnim tokovima posla.\u003C\u002Fp>\n\u003Cp>Ta poslednja stavka je važna. Praktična vrednost kreativne veštačke inteligencije retko se svodi samo na „generisanje videa“. Produkcijski tokovi rada zahtevaju reviziju, zamenu, tajming, kontinuitet stila i razgovor o izmenama. Ako Omni može da učini uređivanje više konverzacijskim uz očuvanje konteksta, on postaje više od običnog generatora noviteta. Postaje kreativni interfejs.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>\u002F\u002F Gemini Omni treba posmatrati kao model kreativnog toka rada\ninput = { text: "Prikaži lansiranje proizvoda u kišnom sajberpank gradu", image: "brand-reference.png", audio: "voiceover.wav", video: "rough-cut.mp4"\n} output = GeminiOmni.generateVideo(input)\nrevision = GeminiOmni.edit(output, "Učini osvetljenje toplijim i zadrži isti dizajn proizvoda")\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Za timove koji grade medijske alate, vizuelne prikaze proizvoda za e-trgovinu, obrazovni sadržaj, prototipove igara, tokove rada u oglašavanju ili kanale za društvene video snimke, ovo je deo I\u002FO 2026 koji je najvažniji. Interfejs se pomera sa ručnog uređivanja vremenske linije ka multimodalnom uređivanju zasnovanom na nameri. To ne uklanja kreativnu procenu, ali menja mesto gde se troši kreativni rad.\u003C\u002Fp>\n\u003Ch2>Gemini 3.5 Flash: Napredna inteligencija sa delovanjem\u003C\u002Fh2>\n\u003Cp>Gemini 3.5 Flash je druga polovina priče o računarstvu. Google opisuje porodicu Gemini 3.5 kao spoj napredne inteligencije i delovanja, i pozicionira 3.5 Flash kao prvo izdanje u toj porodici. Naglasak je na agentima, kodiranju, složenim dugoročnim zadacima i praktičnoj primeni u stvarnom svetu. Taj rečnik nije slučajan. On direktno ukazuje na sisteme koji rade stvari, a ne samo na sisteme koji odgovaraju.\u003C\u002Fp>\n\u003Cp>Za programere, „Flash“ je važan jer su agentski tokovi rada osetljivi na kašnjenje. Model koji je pametan ali spor može da posluži za istraživanje ili pažljivo pisanje. Mnogo ga je teže uklopiti u automatizaciju u terminalu, IDE petlje, višekoračno korišćenje alata, pozadinske agente i korisnička iskustva proizvoda koja zahtevaju trenutnu povratnu informaciju. Google očigledno pokušava da brzo napredno ponašanje učini podrazumevanim motorom za agentske tokove rada.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>\u002F\u002F Klasično korišćenje modela\nanswer = model.generate("Objasni ovu funkciju") \u002F\u002F Agentsko korišćenje modela\nplan = model.plan(task)\ntoolResult = tools.run(plan.nextStep)\nverification = model.verify(toolResult) if verification.failed: rollback()\nelse: continueExecution()\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Ta promena je razlog zašto Gemini 3.5 pripada istoj grupi kao i Antigravity. Antigravity-ju je potreban model koji može da planira, izvršava, verifikuje i nastavi da se kreće bez pretvaranja razvojnog okruženja u čekaonicu. Ovde računarske performanse postaju korisničko iskustvo programera.\u003C\u002Fp>\n\u003Ch2>Zašto brzina i akcija idu zajedno\u003C\u002Fh2>\n\u003Cp>Google-ovo uvodno izlaganje u vezi sa brzinom nije samo marketing. Agentski sistemi umnožavaju latenciju. Jedan odgovor zahteva jedan krug komunikacije sa modelom. Radni tok sa više agenata može imati mnogo koraka: planiranje, izbor alata, uređivanje koda, izvršavanje u terminalu, verifikaciju, popravku, rezimiranje i ljudski pregled. Ako je svaki korak spor, ceo sistem postaje neupotrebljiv.\u003C\u002Fp>\n\u003Cul>\u003Cli>Brz izlaz poboljšava interaktivne petlje kodiranja.\u003C\u002Fli>\u003Cli>Niska latencija čini da agenti sa više koraka deluju manje krhko.\u003C\u002Fli>\u003Cli>Visoka propusnost je važna kada agenti rade na pozadinskim zadacima održavanja.\u003C\u002Fli>\u003Cli>Stabilno rezonovanje je važno jer su brže pogrešne akcije i dalje pogrešne akcije.\u003C\u002Fli>\u003Cli>Verifikacija ostaje od suštinskog značaja jer sama brzina ne znači i poverenje.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Koristan agent nije model koji odgovara samo jednom. To je model koji može da nastavi da deluje, a da radni tok ne deluje kao red za čekanje.\u003Ccite class=\"block mt-2 text-sm\">— Perspektiva agentskog izvršnog okruženja\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Ch2>Gemini Omni protiv Gemini 3.5: Različiti poslovi, isti stek\u003C\u002Fh2>\n\u003Cp>Najjasniji način da se razumeju najave o računarskim resursima sa I\u002FO 2026 jeste da se razdvoje poslovi. Gemini Omni je multimodalni model za kreiranje i uređivanje. Gemini 3.5 Flash je model orijentisan na akciju za agente i kodiranje. Oni su komplementarni, a ne zamenljivi. Proizvodni tim bi mogao da koristi Omni za generisanje medija, a Gemini 3.5 Flash za orkestriranje radnog toka oko tih medija.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>\u002F\u002F Primer toka rada proizvoda\ncreativeAsset = GeminiOmni.generateVideo(brief) agentPlan = Gemini35Flash.plan({ task: "Publish launch campaign", asset: creativeAsset, channels: ["YouTube Shorts", "landing page", "email"]\n}) Gemini35Flash.execute(agentPlan, tools)\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>To je šira arhitektura na koju Google ukazuje. Kreativni modeli generišu bogatije artefakte. Akcioni modeli koordiniraju zadatke oko tih artefakata. Proizvodne površine izlažu te mogućnosti korisnicima. Razvojni alati pružaju orkestraciju i verifikaciju. Površine uređaja čine asistenta dostupnim u više konteksta.\u003C\u002Fp>\n\u003Ch2>Šta ovo znači za programere\u003C\u002Fh2>\n\u003Cp>Programeri ne bi trebalo da posmatraju Gemini 3.5 Flash samo kao još jednu opciju modela. On menja oblik aplikacija koje se mogu izgraditi na vrhu Google-ovog steka. Ako model može da rukuje dužim sekvencama zadataka, pouzdano koristi alate i radi dovoljno brzo za stvarne tokove posla, tada se aplikacije mogu pomeriti sa izolovanih AI funkcija na procese posredovane modelom.\u003C\u002Fp>\n\u003Cul>\u003Cli>Kod-agenti mogu refaktorisati, testirati i verifikovati veće jedinice zadataka.\u003C\u002Fli>\u003Cli>Proizvodni agenti mogu pomoći korisnicima da pređu sa namere na akciju unutar jednog toka.\u003C\u002Fli>\u003Cli>Medijske aplikacije mogu kombinovati Omni generisanje sa Gemini 3.5 orkestracijom.\u003C\u002Fli>\u003Cli>Funkcije pretrage i Workspace-a mogu preći sa odgovaranja na izvršavanje.\u003C\u002Fli>\u003Cli>Mobilne i XR površine mogu koristiti Gemini kao sloj konteksta uživo umesto kao udaljenog asistenta u oblaku.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Opasnost leži u prekomernoj izgradnji pre nego što operativni model sazri. Programerima su i dalje potrebni verzisanje, evaluacija, logovanje, kontrola troškova, zamensko ponašanje (fallback) i ljudska potvrda za akcije visokog uticaja. Jači model ne uklanja potrebu za inženjerskom disciplinom. On povećava vrednost te discipline.\u003C\u002Fp>\n\u003Ch2>Šta ovo znači za proizvodne timove\u003C\u002Fh2>\n\u003Cp>Proizvodni timovi bi trebalo da tumače Gemini Omni i Gemini 3.5 kao signal da Google želi da AI postane sloj izvršavanja širom ekosistema. To stvara i priliku i rizik. Prilika je brži put od namere korisnika do gotovog rezultata. Rizik je u tome što ponašanje proizvoda postaje manje transparentno ako su odluke agenata skrivene iza doteranog interfejsa.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>agenticProductChecklist = { userIntent: "clear", modelRole: "defined", tools: "permissioned", outputs: "reviewable", failures: "recoverable", costs: "measured", userControl: "explicit"\n}\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Ako proizvodni tim ne može da odgovori na to šta je modelu dozvoljeno da radi, kako se evaluira i kako korisnik može da se oporavi od loše akcije, integracija nije spremna za produkciju. Ovo posebno važi kada je Gemini povezan sa Search, Workspace, Shopping, Android platformama ili interfejsima nosivih uređaja.\u003C\u002Fp>\n\u003Ch2>Suočavanje sa realnošću: Benchmarci nisu proizvod\u003C\u002Fh2>\n\u003Cp>Rizik kod svakog lansiranja modela je opijenost benchmark-ovima. Bolje performanse su važne, ali produkciona vrednost zavisi od kvaliteta integracije. Model može biti brži i jači, a da i dalje podbaci u specifičnom procesu rada jer su promptovi slabi, politike alata labave, evaluacija površna ili nedostaju opcije za vraćanje na prethodno stanje.