# Conversational AI Published articles for Conversational AI. 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,... ## Test Complex Interactions Earlier with AI Prototyping DevFeed: [Test Complex Interactions Earlier with AI Prototyping]() Original publisher: [Read original article]() Author: Megan Chan Published: 2026-09-11T17:00:00Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [Artificial Intelligence](), [User interface design](), [cursor]() Tags: [ai](), [article](), [case-studies](), [conversational-ai](), [cursor](), [design](), [prototyping]() ### AI overview AI prototyping tools enable teams to create high-fidelity, interactive prototypes of complex interfaces and test them with users earlier in the design process. The article discusses how Ramp used Cursor to prototype an AI chat-based expense-policy editor and uncover usability issues and edge cases. ### Source excerpt AI tools make it feasible to build fully interactive prototypes of complex interfaces so you can test them with users earlier in the design process. ## 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. ## 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. ## When LLM judges agree, should we believe them? DevFeed: [When LLM judges agree, should we believe them?]() Original publisher: [Read original article]() Author: Krishna Balasubramanian; Sasha Podkopaev Published: 2026-08-26T17:10:40Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Large Language Model](), [Retrieval-Augmented Generation](), [Ising](), [benchmark overfitting machine learning](), [Network](), [AI, ML & Data Engineering]() Tags: [conversational-ai](), [evaluation](), [ising](), [large-language-models-llms](), [llm](), [machine-learning](), [metrics](), [retrieval](), [retrieval-augmented-generation]() ### AI overview The article examines whether agreement among LLM judges is trustworthy when their outputs may be correlated. It presents a dependence-aware aggregation method based on Ising models that discounts shared blind spots and outperforms historical-accuracy-weighted majority voting on three tasks. ### Source excerpt Discounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives. ## SOP-Bench: A new benchmark for evaluating AI agents on real business procedures DevFeed: [SOP-Bench: A new benchmark for evaluating AI agents on real business procedures]() Original publisher: [Read original article]() Author: Rohith Nama; Nandi Subhrangshu Published: 2026-08-21T15:57:17Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Benchmark](), [Artificial Intelligence](), [Ground truth / benchmark quality]() Tags: [agentic-ai](), [ai](), [ai-agents](), [benchmark](), [benchmarks](), [conversational-ai](), [testing]() ### AI overview SOP-Bench is an openly available benchmark for evaluating how well AI agents execute real standard operating procedures authored by domain experts. It combines genuine enterprise procedures, functioning tools, and ground-truth answers to test interpretation, memory, judgment, and tool selection during complete procedures. ### Source excerpt Extendable framework enables testing agents on the full set of capabilities required to successfully complete a procedure, not isolated proxy tasks. ## 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. ## A new benchmark for evaluating patient-facing health AI agents DevFeed: [A new benchmark for evaluating patient-facing health AI agents]() Original publisher: [Read original article]() Author: Korosh Vatanparvar; Ashutosh Joshi Published: 2026-07-29T15:16:52Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [AI Bots]() Tags: [agentic-ai](), [agents](), [ai](), [benchmark](), [conversational-ai](), [evaluation](), [foundation-models](), [health](), [healthcare](), [safety](), [synthetic]() ### AI overview The article introduces PatientAgentBench, a clinician-vetted benchmark for evaluating the safety and task performance of patient-facing healthcare AI agents in realistic, multiturn conversations. ### Source excerpt PatientAgentBench generates a synthetic patient health record, a realistic clinical vignette, and a patient agent that converses with the AI system under evaluation, to capture what a patient-facing agent actually has to do. ## SymptomAI: Towards a conversational AI agent for everyday symptom assessment DevFeed: [SymptomAI: Towards a conversational AI agent for everyday symptom assessment]() Original publisher: [Read original article]() Published: 2026-07-22T21:32:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [AI Agent](), [Artificial Intelligence](), [Language