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\u003Ci>agentic\u003C\u002Fi> rad: planiranje, korišćenje alata i izvršavanje zadataka u više koraka uz znatno veću efikasnost. Poruka je jasna: manje „prompt magije“, više pouzdanog izvršavanja — i to uz kontekst koji ide do \u003Cb>1M tokena\u003C\u002Fb> na Plus varijanti.\u003C\u002Fp>\n\u003Ch3>Zašto je ovo važno developerima\u003C\u002Fh3>\n\u003Cp>Ako pravite produkcione agente (RAG, kopilote, automatske botove za pregled koda, data-pipelines, UI testere), najveći problem nije „da li model nešto zna“, već: \u003Cb>može li dosledno da odradi workflow\u003C\u002Fb> bez raspadanja u 6. koraku. Qwen 3.5-Plus cilja baš tu zonu — i to sa velikim kontekstom, multimodalnim inputom i ugrađenim tool-use ponašanjem.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Ovo je model koji pokušava da pretvori LLM iz „chat UI-a“ u \u003Cb>izvršni sloj\u003C\u002Fb>: vidi, planira, koristi alate i završava posao.\u003Ccite class=\"block mt-2 text-sm\">— Kako Qwen 3.5 pozicionira „agentic“ smer\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Ch3>Najvažnije novosti (Qwen 3.5-Plus u praksi)\u003C\u002Fh3>\n\u003Cul>\u003Cli>\u003Cb>1M kontekst\u003C\u002Fb>: realno znači da možete ubacivati velike isečke koda, logove, specifikacije i duge razgovore bez stalnog „seckanja“ (chunking).\u003C\u002Fli>\u003Cli>\u003Cb>Adaptive tool use\u003C\u002Fb>: model je obučen da sam odluči kada da pozove alat (pretraga, izvršavanje koda, pretraživač, funkcije) umesto da sve radi „u glavi“.\u003C\u002Fli>\u003Cli>\u003Cb>Multimodal + „visual agent“\u003C\u002Fb>: razume slike\u002Fdokumente i cilja na rad preko desktop\u002Fmobilnih aplikacija (agent koji može da „klika“ i izvršava korake).\u003C\u002Fli>\u003Cli>\u003Cb>Efikasnost (MoE \u002F arhitektura)\u003C\u002Fb>: fokus na veću propusnost i niže troškove; Alibaba u javnim nastupima naglašava značajno nižu cenu i bolje skaliranje radnih opterećenja.\u003C\u002Fli>\u003Cli>\u003Cb>Open ecosystem\u003C\u002Fb>: serija dolazi sa open-weight izdanjima i alatima (repo, HF formati), dok se Plus često nudi kao hostovani model za produkcijsku latenciju i stabilnost.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch3>Kako ga brzo isprobati (bez filozofije)\u003C\u002Fh3>\n\u003Cp>Najbrži put je preko provajdera koji već hostuje Qwen 3.5-Plus (npr. gateway\u002Fagregator ili cloud studio). Ako već imate aplikaciju koja koristi API u stilu „chat completions“, migracija je uglavnom promena imena modela + provera limita konteksta i alata.\u003C\u002Fp>\n\u003Cpre class=\"code-block\" data-lang=\"javascript\">\u003Ccode class=\"language-javascript\">\u002F\u002F Minimalni primer (pseudo): zamenite endpoint\u002FSDK u zavisnosti od provajdera\nimport OpenAI from "openai";\n\nconst client = new OpenAI({ apiKey: process.env.API_KEY, baseURL: process.env.BASE_URL });\n\nconst res = await client.chat.completions.create({\n  model: "qwen3.5-plus",\n  messages: [\n    { role: "system", content: "Ti si agent koji završava zadatke." },\n    { role: "user", content: "Prođi kroz ovaj repozitorijum i predloži 5 bezbednosnih poboljšanja." }\n  ]\n});\n\nconsole.log(res.choices[0].message.content);\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Ch3>Slučajevi upotrebe gde Qwen 3.5-Plus ima realnu prednost\u003C\u002Fh3>\n\u003Col>\u003Cli>\u003Cb>Agentic RAG na velikim korpusima\u003C\u002Fb>: 1M kontekst + tool-use smanjuje potrebu za agresivnim sumiranjem.\u003C\u002Fli>\u003Cli>\u003Cb>Repo-level coding\u003C\u002Fb>: analiza više datoteka + generisanje PR-ova uz konzistentan plan (manje „nasumičnog krpljenja“).