\u003C\u002Fp>\n\u003Cul>\u003Cli>Kreativnom modelu su potrebni doslednost, mogućnost uređivanja i kontrola izlaza.\u003C\u002Fli>\u003Cli>Akcionom modelu su potrebni disciplina u korišćenju alata, verifikacija i bezbedni režimi otkazivanja.\u003C\u002Fli>\u003Cli>Programerskom modelu je potrebno ponovljivo ponašanje unutar stvarnih repozitorijuma.\u003C\u002Fli>\u003Cli>Proizvodnom modelu su potrebni poverenje korisnika i jasne granice kontrole.\u003C\u002Fli>\u003Cli>Platformskom modelu su potrebni verziranje, monitoring i vidljivost troškova.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Lansirni model privlači pažnju. Operativni model odlučuje o tome da li će opstati u produkciji.\u003Ccite class=\"block mt-2 text-sm\">— Inženjerska provera realnosti\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Ch2>Kako se ovo uklapa u Google I\u002FO 2026 klaster\u003C\u002Fh2>\n\u003Cp>Ovaj članak pokriva sloj modela u klasteru. \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\">Glavno Google I\u002FO 2026 čvorište\u003C\u002Fa> objašnjava potpunu arhitektonsku promenu. \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\">Antigravity, AI Studio i Google DevTools\u003C\u002Fa> objašnjavaju kako modeli ulaze u razvojne tokove rada. \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\">Android XR i pametne naočare\u003C\u002Fa> objašnjavaju kako se ista inteligencija prenosi na nosive uređaje. \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\">Agentski proizvodi u pretrazi, Workspace-u i kupovini\u003C\u002Fa> objašnjavaju šta se dešava kada ove mogućnosti stignu do potrošačkih i produktivnih proizvoda.\u003C\u002Fp>\n\u003Ch2>Srodni članci u ovom klasteru\u003C\u002Fh2>\n\u003Cul>\u003Cli>\u003Cb>Glavno čvorište:\u003C\u002Fb> \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\">Google I\u002FO 2026: Arhitektonski zaokreti, agentski AI i provera realnosti ujedinjenog ekosistema\u003C\u002Fa>\u003C\u002Fli>\u003Cli>\u003Cb>Alati za programere:\u003C\u002Fb> \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\">Google I\u002FO 2026: Antigravity, AI Studio i Google DevTools\u003C\u002Fa>\u003C\u002Fli>\u003Cli>\u003Cb>Android, XR i površine uređaja:\u003C\u002Fb> \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\">Google I\u002FO 2026: Android XR i inteligentne naočare\u003C\u002Fa>\u003C\u002Fli>\u003Cli>\u003Cb>Agentski potrošački proizvodi:\u003C\u002Fb> \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\">Google I\u002FO 2026: Agentski proizvodi u Pretrazi, Workspace-u i Kupovini\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch2>Završna perspektiva\u003C\u002Fh2>\n\u003Cp>Gemini Omni i Gemini 3.5 definišu računarsku podelu iza Google I\u002FO 2026. Omni je sloj za kreiranje: multimodalni unos, video izlaz, konverzacijsko uređivanje i generisanje utemeljeno u stvarnom svetu. Gemini 3.5 Flash je sloj za akciju: brza napredna inteligencija za agente, kodiranje i dugoročne zadatke. Zajedno, oni objašnjavaju zašto ostatak uvodnog izlaganja izgleda tako kako izgleda. Antigravity zahteva modele akcionog nivoa. Pretrazi i Workspace-u su potrebni agenti koji mogu da rasuđuju i izvršavaju. Android XR zahteva razumevanje konteksta uživo. Uvodno izlaganje nije samo priča o modelima, ali bez ovog računarskog sloja ostatak priče ne funkcioniše.\u003C\u002Fp>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>",{"time":228,"blocks":229,"version":394},1779228337461,[230,234,237,240,244,247,256,261,264,267,270,274,277,280,283,286,289,292,295,298,306,310,313,316,319,322,325,328,336,339,342,345,348,351,354,357,365,369,372,375,378,385,388,391],{"data":231,"type":233},{"text":232},"\u003Cb>Google I\u002FO 2026\u003C\u002Fb> je vratio računarski sloj Google-ove AI strategije ponovo u centar pažnje. Glavno izlaganje bilo je prepuno agenata, programerskih alata, nosivih interfejsa i integracija proizvoda, ali svi ti slojevi zavise od dva smera razvoja modela: \u003Cb>Gemini Omni\u003C\u002Fb> i \u003Cb>Gemini 3.5\u003C\u002Fb>. Jedan gura multimodalno kreiranje i uređivanje. Drugi gura naprednu inteligenciju sa akcijom, posebno kroz Gemini 3.5 Flash.","paragraph",{"data":235,"type":233},{"text":236},"Ovaj članak je \u003Cb>krak računarskog sloja\u003C\u002Fb> unutar šireg \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\">arhitektonskog čvorišta Google I\u002FO 2026\u003C\u002Fa>. Strana sa programerskim alatima pokrivena je u članku \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\">Antigravity, AI Studio i Google DevTools\u003C\u002Fa>. Priča o uređajima i interfejsima pokrivena je u \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\">Android XR i pametne naočare\u003C\u002Fa>. Priča o proizvodima pokrivena je u \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\">Agentski proizvodi u pretrazi, Workspace-u i kupovini\u003C\u002Fa>.",{"data":238,"type":233},{"text":239},"Važno je izbeći posmatranje modela Gemini Omni i Gemini 3.5 kao jedne generičke najave „novog AI modela“. Oni rešavaju različite probleme sa proizvodima i infrastrukturom. Omni se bavi kreativnim generisanjem iz bilo kog unosa, počevši od videa. Gemini 3.5 se bavi brzom inteligencijom orijentisanom na akciju za agente, kodiranje i dugoročne tokove posla. Ta razlika je važna za programere, timove za proizvode i arhitekte platformi.",{"data":241,"type":42},{"text":242,"level":243},"Računarski sloj iza glavnog izlaganja",2,{"data":245,"type":233},{"text":246},"Google-ova sopstvena kolekcija sa I\u002FO 2026 uokviruje događaj oko dva nova modela: Gemini Omni i Gemini 3.5. Gemini Omni je opisan kao korak napred u razumevanju sveta, multimodalnosti i uređivanju, sposoban da kreira izlaz iz unosa kroz tekst, slike, audio i video, počevši od videa. Gemini 3.5 Flash je predstavljen kao prvi model u porodici Gemini 3.5, kombinujući naprednu inteligenciju sa akcijom.",{"data":248,"type":255},{"items":249,"style":254},[250,251,252,253],"Gemini Omni se fokusira na multimodalno kreiranje i uređivanje, počevši od videa.","Gemini 3.5 Flash se fokusira na brzinu na nivou akcije za agente, kodiranje i složene tokove posla.","Podela modela odražava širu podelu Google-ove platforme: kreirajte bogatije medije, a zatim delujte brže kroz proizvode i alate.","Prava strateška vrednost dolazi kada se ovi modeli povežu sa Antigravity, AI Studio, Search, Workspace, Android XR i uređajima koje pokreće Gemini.","unordered","list",{"data":257,"type":260},{"text":258,"caption":259},"Priča o modelima nije samo o većem kontekstu ili boljim demo snimcima. Reč je o specijalizaciji uloga: kreiranje sa jedne strane, akcija sa druge.","Čitanje računarskog sloja","quote",{"data":262,"type":42},{"text":263,"level":243},"Gemini Omni: Kreiranje iz bilo kog unosa",{"data":265,"type":233},{"text":266},"Gemini Omni je Google-ov najdirektniji pokušaj da spoji rasuđivanje sa kreiranjem. Zvanično pozicioniranje je jasno: može da kreira bilo šta iz bilo kog unosa, počevši od videa. Model prihvata kombinacije teksta, slike, zvuka i videa, a zatim generiše visokokvalitetni video zasnovan na realnom znanju Gemini-ja. Takođe podržava konverzacijsko uređivanje videa, što će verovatno biti važnije od jednokratnog generisanja u stvarnim kreativnim tokovima posla.",{"data":268,"type":233},{"text":269},"Ta poslednja stavka je važna. Praktična vrednost kreativne veštačke inteligencije retko se svodi samo na „generisanje videa“. Produkcijski tokovi rada zahtevaju reviziju, zamenu, tajming, kontinuitet stila i razgovor o izmenama. Ako Omni može da učini uređivanje više konverzacijskim uz očuvanje konteksta, on postaje više od običnog generatora noviteta. Postaje kreativni interfejs.",{"data":271,"type":273},{"code":272},"\u002F\u002F Gemini Omni treba posmatrati kao model kreativnog toka rada\ninput = { text: \"Prikaži lansiranje proizvoda u kišnom sajberpank gradu\", image: \"brand-reference.png\", audio: \"voiceover.wav\", video: \"rough-cut.mp4\"\n} output = GeminiOmni.generateVideo(input)\nrevision = GeminiOmni.edit(output, \"Učini osvetljenje toplijim i zadrži isti dizajn proizvoda\")","code",{"data":275,"type":233},{"text":276},"Za timove koji grade medijske alate, vizuelne prikaze proizvoda za e-trgovinu, obrazovni sadržaj, prototipove igara, tokove rada u oglašavanju ili kanale za društvene video snimke, ovo je deo I\u002FO 2026 koji je najvažniji. Interfejs se pomera sa ručnog uređivanja vremenske linije ka multimodalnom uređivanju