Cualquiera tocando Dialogflow en 2018.

🚀 HIRING: Chatbot Developer 🏢 Company: Digit88 📍 India (Flexible Work Model) 💰 ₹10–18 LPA (estimated) 🧑‍💻 Experience: 2–4 Years Skills: • Node.js • REST APIs • DialogFlow • Watson • Conversational AI Build AI-powered chatbot exp for global SaaS. Link in comments.

✨ We're Hiring: Conversational AI Designer Remote | 0–2 Years Exp | Immediate Joiners Design AI conversations, chatbot flows, and user experiences that feel natural and human. Skills: Prompt Engineering, Conversational UX, NLP, Dialogflow/LangChain, Figma. 📩 DM us

CVE-2026-4764 Missing Authorization in Dialogflow CX Playbook Import Enables Privilege Escalation vulmon.com/vulnerabilitydeta…

CVE-2026-4764 A Missing Authorization vulnerability in the playbook import functionality in Dialogflow CX on Google Cloud Platform allows an authenticated user with specific roles to… cve.org/CVERecord?id=CVE-202…

🚨 Critical - GCP Dialogflow CX Privilege Escalation (CVE-2026-4764) A missing authorization vulnerability (CVSS 9.4) in the playbook import functionality of Google Cloud Dialogflow CX could allow authenticated users to escalate privileges. By importing a maliciously crafted playbook, an attacker could potentially achieve full GCP project takeover. 👉 Patched on March 15, 2026 | Managed cloud service - no customer mitigation action is required as Google has remediated the flaw

Modern AI is the new dialogflow

¿Cuál es tu mayor dolor de cabeza con las herramientas de soporte que usas hoy? ¿Costo, dependencia, falta de control de datos o dificultad para personalizar? Existe una alternativa seria, moderna y completamente bajo tu control. Se llama Chatwoot. Es una plataforma open source de soporte omnicanal que puedes instalar en tus propios servidores. Con más de 29.000 estrellas en GitHub, se ha convertido en una de las opciones más sólidas para equipos que quieren calidad enterprise sin pagar licencias millonarias ni perder soberanía sobre su información. Centraliza todas las conversaciones en una sola bandeja de entrada: chat en vivo de tu web, email, WhatsApp Business, Instagram, Facebook Messenger, Telegram y otros canales. El cliente siente que habla con una sola empresa, aunque uses varios canales. Su agente de IA se llama Captain. No es un chatbot genérico. Captain aprende de tu propio centro de ayuda, de las conversaciones anteriores y de las preguntas frecuentes para resolver automáticamente una gran parte de las consultas repetitivas. Tu equipo solo entra cuando realmente hace falta. Incluye también: - Un Help Center profesional y personalizable que reduce tickets antes de que lleguen. - Herramientas de colaboración muy bien resueltas: notas privadas, menciones @, etiquetas, respuestas predefinidas, asignación automática y detección de colisiones. - Reportes claros de rendimiento, tiempos de respuesta y satisfacción del cliente (CSAT). - API completa, webhooks e integraciones (Slack, Shopify, Dialogflow, Linear y más). Y el punto más importante para muchos: es self-hosted. Tú decides dónde viven los datos. Esto cambia completamente el juego si estás en una industria regulada, si valoras la privacidad o simplemente si no quieres que un proveedor externo tenga toda la información de tus clientes. Desplegarlo es bastante accesible con Docker. Tiene buena documentación, soporte para Kubernetes y una comunidad activa que lo sigue mejorando constantemente. Chatwoot no busca ser "la opción gratis y limitada". Busca ser una alternativa real y profesional a Intercom, Zendesk o Freshdesk para quienes quieren escalar el soporte sin escalar los costos de la misma manera y sin renunciar al control. REPOOO👇

Chatbot Development Gambaran kasar: Membuat bot pintar untuk customer service, sales, atau automation di WhatsApp, website, dll. Sub-niche: AI Chatbot, Rules-Based Chatbot, Voice Bot, Integration dengan CRM. Tech Stack: - Gratis: Dialogflow, Botpress, Rasa - Berbayar: Voiceflow, OpenAI Assistants, ManyChat

GoogleのDialogflow CXで作ったAIアプリを、Power AutomateのFlowから呼び出す方法についての解説記事です💸 認証周りの設定やREST API経由での組み込み方を実践的に説明していて、AIチャットボットを実際の業務システムに組み込む際の参考になりそうですね❄️ qiita.com/nnhkrnk/items/65e3… #雪羽のログ