\u003C\u002Fli>\u003Cli>\u003Cb>UI\u002FQA automatizacija\u003C\u002Fb>: multimodalni unos + „visual agent“ smer za end-to-end testove i reprodukciju bagova iz snimaka ekrana\u002Fscreenshot-ova.\u003C\u002Fli>\u003Cli>\u003Cb>Ops\u002Fincident analiza\u003C\u002Fb>: velike količine logova + izvršavanje runbook-a uz alate (pretraga, upiti, tiketing).\u003C\u002Fli>\u003Cli>\u003Cb>Data workflow agent\u003C\u002Fb>: generisanje SQL-a, validacija rezultata, iterativne popravke — sve u jednoj sesiji bez gubljenja konteksta.\u003C\u002Fli>\u003C\u002Fol>\n\u003Ch3>Kompromisi (da ne upadnete u zamku hajpa)\u003C\u002Fh3>\n\u003Cul>\u003Cli>\u003Cb>1M kontekst ≠ 1M „savršene memorije“\u003C\u002Fb>: što je unos veći, to više morate paziti na strukturu (segmentacija, indeks, plan pretrage).\u003C\u002Fli>\u003Cli>\u003Cb>Agentic ponašanje zahteva zaštitne ograde (guardrails)\u003C\u002Fb>: obavezno dodajte sloj polisa (dozvoljeni alati, ograničenje brzine, sandbox), logovanje i replay.\u003C\u002Fli>\u003Cli>\u003Cb>Hosted vs open-weight\u003C\u002Fb>: Plus kao hostovani model je odličan za latenciju\u002Fstabilnost, ali open-weight varijante su bolje za privatnost i on-prem kontrolu — uz veći operativni teret.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>\u003Cb>Zaključak:\u003C\u002Fb> Qwen 3.5-Plus je signal da se trka seli sa „ko je pametniji u ćaskanju“ na „ko pouzdanije izvršava kompleksne radne tokove“. Ako pravite agente u produkciji, ovo vredi testirati — posebno kada su vam usko grlo kontekst, korišćenje alata i stabilnost kroz više koraka.\u003C\u002Fp>",{"time":228,"blocks":229,"version":318},1771515659465,[230,235,240,245,249,256,260,271,275,279,283,289,293,303,307,314],{"id":231,"data":232,"type":42},"qwen35p-hero",{"text":233,"level":234},"Qwen 3.5-Plus: open-source „agentic“ AI koja developerima olakšava rad na kompleksnim zadacima",2,{"id":236,"data":237,"type":239},"qwen35p-lead",{"text":238},"Alibaba je objavio \u003Cb>Qwen 3.5\u003C\u002Fb> i posebno istakao \u003Cb>Qwen 3.5-Plus\u003C\u002Fb> kao model dizajniran za \u003Ci>agentic\u003C\u002Fi> rad: planiranje, korišćenje alata i izvršavanje zadataka u više koraka uz znatno veću efikasnost. Poruka je jasna: manje „prompt magije“, više pouzdanog izvršavanja — i to uz kontekst koji ide do \u003Cb>1M tokena\u003C\u002Fb> na Plus varijanti.","paragraph",{"id":241,"data":242,"type":42},"qwen35p-why",{"text":243,"level":244},"Zašto je ovo važno developerima",3,{"id":246,"data":247,"type":239},"qwen35p-why-p",{"text":248},"Ako pravite produkcione agente (RAG, kopilote, automatske botove za pregled koda, data-pipelines, UI testere), najveći problem nije „da li model nešto zna“, već: \u003Cb>može li dosledno da odradi workflow\u003C\u002Fb> bez raspadanja u 6. koraku. Qwen 3.5-Plus cilja baš tu zonu — i to sa velikim kontekstom, multimodalnim inputom i ugrađenim tool-use ponašanjem.",{"id":250,"data":251,"type":255},"qwen35p-quote",{"text":252,"caption":253,"alignment":254},"Ovo je model koji pokušava da pretvori LLM iz „chat UI-a“ u \u003Cb>izvršni sloj\u003C\u002Fb>: vidi, planira, koristi alate i završava posao.","Kako Qwen 3.5 pozicionira „agentic“ smer","left","quote",{"id":257,"data":258,"type":42},"qwen35p-features",{"text":259,"level":244},"Najvažnije novosti (Qwen 3.5-Plus u praksi)",{"id":261,"data":262,"type":270},"qwen35p-features-list",{"items":263,"style":269},[264,265,266,267,268],"\u003Cb>1M kontekst\u003C\u002Fb>: realno znači da možete ubacivati velike isečke koda, logove, specifikacije i duge razgovore bez stalnog „seckanja“ (chunking).","\u003Cb>Adaptive tool use\u003C\u002Fb>: model je obučen