zasnovanom na nameri. To ne uklanja kreativnu procenu, ali menja mesto gde se troši kreativni rad.",{"data":278,"type":42},{"text":279,"level":243},"Gemini 3.5 Flash: Napredna inteligencija sa delovanjem",{"data":281,"type":233},{"text":282},"Gemini 3.5 Flash je druga polovina priče o računarstvu. Google opisuje porodicu Gemini 3.5 kao spoj napredne inteligencije i delovanja, i pozicionira 3.5 Flash kao prvo izdanje u toj porodici. Naglasak je na agentima, kodiranju, složenim dugoročnim zadacima i praktičnoj primeni u stvarnom svetu. Taj rečnik nije slučajan. On direktno ukazuje na sisteme koji rade stvari, a ne samo na sisteme koji odgovaraju.",{"data":284,"type":233},{"text":285},"Za programere, „Flash“ je važan jer su agentski tokovi rada osetljivi na kašnjenje. Model koji je pametan ali spor može da posluži za istraživanje ili pažljivo pisanje. Mnogo ga je teže uklopiti u automatizaciju u terminalu, IDE petlje, višekoračno korišćenje alata, pozadinske agente i korisnička iskustva proizvoda koja zahtevaju trenutnu povratnu informaciju. Google očigledno pokušava da brzo napredno ponašanje učini podrazumevanim motorom za agentske tokove rada.",{"data":287,"type":273},{"code":288},"\u002F\u002F Klasično korišćenje modela\nanswer = model.generate(\"Objasni ovu funkciju\") \u002F\u002F Agentsko korišćenje modela\nplan = model.plan(task)\ntoolResult = tools.run(plan.nextStep)\nverification = model.verify(toolResult) if verification.failed: rollback()\nelse: continueExecution()",{"data":290,"type":233},{"text":291},"Ta promena je razlog zašto Gemini 3.5 pripada istoj grupi kao i Antigravity. Antigravity-ju je potreban model koji može da planira, izvršava, verifikuje i nastavi da se kreće bez pretvaranja razvojnog okruženja u čekaonicu. Ovde računarske performanse postaju korisničko iskustvo programera.",{"data":293,"type":42},{"text":294,"level":243},"Zašto brzina i akcija idu zajedno",{"data":296,"type":233},{"text":297},"Google-ovo uvodno izlaganje u vezi sa brzinom nije samo marketing. Agentski sistemi umnožavaju latenciju. Jedan odgovor zahteva jedan krug komunikacije sa modelom. Radni tok sa više agenata može imati mnogo koraka: planiranje, izbor alata, uređivanje koda, izvršavanje u terminalu, verifikaciju, popravku, rezimiranje i ljudski pregled. Ako je svaki korak spor, ceo sistem postaje neupotrebljiv.",{"data":299,"type":255},{"items":300,"style":254},[301,302,303,304,305],"Brz izlaz poboljšava interaktivne petlje kodiranja.","Niska latencija čini da agenti sa više koraka deluju manje krhko.","Visoka propusnost je važna kada agenti rade na pozadinskim zadacima održavanja.","Stabilno rezonovanje je važno jer su brže pogrešne akcije i dalje pogrešne akcije.","Verifikacija ostaje od suštinskog značaja jer sama brzina ne znači i poverenje.",{"data":307,"type":260},{"text":308,"caption":309},"Koristan agent nije model koji odgovara samo jednom. To je model koji može da nastavi da deluje, a da radni tok ne deluje kao red za čekanje.","Perspektiva agentskog izvršnog okruženja",{"data":311,"type":42},{"text":312,"level":243},"Gemini Omni protiv Gemini 3.5: Različiti poslovi, isti stek",{"data":314,"type":233},{"text":315},"Najjasniji način da se razumeju najave o računarskim resursima sa I\u002FO 2026 jeste da se razdvoje poslovi. Gemini Omni je multimodalni model za kreiranje i uređivanje. Gemini 3.5 Flash je model orijentisan na akciju za agente i kodiranje. Oni su komplementarni, a ne zamenljivi. Proizvodni tim bi mogao da koristi Omni za generisanje medija, a Gemini 3.5 Flash za orkestriranje radnog toka oko tih medija.",{"data":317,"type":273},{"code":318},"\u002F\u002F Primer toka rada proizvoda\ncreativeAsset = GeminiOmni.generateVideo(brief) agentPlan = Gemini35Flash.plan({ task: \"Publish launch campaign\", asset: creativeAsset, channels: [\"YouTube Shorts\", \"landing page\", \"email\"]\n}) Gemini35Flash.execute(agentPlan, tools)",{"data":320,"type":233},{"text":321},"To je šira arhitektura na koju Google ukazuje. Kreativni modeli generišu bogatije artefakte. Akcioni modeli koordiniraju zadatke oko tih artefakata. Proizvodne površine izlažu te mogućnosti korisnicima. Razvojni alati pružaju orkestraciju i verifikaciju. Površine uređaja čine asistenta dostupnim u više konteksta.",{"data":323,"type":42},{"text":324,"level":243},"Šta ovo znači za programere",{"data":326,"type":233},{"text":327},"Programeri ne bi trebalo da posmatraju Gemini 3.5 Flash samo kao još jednu opciju modela. On menja oblik aplikacija koje se mogu izgraditi na vrhu Google-ovog steka. Ako model može da rukuje dužim sekvencama zadataka, pouzdano koristi alate i radi dovoljno brzo za stvarne tokove posla, tada se aplikacije mogu pomeriti sa izolovanih AI funkcija na procese posredovane modelom.",{"data":329,"type":255},{"items":330,"style":254},[331,332,333,334,335],"Kod-agenti mogu refaktorisati, testirati i verifikovati veće jedinice zadataka.","Proizvodni agenti mogu pomoći korisnicima da pređu sa namere na akciju unutar jednog toka.","Medijske aplikacije mogu kombinovati Omni generisanje sa Gemini 3.5 orkestracijom.","Funkcije pretrage i Workspace-a mogu preći sa odgovaranja na izvršavanje.","Mobilne i XR površine mogu koristiti Gemini kao sloj konteksta uživo umesto kao udaljenog asistenta u oblaku.",{"data":337,"type":233},{"text":338},"Opasnost leži u prekomernoj izgradnji pre nego što operativni model sazri. Programerima su i dalje potrebni verzisanje, evaluacija, logovanje, kontrola troškova, zamensko ponašanje (fallback) i ljudska potvrda za akcije visokog uticaja. Jači model ne uklanja potrebu za inženjerskom disciplinom. On povećava vrednost te discipline.",{"data":340,"type":42},{"text":341,"level":243},"Šta ovo znači za proizvodne timove",{"data":343,"type":233},{"text":344},"Proizvodni timovi bi trebalo da tumače Gemini Omni i Gemini 3.5 kao signal da Google želi da AI postane sloj izvršavanja širom ekosistema. To stvara i priliku i rizik. Prilika je brži put od namere korisnika do gotovog rezultata. Rizik je u tome što ponašanje proizvoda postaje manje transparentno ako su odluke agenata skrivene iza doteranog interfejsa.",{"data":346,"type":273},{"code":347},"agenticProductChecklist = { userIntent: \"clear\", modelRole: \"defined\", tools: \"permissioned\", outputs: \"reviewable\", failures: \"recoverable\", costs: \"measured\", userControl: \"explicit\"\n}",{"data":349,"type":233},{"text":350},"Ako proizvodni tim ne može da odgovori na to šta je modelu dozvoljeno da radi, kako se evaluira i kako korisnik može da se oporavi od loše akcije, integracija nije spremna za produkciju. Ovo posebno važi kada je Gemini povezan sa Search, Workspace, Shopping, Android platformama ili interfejsima nosivih uređaja.",{"data":352,"type":42},{"text":353,"level":243},"Suočavanje sa realnošću: Benchmarci nisu proizvod",{"data":355,"type":233},{"text":356},"Rizik kod svakog lansiranja modela je opijenost benchmark-ovima. Bolje performanse su važne, ali produkciona vrednost zavisi od kvaliteta integracije. Model može biti brži i jači, a da i dalje podbaci u specifičnom procesu rada jer su promptovi slabi, politike alata labave, evaluacija površna ili nedostaju opcije za vraćanje na prethodno stanje.",{"data":358,"type":255},{"items":359,"style":254},[360,361,362,363,364],"Kreativnom modelu su potrebni doslednost, mogućnost uređivanja i kontrola izlaza.","Akcionom modelu su potrebni disciplina u korišćenju alata, verifikacija i bezbedni režimi otkazivanja.","Programerskom modelu je potrebno ponovljivo ponašanje unutar stvarnih repozitorijuma.","Proizvodnom modelu su potrebni poverenje korisnika i jasne granice kontrole.","Platformskom modelu su potrebni verziranje, monitoring i vidljivost troškova.",{"data":366,"type":260},{"text":367,"caption":368},"Lansirni model privlači pažnju. Operativni model odlučuje o tome da li će opstati u produkciji.","Inženjerska provera realnosti",{"data":370,"type":42},{"text":371,"level":243},"Kako se ovo uklapa u Google I\u002FO 2026 klaster",{"data":373,"type":233},{"text":374},"Ovaj članak pokriva sloj modela u klasteru. \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\">Glavno Google I\u002FO 2026 čvorište\u003C\u002Fa> objašnjava potpunu arhitektonsku promenu. \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\">Antigravity, AI Studio i Google DevTools\u003C\u002Fa> objašnjavaju kako modeli ulaze u razvojne tokove