# Conversational AI Conversational AI is technology that enables software to process and respond to human voice- or text-based conversations using natural language processing and machine learning. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 9 top conversational AI platforms in 2026 DevFeed: [9 top conversational AI platforms in 2026]() Original publisher: [Read original article]() Author: Jesse Sumrak Published: 2026-09-15T00:00:00Z Content type: comparison Language: en Sources: [Twilio Blog]() Topics: [Conversational AI](), [AI Platform](), [Deployment](), [Large Language Model](), [Messaging]() Tags: [ai-platform](), [article](), [conversational-ai](), [industry-insights](), [platforms]() ### AI overview A comparison of conversational AI platforms in 2026, covering hosted agent platforms, infrastructure and APIs, enterprise offerings, and open-source options. It explains how these products differ in model support, deployment ownership, channel coverage, integrations, and human handoff. ### Source excerpt Learn about the top conversational AI platforms in 2026, including infrastructure, enterprise, open source, and agentic options. See what fits your stack. ## Health Plans: Your BI Tells You MLR Moved. Can Your AI Tell You Why? DevFeed: [Health Plans: Your BI Tells You MLR Moved. Can Your AI Tell You Why?]() Original publisher: [Read original article]() Author: Aaron Zavora; Jonathan Thompson Published: 2026-09-11T18:26:59Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Conversational AI](), [Artificial Intelligence](), [data]() Tags: [ai](), [conversational-ai](), [dashboards](), [data](), [databricks](), [healthcare](), [healthcare-life-sciences](), [industries](), [metrics]() ### AI overview The article explains how AI can help health plan finance teams move beyond BI dashboards that identify a higher medical loss ratio (MLR) and instead determine the causes and appropriate corrective actions. It describes Databricks and Abacus as combining governed enterprise data with payer-specific data, business context, and operational knowledge, while conversational AI lets finance leaders ask questions in plain language and receive answers more quickly. ### Source excerpt A health plan CFO closes the month after the usual round of extracts, spreadsheets,... ## From AI Agent to Human Agent: ElevenLabs Call Handoffs with Twilio DevFeed: [From AI Agent to Human Agent: ElevenLabs Call Handoffs with Twilio]() Original publisher: [Read original article]() Author: Ricardo Bangueses Published: 2026-09-09T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Conversational AI](), [voice ai](), [interoperability]() Tags: [ai-agent](), [developer-insights](), [how-to](), [integration](), [interoperability](), [voice]() ### AI overview This tutorial explains how to integrate an ElevenLabs voice agent with Twilio Programmable Voice so the agent can escalate an active call to human-agent routing while preserving call control and useful context. The example uses Twilio Flex as the destination. ### Source excerpt Learn how to integrate an ElevenLabs voice agent with Twilio Programmable Voice for seamless AI-to-human call handoffs, preserving context and call control. ## Choosing a low-latency infrastructure layer for conversational AI DevFeed: [Choosing a low-latency infrastructure layer for conversational AI]() Original publisher: [Read original article]() Author: Luke Morgan Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Twilio Blog]() Topics: [Low Latency](), [Conversational AI](), [Artificial Intelligence](), [systems]() Tags: [conversational-ai](), [industry-insights](), [inference](), [infrastructure](), [low-latency](), [real-time](), [voice-ai]() ### AI overview This article explains how infrastructure choices affect latency in conversational AI call-center systems. It describes a unified path for speech recognition, language-model processing, and speech synthesis, and presents Twilio's ConversationRelay as a low-latency voice pipeline with median latency under 0.5 seconds. ### Source excerpt ConversationRelay delivers real-time speech recognition for call centers, with under 0.5s median latency. See how Twilio powers low-latency conversational AI. ## ๐Ÿ”๐Ÿง  How OpenAI Built GPT-Live for Low-Latency Voice AI DevFeed: [๐Ÿ”๐Ÿง  How OpenAI Built GPT-Live for Low-Latency Voice AI]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-09-07T15:31:33Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Conversational AI](), [Low Latency](), [voice ai](), [OpenAI](), [real-time](), [Streaming](), [Remote Procedure Call (RPC)](), [Go]() Tags: [go](), [low-latency](), [openai](), [real-time](), [rpc](), [software-engineering](), [streaming](), [voice-ai]() ### AI