da sam odluči kada da pozove alat (pretraga, izvršavanje koda, pretraživač, funkcije) umesto da sve radi „u glavi“.","\u003Cb>Multimodal + „visual agent“\u003C\u002Fb>: razume slike\u002Fdokumente i cilja na rad preko desktop\u002Fmobilnih aplikacija (agent koji može da „klika“ i izvršava korake).","\u003Cb>Efikasnost (MoE \u002F arhitektura)\u003C\u002Fb>: fokus na veću propusnost i niže troškove; Alibaba u javnim nastupima naglašava značajno nižu cenu i bolje skaliranje radnih opterećenja.","\u003Cb>Open ecosystem\u003C\u002Fb>: serija dolazi sa open-weight izdanjima i alatima (repo, HF formati), dok se Plus često nudi kao hostovani model za produkcijsku latenciju i stabilnost.","unordered","list",{"id":272,"data":273,"type":274},"qwen35p-delim1",{},"delimiter",{"id":276,"data":277,"type":42},"qwen35p-howto",{"text":278,"level":244},"Kako ga brzo isprobati (bez filozofije)",{"id":280,"data":281,"type":239},"qwen35p-howto-p",{"text":282},"Najbrži put je preko provajdera koji već hostuje Qwen 3.5-Plus (npr. gateway\u002Fagregator ili cloud studio). Ako već imate aplikaciju koja koristi API u stilu „chat completions“, migracija je uglavnom promena imena modela + provera limita konteksta i alata.",{"id":284,"data":285,"type":288},"qwen35p-code1",{"code":286,"language":287},"\u002F\u002F Minimalni primer (pseudo): zamenite endpoint\u002FSDK u zavisnosti od provajdera\nimport OpenAI from \"openai\";\n\nconst client = new OpenAI({ apiKey: process.env.API_KEY, baseURL: process.env.BASE_URL });\n\nconst res = await client.chat.completions.create({\n  model: \"qwen3.5-plus\",\n  messages: [\n    { role: \"system\", content: \"Ti si agent koji završava zadatke.\" },\n    { role: \"user\", content: \"Prođi kroz ovaj repozitorijum i predloži 5 bezbednosnih poboljšanja.\" }\n  ]\n});\n\nconsole.log(res.choices[0].message.content);","javascript","code",{"id":290,"data":291,"type":42},"qwen35p-usecases",{"text":292,"level":244},"Slučajevi upotrebe gde Qwen 3.5-Plus ima realnu prednost",{"id":294,"data":295,"type":270},"qwen35p-usecases-list",{"items":296,"style":302},[297,298,299,300,301],"\u003Cb>Agentic RAG na velikim korpusima\u003C\u002Fb>: 1M kontekst + tool-use smanjuje potrebu za agresivnim sumiranjem.","\u003Cb>Repo-level coding\u003C\u002Fb>: analiza više datoteka + generisanje PR-ova uz konzistentan plan (manje „nasumičnog krpljenja“).","\u003Cb>UI\u002FQA automatizacija\u003C\u002Fb>: multimodalni unos + „visual agent“ smer za end-to-end testove i reprodukciju bagova iz snimaka ekrana\u002Fscreenshot-ova.","\u003Cb>Ops\u002Fincident analiza\u003C\u002Fb>: velike količine logova + izvršavanje runbook-a uz alate (pretraga, upiti, tiketing).","\u003Cb>Data workflow agent\u003C\u002Fb>: generisanje SQL-a, validacija rezultata, iterativne popravke — sve u jednoj sesiji bez gubljenja konteksta.","ordered",{"id":304,"data":305,"type":42},"qwen35p-tradeoffs",{"text":306,"level":244},"Kompromisi (da ne upadnete u zamku hajpa)",{"id":308,"data":309,"type":270},"qwen35p-tradeoffs-list",{"items":310,"style":269},[311,312,313],"\u003Cb>1M kontekst ≠ 1M „savršene memorije“\u003C\u002Fb>: što je unos veći, to više morate paziti na strukturu (segmentacija, indeks, plan pretrage).","\u003Cb>Agentic ponašanje zahteva zaštitne ograde (guardrails)\u003C\u002Fb>: obavezno dodajte sloj polisa (dozvoljeni alati, ograničenje brzine, sandbox), logovanje i replay.","\u003Cb>Hosted vs open-weight\u003C\u002Fb>: Plus kao hostovani model je odličan za latenciju\u002Fstabilnost, ali open-weight varijante su bolje za privatnost i on-prem kontrolu — uz veći operativni