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AI: guida tecnica al decision model di TypeSafe","\u003Ch1>\u003Cstrong>\u003Cspan class=\"text-color-primary-400\">Jev AI:\u003C/span> guida tecnica al decision model di TypeSafe\u003C/strong>\u003C/h1>\n","Jev AI è un modello di TypeSafe che restituisce giudizi strutturati su domande definite dall’applicazione. Puoi chiedergli di scegliere una coda, stimare l’urgenza e indicare la probabilità che serva un’escalation. Il software riceve valori che può usare nelle proprie regole. È il genere di compito per cui TypeSafe ha introdotto la categoria System One. La […]",30162,"10 Novembre 2026",{"url":1019,"width":1020,"height":1020,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/11/cover-21.png",1254,[1022,1026,1050,1052],{"block_layout":1023,"classes":1024,"text":1025},"simple_text","above-the-fold","\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cb>Jev AI\u003C/b>\u003Cspan style=\"font-weight: 400;\"> è un modello di TypeSafe che restituisce giudizi strutturati su domande definite dall’applicazione. Puoi chiedergli di scegliere una coda, stimare l’urgenza e indicare la probabilità che serva un’escalation. Il software riceve valori che può usare nelle proprie regole. È il genere di compito per cui TypeSafe ha introdotto la categoria \u003C/span>\u003Ci>\u003Cspan style=\"font-weight: 400;\">System One\u003C/span>\u003C/i>\u003Cspan style=\"font-weight: 400;\">. La parte delicata resta progettare l’azione successiva, soprattutto quando le alternative sono quasi alla pari.\u003C/span>\u003Cspan style=\"font-weight: 400;\">\u003Cbr />\n\u003C/span>\u003Cspan style=\"font-weight: 400;\">\u003Cbr />\n\u003C/span>\u003Cspan style=\"font-weight: 400;\">In azienda puoi usarlo per smistare ticket e ordinare i lead di un CRM, oppure per mandare a un moderatore i contenuti dubbi. Resta da progettare che cosa fa l’applicazione dopo la risposta, soprattutto quando due opzioni hanno probabilità quasi uguali.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Come funziona Jev, dalle domande tipizzate alle probabilità\u003C/strong>\u003C/h2>\n\u003Ch3>\u003Cstrong>Stato, istruzioni e opzioni\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">L’API riceve uno \u003C/span>\u003Cb>stato\u003C/b>\u003Cspan style=\"font-weight: 400;\"> (\u003C/span>\u003Cspan style=\"font-weight: 400;\">state\u003C/span>\u003Cspan style=\"font-weight: 400;\">), che può essere testo o un oggetto strutturato, e un insieme di domande (\u003C/span>\u003Cspan style=\"font-weight: 400;\">questions\u003C/span>\u003Cspan style=\"font-weight: 400;\">). Ogni domanda ha un identificativo scelto dallo sviluppatore, un tipo e istruzioni. Per la classificazione aggiungi una descrizione per ogni etichetta. «Accesso» può voler dire credenziali, permessi o indisponibilità del pannello. Chiarirlo nei criteri evita di chiedere al modello di indovinare i confini fra reparti.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch3>\u003Cstrong>Choice, Score e Noul\u003C/strong>\u003C/h3>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cb>Tipo\u003C/b>\u003C/span>\u003C/th>\n\u003Cth class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cb>Che cosa chiedi\u003C/b>\u003C/span>\u003C/th>\n\u003Cth class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cb>Che cosa ricevi\u003C/b>\u003C/span>\u003C/th>\n\u003C/tr>\n\u003C/thead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">choice\u003C/span>\u003C/td>\n\u003Ctd class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">Qual è la coda più adatta fra quelle consentite?\u003C/span>\u003C/td>\n\u003Ctd class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">L’opzione selezionata, le probabilità delle opzioni e la \u003C/span>\u003Cspan style=\"font-weight: 400;\">confidence\u003C/span>\u003Cspan style=\"font-weight: 400;\">.\u003C/span>\u003C/span>\u003C/td>\n\u003C/tr>\n\u003Ctr>\n\u003Ctd class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">score\u003C/span>\u003C/td>\n\u003Ctd class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">Quanto è urgente secondo una scala ordinata?