models](), [Chat Bot](), [Google](), [LLM evaluation / benchmarking]() Tags: [ai](), [ai-agent](), [benchmarking](), [conversational-ai](), [general-science](), [google](), [health-bioscience](), [healthcare](), [language-models](), [natural-language-processing](), [research](), [responsible-ai]() ### AI overview Google Research presents SymptomAI, a study of conversational AI agents for everyday symptom interviews and differential-diagnosis assessment. The national-scale study involved 13,917 participants interacting with one of five Gemini Flash 2.0 SymptomAI agents, with performance compared against clinical assessments for research benchmarking. ### Source excerpt General Science ## Getting started with ChatGPT DevFeed: [Getting started with ChatGPT]() Original publisher: [Read original article]() Published: 2026-07-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [Prompt Engineering]() Tags: [ai](), [chatgpt](), [conversational-ai](), [getting-started](), [guides](), [how-to](), [large-language-models](), [learn](), [openai-academy](), [skills](), [work](), [workflows]() ### AI overview A beginner-friendly guide to using ChatGPT. It explains how to start conversations, write effective prompts, and apply ChatGPT to tasks such as drafting, brainstorming, summarizing, and problem-solving. It also introduces Work, Projects, custom GPTs, and Skills for larger or repeatable workflows. ### Source excerpt Learn how to use ChatGPT, start your first conversation, and discover simple ways to write, brainstorm, and solve problems with AI. ## 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. ## How Omio is building the future of conversational travel DevFeed: [How Omio is building the future of conversational travel]() Original publisher: [Read original article]() Published: 2026-06-23T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat]() Tags: [chatgpt](), [conversational-ai](), [development](), [integration](), [models](), [openai](), [real-time]() ### AI overview Omio describes using OpenAI models and real-time transportation inventory to let travelers discover and book journeys through natural-language conversation. The company also positions this work as part of a broader AI-native transformation and faster product development. ### Source excerpt Discover how Omio uses OpenAI to power conversational travel experiences, accelerate product development, and transform into an AI-native company. ## DASH 2026 Harnessing AI: Guide to Datadog's newest announcements DevFeed: [DASH 2026 Harnessing AI: Guide to Datadog's newest announcements]() Original publisher: [Read original article]() Author: Datadog Published: 2026-06-09T00:00:00Z Content type: release Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Chat](), [dashboards]() Tags: [agentic](), [ai](), [ai-observability](), [announcements](), [autonomous](), [bits-ai](), [coding](), [conversational-ai](), [dash](), [dashboards](), [incident](), [logs](), [mcp](), [metrics](), [observability](), [telemetry](), [traces]() ### AI overview A DASH 2026 release roundup covering Datadog AI features, including Bits Chat for natural-language search, incident investigation, and dashboard generation. ### Source excerpt A roundup of everything we announced at DASH 2026, including Datadog MCP Apps, Bits Code, AI Observability, and Pup CLI. ## Bridging intent and execution in agentic systems DevFeed: [Bridging intent and execution in agentic systems]() Original publisher: [Read original article]() Author: Gaurav Gupta; Vatshank Chaturvedi Published: 2026-06-08T17:00:00Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Agent Harness](), [AI Agent](), [systems](), [Large Language Model](), [Terminal](), [cloud-infrastructure](), [Open Source](), [Code]() Tags: [agent](), [agentic](), [agentic-ai](), [ai](), [ai-agent](), [benchmarks](), [cloud-and-systems](), [code](), [conversational-ai](), [infrastructure](), [llm](), [software](), [tools]() ### AI overview The article argues that performance in agentic systems is fundamentally a systems problem involving the interaction between a large language model and its harness. It defines the intent-execution gap between model intentions and harness actions, shows that reducing this gap can improve benchmark results without task-specific tuning, and emphasizes the effects of tools, execution graphs, infrastructure, timeouts, and resource constraints. It also introduces Simple Strands Agent (SSA), a lightweight customizable harness, and argues that model-harness codesign is important because model families differ in tool use and feedback interpretation. ### Source excerpt The harnesses that mediate between models and tools in agentic systems are becoming their own performance bottleneck, but a few simple design principles can fix what ails them. ## Ground truth is a process, not a dataset DevFeed: [Ground truth is a process, not a dataset]() Original publisher: [Read original article]() Author: Venkatesh Saligrama Published: 2026-06-03T15:56:57Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [ai observability](), [AI Chat]() Tags: [ai](), [ai-fact-checking](), [ai-generated-research-reports](), [audit-then-score](), [benchmarking](), [conversational-ai](), [datasets](), [deep-research-verification](), [deepfact-bench](), [evaluation](), [fact-verification](), [fact-verification-benchmark](), [generative-ai](), [ground-truth-benchmark-quality](), [hallucination-detection](), [hallucinations](), [human-ai-evaluation](), [large-language-models-llms](), [llm-evaluation-benchmarking](), [long-context](), [machine-learning](), [responsible-ai](), [retrieval-augmented-generation-rag]() ### AI overview The article argues that evaluating factuality in long AI-generated research reports requires a process-based approach to ground truth. It introduces audit-then-score and accompanying datasets for benchmarking AI fact checkers. ### Source excerpt Automatically fact-checking long, AI-generated research reports poses new challenges -- including benchmarking. ## Diverse reasoning traces teach LLMs to make better decisions DevFeed: [Diverse reasoning traces teach LLMs to make better decisions]() Original publisher: [Read original article]() Author: Sheng Jia; Xiao Wang; Shiva Kasiviswanathan Published: 2026-05-26T15:17:06Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Language models]() Tags: [benchmarks](), [chain-of-thought](), [code-generation](), [conversational-ai](), [deep-learning](), [evaluation](), [fine-tuning](), [iclr](), [iclr-2026](), [large-language-models-llms](), [llms](), [math-reasoning](), [parallel-reasoning](), [post-training](), [post-training-optimization](), [reasoning](), [reinforcement-learning](), [traces]() ### AI overview The article presents set-supervised fine tuning and global forking policy optimization to train LLMs on multiple distinct reasoning paths. It reports 5% to 7% single-shot accuracy gains on standard benchmarks. ### Source excerpt How to train language models to generate diverse, accurate reasoning paths using tokens that control distinct reasoning strategies. ## Making LLMs faster without sacrificing accuracy DevFeed: [Making LLMs faster without sacrificing accuracy]() Original publisher: [Read original article]() Author: Tao Yu; Youngsuk Park Published: 2026-05-15T13:00:00Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Language models](), [Training AI Models](), [web applications]() Tags: [architecture](), [chinchilla-scaling-law](), [conversational-ai](), [generative-ai](), [grouped-query-attention](), [hyperparameter-optimization](), [iclr-2026](), [inference](), [inference-efficiency](), [large-language-models](), [large-language-models-llms](), [llm-optimization](), [llms](), [machine-learning](), [model-architecture](), [network-architectures](), [scaling-laws](), [training](), [transformer-architecture]() ### AI overview The article presents scaling laws that connect LLM architectural choices to the tradeoff between accuracy and efficiency. It describes how these choices can improve inference throughput without reducing accuracy. ### Source excerpt A new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy. ## Promptimus: Improving already good LLM prompts with zero manual engineering DevFeed: [Promptimus: Improving already good LLM prompts with zero manual engineering]() Original publisher: [Read original article]() Author: Zhengyuan Shen; Yunfei Bai; Sullam Jeoung; Shuai Wang Published: 2026-05-14T13:47:45Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Automated prompt engineering](), [Prompt Engineering](), [Optimization](), [Large Language Model](), [AI Agent](), [Artificial Intelligence](), [multimodal](), [Code]() Tags: [ai](), [ai-agent](), [amazon-bedrock](), [automated-prompt-engineering](), [code-generation](), [conversational-ai](), [engineering](), [generative-ai](), [large-language-models-llms](), [llm](), [multimodal](), [optimization](), [performance](), [prompt-engineering](), [prompt-optimization]() ### AI overview Promptimus is an automated prompt-engineering method for improving already strong prompts without manual engineering. It uses