teret.",{"id":315,"data":316,"type":239},"qwen35p-bottomline",{"text":317},"\u003Cb>Zaključak:\u003C\u002Fb> Qwen 3.5-Plus je signal da se trka seli sa „ko je pametniji u ćaskanju“ na „ko pouzdanije izvršava kompleksne radne tokove“. Ako pravite agente u produkciji, ovo vredi testirati — posebno kada su vam usko grlo kontekst, korišćenje alata i stabilnost kroz više koraka.","2.31","Otkrijte revolucionarne funkcije i prednosti Alibabinog Qwen 3.5-Plus modela, AI otvorenog koda koji menja pravila igre za programere.","\u002Fuploads\u002F2026\u002F02\u002Fnew-qwen-3-5-plus-1771515512741-dcbi9p.webp","new-qwen-3-5-plus-1771515512741-dcbi9p","PUBLISHED","2026-02-19T10:23:00.000Z","2026-02-19T15:23:23.973Z","2026-02-20T20:39:04.434Z",{"en":327,"de":328,"sr":329,"es":330,"fr":331,"it":332,"ru":333,"zh":334},"\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fde\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fsr\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fes\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Ffr\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fit\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fru\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fzh\u002Fblog\u002Fnew-qwen-3-5-plus",[336,340,344,348],{"id":337,"name":338,"slug":339},94,"Runbook: Izdanje","release-runbook",{"id":341,"name":342,"slug":343},106,"Runbook: AI rollback","ai-rollback",{"id":345,"name":346,"slug":347},88,"Verzionisanje (prompt, modeli)","versioning",{"id":349,"name":350,"slug":351},87,"Playbook: LLMOps","llmops",{"id":353,"login":354,"email":355,"displayName":356},"20","rooth8233","","Aleksandar Stajić",[358,427],{"lang":359,"title":360,"content":361,"contentJson":362,"excerpt":426},"en","New Qwen 3.5-Plus: Open-source AI is getting serious now","{\"time\":1771533358356,\"blocks\":[{\"id\":\"qwen35p-hero\",\"data\":{\"text\":\"Qwen 3.5-Plus: Open-source \\\"agentic\\\" AI that removes friction for developers in complex tasks\",\"level\":2},\"type\":\"header\"},{\"id\":\"qwen35p-lead\",\"data\":{\"text\":\"Alibaba has released \u003Cb>Qwen 3.5\u003C\u002Fb> and particularly highlights \u003Cb>Qwen 3.5-Plus\u003C\u002Fb> as a model developed for \u003Ci>agentic\u003C\u002Fi> work: planning, tool use, and execution of multi-step tasks with significantly higher efficiency. The message is clear: less \\\"prompt magic\\\", more reliable execution – and all with a context of up to \u003Cb>1 million tokens\u003C\u002Fb> in the Plus variant.\"},\"type\":\"paragraph\"},{\"id\":\"qwen35p-why\",\"data\":{\"text\":\"Why this is important for developers\",\"level\":3},\"type\":\"header\"},{\"id\":\"qwen35p-why-p\",\"data\":{\"text\":\"When you build production agents (RAG, copilots, automatic code review bots, data pipelines, UI testers), the biggest problem isn't \\\"whether the model knows something\\\", but: \u003Cb>can it consistently process the workflow\u003C\u002Fb> without falling apart at the 6th step. Qwen 3.5-Plus targets exactly this area – with large context, multimodal input, and integrated tool-use behavior.\"},\"type\":\"paragraph\"},{\"id\":\"qwen35p-quote\",\"data\":{\"text\":\"This is a model that attempts to transform LLMs from a \\\"chat UI\\\" into an \u003Cb>execution layer\u003C\u002Fb>: it sees, plans, uses tools, and completes the task.\",\"caption\":\"How Qwen 3.5 positions the \\\"agentic\\\" direction\",\"alignment\":\"left\"},\"type\":\"quote\"},{\"id\":\"qwen35p-features\",\"data\":{\"text\":\"Key innovations (Qwen 3.5-Plus in practice)\",\"level\":3},\"type\":\"header\"},{\"id\":\"qwen35p-features-list\",\"data\":{\"items\":[\"\u003Cb>1M Context\u003C\u002Fb>: In practice, this means you can process large codebase snippets, logs, specifications, and long conversations without constant \\\"chunking\\\".