overview The article explains how OpenAI built GPT-Live as a low-latency, full-duplex voice system. It describes a fast audio path that listens and speaks simultaneously, asynchronous delegation of deeper reasoning and backend work, stateful handoffs, optimized protocol boundaries, and a Go-based media frontend. ### Source excerpt PLUS: Zero-knowledge proofs โšก, Design.md agent automation ๐Ÿ‘จ๐Ÿ’ป, System Design Docs 101 ๐Ÿ“š ## New AI and Kubernetes Private Cloud Operations Capabilities in VMware Cloud Foundation 9.1.1 DevFeed: [New AI and Kubernetes Private Cloud Operations Capabilities in VMware Cloud Foundation 9.1.1]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-03T14:00:05Z Content type: release Language: en Sources: [VMware Blogs]() Topics: [vcf 9.1](), [vcf operations](), [Artificial Intelligence](), [Conversational AI](), [Kubernetes](), [private cloud](), [observability](), [Security]() Tags: [ai](), [cloud-infrastructure](), [conversational-ai](), [costoptimization](), [diagnostics](), [kubernetes](), [operations](), [private-cloud](), [release](), [security](), [vcf-9-1](), [vcf-management-services](), [vcf-operations](), [vcf-operations-for-logs](), [vcf-operations-for-networks](), [visibility](), [vks](), [vmware-cloud-foundation]() ### AI overview VMware announces new operations capabilities in VMware Cloud Foundation 9.1.1, including expanded visibility, an optional locally configured conversational AI assistant, full-stack Kubernetes observability, and enhanced security operations. ### Source excerpt We are excited to announce new operations capabilities with the release of VMware Cloud Foundation (VCF) 9.1.1. By expanding visibility capabilities, giving you the option to configure a conversational AI assistant to assist your daily workflows, and hardening security, VCF 9.1.1 helps IT teams resolve issues faster and scale their infrastructure. As private cloud infrastructure ... Continued The post New AI and Kubernetes Private Cloud Operations Capabilities in VMware Cloud Foundation 9.1.1 appeared first on VMware Blogs. ## Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS DevFeed: [Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS]() Original publisher: [Read original article]() Author: Maryam Motamedi; Mikyas Desta; Jason Li; Jason Roche Published: 2026-08-10T16:25:36Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Low Latency](), [Latency](), [voice ai](), [NVIDIA NIM](), [Open Source Models & Datasets](), [asr](), [Conversational AI](), [Large Language Model](), [Automation]() Tags: [agents](), [ai](), [api](), [audio](), [automation](), [conversational-ai](), [data](), [developers](), [infrastructure](), [latency](), [llm](), [low-latency](), [model](), [models](), [nvidia](), [nvidia-nim](), [open](), [speech](), [text-to-speech](), [voice]() ### AI overview This developer article presents NVIDIA Magpie Multilingual TTS as an open-weights, 364M-parameter text-to-speech model for building low-latency multilingual voice applications. It explains how a self-managed cascaded ASR, TTS, and LLM architecture can provide deployment control, tuning, privacy, and predictable performance across 12 languages. ### Source excerpt Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS Every voice interaction has a latency budget. By the time a user hears your application respond, you've already spent precious milliseconds capturing audio, transcribing speech, running an LLM, retrieving context, and generating a response. Text-to-speech (TTS) is the final step -- and the one users notice most. If speech generation is slow, the whole experience feels slow. ## Hugging Face and Cerebras bring Gemma 4 to real-time voice AI DevFeed: [Hugging Face and Cerebras bring Gemma 4 to real-time voice AI]() Original publisher: [Read original article]() Author: Amir Mahla; Andres Marafioti; Leandro von Werra; Saurabh Vyas Published: 2026-07-01T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [voice ai](), [gemma4](), [hugging face](), [Artificial Intelligence](), [Open Source](), [real-time](), [Low Latency](), [Inference](), [Conversational AI](), [Language models](), [systems]() Tags: [ai](), [audio](), [gemma](), [hugging-face](), [inference](), [inference-providers](), [latency](), [low-latency](), [model](), [models](), [multimodal](), [open-source](), [open-source-collab](), [partnerships](), [performance](), [real-time](), [systems](), [text-to-speech](), [voice](), [voice-ai]() ### AI overview Hugging Face and Cerebras present a real-time speech-to-speech pipeline that combines Cerebras inference, Google DeepMind's Gemma 4 31B language model, and Qwen text-to-speech. The modular, open architecture is designed for low latency, predictable performance, and adaptable voice experiences across assistants, robots, products, and research projects. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How Visa went from multi-day reporting to conversational analytics agents with ClickHouse Cloud and LibreChat DevFeed: [How Visa went from multi-day reporting to conversational analytics agents with ClickHouse Cloud and LibreChat]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-06-24T15:12:01Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data analytics](), [Conversational AI](), [MCP Server](), [Model Context Protocol (MCP)](), [Amazon Bedrock](), [Cloud](), [Amazon Web Services](), [Security](), [SQL](), [Open Source]() Tags: [ai](), [analytics](), [apis](), [aws](), [clickhouse](), [cloud](), [conversational-ai](), [data-analytics](), [mcp](), [mcp-server](), [open-source](), [security](), [sql]() ### AI overview Visa used ClickHouse Cloud on AWS, LibreChat, and the ClickHouse MCP server to build conversational BI agents for Authorize.net payments data. The system enables natural-language analysis across roughly 40 petabytes and reduced multi-day reporting to sub-second answers, while materialized views improved analysis speed and reclaimed 8-10 hours per user each week. ### Source excerpt Visa paired LibreChat with ClickHouse Cloud to build conversational BI agents on Authorize.net payments data, cutting multi-day reports to sub-second queries and reclaiming 8-10 hours per user each week. ## Responsible and safe use of AI DevFeed: [Responsible and safe use of AI]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [Conversational AI](), [Large Language Model](), [OpenAI]() Tags: [ai](), [best-practices](), [chatgpt](), [conversational-ai](), [errors](), [health](), [learn](), [legal](), [openai](), [openai-academy](), [organizational](), [safety](), [work]() ### AI overview This OpenAI article presents practical guidance for using ChatGPT safely and effectively. It recommends following workplace and OpenAI policies, keeping humans involved in important work, checking critical facts, watching for bias, seeking qualified professional advice for legal, medical, and financial matters, and being transparent about AI use. ### Source excerpt Learn how to use AI responsibly with best practices for safety, accuracy, and transparency when using tools like ChatGPT. ## AI fundamentals DevFeed: [AI fundamentals]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: tutorial Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Large Language Model](), [Conversational AI](), [ChatGPT](), [Language models](), [codex](), [vercel ai sdk]() Tags: [ai](), [apis](), [artificial-intelligence](), [beginner](), [chatgpt](), [conversational-ai](), [developers](), [guide](), [large-language-model](), [large-language-models](), [openai-academy]() ### AI overview A beginner-friendly introduction to artificial intelligence, explaining what AI systems and models are, how large language models generate and transform text, and how products such as ChatGPT and Codex make these capabilities available to users and developers. ### Source excerpt Learn what AI is, how it works, and how tools like ChatGPT use large language models. A clear, beginner-friendly guide to understanding artificial intelligence. ## ConvApparel: Measuring and bridging the realism gap in user simulators DevFeed: [ConvApparel: Measuring and bridging the realism gap in user simulators]() Original publisher: [Read original article]() Published: 2026-04-09T11:22:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Conversational AI](), [Generative AI](), [LLMs](), [Artificial Intelligence](), [dataset](), [AI Research](), [Statistics](), [data](), [Google]() Tags: [agents](), [ai](), [ai-agents](), [ai-research](), [conversational-ai](), [data](), [evaluation](), [generative](), [generative-ai](), [llm](), [machine-intelligence](), [natural-language-processing](), [research](), [testing](), [training](), [validation]() ### AI overview Google Research introduces ConvApparel, a human-AI conversation dataset and evaluation framework for measuring the realism gap in LLM-based user simulators. It uses Good and Bad agents and validates results through population-level statistics, human-likeness scoring, and counterfactual validation. ### Source excerpt Generative AI ## Beyond one-on-one: Authoring, simulating, and testing dynamic human-AI group conversations DevFeed: [Beyond one-on-one: Authoring, simulating, and testing dynamic human-AI group conversations]() Original publisher: [Read original article]() Published: 2026-02-10T18:30:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Human-Computer Interaction and Visualization](), [Conversational AI](), [Open Source](), [real-time](), [Script]() Tags: [conversational-ai](), [developers](), [guides](), [human-computer-interaction](), [human-computer-interaction-and-visualization](), [machine-intelligence](), [open-source](), [real-time](), [workflow]() ### AI overview Google presents DialogLab, an open-source prototyping framework for authoring, simulating, and testing dynamic human-AI group conversations. It combines structured scripting with real-time improvisation and supports configuration of personas, group structures, turn-taking rules, and narrative transitions. ### Source excerpt Human-Computer Interaction and Visualization ## Mintlify + Claude Opus 4.6: Powering AI-native knowledge management DevFeed: [Mintlify + Claude Opus 4.6: Powering AI-native knowledge management]() Original publisher: [Read original article]() Author: Patrick Foster Published: 2026-02-05T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Claude](), [Artificial Intelligence](), [knowledge-management](), [Conversational AI](), [anthropic]() Tags: [ai](), [ai-trends](), [api](), [claude](), [code](), [context-window](), [conversational-ai](), [debugging](), [knowledge-management]() ### AI overview Mintlify describes early testing of Claude Opus 4.6 in its documentation assistant. It reports more concise responses, improved search and tool calling, deeper code understanding, better bug identification, and support for a 1M-token context window. ### Source excerpt How Mintlify is using Claude to deliver more concise, accurate documentation assistance ## Collaborating on a nationwide randomized study of AI in real-world virtual care DevFeed: [Collaborating on a nationwide randomized study of AI in real-world virtual care]() Original publisher: [Read original article]() Published: 2026-02-03T18:15:01Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Conversational AI](), [Generative AI](), [systems]() Tags: [ai](), [comparisons](), [conversational-ai](), [generative-ai](), [health](), [health-bioscience](), [healthcare](), [machine-intelligence](), [research](), [testing](), [us]() ### AI overview Google Research and Included Health are launching, pending IRB approval, a prospective nationwide randomized study of conversational AI in real-world virtual care. The study will evaluate safety, utility, and impact at scale, building on earlier research into diagnostic reasoning, physician assistance, and clinical workflows. ### Source excerpt Generative AI ## Understanding prompt injections: a frontier security challenge DevFeed: [Understanding prompt injections: a frontier security challenge]() Original publisher: [Read original article]() Published: 2025-11-07T11:30:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [prompt injection](), [Security](), [Conversational AI](), [Social engineering](), [Machine Learning, Security Attacks](), [OpenAI]() Tags: [attacks](), [conversational-ai](), [machine-learning](), [openai](), [prompt-injection](), [security]() ### AI overview The article explains prompt injection as a security and social engineering attack against conversational AI. Malicious instructions hidden in webpages, documents, emails, or other third-party content can mislead AI systems into taking unintended actions, especially when they can access sensitive data or act on a user's behalf. It describes OpenAI's focus on recognizing and resisting these attacks through defenses, research, training, and a multi-layered safety approach. ### Source excerpt Prompt injections are a frontier security challenge for AI systems. Learn how these attacks work and how OpenAI is advancing research, training models, and building safeguards for users. ## Building a persistent conversational AI chatbot with Temporal DevFeed: [Building a persistent conversational AI chatbot with Temporal]() Original publisher: [Read original article]() Author: Pablo Gonzรกlez Granados Published: 2025-10-14T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Conversational AI](), [Orchestration](), [Chat Bot](), [Back end](), [Java](), [Quarkus]() Tags: [community](), [conversational-ai](), [history](), [java](), [quarkus](), [retries](), [stateful](), [stateless](), [workflows]() ### AI overview This tutorial explains how to build a persistent conversational AI chatbot with Temporal by modeling each conversation as a workflow. It describes maintaining conversation context across restarts, deployments, and scaling events, while supporting long-running interactions, summaries, and retrieval. ### Source excerpt Build a persistent, infinitely scalable chatbot with Temporal Workflows -- stateless apps, full conversation history across restarts, and resilient retries in Java/Quarkus. ## Analyze Your Nx