\u003C/span>\u003C/td>\n\u003Ctd class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Un punteggio sulla scala, la distribuzione sui livelli e la \u003C/span>\u003Cspan style=\"font-weight: 400;\">confidence\u003C/span>\u003Cspan style=\"font-weight: 400;\">.\u003C/span>\u003C/span>\u003C/td>\n\u003C/tr>\n\u003Ctr>\n\u003Ctd class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">noul\u003C/span>\u003C/td>\n\u003Ctd class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">Il cliente è impossibilitato a usare il servizio?\u003C/span>\u003C/td>\n\u003Ctd class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Un valore \u003C/span>\u003Cspan style=\"font-weight: 400;\">noul\u003C/span>\u003Cspan style=\"font-weight: 400;\"> fra 0 e 1, interpretabile come probabilità del sì.\u003C/span>\u003C/span>\u003C/td>\n\u003C/tr>\n\u003C/tbody>\n\u003C/table>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">L’API valuta le domande \u003C/span>\u003Cb>in parallelo e in modo indipendente\u003C/b>\u003Cspan style=\"font-weight: 400;\"> sullo stesso stato. Il punteggio di urgenza si basa quindi sul ticket, come la scelta della coda. Se serve una seconda valutazione condizionata alla prima, componi i risultati nel codice o invia una seconda richiesta con il nuovo contesto. TypeSafe descrive Jev come un modello che campiona le risposte in parallelo, evitando la decodifica sequenziale tipica della generazione di testo. Con la versione 1.14 ha introdotto anche funzioni sperimentali per immagini e il tipo \u003C/span>\u003Cspan style=\"font-weight: 400;\">box\u003C/span>\u003Cspan style=\"font-weight: 400;\"> per localizzare un elemento.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch3>\u003Cstrong>Probabilità calibrate e confidence score\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">La \u003C/span>\u003Cb>calibrazione\u003C/b>\u003Cspan style=\"font-weight: 400;\"> si misura su molti esempi. Se il modello attribuisce circa 0,8 alla necessità di escalation su un gruppo di ticket, la quota di escalation confermate dovrebbe avvicinarsi all’80% in quel gruppo. TypeSafe addestra Jev per produrre stime calibrate. Verifica la corrispondenza sul tuo storico, per lingua e categoria, prima di basarci un’automazione.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">La \u003C/span>\u003Cb>confidence\u003C/b>\u003Cspan style=\"font-weight: 400;\"> di \u003C/span>\u003Cspan style=\"font-weight: 400;\">choice\u003C/span>\u003Cspan style=\"font-weight: 400;\"> e \u003C/span>\u003Cspan style=\"font-weight: 400;\">score\u003C/span>\u003Cspan style=\"font-weight: 400;\"> sintetizza la distribuzione delle probabilità secondo la formula di TypeSafe. Per un \u003C/span>\u003Cspan style=\"font-weight: 400;\">choice\u003C/span>\u003Cspan style=\"font-weight: 400;\"> puoi anche confrontare le due opzioni più probabili. Un piccolo distacco fra «fatturazione» e «accesso» può bastare per mandare il ticket in revisione, anche se il formato della risposta è perfetto. \u003C/span>\u003Cspan style=\"font-weight: 400;\">noul\u003C/span>\u003Cspan style=\"font-weight: 400;\"> restituisce direttamente la probabilità del sì.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Esempi di Jev in azienda e nei workflow con agenti AI\u003C/strong>\u003C/h2>\n\u003Ch3>\u003Cstrong>Classificazione di ticket, email e richieste clienti\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Un help desk può usare Jev per il \u003C/span>\u003Cb>primo smistamento\u003C/b>\u003Cspan style=\"font-weight: 400;\"> di email e ticket. L’applicazione unisce il messaggio all’informazione che serve per decidere, per esempio se il servizio risulta attivo. Chiede la coda di destinazione con \u003C/span>\u003Cspan style=\"font-weight: 400;\">choice\u003C/span>\u003Cspan style=\"font-weight: 400;\">, l’urgenza con \u003C/span>\u003Cspan style=\"font-weight: 400;\">score\u003C/span>\u003Cspan style=\"font-weight: 400;\"> e la necessità di passare a un operatore con \u003C/span>\u003Cspan style=\"font-weight: 400;\">noul\u003C/span>\u003Cspan style=\"font-weight: 400;\">. Se fatturazione e accesso restano vicini, il ticket va a un operatore con entrambe le ipotesi visibili.