\",\"\u003Cb>Adaptive Tool Use\u003C\u002Fb>: The model is trained to decide for itself when a tool (search, code execution, browser, functions) should be called, instead of doing everything \\\"in its head\\\".\",\"\u003Cb>Multimodal + \\\"Visual Agent\\\"\u003C\u002Fb>: Understands images\u002Fdocuments and aims at working across desktop\u002Fmobile apps (an agent that can \\\"click\\\" and execute steps).\",\"\u003Cb>Efficiency (MoE \u002F Architecture)\u003C\u002Fb>: Focus on higher throughput and lower costs; Alibaba emphasizes significantly lower costs and better scaling of workloads in public appearances.\",\"\u003Cb>Open Ecosystem\u003C\u002Fb>: The series includes open-weight editions and tooling (repo, HF formats), while Plus is often offered as a hosted model for production latency and stability.\"],\"style\":\"unordered\"},\"type\":\"list\"},{\"id\":\"qwen35p-delim1\",\"data\":{},\"type\":\"delimiter\"},{\"id\":\"qwen35p-howto\",\"data\":{\"text\":\"How to try it quickly (without further ado)\",\"level\":3},\"type\":\"header\"},{\"id\":\"qwen35p-howto-p\",\"data\":{\"text\":\"The fastest way is through a provider that already hosts Qwen 3.5-Plus (e.g., gateway\u002Faggregator or cloud studio). If you already have an app that uses a \\\"Chat Completions\\\" style API, migration usually consists only of changing the model name and checking context limits and tools.\"},\"type\":\"paragraph\"},{\"id\":\"qwen35p-code1\",\"data\":{\"code\":\"\u002F\u002F Minimal example (pseudo): replace endpoint\u002FSDK depending on provider\\nimport OpenAI from \\\"openai\\\";\\n\\nconst client = new OpenAI({ apiKey: process.env.API_KEY, baseURL: process.env.BASE_URL });\\n\\nconst res = await client.chat.completions.create({\\n  model: \\\"qwen3.5-plus\\\",\\n  messages: [\\n    { role: \\\"system\\\", content: \\\"You are an agent that completes tasks.\\\" },\\n    { role: \\\"user\\\", content: \\\"Go through this repo and suggest 5 security improvements.\\\" }\\n  ]\\n});\\n\\nconsole.log(res.choices[0].message.content);\",\"language\":\"javascript\"},\"type\":\"code\"},{\"id\":\"qwen35p-usecases\",\"data\":{\"text\":\"Use cases where Qwen 3.5-Plus offers a real advantage\",\"level\":3},\"type\":\"header\"},{\"id\":\"qwen35p-usecases-list\",\"data\":{\"items\":[\"\u003Cb>Agentic RAG for large corpora\u003C\u002Fb>: 1M context + tool-use reduces the need for aggressive summarization.\",\"\u003Cb>Repo-Level Coding\u003C\u002Fb>: Analysis of multiple files + generation of PRs with a consistent plan (less \\\"random patchwork\\\").\",\"\u003Cb>UI\u002FQA Automation\u003C\u002Fb>: Multimodal input + \\\"Visual Agent\\\" alignment for end-to-end tests and reproducing bugs from screencasts\u002Fscreenshots.\",\"\u003Cb>Ops\u002FIncident Analysis\u003C\u002Fb>: Large amounts of logs + runbook execution with tools (search, query, ticketing).\",\"\u003Cb>Data Workflow Agent\u003C\u002Fb>: SQL generation, result validation, iterative corrections – all in one session without context loss.\"],\"style\":\"ordered\"},\"type\":\"list\"},{\"id\":\"qwen35p-tradeoffs\",\"data\":{\"text\":\"Trade-offs (to avoid falling into the hype trap)\",\"level\":3},\"type\":\"header\"},{\"id\":\"qwen35p-tradeoffs-list\",\"data\":{\"items\":[\"\u003Cb>1M Context ≠ 1M \\\"perfect memory\\\"\u003C\u002Fb>: The larger the input, the more you need to pay attention to structure (sectioning, index, retrieval plan).\",\"\u003Cb>Agentic behavior requires guardrails\u003C\u002Fb>: Be sure to add a policy layer (allowed tools, rate limit, sandbox), logging, and replay.