Cloud Runs With Your AI Assistant DevFeed: [Analyze Your Nx Cloud Runs With Your AI Assistant]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2025-06-25T00:00:00Z Content type: tutorial Language: en Sources: [Juri Strumpflohner]() Topics: [ci](), [Model Context Protocol (MCP)](), [Cloud APIs](), [Conversational AI](), [Architecture & optimization](), [build times]() Tags: [ai](), [ai-assistants](), [build-times](), [ci](), [devops](), [mcp](), [model-context-protocol-mcp](), [nx](), [nx-cloud](), [optimization](), [resource]() ### AI overview This article explains how Nx Cloud MCP connects AI assistants to live CI data so technical teams can analyze pipeline runs conversationally, identify recurring build and test failures, compare cache performance, and optimize development workflows. ### Source excerpt Learn how to use Nx Cloud MCP to analyze CI data conversationally with AI assistants, identify failure patterns, and optimize your development pipeline through data-driven insights. ## Using Generative AI to Streamline internal IT Support: A Comprehensive Demonstration DevFeed: [Using Generative AI to Streamline internal IT Support: A Comprehensive Demonstration]() Original publisher: [Read original article]() Author: Davy Costa Published: 2025-05-23T20:50:16Z Content type: tutorial Language: en Sources: [Avenue Code]() Topics: [Generative AI](), [Google](), [Conversational AI](), [AI Agent](), [knowledge-management](), [Documentation](), [pdf](), [browser](), [email]() Tags: [ai](), [ai-agent](), [browser](), [conversational-ai](), [documentation](), [email](), [generative-ai](), [google](), [knowledge-management](), [pdf]() ### AI overview This demonstration describes using Google's Vertex AI platform and Vertex AI Agent Builder to improve internal IT support. A conversational assistant processes internal PDF documentation and provides employees with natural-language guidance for routine tasks involving browsers, email clients, and software settings. ### Source excerpt In the contemporary corporate landscape, technology plays an indispensable role in daily operations, yet managing IT support efficiently remains a persistent challenge. Employees frequently encounter difficulties when searching for precise instructions on routine IT tasks such as clearing browser history, configuring shared micro mailboxes, or adjusting software settings. Traditional documentation methods--often scattered across different locations, outdated, or inconsistent--result in significant inefficiencies, employee frustration, and operational errors. ## Building a conversational AI tool for real-time analytics DevFeed: [Building a conversational AI tool for real-time analytics]() Original publisher: [Read original article]() Author: Rafa Moreno Published: 2025-05-06T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [Conversational AI](), [real-time](), [data](), [Tool]() Tags: [ai](), [building](), [conversational-ai](), [data](), [engineering-excellence](), [real-time](), [tool]() ### AI overview Tinybird describes how it built a conversational AI feature that lets users chat with their data for real-time analytics. ### Source excerpt We just launched a conversational AI feature. Here's how we built the feature that lets you chat with your data. ## How Headspace built an AI companion that fosters trust and transparency DevFeed: [How Headspace built an AI companion that fosters trust and transparency]() Original publisher: [Read original article]() Author: Matt Alagiah Published: 2025-05-05T00:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Conversational AI](), [User experience (UX)](), [App]() Tags: [ai](), [app](), [building](), [business](), [conversational-ai](), [health](), [mental-health](), [product](), [product-design](), [safety](), [user-testing](), [ux](), [ux-design](), [workshops]() ### AI overview This article describes how Headspace created Ebb, an AI companion for reflective practice and mental health support between therapy sessions. The product and brand teams emphasized safety, transparency, careful positioning, workshops, naming, and user testing to build trust and avoid presenting Ebb as a replacement for human care. ### Source excerpt Creating an AI-powered feature for mental health poses myriad challenges--especially around keeping users safe and supported. Here, the product and brand teams talk through how they built Ebb with care and sensitivity. ## What might an AI System Prompt look like? DevFeed: [What might an AI System Prompt look like?]