\u003C/span>\u003C/span>\u003C/p>\n\u003Cdiv style=\"width: 861px\" class=\"wp-caption alignnone\">\u003Cimg fetchpriority=\"high\" decoding=\"async\" style=\"font-size: 16px; letter-spacing: -0.16px;\" src=\"https://website-prod.datamasters.it/wp-content/uploads/2026/11/inline-01-27.png\" alt=\"jev ai\" width=\"851\" height=\"567\" />\u003Cp class=\"wp-caption-text\">La coda, la priorità e l’escalation sono tre giudizi separati sullo stesso ticket.\u003C/p>\u003C/div>\n\u003Cp> \u003C/p>\n",{"block_layout":1027,"gutter_top":1028,"gutter_bottom":1028,"list":1029,"h_align":1049,"v_align":1049},"cta_list","xl",[1030],{"link":1031,"config":1033,"modal":1037},{"title":1032,"url":149,"target":19},"ISCRIVITI ALLA NEWSLETTER",{"variant":161,"theme":1034,"size":1035,"class":1036},"theme-primary","size-default","iscritto-nl qs-link",{"api_url":1038,"title":1039,"text":1040,"name_label":1041,"name_placeholder":1042,"surname_label":1043,"surname_placeholder":1044,"email_label":1045,"email_placeholder":1046,"privacy_text":1047,"submit_text":1048},"/wp-json/dm/v1/modal-form/26876","Iscriviti alla Newletter","\u003Cp>Ricevi direttamente sulla tua mail gli ultimi articoli pubblicati nella nostra sezione AI NEWS per rimanere sempre aggiornato e non perderti nessun contenuto.\u003C/p>\n","Nome","Inserisci il tuo nome","Cognome","Inserisci il tuo cognome","E-mail","Inserisci la tua e-mail","Dichiaro di aver letto l’informativa \u003Ca href=\"https://datamasters.it/privacy-policy/\" target=\"_blank\" rel=\"noopener\">privacy\u003C/a> e di prestare espresso consenso al \u003Ca href=\"https://datamasters.it/trattamento-dati/\" target=\"_blank\" rel=\"noopener\">trattamento dei dati\u003C/a> personali","ISCRIVITI ORA","center",{"block_layout":1023,"text":1051},"\u003Ch3>\u003Cstrong>Scoring, moderazione e interfacce SaaS\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">In un CRM puoi definire una scala di \u003C/span>\u003Cb>qualificazione dei lead\u003C/b>\u003Cspan style=\"font-weight: 400;\"> a partire da criteri osservabili, come la presenza di un’esigenza esplicita. Il punteggio può ordinare una coda di contatti da rivedere. In moderazione, \u003C/span>\u003Cspan style=\"font-weight: 400;\">noul\u003C/span>\u003Cspan style=\"font-weight: 400;\"> può stimare se un contenuto rientra in una policy specifica e inviare i casi dubbi a un moderatore. In un’app SaaS, \u003C/span>\u003Cspan style=\"font-weight: 400;\">choice\u003C/span>\u003Cspan style=\"font-weight: 400;\"> può selezionare quale scheda di aiuto mostrare. Le azioni visibili all’utente e le autorizzazioni restano governate dall’applicazione.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch3>\u003Cstrong>Jev vs ChatGPT e Claude\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Un LLM può scrivere al cliente, spiegare un errore o restituire JSON secondo uno schema. Jev è specializzato in giudizi con \u003C/span>\u003Cb>uno spazio di risposta definito\u003C/b>\u003Cspan style=\"font-weight: 400;\">. In un agente multi-step, Jev può scegliere se consultare il CRM o aprire un ticket, una regola può verificare i permessi e un LLM può redigere la risposta. Per un codice prodotto presente in un elenco basta invece un controllo deterministico. Gli agenti che producono documenti o usano connettori sono un’altra categoria di prodotto, come si vede nella nostra guida a \u003C/span>\u003Ca href=\"https://datamasters.it/blog/chatgpt-work/\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">ChatGPT Work\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\">.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Prestazioni, costi e limiti di Jev\u003C/strong>\u003C/h2>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">A novembre 2026, TypeSafe indica \u003C/span>\u003Cb>0,042 USD per milione di token in ingresso\u003C/b>\u003Cspan style=\"font-weight: 400;\">, con output gratuito. Con un consumo fatturato ipotetico di 1.000 token per richiesta, 100.000 richieste costerebbero circa 4,20 USD per la sola inferenza. Misura l’uso reale riportato dall’API e includi nel conto code, retry e tempo degli operatori. Raggruppare domande sullo stesso stato può evitare richieste distinte, purché possano essere valutate indipendentemente. \u003C/span>\u003Ca href=\"https://help.typesafe.ai/articles/5187202169-how-much-does-jev-cost\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">Listino Jev\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\">.