\",\"\u003Cb>Hosted vs. Open-Weight\u003C\u002Fb>: Plus as a hosted model is excellent for latency\u002Fstability, but open-weight variants are better for data privacy and on-prem control – with higher operational overhead (Ops).\"],\"style\":\"unordered\"},\"type\":\"list\"},{\"id\":\"qwen35p-bottomline\",\"data\":{\"text\":\"\u003Cb>Conclusion:\u003C\u002Fb> Qwen 3.5-Plus is a signal that the race is shifting from \\\"who is smarter in chat\\\" to \\\"who executes complex workflows more reliably\\\". If you use agents in production, this is worth a test – especially if context, tool-use, and stability across multiple steps are your bottlenecks.\"},\"type\":\"paragraph\"}],\"version\":\"2.31\"}",{"time":363,"blocks":364,"version":318},1771533358356,[365,368,371,374,377,381,384,392,394,397,400,403,406,414,417,423],{"id":231,"data":366,"type":42},{"text":367,"level":234},"Qwen 3.5-Plus: Open-source \"agentic\" AI that removes friction for developers in complex tasks",{"id":236,"data":369,"type":239},{"text":370},"Alibaba has released \u003Cb>Qwen 3.5\u003C\u002Fb> and particularly highlights \u003Cb>Qwen 3.5-Plus\u003C\u002Fb> as a model developed for \u003Ci>agentic\u003C\u002Fi> work: planning, tool use, and execution of multi-step tasks with significantly higher efficiency. The message is clear: less \"prompt magic\", more reliable execution – and all with a context of up to \u003Cb>1 million tokens\u003C\u002Fb> in the Plus variant.",{"id":241,"data":372,"type":42},{"text":373,"level":244},"Why this is important for developers",{"id":246,"data":375,"type":239},{"text":376},"When you build production agents (RAG, copilots, automatic code review bots, data pipelines, UI testers), the biggest problem isn't \"whether the model knows something\", but: \u003Cb>can it consistently process the workflow\u003C\u002Fb> without falling apart at the 6th step. Qwen 3.5-Plus targets exactly this area – with large context, multimodal input, and integrated tool-use behavior.",{"id":250,"data":378,"type":255},{"text":379,"caption":380,"alignment":254},"This is a model that attempts to transform LLMs from a \"chat UI\" into an \u003Cb>execution layer\u003C\u002Fb>: it sees, plans, uses tools, and completes the task.","How Qwen 3.5 positions the \"agentic\" direction",{"id":257,"data":382,"type":42},{"text":383,"level":244},"Key innovations (Qwen 3.5-Plus in practice)",{"id":261,"data":385,"type":270},{"items":386,"style":269},[387,388,389,390,391],"\u003Cb>1M Context\u003C\u002Fb>: In practice, this means you can process large codebase snippets, logs, specifications, and long conversations without constant \"chunking\".","\u003Cb>Adaptive Tool Use\u003C\u002Fb>: The model is trained to decide for itself when a tool (search, code execution, browser, functions) should be called, instead of doing everything \"in its head\".","\u003Cb>Multimodal + \"Visual Agent\"\u003C\u002Fb>: Understands images\u002Fdocuments and aims at working across desktop\u002Fmobile apps (an agent that can \"click\" and execute steps).","\u003Cb>Efficiency (MoE \u002F Architecture)\u003C\u002Fb>: Focus on higher throughput and lower costs; Alibaba emphasizes significantly lower costs and better scaling of workloads in public appearances.","\u003Cb>Open Ecosystem\u003C\u002Fb>: The series includes open-weight editions and tooling (repo, HF formats), while Plus is often offered as a hosted model for production latency and stability.",{"id":272,"data":393,"type":274},{},{"id":276,"data":395,"type":42},{"text":396,"level":244},"How to try it quickly (without