task data, user-defined performance metrics, failure analysis, debugging agents, sanitization, and targeted edit mode to refine prompts while preserving working business logic. The method is model agnostic and supports textual and multimodal LLM tasks, including classification, extraction, generation, summarization, code generation, and tool use. ### Source excerpt By focusing on specific failure points and suggesting targeted solutions, a new automated prompt-engineering framework improves prompt performance without compromising existing functionality. ## 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. ## From Drift to Direction: The Architecture Behind Digression Control (Role Play) DevFeed: [From Drift to Direction: The Architecture Behind Digression Control (Role Play)]() Original publisher: [Read original article]() Author: Raka Dalal Published: 2026-04-09T12:25:37Z Content type: article Language: en Sources: [Udemy Engineering]() Topics: [Large Language Model](), [Artificial Intelligence](), [systems](), [Learning]() Tags: [ai](), [architecture](), [blocking](), [conversational-ai](), [data-science](), [large-language-models](), [learning](), [llm](), [llm-evaluation](), [prompt-engineering](), [systems]() ### AI overview Udemy's Role Play experience uses open-ended LLM-mediated conversations for practice with defined learning goals. The article examines digression as a systems challenge and introduces a Response Steering Layer intended to preserve conversational naturalness while improving goal adherence. ### Source excerpt Role Play @UdemyIntroduction AI-powered role play systems aim to recreate one of the most effective learning modalities: practicing real-world conversations in a safe, repeatable environment. In Udemy's Role Play experience, learners engage in open-ended, free-text dialogues with an AI character that embodies a specific persona -- such as an interviewer, a stakeholder, or a customer -- while working toward clearly defined learning goals. This contrasts with many traditional digital learning tools, which rely on static content, multiple-choice interactions, or scripted simulations that limit conversational variability. This design prioritizes realism and skill transfer, allowing learners to respond naturally rather than selecting from predefined options. Open-ended conversations mediated by LLM-based systems, however, introduce a core systems challenge: digression. In real conversations, people may drift off-topic, but human tutors actively manage and redirect such drift. In a learning-oriented role play mediated by an AI system, persistent digressions undermine the experience in more subtle but consequential ways. Learners may shift into meta-conversations, attempt to override the role being played, or explore tangents unrelated to the scenario's objectives. When this happens, learning goals become harder to assess, feedback loses grounding in the conversation, and the interaction no longer reflects the real-world situation the learner is meant to practice. A naive solution is to tightly constrain the conversation -- blocking inputs, rejecting turns, or forcing the dialogue back onto a scripted path. While this approach improves goal adherence, it comes at the cost of immersion and learner agency. Overly rigid controls make the AI feel less like a conversational partner and more like an instructional interface. On the other hand, allowing unrestricted conversational freedom preserves naturalness but leads to goal drift, inconsistent learning signals, and reduced reliab ## 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 ## February 2026 newsletter DevFeed: [February 2026 newsletter]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-02-19T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [Data visualization](), [Open Source](), [Streaming](), [anthropic](), [Claude](), [Conversational AI](), [Database](), [Python]() Tags: [anthropic](), [claude](), [clickhouse](), [conversational-ai](), [data](), [data-visualization](), [newsletter](), [python](), [real-time](), [streaming]() ### AI overview The February 2026 ClickHouse newsletter covers the company's Series D funding, its official Kubernetes operator release, data modeling and query optimization guidance, community work using ClickHouse with Claude and LibreChat, and upcoming training sessions and events. ### Source excerpt Welcome to the February 2026 ClickHouse newsletter, which will round up what's happened in real-time data warehouses over the last month. 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