HaystackConf Highlights: AI Search Tuning and Relevan

Super excited that more RI:Pod Local AI inference units will be going live this coming Friday. At Pixl8 Group we've been running a high percentage of our own agent workloads using local models for a while now and happy to be able to offer this to our ReadyIntelligence and ReadyMembership customers. Frontier models providers like Claude, OpenAI and Mistral offer market leading offerings for the vast majority of everyday uses cases, but having the data compliance, cost predictability, data flexibility and more importantly control over intelligence access is important. Built to our own custom specifications and tuning it's an exciting time for Local AI.

mem0ai/mem0 Universal memory layer for AI agents. Useful if you're building agents that need to remember users, preferences, context, and all the tiny facts we normally duct-tape into prompts and hope survive the next run. The catch is the same one every agent stack ships with: measure the time saved after you include setup, debugging, and the emotional support work of watching an agent confidently invent side quests. Still, this looks worth testing if you're building coding-agent or assistant workflows. Resource: https://lnkd.in/dxdE9pVy

Ever wondered about the power behind plugins in AI? They're more than just add-ons; they're a sophisticated combination of skills, agents, and hooks. Take music catalog diligence, for example. Manually sifting through messy data rooms for financial underwriting can take weeks. A specialized plugin streamlines this, ingesting data, performing financial analysis, and ensuring metadata accuracy through sub-agents. It even includes hooks for automated actions, like protecting source files during processing. Ultimately, plugins are installable bundles designed to enhance AI capabilities, making complex tasks more efficient and manageable. #AI #Plugins #Technology #Innovation #DataAnalysis

The AI DIY trend in buyer data Director of RevOps deprecating ZoomInfo and Apollo.io in favor of Clay and "building our own AI-powered" workflows:

AI pilots are all fun and games until they fall flat. In fact, 95% of them fail when they’re not built on a secure foundation. It might sound like a tall ask, but as Bisma Malik explains, scaling enterprise-ready AI takes four trusted systems built from the ground up. Learn how humans and trusted AI agents work together on Agentforce 360: https://lnkd.in/e_Snhj5y

This might be the most accurate one-liner about AI agents I've seen. Martin Varsavsky called multi-agent setups without execution discipline "committee theatre with GPUs." And he's right. Everyone's racing to make agents smarter, more conversational, more autonomous. But that's not where they fail. They fail when there's no ownership, no definition of done, and zero accountability between agents. At Invofox we see this constantly. You throw a prompt at an LLM, get a result that looks right. Then you look closer. Edge cases break it. You need retries. Specialized models for specific tasks. Fallback logic when confidence is low. The first result is never the product. The engineering around it is. The gap between demo and production is always a design problem.

Agentic AI workflows just killed traditional automation. 🚀 Sounds insane, right? But here is the twist. OpenAI just dropped GPT-5.5 Instant. 🤯 And Anthropic rolled out multiagent orchestration for Claude. Honestly, mapping out old-school Zapier flows used to make my brain hurt. 😅 We aren't just connecting apps anymore. We are deploying autonomous agents that think, route, and execute. 🧠 Are you still using static triggers in your business? The shift is undeniable. Agents are greater than apps, and autonomy wins. Adapt fast, lead the future, and stop building brittle systems. ⚙️ How many agents are running in your business right now? #AgenticAI #GPT5 #FutureOfWork ------- ♻️ Repost if you are playing the Long Game. 🔔 Follow DEEPAK SINGH to stop chasing tactics and start Architecting Systems.

I've had this conversation 100 times: "Our model works great in testing. But in production...it hallucinates, repeats itself, and we have no idea why." Welcome to the black box problem. Today, Qwen released something that cracks it open: Qwen-Scope 🔭 It's a suite of sparse autoencoders that lets you see, and control what's happening inside Qwen models: 🎯 Steer outputs by manipulating internal features (no more prompt hell) 📂 Build long-tail capabilities with minimal training data 🏋️ Trace bugs like code-switching & repetition to their root cause 📊 Design evals that actually matter This is what production AI infrastructure looks like. Not just "run inference faster" — but "understand and fix what's broken." If you're building with Qwen (or evaluating MaaS platforms), this changes what's possible. What's the hardest production AI problem you're facing right now? 👇 🔗 Explore Qwen-Scope: Blog: https://lnkd.in/gJTXt_k3 HuggingFace: https://lnkd.in/g7akNCrF Technical Report: https://lnkd.in/gDGaVmpU #ProductionAI #Qwen #MaaS #AIEngineering

Totally confirmed by experience !! Service reps spend only 45% of their time actually helping customers. The rest goes to toggling between systems, hunting for context, and stitching things together manually. We talk a lot about AI improving CX—this piece is an honest look at what's actually standing in the way. https://lnkd.in/eCvDPyvx