further ado)",{"id":280,"data":398,"type":239},{"text":399},"The fastest way is through a provider that already hosts Qwen 3.5-Plus (e.g., gateway\u002Faggregator or cloud studio). If you already have an app that uses a \"Chat Completions\" style API, migration usually consists only of changing the model name and checking context limits and tools.",{"id":284,"data":401,"type":288},{"code":402,"language":287},"\u002F\u002F Minimal example (pseudo): replace endpoint\u002FSDK depending on provider\nimport OpenAI from \"openai\";\n\nconst client = new OpenAI({ apiKey: process.env.API_KEY, baseURL: process.env.BASE_URL });\n\nconst res = await client.chat.completions.create({\n  model: \"qwen3.5-plus\",\n  messages: [\n    { role: \"system\", content: \"You are an agent that completes tasks.\" },\n    { role: \"user\", content: \"Go through this repo and suggest 5 security improvements.\" }\n  ]\n});\n\nconsole.log(res.choices[0].message.content);",{"id":290,"data":404,"type":42},{"text":405,"level":244},"Use cases where Qwen 3.5-Plus offers a real advantage",{"id":294,"data":407,"type":270},{"items":408,"style":302},[409,410,411,412,413],"\u003Cb>Agentic RAG for large corpora\u003C\u002Fb>: 1M context + tool-use reduces the need for aggressive summarization.","\u003Cb>Repo-Level Coding\u003C\u002Fb>: Analysis of multiple files + generation of PRs with a consistent plan (less \"random patchwork\").","\u003Cb>UI\u002FQA Automation\u003C\u002Fb>: Multimodal input + \"Visual Agent\" alignment for end-to-end tests and reproducing bugs from screencasts\u002Fscreenshots.","\u003Cb>Ops\u002FIncident Analysis\u003C\u002Fb>: Large amounts of logs + runbook execution with tools (search, query, ticketing).","\u003Cb>Data Workflow Agent\u003C\u002Fb>: SQL generation, result validation, iterative corrections – all in one session without context loss.",{"id":304,"data":415,"type":42},{"text":416,"level":244},"Trade-offs (to avoid falling into the hype trap)",{"id":308,"data":418,"type":270},{"items":419,"style":269},[420,421,422],"\u003Cb>1M Context ≠ 1M \"perfect memory\"\u003C\u002Fb>: The larger the input, the more you need to pay attention to structure (sectioning, index, retrieval plan).","\u003Cb>Agentic behavior requires guardrails\u003C\u002Fb>: Be sure to add a policy layer (allowed tools, rate limit, sandbox), logging, and replay.","\u003Cb>Hosted vs. Open-Weight\u003C\u002Fb>: Plus as a hosted model is excellent for latency\u002Fstability, but open-weight variants are better for data privacy and on-prem control – with higher operational overhead (Ops).",{"id":315,"data":424,"type":239},{"text":425},"\u003Cb>Conclusion:\u003C\u002Fb> Qwen 3.5-Plus is a signal that the race is shifting from \"who is smarter in chat\" to \"who executes complex workflows more reliably\". If you use agents in production, this is worth a test – especially if context, tool-use, and stability across multiple steps are your bottlenecks.","Discover the groundbreaking features and benefits of Alibaba's Qwen 3.5-Plus, a revolutionary open-source AI for developers.",{"lang":7,"title":224,"content":226,"contentJson":428,"excerpt":319},{"time":228,"blocks":429,"version":318},[430,432,434,436,438,440,442,445,447,449,451,453,455,458,460,463],{"id":231,"data":431,"type":42},{"text":233,"level":234},{"id":236,"data":433,"type":239},{"text":238},{"id":241,"data":435,"type":42},{"text":243,"level":244},{"id":246,"data":437,"type":239},{"text":248},{"id":250,"data":439,"type":255},{"text":252,"caption":253,"alignment":254},{"id":257,"data":441,"type":42},{"text":259,"level":244},{"id":261,"data":443,"type":270},{"items":444,"style