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Per un test in azienda, esegui Jev in \u003C/span>\u003Cb>modalità osservazione\u003C/b>\u003Cspan style=\"font-weight: 400;\"> su ticket già risolti o in parallelo agli operatori. Misura latenza al 95° percentile, errori per coda e quota di richieste smistabili automaticamente a parità di errore. Una categoria rara come «incidente di sicurezza» merita un controllo separato perché un buon dato medio può nascondere errori sistematici proprio lì. Il nostro consiglio è che il valore di Jev si vede quando il team riesce a descrivere un giudizio breve, controllare i casi al limite e correggere la policy senza cambiare il resto del flusso.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Se vuoi esercitarti anche sulla parte generativa, nel \u003C/span>\u003Ca href=\"https://datamasters.it/catalogo/corsi/chatgpt-il-corso-completo/\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">Corso ChatGPT Completo\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> puoi approfondire l’uso di ChatGPT nei flussi di lavoro.\u003C/span>\u003C/span>\u003C/p>\n",{"block_layout":1053,"classes":1054,"bg_color":1055,"api_url":1056,"text":1057,"form":1058},"newsletter_form","gutter-mt-xl","color-primary-50","/wp-json/dm/v1/newsletter","\u003Ch6>NEWSLETTER\u003C/h6>\n\u003Cp class=\"font-size-body-1\">Ricevi direttamente sulla tua mail gli ultimi articoli pubblicati nella nostra sezione AI NEWS per rimanere sempre aggiornato e non perderti nessun contenuto.\u003C/p>\n",{"name_label":1041,"name_placeholder":1042,"surname_label":1043,"surname_placeholder":1044,"email_label":1045,"email_placeholder":1046,"privacy_text":1047,"submit_text":1048,"confirm_message":1059,"ac_list":1060,"ac_tags":1061},"Grazie per esserti iscritto alla nostra newsletter!\u003Cbr />\r\nOra riceverai direttamente nella tua casella di posta gli ultimi aggiornamenti dal mondo AI e Data Science per essere sempre al passo con le novità del settore.","5821e8498b0d54c0:0e0953c53171f532a8549bcddf9432636b38a34d:tG3LxS4YzARBO+q/lIEjJg==","8b2b6bdca06d3b0d:c5ce451792947faabc6b2b27e91c720d68e3f243:211egZVr/FqdoJyUT5tmHQ==",{"first_name":1063,"last_name":1064,"avatar":1065,"linkedin_url":1080,"description":1081},"Simone","Truglia",{"ID":1066,"id":1066,"title":1067,"filename":1068,"filesize":1069,"url":1070,"alt":977,"author":1071,"description":19,"caption":19,"name":1067,"status":1072,"uploaded_to":1073,"date":1074,"modified":1075,"menu_order":1076,"mime_type":957,"type":1077,"subtype":1078,"width":1079,"height":1079},1330,"simone-truglia","simone-truglia-1.png",667231,"https://website-prod.datamasters.it/wp-content/uploads/2024/06/simone-truglia-1.png","7","inherit",1313,"2024-06-17 13:55:06","2024-06-17 13:56:20",0,"image","png",1080,"https://www.linkedin.com/in/simonetruglia/","\u003Cp>Simone è un \u003Cstrong>Ingegnere Informatico\u003C/strong> con specializzazione nei \u003Cb>sistemi automatici \u003C/b>e con una grande passione per la \u003Cb>matematica\u003C/b>, la \u003Cb>programmazione\u003C/b> e l’\u003Cb>intelligenza artificiale\u003C/b>. Ha lavorato con diverse aziende europee, aiutandole ad acquisire e ad estrarre il massimo valore dai principali dati a loro disposizione.