rada. \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\">Android XR i pametne naočare\u003C\u002Fa> objašnjavaju kako se ista inteligencija prenosi na nosive uređaje. \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\">Agentski proizvodi u pretrazi, Workspace-u i kupovini\u003C\u002Fa> objašnjavaju šta se dešava kada ove mogućnosti stignu do potrošačkih i produktivnih proizvoda.",{"data":376,"type":42},{"text":377,"level":243},"Srodni članci u ovom klasteru",{"data":379,"type":255},{"items":380,"style":254},[381,382,383,384],"\u003Cb>Glavno čvorište:\u003C\u002Fb> \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\">Google I\u002FO 2026: Arhitektonski zaokreti, agentski AI i provera realnosti ujedinjenog ekosistema\u003C\u002Fa>","\u003Cb>Alati za programere:\u003C\u002Fb> \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\">Google I\u002FO 2026: Antigravity, AI Studio i Google DevTools\u003C\u002Fa>","\u003Cb>Android, XR i površine uređaja:\u003C\u002Fb> \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\">Google I\u002FO 2026: Android XR i inteligentne naočare\u003C\u002Fa>","\u003Cb>Agentski potrošački proizvodi:\u003C\u002Fb> \u003Ca href=\"\u002Fsr\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\">Google I\u002FO 2026: Agentski proizvodi u Pretrazi, Workspace-u i Kupovini\u003C\u002Fa>",{"data":386,"type":42},{"text":387,"level":243},"Završna perspektiva",{"data":389,"type":233},{"text":390},"Gemini Omni i Gemini 3.5 definišu računarsku podelu iza Google I\u002FO 2026. Omni je sloj za kreiranje: multimodalni unos, video izlaz, konverzacijsko uređivanje i generisanje utemeljeno u stvarnom svetu. Gemini 3.5 Flash je sloj za akciju: brza napredna inteligencija za agente, kodiranje i dugoročne zadatke. Zajedno, oni objašnjavaju zašto ostatak uvodnog izlaganja izgleda tako kako izgleda. Antigravity zahteva modele akcionog nivoa. Pretrazi i Workspace-u su potrebni agenti koji mogu da rasuđuju i izvršavaju. Android XR zahteva razumevanje konteksta uživo. Uvodno izlaganje nije samo priča o modelima, ali bez ovog računarskog sloja ostatak priče ne funkcioniše.",{"data":392,"type":393},{},"delimiter","2.31","Google I\u002FO 2026 je postavio Gemini Omni i Gemini 3.5 u središte Google-ove strategije za agentsku veštačku inteligenciju. Ovaj članak analizira razliku između multimodalnog kreiranja i inteligencije na nivou delovanja, zašto je Gemini 3.5 Flash važan za agente i kodiranje, i kako ovi modeli pokreću širu promenu platforme Google I\u002FO 2026.","\u002Fuploads\u002F2026\u002F05\u002Fgoogle-io-2026-gemini-omni-and-gemini-3-5-1779227791401-6hzln1.webp","google-io-2026-gemini-omni-and-gemini-3-5-1779227791401-6hzln1","PUBLISHED","2026-05-21T11:01:00.000Z","2026-05-19T21:24:25.091Z","2026-05-21T11:07:38.647Z",{"en":403,"de":404,"sr":405,"es":406,"fr":407,"it":408,"ru":409,"zh":410},"\u002Fblog\u002Fgoogle-io-2026-gemini-omni-and-gemini-3-5","\u002Fde\u002Fblog\u002Fgoogle-io-2026-gemini-omni-and-gemini-3-5","\u002Fsr\u002Fblog\u002Fgoogle-io-2026-gemini-omni-and-gemini-3-5","\u002Fes\u002Fblog\u002Fgoogle-io-2026-gemini-omni-and-gemini-3-5","\u002Ffr\u002Fblog\u002Fgoogle-io-2026-gemini-omni-and-gemini-3-5","\u002Fit\u002Fblog\u002Fgoogle-io-2026-gemini-omni-and-gemini-3-5","\u002Fru\u002Fblog\u002Fgoogle-io-2026-gemini-omni-and-gemini-3-5","\u002Fzh\u002Fblog\u002Fgoogle-io-2026-gemini-omni-and-gemini-3-5",[],{"id":413,"login":414,"email":415,"displayName":416},"20","rooth8233","","Aleksandar Stajić",[418,583],{"lang":419,"title":420,"content":421,"contentJson":422,"excerpt":582},"en","Google I\u002FO 2026: Gemini Omni, Gemini 3.5, and the Compute Layer Behind Agentic AI","{\"time\":1774828800000,\"blocks\":[{\"data\":{\"text\":\"\u003Cb>Google I\u002FO 2026\u003C\u002Fb> put the compute layer of Google’s AI strategy back in the center. The keynote was full of agents, developer tools, wearable interfaces, and product integrations, but all of those layers depend on two model directions: \u003Cb>Gemini Omni\u003C\u002Fb> and \u003Cb>Gemini 3.5\u003C\u002Fb>. One pushes multimodal creation and editing. The other pushes frontier intelligence with action, especially through Gemini 3.5 Flash.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"This article is the \u003Cb>compute-layer spoke\u003C\u002Fb> inside the larger \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\\\">Google I\u002FO 2026 architectural hub\u003C\u002Fa>. The developer-tooling side is covered in \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\\\">Antigravity, AI Studio, and Google DevTools\u003C\u002Fa>. The device-surface story is covered in \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\\\">Android XR and Intelligent Eyewear\u003C\u002Fa>. The product-surface story is covered in \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\\\">Agentic Products Across Search, Workspace, and Shopping\u003C\u002Fa>.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"The important thing is to avoid reading Gemini Omni and Gemini 3.5 as one generic “new AI model” announcement. They solve different product and infrastructure problems. Omni is about any-input creative generation, starting with video. Gemini 3.5 is about fast, action-oriented intelligence for agents, coding, and long-horizon workflows. That separation matters for developers, product teams, and platform architects.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"The Compute Layer Behind the Keynote\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"Google’s own I\u002FO 2026 collection frames the event around two new models: Gemini Omni and Gemini 3.5. Gemini Omni is described as a leap forward in world understanding, multimodality, and editing, able to create output from input across text, images, audio, and video, starting with video. Gemini 3.5 Flash is introduced as the first model in the Gemini 3.5 family, combining frontier intelligence with action.\"},\"type\":\"paragraph\"},{\"data\":{\"items\":[\"Gemini Omni focuses on multimodal creation and editing, starting with video.\",\"Gemini 3.5 Flash focuses on action-grade speed for agents, coding, and complex workflows.\",\"The model split mirrors Google’s broader platform split: create richer media, then act faster across products and tools.\",\"The real strategic value comes when these models are connected to Antigravity, AI Studio, Search, Workspace, Android XR, and Gemini-powered devices.\"],\"style\":\"unordered\"},\"type\":\"list\"},{\"data\":{\"text\":\"The model story is not just bigger context or better demos. It is role specialization: creation on one side, action on the other.\",\"caption\":\"Compute-layer reading\"},\"type\":\"quote\"},{\"data\":{\"text\":\"Gemini Omni: Creation From Any Input\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"Gemini Omni is Google’s most direct attempt to fuse reasoning with creation. The official positioning is clear: it can create anything from any input, starting with video. The model accepts combinations of text, image, audio, and video, then generates high-quality video grounded in Gemini’s real-world knowledge. It also supports conversational video editing, which is likely to matter more than one-shot generation in actual creative workflows.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"That last point is important. The practical value of creative AI is rarely only “generate a video.” Production workflows require revision, replacement, timing, style continuity, and conversation around changes. If Omni can make editing more conversational while preserving context, it becomes more than a novelty generator. It becomes a creative interface.\"},\"type\":\"paragraph\"},{\"data\":{\"code\":\"\u002F\u002F Gemini Omni should be read as a creative workflow model\\ninput = { text: \\\"Show a product launch in a rainy cyberpunk city\\\", image: \\\"brand-reference.png\\\", audio: \\\"voiceover.wav\\\", video: \\\"rough-cut.mp4\\\"\\n} output = GeminiOmni.generateVideo(input)\\nrevision = GeminiOmni.edit(output, \\\"Make the lighting warmer and keep the same product design\\\")\"},\"type\":\"code\"},{\"data\":{\"text\":\"For teams building media tools, ecommerce product visuals, education content, game prototyping, advertising workflows, or social video pipelines, this is the part of I\u002FO 2026 that matters most. The interface moves from manual timeline editing toward intent-based multimodal editing. That does not remove creative judgment, but it changes where creative labor is spent.