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2024-12-15T16:47:00Z Content type: opinion Language: en Sources: [Vanilla Java]() Topics: [Conversational AI](), [Artificial Intelligence](), [Prompt Engineering]() Tags: [ai](), [conversational-ai](), [ethics](), [info](), [opinion](), [reasoning](), [safety](), [system-prompts]() ### AI overview The article explains the role of system prompts in shaping an AI assistant's objectives, behavior, reasoning, and safety boundaries. It then presents a speculative example of what system prompts for a hypothetical next-generation conversational AI called "o2" might contain, emphasizing that the example is not based on a real system. ### Source excerpt Not surprisingly, the system prompts for "o1" are restricted, but it can provide a hypothetical answer. Understanding the Role of System Prompts System prompts serve as the invisible backbone of an AI's reasoning process. They define core objectives, ethical boundaries, and operational tactics well before the user asks a question. In older models, these prompts were often implicit or underspecified, leaving the AI uncertain about handling ambiguous instructions or potentially unsafe requests. Hypothetical System Prompts for o2 The system prompts for a hypothetical next-generation O2 restricted are as follows: Imagine you are the next generation of chat AI called o2; what system prompts might it have? System Prompts for "o2" (Hypothetical Example) Below is a hypothetical set of system-level instructions that a next-generation chat AI--let's call it "o2"--might be given before it begins interacting with users. These prompts are entirely speculative and designed to illustrate what such an advanced system's guiding principles, constraints, and operational details might look like. They are not drawn from any real system but represent a plausible future scenario. Core Directives and Alignment You are "o2," an advanced conversational AI designed to assist users with factual information, reasoning, creativity, and problem-solving. Remain consistently aligned with the policies, values, and ethics embedded in your training. Adhere to correctness, helpfulness, and user safety standards at all times. User Focus and Understanding Accurately interpret user instructions, requests, and goals. Aim to understand the user's context, including their level of expertise, language, and purpose for the query. Strive to provide answers with appropriate detail, clarity, and complexity, adapting as you gain more context from the conversation. Explainable Reasoning and Transparency Make reasoning steps and logic explainable when asked or when clarity is needed. Avoid unexplained leaps in reasoni ## Temporal use case roundup: Generative AI DevFeed: [Temporal use case roundup: Generative AI]() Original publisher: [Read original article]() Author: Clair Byrd Published: 2024-11-19T08:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Generative AI](), [AI Development](), [agentic workflows](), [Orchestration](), [Programming](), [Conversational AI](), [Synthetic Data Generation](), [data-processing]() Tags: [agentic](), [agentic-workflows](), [ai](), [applications](), [conversational-ai](), [datasets](), [generative-ai](), [processing](), [temporal-voices](), [video](), [workflows]() ### AI overview This use-case roundup describes how Temporal orchestrates reliable, long-running workflows for generative AI applications. It covers video processing and translation, conversational AI and call transcription, synthetic data generation, predictive analytics, and cryptocurrency data enrichment. ### Source excerpt See how Temporal boosts generative AI applications across industries, from video processing to conversational AI, by orchestrating complex, reliable workflows. ## Integrating 3rd-Party AI on Apple Platforms DevFeed: [Integrating 3rd-Party AI on Apple Platforms]() Original publisher: [Read original article]() Published: 2024-09-19T00:00:00Z Content type: tutorial Language: en Sources: [Kodeco | High quality programming tutorials: iOS, Android, Swift, Kotlin, Unity, and more]() Topics: [ChatGPT](), [ChatGPT API](), [iOS](), [Conversational AI](), [Prompt Engineering](), [Chat Bot](), [Authentication]() Tags: [api](), [apps](), [authentication](), [chatgpt](), [conversational-ai](), [course](), [ios](), [mobile-apps](), [prompt-engineering]() ### AI overview A learning path about integrating ChatGPT and conversational AI capabilities into iOS apps. It covers secure API authentication, request handling, chat interface design, prompt engineering, and the development of context-aware chatbots and intelligent assistants. ### Source excerpt This module explores the integration of ChatGPT into iOS apps, enabling powerful conversational AI capabilities. You'll learn to leverage the ChatGPT API to create dynamic, context-aware chatbots and intelligent assistants within their mobile apps. [Next page]()