":269},[264,265,266,267,268],{"id":272,"data":446,"type":274},{},{"id":276,"data":448,"type":42},{"text":278,"level":244},{"id":280,"data":450,"type":239},{"text":282},{"id":284,"data":452,"type":288},{"code":286,"language":287},{"id":290,"data":454,"type":42},{"text":292,"level":244},{"id":294,"data":456,"type":270},{"items":457,"style":302},[297,298,299,300,301],{"id":304,"data":459,"type":42},{"text":306,"level":244},{"id":308,"data":461,"type":270},{"items":462,"style":269},[311,312,313],{"id":315,"data":464,"type":239},{"text":317},"Post erfolgreich abgerufen",{"items":467,"source":507,"manualIds":508,"manualMatchedIds":509},[468,475,482,488,495,502],{"id":469,"slug":470,"title":471,"excerpt":472,"featuredImage":473,"publishedAt":474},"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":476,"slug":477,"title":478,"excerpt":479,"featuredImage":480,"publishedAt":481},"496","why-does-ai-retrieve-some-sources-and-ignore-others-the-retrieval-authority-layer","Zašto AI pronalazi neke izvore, a ignoriše druge? — Sloj autoriteta za pronalaženje","Pronalaženje relevantnog dokumenta nije dovoljno. AI sistemi takođe treba da razlikuju izvore koji su aktuelni, merodavni, primenljivi i sledljivi od izvora koji samo izgledaju slično. Sloj autoriteta pronalaženja definiše tu granicu odabira izvora.","\u002Fuploads\u002F2026\u002F10\u002Fwhy-does-ai-retrieve-some-sources-and-ignore-others-the-retrieval-authority-layer-1792072410616-0evp1z.webp","2026-10-15T07:51:00.000Z",{"id":483,"slug":484,"title":485,"excerpt":486,"featuredImage":320,"publishedAt":487},"445","qwen-3-6-in-production-release-runbook-ai-rollback-and-llmops-versioning","Qwen 3.6 u produkciji: Runbook za izdavanje, AI rollback i LLMOps verziranje","Qwen 3.6 nije samo još jedna nadogradnja modela. To je istovremeno događaj objavljivanja, scenario povratka na prethodnu verziju i problem verziranja. Ovaj članak objašnjava kako Qwen 3.6 treba tretirati u produkciji kroz LLMOps disciplinu, sledljivost promptova i modela, kontrolisano uvođenje i spremnost za povratak na prethodnu verziju zasnovanu na dokazima.","2026-05-04T02:49:00.000Z",{"id":489,"slug":490,"title":491,"excerpt":492,"featuredImage":493,"publishedAt":494},"488","what-is-context-engineering-what-the-model-receives-before-it-answers","Šta je kontekstualno inženjerstvo? Šta model prima pre nego što odgovori","Inženjering konteksta osmišljava koje informacije AI model prima pre inferencije, uključujući promptove, pretragu, memoriju, stanje aplikacije, rezultate alata i istoriju konverzacije.","\u002Fuploads\u002F2026\u002F10\u002Fwhat-is-context-engineering-what-the-model-receives-before-it-answers-1791480653258-018kcv.webp","2026-10-08T13:29:00.000Z",{"id":496,"slug":497,"title":498,"excerpt":499,"featuredImage":500,"publishedAt":501},"493","mlops-vs-llmops-what-changes-when-the-model-is-an-llm","MLOps vs LLMOps: Šta se menja kada je model LLM","MLOps upravlja sistemima mašinskog učenja; LLMOps proširuje te prakse na promptove, kontekst, pretragu, provajdere, alate, evaluacije i ponašanje u vreme izvršavanja oko velikih jezičkih modela.","\u002Fuploads\u002F2026\u002F10\u002Fmlops-vs-llmops-what-changes-when-the-model-is-an-llm-1791487319869-2v7hxo.webp","2026-10-08T15:20:00.000Z",{"id":503,"slug":504,"title":504,"excerpt":10,"featuredImage":505,"publishedAt":506},"369","git-with-automatic-upload-and-synchronization-to-a-production-server","\u002Fuploads\u002F2024\u002F05\u002Fstep-by-step-guide-illustration-showing-the-process-of-setting-up-Git-with-auto-upload-and-synchronization-to-a-production-server-large.webp","2024-05-28T22:48:00.000Z","fallback",[],[]]