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Gemini 3.5 Flash: Frontier Intelligence With Action\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"Gemini 3.5 Flash is the other half of the compute story. Google describes the Gemini 3.5 family as combining frontier intelligence with action, and positions 3.5 Flash as the first release in that family. The emphasis is on agents, coding, complex long-horizon tasks, and real-world utility. That language is not accidental. It points directly toward systems that do things, not just systems that answer.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"For developers, “Flash” matters because agentic workflows are latency-sensitive. A model that is smart but slow can work for research or careful writing. It is much harder to fit into terminal-native automation, IDE loops, multi-step tool usage, background agents, and product experiences that need immediate feedback. Google is clearly trying to make fast frontier behavior the default engine for agentic workflows.\"},\"type\":\"paragraph\"},{\"data\":{\"code\":\"\u002F\u002F Classic model usage\\nanswer = model.generate(\\\"Explain this function\\\") \u002F\u002F Agentic model usage\\nplan = model.plan(task)\\ntoolResult = tools.run(plan.nextStep)\\nverification = model.verify(toolResult) if verification.failed: rollback()\\nelse: continueExecution()\"},\"type\":\"code\"},{\"data\":{\"text\":\"That shift is why Gemini 3.5 belongs in the same cluster as Antigravity. Antigravity needs a model that can plan, execute, verify, and keep moving without turning the development environment into a waiting room. This is where compute performance becomes developer experience.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Why Speed and Action Belong Together\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"Google’s keynote framing around speed is not just marketing. Agentic systems multiply latency. A single answer has one model round trip. A multi-agent workflow can have many: planning, tool selection, code editing, terminal execution, verification, repair, summarization, and human review. If each step is slow, the whole system becomes unusable.\"},\"type\":\"paragraph\"},{\"data\":{\"items\":[\"Fast output improves interactive coding loops.\",\"Low latency makes multi-step agents feel less brittle.\",\"High throughput matters when agents run in background maintenance tasks.\",\"Stable reasoning matters because faster wrong actions are still wrong actions.\",\"Verification remains essential because speed alone does not equal trust.\"],\"style\":\"unordered\"},\"type\":\"list\"},{\"data\":{\"text\":\"The useful agent is not the model that answers once. It is the model that can keep acting without making the workflow feel like a queue.\",\"caption\":\"Agentic runtime perspective\"},\"type\":\"quote\"},{\"data\":{\"text\":\"Gemini Omni vs Gemini 3.5: Different Jobs, Same Stack\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"The cleanest way to understand the I\u002FO 2026 compute announcements is to separate the jobs. Gemini Omni is a multimodal creation and editing model. Gemini 3.5 Flash is an action-oriented model for agents and coding. They are complementary, not interchangeable. A product team might use Omni to generate media and use Gemini 3.5 Flash to orchestrate the workflow around that media.\"},\"type\":\"paragraph\"},{\"data\":{\"code\":\"\u002F\u002F Product workflow example\\ncreativeAsset = GeminiOmni.generateVideo(brief) agentPlan = Gemini35Flash.plan({ task: \\\"Publish launch campaign\\\", asset: creativeAsset, channels: [\\\"YouTube Shorts\\\", \\\"landing page\\\", \\\"email\\\"]\\n}) Gemini35Flash.execute(agentPlan, tools)\"},\"type\":\"code\"},{\"data\":{\"text\":\"That is the broader architecture Google is pointing toward. Creative models generate richer artifacts. Action models coordinate tasks around those artifacts. Product surfaces expose those capabilities to users. Developer tools provide orchestration and verification. Device surfaces make the assistant available in more contexts.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"What This Means for Developers\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"Developers should not treat Gemini 3.5 Flash as only another model option. It changes the shape of applications that can be built on top of Google’s stack. If a model can handle longer task sequences, use tools reliably, and operate fast enough for real workflows, then applications can shift from isolated AI features to model-mediated processes.\"},\"type\":\"paragraph\"},{\"data\":{\"items\":[\"Code agents can refactor, test, and verify larger task units.\",\"Product agents can help users move from intent to action inside one flow.\",\"Media applications can combine Omni generation with Gemini 3.5 orchestration.\",\"Search and Workspace features can move from answering toward executing.\",\"Mobile and XR surfaces can use Gemini as a live context layer instead of a distant cloud assistant.\"],\"style\":\"unordered\"},\"type\":\"list\"},{\"data\":{\"text\":\"The danger is overbuilding before the operational model is mature. Developers still need versioning, evaluation, logging, cost controls, fallback behavior, and human confirmation for high-impact actions. A stronger model does not remove the need for engineering discipline. It increases the value of that discipline.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"What This Means for Product Teams\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"Product teams should read Gemini Omni and Gemini 3.5 as a signal that Google wants AI to become an execution layer across the ecosystem. That creates opportunity and risk. The opportunity is a faster path from user intent to finished output. The risk is that product behavior becomes less transparent if agent decisions are hidden behind a polished interface.\"},\"type\":\"paragraph\"},{\"data\":{\"code\":\"agenticProductChecklist = { userIntent: \\\"clear\\\", modelRole: \\\"defined\\\", tools: \\\"permissioned\\\", outputs: \\\"reviewable\\\", failures: \\\"recoverable\\\", costs: \\\"measured\\\", userControl: \\\"explicit\\\"\\n}\"},\"type\":\"code\"},{\"data\":{\"text\":\"If a product team cannot answer what the model is allowed to do, how it is evaluated, and how the user can recover from a bad action, the integration is not ready for production. This is especially true when Gemini is connected to Search, Workspace, Shopping, Android, or wearable interfaces.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"The Reality Check: Benchmarks Are Not the Product\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"The risk with every model launch is benchmark intoxication. Better performance matters, but production value depends on integration quality. A model can be faster and stronger while still failing a specific workflow because the prompts are weak, tool policies are loose, evaluation is shallow, or rollback paths are missing.\"},\"type\":\"paragraph\"},{\"data\":{\"items\":[\"A creative model needs consistency, editability, and output control.\",\"An action model needs tool discipline, verification, and safe failure modes.\",\"A developer model needs repeatable behavior inside real repositories.\",\"A product model needs user trust and clear control boundaries.\",\"A platform model needs versioning, monitoring, and cost observability.\"],\"style\":\"unordered\"},\"type\":\"list\"},{\"data\":{\"text\":\"The launch model gets attention. The operating model decides whether it survives production.\",\"caption\":\"Engineering reality check\"},\"type\":\"quote\"},{\"data\":{\"text\":\"How This Fits the Google I\u002FO 2026 Cluster\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"This article covers the model layer of the cluster. The \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\\\">main Google I\u002FO 2026 hub\u003C\u002Fa> explains the full architectural shift. \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\\\">Antigravity, AI Studio, and Google DevTools\u003C\u002Fa> explains how the models enter developer workflows. \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\\\">Android XR and Intelligent Eyewear\u003C\u002Fa> explains how the same intelligence moves into wearable surfaces. \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\\\">Agentic Products Across Search, Workspace, and Shopping\u003C\u002Fa> explains what happens when these capabilities reach consumer and productivity products.