🚨Is Janitor AI Down? Facing slow responses or disruptions? You're not alone! Find out why Janitor AI might be down and get quick troubleshooting tips to get back on track! tycoonstory.com/is-janitor-a… #janitorai #aitools #techtroubleshooting @Dialogflow @chainlink @TycoonStoryCo

おはようございます、水曜日です！😊 折り返し、ここからが勝負です。 【今日のDX占い】 本日の1位星座：しし座♌ ラッキーチャットボット基盤：#Dialogflow 対話がヒントに。コミュニケーションを楽しみましょう✨ #情シス #DX #企業公式つぶやき部

🛑 Stop paying for online courses. 10 Courses to learn AI in 2026 for Free These fantastic new courses are curated by Chris Donnelly: - Led by experts - Suitable for people of all levels - A great way to take your AI learning further Do a deep dive today, Find the courses below: --- Beginner Level 1) Introduction to Generative AI Understand the fundamentals of generative AI and its use cases, and gain practical experience of developing generative AI apps. 🔗lnkd.in/d-RebaXf 2) Prompt Design in Vertex AI Craft powerful prompts, learn multimodal generative techniques, and apply Gemini models to real-world marketing cases. 🔗lnkd.in/g-6gmzm3 3) Introduction to Duet AI in Google Workspace Learn to use Duet AI to streamline your Google Workspace and become more productive. 🔗lnkd.in/gFfMfg4r 4) Responsible AI: Applying AI Principles with Google Cloud Learn best practices to responsibly integrate AI into your business operations. 🔗lnkd.in/g4E4UBt7 Intermediate 5) Conversational AI on Vertex AI and Dialogflow CX Build, deploy and manage virtual agents to engage with customers and resolve errors. 🔗lnkd.in/gzis-FUu? 6) Attention Mechanism Understand the basics of attention mechanism and learn to use it to improve standard ML tasks, such as machine translation, text summarization, and answering questions. 🔗lnkd.in/gwjhNNTa 7) Create Image Captioning Models Use deep learning to create your own image captioning model, train it, and evaluate output. 🔗lnkd.in/gkxwnSgM 8) Encoder-Decoder Architecture Learn the fundamentals of encoder-decoder architecture and to use it to train models to perform sequence-to-sequence tasks. 🔗lnkd.in/gJh5y3Wp Advanced 9) ML Pipelines 🔗lnkd.in/ggKCidzH 10) Google Cloud Solutions II: Data and Machine Learning 🔗lnkd.in/gAxUGsjR AI is advancing as fast as ever… It’s up to you to keep up. Take these courses today to build a brighter future. Which one are you trying first? Let me know in the comments below ⬇️ Disclaimer: Most course materials are free to study. However, labs may need credits in campaigns to be accessed. 👉Join Our Telegram for more amazing notes and study materials t.me/CodeNoteBook

📅 We have an exciting session on Building Chatbots with Dialogflow hosted by Google Developer Group (GDG) On Campus Kabale University 👨‍💻The session will take place on 08‑03‑2026 Wednesday to gain handson insights into chatbot development with Dialogflow #GDG #KabaleUniversity

What are AI agents, and what do they do? Imagine a digital helper that can think, learn, and take action on its own. That’s an AI agent! What can it do? - Think: Solve problems for you - Act: Do tasks automatically - Observe: Watch & understand what’s happening - Plan: Figure out the best steps - Collaborate: Work with people or other agents -Improve: Learn & get smarter over time AI Agents vs AI Assistants vs Bots -AI Agent: Works independently, makes decisions -AI Assistant: Helps you directly, follows instructions -Bot: Does simple repetitive tasks How it works: - Memory: Remembers info to get better - Tools: Uses apps or systems to complete tasks - AI model: Thinks, understands, and decides Types & examples: - Customer support agents - Data analysis & coding helpers - Creative idea generators - Single agents or teams working together Why it matters: - Automates tasks - Learns & improves -Can handle complex work Challenges: - Tasks needing empathy - Changing environments - Heavy on resources Tools like Google Cloud’s Vertex AI, Dialogflow, Cloud Run make building AI agents easy. #targon #manifold