\u003C/p>\n",[1083,1085],{"label":934,"link":1084},"https://datamasters.it/blog/?tag=jev-ai",{"label":936,"link":1086},"https://datamasters.it/blog/?tag=modelli-ai",[1088,1106,1121,1138,1152],{"id":1089,"image":1090,"raw_date":1092,"date":1093,"date_day":1094,"date_month":1095,"title":1096,"excerpt":1097,"url":1098,"taxonomies":1099,"is_webinar":712},30124,{"url":1091,"width":1020,"height":1020,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/11/cover-20.png","2026-11-04T10:32:45+01:00","4 Novembre 2026","04","Nov","Fine-tuning degli LLM: guida pratica per personalizzare un modello","Hai un modello linguistico che funziona quasi sempre. Estrae il prodotto, estrae la quantità, e poi su una richiesta come «Ci servono otto monitor» aggiunge una data di consegna inventata. […]","https://datamasters.it/blog/fine-tuning-llm/",{"category":1100,"tag":1102},[1101],"blog",[1103,1104,1105],"fine-tuning","llm","modelli-ai",{"id":1107,"image":1108,"raw_date":1110,"date":1111,"date_day":1112,"date_month":1113,"title":1114,"excerpt":1115,"url":1116,"taxonomies":1117,"is_webinar":712},30103,{"url":1109,"width":1020,"height":1020,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/10/cover-18.png","2026-10-30T12:49:08+01:00","30 Ottobre 2026","30","Ott","LLM in locale: guida per installare e usare modelli AI","Usare un LLM in locale significa eseguire un modello linguistico sul tuo computer, invece di inviare ogni richiesta a un servizio remoto. Puoi tenere i dati sotto il tuo controllo, […]","https://datamasters.it/blog/llm-locale/",{"category":1118,"tag":1119},[],[1104,1120,1105],"llm-in-locale",{"id":1122,"image":1123,"raw_date":1127,"date":1128,"date_day":1129,"date_month":1113,"title":1130,"excerpt":1131,"url":1132,"taxonomies":1133,"is_webinar":712},30032,{"url":1124,"width":1125,"height":1126,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/10/nemotron-cose-come-funziona-e-quali-modelli-include.jpg",1014,677,"2026-10-28T20:30:41+01:00","28 Ottobre 2026","28","Nemotron: cos’è, come funziona e quali modelli include","Un agente AI che deve controllare un contratto può impiegare pochi secondi per leggere una clausola e molto più tempo per confrontarla con altri documenti. Se poi deve interrogare un […]","https://datamasters.it/blog/nemotron-nvidia/",{"category":1134,"tag":1135},[1101],[1105,1136,1137],"nemotron","nvidia",{"id":1139,"image":1140,"raw_date":1142,"date":1143,"date_day":1144,"date_month":1113,"title":1145,"excerpt":1146,"url":1147,"taxonomies":1148,"is_webinar":712},29896,{"url":1141,"width":1020,"height":1020,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/10/cover-16.png","2026-10-21T14:52:37+02:00","21 Ottobre 2026","21","Guida a GLM 5.3: cos’è, caratteristiche e come utilizzarlo al meglio","GLM 5.3 è un modello AI di Z.ai pensato per la programmazione e per attività che richiedono più passaggi, come analizzare un progetto software, proporre modifiche e verificarne il risultato. […]","https://datamasters.it/blog/glm-5-3/",{"category":1149,"tag":1150},[],[1151,1105],"glm-5-3",{"id":1153,"image":1154,"raw_date":1157,"date":1158,"date_day":1159,"date_month":1160,"title":1161,"excerpt":1162,"url":1163,"taxonomies":1164,"is_webinar":712},28438,{"url":1155,"width":1156,"height":1156,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/02/copertina-1.png",1024,"2026-02-20T13:14:08+01:00","20 Febbraio 2026","20","Feb","Something Big Is Happening: perché i nuovi modelli AI stanno cambiando il lavoro (e cosa devi fare ora)","Negli ultimi mesi c’è una sensazione strana nell’aria, soprattutto se lavori nel digitale, non si tratta della solita ondata di hype, ma qualcosa di più viscerale, quasi fastidioso. Per anni […]","https://datamasters.it/blog/something-big-is-happening-perche-i-nuovi-modelli-ai-stanno-cambiando-il-lavoro-e-cosa-devi-fare-ora/",{"category":1165,"tag":1166},[1101],[1167,1168,1169,1105],"ai-developer","claude-opus-4-6","gpt-5-3",[1171],{"block_layout":1172,"gutter_top":1173,"gutter_bottom":1173,"bg_color":1174,"fg_color":185,"text":1175,"cta":1176,"small":712},"banner_icon_cta","xxl","color-primary-600","\u003Ch4 class=\"fw-semibold\" style=\"text-align: center;\">ISCRIVITI ED INIZIA\u003Cbr />\nAD IMPARARE SUBITO GRATIS\u003C/h4>\n\u003Cp class=\"font-size-body-1\" style=\"text-align: center;\">Accedi ora a risorse di apprendimento gratuite di alta qualità e trasforma le tue ambizioni\u003Cbr />\nin competenze concrete. Il tuo viaggio verso il successo inizia oggi!\u003C/p>\n",{"link":1177,"config":1179},{"title":119,"url":1178,"target":15},"https://datamasters.it/catalogo/?price=gratuito",{"variant":161,"theme":1180,"size":1035},"theme-white",1794322289018]