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Related Articles in This Cluster\",\"level\":2},\"type\":\"header\"},{\"data\":{\"items\":[\"\u003Cb>Main Hub:\u003C\u002Fb> \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\\\">Google I\u002FO 2026: Architectural Pivots, Agentic AI, and the Unified Ecosystem Reality Check\u003C\u002Fa>\",\"\u003Cb>Developer Tooling:\u003C\u002Fb> \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\\\">Google I\u002FO 2026: Antigravity, AI Studio, and Google DevTools\u003C\u002Fa>\",\"\u003Cb>Android, XR, and Device Surfaces:\u003C\u002Fb> \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\\\">Google I\u002FO 2026: Android XR and Intelligent Eyewear\u003C\u002Fa>\",\"\u003Cb>Agentic Consumer Products:\u003C\u002Fb> \u003Ca href=\\\"\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\\\">Google I\u002FO 2026: Agentic Products Across Search, Workspace, and Shopping\u003C\u002Fa>\"],\"style\":\"unordered\"},\"type\":\"list\"},{\"data\":{\"text\":\"Final Perspective\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"Gemini Omni and Gemini 3.5 define the compute split behind Google I\u002FO 2026. Omni is the creation layer: multimodal input, video output, conversational editing, and world-grounded generation. Gemini 3.5 Flash is the action layer: fast frontier intelligence for agents, coding, and long-horizon tasks. Together, they explain why the rest of the keynote looks the way it does. Antigravity needs action-grade models. Search and Workspace need agents that can reason and execute. Android XR needs live context understanding. The keynote is not just a model story, but without this compute layer the rest of the story does not work.\"},\"type\":\"paragraph\"},{\"data\":{},\"type\":\"delimiter\"}],\"version\":\"2.30.8\"}",{"time":423,"blocks":424,"version":581},1774828800000,[425,428,431,434,437,440,447,451,454,457,460,463,466,469,472,475,478,481,484,487,495,499,502,505,508,511,514,517,525,528,531,534,536,539,542,545,553,557,560,563,566,573,576,579],{"data":426,"type":233},{"text":427},"\u003Cb>Google I\u002FO 2026\u003C\u002Fb> put the compute layer of Google’s AI strategy back in the center. The keynote was full of agents, developer tools, wearable interfaces, and product integrations, but all of those layers depend on two model directions: \u003Cb>Gemini Omni\u003C\u002Fb> and \u003Cb>Gemini 3.5\u003C\u002Fb>. One pushes multimodal creation and editing. The other pushes frontier intelligence with action, especially through Gemini 3.5 Flash.",{"data":429,"type":233},{"text":430},"This article is the \u003Cb>compute-layer spoke\u003C\u002Fb> inside the larger \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\">Google I\u002FO 2026 architectural hub\u003C\u002Fa>. The developer-tooling side is covered in \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\">Antigravity, AI Studio, and Google DevTools\u003C\u002Fa>. The device-surface story is covered in \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\">Android XR and Intelligent Eyewear\u003C\u002Fa>. The product-surface story is covered in \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\">Agentic Products Across Search, Workspace, and Shopping\u003C\u002Fa>.",{"data":432,"type":233},{"text":433},"The important thing is to avoid reading Gemini Omni and Gemini 3.5 as one generic “new AI model” announcement. They solve different product and infrastructure problems. Omni is about any-input creative generation, starting with video. Gemini 3.5 is about fast, action-oriented intelligence for agents, coding, and long-horizon workflows. That separation matters for developers, product teams, and platform architects.",{"data":435,"type":42},{"text":436,"level":243},"The Compute Layer Behind the Keynote",{"data":438,"type":233},{"text":439},"Google’s own I\u002FO 2026 collection frames the event around two new models: Gemini Omni and Gemini 3.5. Gemini Omni is described as a leap forward in world understanding, multimodality, and editing, able to create output from input across text, images, audio, and video, starting with video. Gemini 3.5 Flash is introduced as the first model in the Gemini 3.5 family, combining frontier intelligence with action.",{"data":441,"type":255},{"items":442,"style":254},[443,444,445,446],"Gemini Omni focuses on multimodal creation and editing, starting with video.","Gemini 3.5 Flash focuses on action-grade speed for agents, coding, and complex workflows.","The model split mirrors Google’s broader platform split: create richer media, then act faster across products and tools.","The real strategic value comes when these models are connected to Antigravity, AI Studio, Search, Workspace, Android XR, and Gemini-powered devices.",{"data":448,"type":260},{"text":449,"caption":450},"The model story is not just bigger context or better demos. It is role specialization: creation on one side, action on the other.","Compute-layer reading",{"data":452,"type":42},{"text":453,"level":243},"Gemini Omni: Creation From Any Input",{"data":455,"type":233},{"text":456},"Gemini Omni is Google’s most direct attempt to fuse reasoning with creation. The official positioning is clear: it can create anything from any input, starting with video. The model accepts combinations of text, image, audio, and video, then generates high-quality video grounded in Gemini’s real-world knowledge. It also supports conversational video editing, which is likely to matter more than one-shot generation in actual creative workflows.",{"data":458,"type":233},{"text":459},"That last point is important. The practical value of creative AI is rarely only “generate a video.” Production workflows require revision, replacement, timing, style continuity, and conversation around changes. If Omni can make editing more conversational while preserving context, it becomes more than a novelty generator. It becomes a creative interface.",{"data":461,"type":273},{"code":462},"\u002F\u002F Gemini Omni should be read as a creative workflow model\ninput = { text: \"Show a product launch in a rainy cyberpunk city\", image: \"brand-reference.png\", audio: \"voiceover.wav\", video: \"rough-cut.mp4\"\n} output = GeminiOmni.generateVideo(input)\nrevision = GeminiOmni.edit(output, \"Make the lighting warmer and keep the same product design\")",{"data":464,"type":233},{"text":465},"For teams building media tools, ecommerce product visuals, education content, game prototyping, advertising workflows, or social video pipelines, this is the part of I\u002FO 2026 that matters most. The interface moves from manual timeline editing toward intent-based multimodal editing. That does not remove creative judgment, but it changes where creative labor is spent.",{"data":467,"type":42},{"text":468,"level":243},"Gemini 3.5 Flash: Frontier Intelligence With Action",{"data":470,"type":233},{"text":471},"Gemini 3.5 Flash is the other half of the compute story. Google describes the Gemini 3.5 family as combining frontier intelligence with action, and positions 3.5 Flash as the first release in that family. The emphasis is on agents, coding, complex long-horizon tasks, and real-world utility. That language is not accidental. It points directly toward systems that do things, not just systems that answer.",{"data":473,"type":233},{"text":474},"For developers, “Flash” matters because agentic workflows are latency-sensitive. A model that is smart but slow can work for research or careful writing. It is much harder to fit into terminal-native automation, IDE loops, multi-step tool usage, background agents, and product experiences that need immediate feedback. Google is clearly trying to make fast frontier behavior the default engine for agentic workflows.",{"data":476,"type":273},{"code":477},"\u002F\u002F Classic model usage\nanswer = model.generate(\"Explain this function\") \u002F\u002F Agentic model usage\nplan = model.plan(task)\ntoolResult = tools.run(plan.nextStep)\nverification = model.verify(toolResult) if verification.failed: rollback()\nelse: continueExecution()",{"data":479,"type":233},{"text":480},"That shift is why Gemini 3.5 belongs in the same cluster as Antigravity. Antigravity needs a model that