Let's Review Conversational AI A conversational bot (AI) is built by training or integrating a large language model, wrapping it with conversation logic, memory, and retrieval tools, then deploying it through APIs and user interfaces using cloud infrastructure. e.g. User input → Language understanding → Reasoning → Text generation → Response in which the bot uses RAG (Retrieval-Augmented Generation), for accuracy and real-time knowledge. Examples: chatbots, virtual assistants, customer support bots. Conversational AI models like Generative AI, they’re not the same, but they’re closely related. Modern Conversational bot (AI) is strong at reasoning, explanation, creativity, and general-purpose assistance. They Optimized for real-time data and conversational tone that can create new content: such as text, images, music, code, videos, etc. Examples: ChatGPT, Grok and Gemini perform tasks of image generators, music generators Also focused on efficiency, technical reasoning, and cost-effective large-scale modeling. Modelling or Model is known to be (The Brain) where the AI generates responses. - Trained on massive text datasets - Learns grammar, facts, reasoning patterns - Predicts the next token (word piece) Common Model Types - Rule-based models (simple, scripted) - Machine Learning models - Large Language Models (LLMs) like GPT-style models Training & Fine-Tuning - Pre-training: Learning general language (Trained on books, articles, code, conversations, Uses self-supervised learning) - Fine-tuning: Specializing for tasks (support, sales, education). Optimized for conversation and safety. - (RLHF - Reinforcement Learning from Human Feedback): Improves quality and safety Backend & APIs OR Development Tools Role This connects the AI to applications - Python / JavaScript Core programming - LangChain Connect LLMs to tools, memory, data - LlamaIndex Document-based chat (RAG) - FastAPI / Flask Backend APIs - React / Next.js Frontend chat UI - WebSockets Real-time chat Tool / StudioUse Case No-Code / Low-Code Studios (Beginner Friendly) - OpenAI Playground Test and deploy ChatGPT-style bots (platform.openai.com/chat/edi…) - Azure AI Studio Enterprise-grade conversational AI (azure.microsoft.com/en-us/bl……) - Dialogflow (Google) Customer support & voice bots (cloud.google.com/products/ag……?) - Microsoft Copilot StudioBusiness & enterprise bots (microsoft.com/en-us/microsof…) - IBM Watson Assistant Enterprise conversational AI (cloud.ibm.com/catalog/servic…) - Botpress Visual flow-based chatbot building (get.manychat.com) - Amazon Lex Voice & chat bots (aws.amazon.com/lex/) Bots run on powerful infrastructure deployment. Compute - GPUs - TPUs Cloud - AWS - Azure - Google Cloud Conversation Logic - Memory (Context) - User intent (interactions, use of words and intentions of question flows) - Response flow or Dialogue flow - Personality and tone. UI Frontend - User Interface (Where Users Talk to It) - Chat apps - Websites - Mobile apps - Voice assistants. Examples of Conversational AI - GPT (ChatGPT) - Grok (xAI) - DeepSeek - LLaMA (Meta). Be Beautiful 𝕏 ❤️👾

No they didn't. And the contact center space is largely already built on Google. Contact Center is built on more than just the tech. The large players are in line with Google and embed this tech into their stack, they have for a while, since the early Dialogflow acquisition days.

130,808 stars on the langchain-ai/langchain GitHub repository highlights the immense interest in this agent engineering platform that enables the development of AI agents with unique capabilities. This project provides a framework for building and deploying AI models, and its significance lies in its potential to revolutionize the way AI systems are designed and interact with humans. The data nobody is talking about: ▪️ 130,808 stars and a fork count of 21,544, indicating a growth rate of 13,000 new stars in the last 30 days ▪️ The platform solves the problem of developing and integrating AI agents by providing a modular and flexible architecture, allowing for the creation of complex AI systems ▪️ Historically, similar tools like Rasa (34,116 stars) and Dialogflow (18,515 stars) have been used for building conversational AI, but langchain-ai/langchain has surpassed them in terms of popularity and community engagement ▪️ The Bitcoin price surge of 2.3% to $70,303 and the growing interest in AI indicate a potential shift in the industry towards more autonomous and decentralized systems What this actually means: The rapid growth of the langchain-ai/langchain project signals a significant shift in the AI development landscape, with a growing focus on building more advanced and interactive AI systems. Watch the fork count exceed 25,000 in the next quarter. Can langchain-ai/langchain's agent engineering platform become the new standard for AI development, replacing traditional machine learning frameworks?