Vik Paruchuri

The paper proposes a system that integrates large language model–based agents directly into multi-participant group chats to improve engagement and coordination. The authors note that while LLM-powered assistants are effective in one-to-one conversations, group chats present unique challenges such as inactivity, fragmented discussions, and moderation difficulties. To address this, the paper introduces GCAgent, a framework designed to support both entertainment-oriented and utility-oriented dialogue agents within group messaging environments. The architecture of GCAgent contains three main components: Agent Builder, Dialogue Manager, and Interface Plugins. The Agent Builder allows users or administrators to create customized agents tailored to specific roles or interests—such as tutoring, moderation, or social interaction. The Dialogue Manager orchestrates multi-party conversations, determining when an agent should respond and coordinating interactions among participants. Interface Plugins provide supporting tools such as speech recognition, text-to-speech, and interaction management to reduce user friction. Experimental evaluations and long-term deployment show that the system improves group engagement, achieving an average user rating of 4.68 and increasing message volume by about 28.8% during real-world use. Overall, the paper demonstrates that embedding specialized dialogue agents within group chats can meaningfully enhance communication quality and participation in online communities. https://lnkd.in/gaDN7TxT

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Most subreddit threads will disagree with this, but Sonnet 4.5 seems to be better than GPT-5 Codex when it comes to working in large complex codebases such as ours. It's often not necessary for LLM benchmarks to reflect the real world reality since it's practically impossible to independently verify for training data contamination or bias. But in the case of Sonnet 4.5, the benchmarks have lived upto their promise. Having said that, we're all software engineers and usually depend on these models for repetitive tasks and cannot account for what the experience is like for them vibe-coders.

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Question to those using LLMs every day: Which LLM work best for team collaboration and shared AI workflows? I'm personally split 50/50 between ChatGPT and Claude. But as we look to invest in an Enterprise account for Remento, I'm realizing I definitely have some blind spots... Our primary goal is to easily create reusable AI assistants that any team member can access, trained on our specific knowledge base. For example: 👉 Marketing copy aligned with brand voice 👉 Product feedback based on customer research 👉 Content strategy from audience data For those who've made this transition: what's working?

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When AI can teach better than any professor, diagnose better than any doctor, and code better than any developer, what happens to human work? To education? To you? 🎧 Listen to the full episode for a deep dive into the timeline, the impact, and the paths forward. https://lnkd.in/eTRukN2G

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As infrastructure spending rapidly outpaces software engineering costs, the need for intelligent abstraction layers has never been more critical. For 5 years, Facets.cloud has been building powerful abstractions up to Layer 3 (composition and intent). Now, with the rise of agentic AI, we're unlocking the critical top 2 layers that complete the stack. This evolution represents a fundamental shift in how teams will interact with and manage infrastructure . And it couldn't come at a better time.

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Claude Code is helping government too. I was amazed to hear from two software engineers inside government IT departments last week: - "Claude Code makes my 20 engineers feel like 200" and - "A project that used to take 1 month took just 1 weekend with Claude Code." Government software engineers build mission-critical systems affecting millions of residents, yet they're stretched thin maintaining legacy systems. Empowering them means better public services built in-house. Safe AI models and responsible deployment also matter even more here given the high stakes of government production code.

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The latest UPCEA report, The Future is Now: Essential Conversations for Building Tomorrow’s University Today, captures exactly where higher education is heading — and why our partnerships at Riipen are focused on helping institutions get there faster. UPCEA highlights a few urgent truths: - Students are making choices based on ROI and career outcomes - Credentials need to be stackable and connected to real career progress - Institutions must strengthen collaboration with employers to keep programs relevant - Professional, Continuing, and Online (PCO) units will lead the way in innovation At Riipen, we see this shift every day in our partnerships. Together with universities and colleges, we’re building industry micro-credentials combined with project-based work experiences - helping learners demonstrate skills, helping educators make curriculum more applied, and helping employers find talent ready to contribute on day one. By connecting industry, education, and learners through authentic projects, we’re helping institutions turn these UPCEA insights into scalable, measurable action. You can read the full UPCEA report here: https://lnkd.in/gxw7iSh2

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