can plan, execute, verify, and keep moving without turning the development environment into a waiting room. This is where compute performance becomes developer experience.",{"data":482,"type":42},{"text":483,"level":243},"Why Speed and Action Belong Together",{"data":485,"type":233},{"text":486},"Google’s keynote framing around speed is not just marketing. Agentic systems multiply latency. A single answer has one model round trip. A multi-agent workflow can have many: planning, tool selection, code editing, terminal execution, verification, repair, summarization, and human review. If each step is slow, the whole system becomes unusable.",{"data":488,"type":255},{"items":489,"style":254},[490,491,492,493,494],"Fast output improves interactive coding loops.","Low latency makes multi-step agents feel less brittle.","High throughput matters when agents run in background maintenance tasks.","Stable reasoning matters because faster wrong actions are still wrong actions.","Verification remains essential because speed alone does not equal trust.",{"data":496,"type":260},{"text":497,"caption":498},"The useful agent is not the model that answers once. It is the model that can keep acting without making the workflow feel like a queue.","Agentic runtime perspective",{"data":500,"type":42},{"text":501,"level":243},"Gemini Omni vs Gemini 3.5: Different Jobs, Same Stack",{"data":503,"type":233},{"text":504},"The cleanest way to understand the I\u002FO 2026 compute announcements is to separate the jobs. Gemini Omni is a multimodal creation and editing model. Gemini 3.5 Flash is an action-oriented model for agents and coding. They are complementary, not interchangeable. A product team might use Omni to generate media and use Gemini 3.5 Flash to orchestrate the workflow around that media.",{"data":506,"type":273},{"code":507},"\u002F\u002F Product workflow example\ncreativeAsset = GeminiOmni.generateVideo(brief) agentPlan = Gemini35Flash.plan({ task: \"Publish launch campaign\", asset: creativeAsset, channels: [\"YouTube Shorts\", \"landing page\", \"email\"]\n}) Gemini35Flash.execute(agentPlan, tools)",{"data":509,"type":233},{"text":510},"That is the broader architecture Google is pointing toward. Creative models generate richer artifacts. Action models coordinate tasks around those artifacts. Product surfaces expose those capabilities to users. Developer tools provide orchestration and verification. Device surfaces make the assistant available in more contexts.",{"data":512,"type":42},{"text":513,"level":243},"What This Means for Developers",{"data":515,"type":233},{"text":516},"Developers should not treat Gemini 3.5 Flash as only another model option. It changes the shape of applications that can be built on top of Google’s stack. If a model can handle longer task sequences, use tools reliably, and operate fast enough for real workflows, then applications can shift from isolated AI features to model-mediated processes.",{"data":518,"type":255},{"items":519,"style":254},[520,521,522,523,524],"Code agents can refactor, test, and verify larger task units.","Product agents can help users move from intent to action inside one flow.","Media applications can combine Omni generation with Gemini 3.5 orchestration.","Search and Workspace features can move from answering toward executing.","Mobile and XR surfaces can use Gemini as a live context layer instead of a distant cloud assistant.",{"data":526,"type":233},{"text":527},"The danger is overbuilding before the operational model is mature. Developers still need versioning, evaluation, logging, cost controls, fallback behavior, and human confirmation for high-impact actions. A stronger model does not remove the need for engineering discipline. It increases the value of that discipline.",{"data":529,"type":42},{"text":530,"level":243},"What This Means for Product Teams",{"data":532,"type":233},{"text":533},"Product teams should read Gemini Omni and Gemini 3.5 as a signal that Google wants AI to become an execution layer across the ecosystem. That creates opportunity and risk. The opportunity is a faster path from user intent to finished output. The risk is that product behavior becomes less transparent if agent decisions are hidden behind a polished interface.",{"data":535,"type":273},{"code":347},{"data":537,"type":233},{"text":538},"If a product team cannot answer what the model is allowed to do, how it is evaluated, and how the user can recover from a bad action, the integration is not ready for production. This is especially true when Gemini is connected to Search, Workspace, Shopping, Android, or wearable interfaces.",{"data":540,"type":42},{"text":541,"level":243},"The Reality Check: Benchmarks Are Not the Product",{"data":543,"type":233},{"text":544},"The risk with every model launch is benchmark intoxication. Better performance matters, but production value depends on integration quality. A model can be faster and stronger while still failing a specific workflow because the prompts are weak, tool policies are loose, evaluation is shallow, or rollback paths are missing.",{"data":546,"type":255},{"items":547,"style":254},[548,549,550,551,552],"A creative model needs consistency, editability, and output control.","An action model needs tool discipline, verification, and safe failure modes.","A developer model needs repeatable behavior inside real repositories.","A product model needs user trust and clear control boundaries.","A platform model needs versioning, monitoring, and cost observability.",{"data":554,"type":260},{"text":555,"caption":556},"The launch model gets attention. The operating model decides whether it survives production.","Engineering reality check",{"data":558,"type":42},{"text":559,"level":243},"How This Fits the Google I\u002FO 2026 Cluster",{"data":561,"type":233},{"text":562},"This article covers the model layer of the cluster. The \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\">main Google I\u002FO 2026 hub\u003C\u002Fa> explains the full architectural shift. \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\">Antigravity, AI Studio, and Google DevTools\u003C\u002Fa> explains how the models enter developer workflows. \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\">Android XR and Intelligent Eyewear\u003C\u002Fa> explains how the same intelligence moves into wearable surfaces. \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\">Agentic Products Across Search, Workspace, and Shopping\u003C\u002Fa> explains what happens when these capabilities reach consumer and productivity products.",{"data":564,"type":42},{"text":565,"level":243},"Related Articles in This Cluster",{"data":567,"type":255},{"items":568,"style":254},[569,570,571,572],"\u003Cb>Main Hub:\u003C\u002Fb> \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-architectural-pivots-agentic-ai-and-the-unified-ecosystem-reality-check\">Google I\u002FO 2026: Architectural Pivots, Agentic AI, and the Unified Ecosystem Reality Check\u003C\u002Fa>","\u003Cb>Developer Tooling:\u003C\u002Fb> \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-antigravity-ai-studio-and-google-devtools\">Google I\u002FO 2026: Antigravity, AI Studio, and Google DevTools\u003C\u002Fa>","\u003Cb>Android, XR, and Device Surfaces:\u003C\u002Fb> \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear\">Google I\u002FO 2026: Android XR and Intelligent Eyewear\u003C\u002Fa>","\u003Cb>Agentic Consumer Products:\u003C\u002Fb> \u003Ca href=\"\u002Fblog\u002Fgoogle-io-2026-agentic-products-search-workspace-and-shopping\">Google I\u002FO 2026: Agentic Products Across Search, Workspace, and Shopping\u003C\u002Fa>",{"data":574,"type":42},{"text":575,"level":243},"Final Perspective",{"data":577,"type":233},{"text":578},"Gemini Omni and Gemini 3.5 define the compute split behind Google I\u002FO 2026. Omni is the creation layer: multimodal input, video output, conversational editing, and world-grounded generation. Gemini 3.5 Flash is the action layer: fast frontier intelligence for agents, coding, and long-horizon tasks. Together, they explain why the rest of the keynote looks the way it does. Antigravity needs action-grade models. Search and Workspace need agents that can reason and execute. Android XR needs live context understanding. The keynote is not just a model story, but without this compute layer the rest of the story does not