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Alibaba Qwen Team Introduces Qwen3.5-LiveTranslate-Flash: Real-Time Multimodal Interpretation Across 60 Languages at 2.8-Second Latency

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Simultaneous interpretation is one of the harder problems in applied AI. You’re asking a model to translate speech before the speaker has finished a sentence. Every extra second of delay breaks the illusion of real-time communication. Alibaba’s Qwen team has been chipping away at this with each release. Their latest model, Qwen3.5-LiveTranslate-Flash, brings that latency […]

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From SAS/IntrNet to agentic AI: Watching two technology shifts unfold

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When I joined SAS in 1997, most analytics workflows still revolved around desktops, batch processing and highly technical users. Later that same year, SAS introduced SAS/IntrNet – a technology that helped bring SAS analytics into the growing world of web applications. At the time, it felt like a major shift […] The post From SAS/IntrNet

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Google Introduces Gemini 3.5 Flash at I/O 2026: A Faster and Cheaper Model for AI Agents and Coding

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Google just released Gemini 3.5 Flash at Google I/O May, 2026. It is the first Gemini 3.5 model. The series combines frontier intelligence with action. Google calls it a major leap for intelligent agents. The Flash tier has historically been faster and cheaper. 3.5 Flash outperforms Gemini 3.1 Pro on challenging benchmarks. The previous premium

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Upstash for Redis vs Supabase vs Neon: Which One Fits Vibe Coding Workflows in 2026?

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The short answer most comparison articles skip: these three tools are not competing for the same job. Before picking one, it helps to understand what each is actually designed to do, where they genuinely overlap, and where the real tradeoffs land when you are shipping code with an AI assistant at your side. What These

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Google Launches Antigravity 2.0 at I/O 2026: A Standalone Agent-First Platform with CLI, SDK, Managed Execution, and Enterprise Support

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Google used its I/O 2026 developer keynote to ship a meaningful architectural shift in how it packages AI-assisted development. The company announced Google Antigravity 2.0 — a standalone desktop application built entirely around agent orchestration alongside an Antigravity CLI, an Antigravity SDK, Managed Agents in the Gemini API, and enterprise support through the Gemini Enterprise

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Best Enterprise Level Agentic AI Platforms for 2026

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In 2026, enterprise agentic AI has moved from pilot budgets to production commitments. Salesforce is closing Agentforce deals at 29,000 since launch with $800M ARR. Microsoft Copilot Studio has 160,000 organizations running 400,000+ custom agents. ServiceNow has restructured its entire commercial model around autonomous AI tiers. The question is no longer whether to deploy —

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How to Build an Advanced Agentic AI System with Planning, Tool Calling, Memory, and Self-Critique Using OpenAI API

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In this tutorial, we build an advanced agentic AI system using the OpenAI API and a hidden terminal prompt for the API key. We design the agent as a small pipeline of specialized roles: planner, tool-using executor, and critic, so that we can separate strategy, action, and quality control. We also integrate structured tools (calculator,

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Vercel Labs Introduces Zero, a Systems Programming Language Designed So AI Agents Can Read, Repair, and Ship Native Programs

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Most programming languages were designed for humans who read error messages, interpret warnings, and manually trace through stack output to fix bugs. AI agents do none of those things well. They work better with structured data: predictable tokens, stable codes, and machine-parseable repair hints. That gap is what Vercel Labs is trying to close by

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Meet LiteLLM Agent Platform: A Kubernetes-Based, Self-Hosted Infrastructure Layer for Isolated Agent Sandboxes and Persistent Session Management in Production

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Running AI agents in a local script is straightforward. Running them reliably in production across teams, across restarts, with isolated environments per context is a different problem entirely. BerriAI, the company behind the LiteLLM AI Gateway, is now open-sourcing a purpose-built answer to that problem: the LiteLLM Agent Platform. The platform is described as a

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How to Build Repository-Level Code Intelligence with Repowise Using Graph Analysis, Dead-Code Detection, Decisions, and AI Context

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In this tutorial, we explore how to use Repowise to build repository-level intelligence for the itsdangerous Python project in a practical and reproducible way. We start with an already cloned repository, configure Repowise using the available LLM credentials, and initialize its indexing pipeline. We then inspect the generated .repowise artifacts, analyze the repository graph with

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