work.",{"data":580,"type":393},{},"2.30.8","Google I\u002FO 2026 put Gemini Omni and Gemini 3.5 at the center of Google’s agentic AI strategy. This article breaks down the difference between multimodal creation and action-grade intelligence, why Gemini 3.5 Flash matters for agents and coding, and how these models power the wider Google I\u002FO 2026 platform shift.",{"lang":7,"title":224,"content":226,"contentJson":584,"excerpt":395},{"time":228,"blocks":585,"version":394},[586,588,590,592,594,596,599,601,603,605,607,609,611,613,615,617,619,621,623,625,628,630,632,634,636,638,640,642,645,647,649,651,653,655,657,659,662,664,666,668,670,673,675,677],{"data":587,"type":233},{"text":232},{"data":589,"type":233},{"text":236},{"data":591,"type":233},{"text":239},{"data":593,"type":42},{"text":242,"level":243},{"data":595,"type":233},{"text":246},{"data":597,"type":255},{"items":598,"style":254},[250,251,252,253],{"data":600,"type":260},{"text":258,"caption":259},{"data":602,"type":42},{"text":263,"level":243},{"data":604,"type":233},{"text":266},{"data":606,"type":233},{"text":269},{"data":608,"type":273},{"code":272},{"data":610,"type":233},{"text":276},{"data":612,"type":42},{"text":279,"level":243},{"data":614,"type":233},{"text":282},{"data":616,"type":233},{"text":285},{"data":618,"type":273},{"code":288},{"data":620,"type":233},{"text":291},{"data":622,"type":42},{"text":294,"level":243},{"data":624,"type":233},{"text":297},{"data":626,"type":255},{"items":627,"style":254},[301,302,303,304,305],{"data":629,"type":260},{"text":308,"caption":309},{"data":631,"type":42},{"text":312,"level":243},{"data":633,"type":233},{"text":315},{"data":635,"type":273},{"code":318},{"data":637,"type":233},{"text":321},{"data":639,"type":42},{"text":324,"level":243},{"data":641,"type":233},{"text":327},{"data":643,"type":255},{"items":644,"style":254},[331,332,333,334,335],{"data":646,"type":233},{"text":338},{"data":648,"type":42},{"text":341,"level":243},{"data":650,"type":233},{"text":344},{"data":652,"type":273},{"code":347},{"data":654,"type":233},{"text":350},{"data":656,"type":42},{"text":353,"level":243},{"data":658,"type":233},{"text":356},{"data":660,"type":255},{"items":661,"style":254},[360,361,362,363,364],{"data":663,"type":260},{"text":367,"caption":368},{"data":665,"type":42},{"text":371,"level":243},{"data":667,"type":233},{"text":374},{"data":669,"type":42},{"text":377,"level":243},{"data":671,"type":255},{"items":672,"style":254},[381,382,383,384],{"data":674,"type":42},{"text":387,"level":243},{"data":676,"type":233},{"text":390},{"data":678,"type":393},{},"Post erfolgreich abgerufen",{"items":681,"source":766,"manualIds":767,"manualMatchedIds":768},[682,689,696,703,710,717,724,731,738,745,752,759],{"id":683,"slug":684,"title":685,"excerpt":686,"featuredImage":687,"publishedAt":688},"468","ai-agent-memory-is-not-rag-how-to-separate-memory-retrieval-state-and-context","Memorija AI agenta nije RAG: Kako razdvojiti memoriju, pronalaženje, stanje i kontekst","Memorija agenta, RAG, stanje i kontekst često se koriste kao da su međusobno zamenjivi. Oni to nisu. Ovaj praktični arhitektonski model razdvaja ova četiri sloja, pokazuje gde svaki pripada i objašnjava šta se kvari kada ih sistemi stope u jedno.","\u002Fuploads\u002F2026\u002F09\u002Fai-agent-memory-is-not-rag-how-to-separate-memory-retrieval-state-and-context-1790350560308-np0xy6.webp","2026-09-25T11:34:00.000Z",{"id":690,"slug":691,"title":692,"excerpt":693,"featuredImage":694,"publishedAt":695},"381","enterprise-grade-multi-tenant-architecture-for-an-international-platform","Višezakupna arhitektura korporativnog nivoa za međunarodnu platformu","Loving Rocks je platforma za venčanja poslovne klase, dizajnirana sa istinskom više-zakupnom arhitekturom, izolovanim bazama podataka po zakupcu i ugrađenom internacionalizacijom za globalnu skalabilnost, bezbednost i dugoročnu operativnu stabilnost.","\u002Fuploads\u002F2026\u002F01\u002Fenterprise-grade-multi-tenant-architecture-for-an-international-platform-1769789121298-b6v7ak.webp","2026-01-30T12:04:00.000Z",{"id":697,"slug":698,"title":699,"excerpt":700,"featuredImage":701,"publishedAt":702},"494","air-gapped-ai-how-ai-systems-work-without-internet-or-cloud-access","Air-Gapped AI: Kako AI sistemi funkcionišu bez interneta ili pristupa oblaku","Air-gapped AI pokreće modele, RAG i AI aplikacije unutar izolovanog bezbednosnog domena bez internet ili cloud zavisnosti. Saznajte kako modeli, podaci, ažuriranja i alati funkcionišu offline.","\u002Fuploads\u002F2026\u002F10\u002Fair-gapped-ai-how-ai-systems-work-without-internet-or-cloud-access-1791487983978-e6xqf0.webp","2026-10-08T11:32:00.000Z",{"id":704,"slug":705,"title":706,"excerpt":707,"featuredImage":708,"publishedAt":709},"466","the-gpu-is-not-the-product-future-proof-private-ai-architecture","GPU nije proizvod: Privatna AI arhitektura spremna za budućnost","Privatna AI infrastruktura ne bi trebalo da bude projektovana oko jednog GPU-a ili jednog modela. Otporniji pristup kombinuje brze GPU-ove za inferenciju, memorijski bogate AI sisteme, čvorove za fizički AI i opcione vodeće modele u oblaku iza sloja za rutiranje koji prepoznaje mogućnosti.","\u002Fuploads\u002F2026\u002F09\u002Fthe-gpu-is-not-the-product-future-proof-private-ai-architecture-1790140878812-8hsl39.webp","2026-09-23T01:19:00.000Z",{"id":711,"slug":712,"title":713,"excerpt":714,"featuredImage":715,"publishedAt":716},"449","google-io-2026-agentic-products-search-workspace-and-shopping","Google I\u002FO 2026: Agentski proizvodi širom Pretrage, Workspace-a i Kupovine","Google I\u002FO 2026 je pokazao da agentska veštačka inteligencija prevazilazi demonstracije modela i razvojne alate i ulazi u svakodnevne površine proizvoda. 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Otkrijte ključne indikatore, metode izračunavanja i najbolje prakse za usklađivanje metrika sa poslovnim ishodima.","\u002Fuploads\u002F2026\u002F06\u002Fmetrics-1781624416316-4nsuwg.webp","2026-03-01T17:51:00.000Z",{"id":732,"slug":733,"title":734,"excerpt":735,"featuredImage":736,"publishedAt":737},"455","zbt-z8102ax-dual-sim-failover-test","ZBT Z8102AX Dual-SIM failover: Šta radi, šta nedostaje i šta zahteva bolji firmver","ZBT Z8102AX je dual-SIM 5G OpenWrt ruter, ali sam dual-SIM hardver nije isto što i inteligentni failover. Ruter prepoznaje SIM karticu i uspešno se povezuje, ali automatsko prebacivanje, oporavak modema, odluke zasnovane na signalu i čista failover logika i dalje zahtevaju dublje testiranje.","\u002Fuploads\u002F2026\u002F06\u002Fopenwrt-router-review-dual-sim-03-1781620592829-7t77j7.webp","2026-06-16T10:40:00.000Z",{"id":739,"slug":740,"title":741,"excerpt":742,"featuredImage":743,"publishedAt":744},"469","rag-failed-but-which-layer-actually-failed-a-diagnostic-method","RAG nije uspeo — ali koji sloj je zapravo zakazao? Dijagnostička metoda","Kada je RAG odgovor pogrešan, kriviti pretragu ili model je previše neodređeno. Ova dijagnostička metoda izoluje pokrivenost izvora, konstrukciju upita, pretragu, rangiranje, sastavljanje konteksta, generisanje, pripisivanje dokaza i svežinu—tako da se stvarni kvar može reprodukovati i ispraviti.","\u002Fuploads\u002F2026\u002F09\u002Frag-failed-but-which-layer-actually-failed-a-diagnostic-method-1790350847177-pior4c.webp","2026-09-24T19:39:00.000Z",{"id":746,"slug":747,"title":748,"excerpt":749,"featuredImage":750,"publishedAt":751},"459","ollama-is-not-the-product-building-production-ready-open-llm-applications","Ollama nije proizvod: Izgradnja aplikacija spremnih za produkciju sa otvorenim LLM-ovima","Pokretanje lokalnog modela pomoću Ollama-e je jednostavno. Izgradnja Open-LLM aplikacije spremne za produkciju je teža: zahteva RAG, kontrolu pristupa, apstrakciju provajdera, evaluaciju, logovanje, disciplinu puštanja u rad i kontrolisani aplikativni sloj oko modela.","\u002Fuploads\u002F2026\u002F06\u002Follama-is-not-the-product-building-production-ready-open-llm-applications-1782679361640-h0usqf.webp","2026-06-28T16:39:00.000Z",{"id":753,"slug":754,"title":755,"excerpt":756,"featuredImage":757,"publishedAt":758},"480","when-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","Kada bi AI trebalo da prestane da veruje sopstvenom znanju? — Okidač za pretragu","AI model ne zahteva pretragu za svako pitanje. Važan problem je znati kada njegovo interno znanje više nije dovoljno. 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