Arcjet’s Post

🌟 **Introducing the Latest Update to JARVIS MK7 - Advanced AI Control System** 🌟 We are excited to share that the new version of JARVIS MK7 is now available! This update brings a host of robust features and improvements designed to enhance your control and automation capabilities. **Key Highlights:** - **CodeBrew Engine:** Now featuring asynchronous code execution and real-time output capture, ensuring a seamless and efficient performance. - **Android Bridge Server:** Enhanced with WebSocket-based real-time communication and secure device authentication. - **LLM Integration:** Expanded support for multiple LLM providers including OpenAI, Groq, and Cohere, alongside advanced features like streaming responses and concurrent request handling. **Explore More:** For a detailed overview of all new features and enhancements, please visit our [GitHub Repository](https://lnkd.in/enH3XjJB) **Join Us:** We invite you to join our community on LinkedIn and GitHub to stay updated with the latest developments and participate in making AI integration more powerful and accessible. Thank you for your support and enthusiasm. We look forward to seeing how you leverage the new capabilities of JARVIS MK7 in your projects! #AI #Automation #TechUpdate #JARVISMk7 #SoftwareRelease

Making sense of your UNS with the help of LLMs? A UNS can be awesome and overwhelming at the same time. - Awesome, because it gives you access to all your data in one place. - Overwhelming because it gives you access to all your data in one place. A UNS makes it rather easy to build new solutions on top of your data, but if the UNS reaches a certain size it can be hard to figure out what is even there. What if you could talk to your UNS, just like you would talk to your colleague on the shop floor: - "How is the assembly line performing today?" - "Are there any active alarms in my department?" - "Which order is currently active at the stations XYZ?" LLMs make it easy to extract relevant information out of large amounts of text. So… why not hook your UNS to an LLM? The below example shows exactly that: A Chatbot using OpenAI gpt4o in the background and Ignition from Inductive Automation as frontend to talk to the UNS (more in the Article): https://lnkd.in/dWFSQ8WE Thanks to Anthony Olazabal for building the UNS Simulator and for the HiveMQ UNS show case at HannoverMesse, which gave me the idea for my UNS Chat. Link in the Comments. #UNS, #SmartFactory, #LLM

Interesting demo on how to uncover more with the help of #LLM in industrial settings when you are using #UNS. What are some other use cases you can think of? Great work Henning Heine (MaibornWolff GmbH)

OpenAI is great but there are organizations have no real-time access to the internet and hence hampering their AI ambitions. Thanks to the democratization of AI/ML, they can now host #LLM locally and even run their local #RAG system to produce accurate/updated/custom information for the users. Check out my latest article detailing on how to devise a scalable RAG-LLM system on the air-gapped K8s platform. #openshift #cml https://lnkd.in/gnSzsRDd

when you want to learn LLM this is the really good stack one should follow to deep dive into LLM models and their Agents, one good thing is that this models are rapidly evolving and making good impact on industry revolution.

AI Agents Stack AI Agents are advanced computer programs that use LLMs to automate complex tasks. AI Agents Stack includes - Vertical Agents - Hosting & Serving - Observability - Agent Frameworks - Memory - Tool libraries - SandBoxes - Storage - Model Serving Pic credits: Letta #aiagents #agents #llms #nlproc

"Excellent post! 🌟 The AI Agents Stack offers a detailed framework for building scalable and efficient agents powered by LLMs. It’s impressive to see the ecosystem broken down into components that address the various technical challenges of modern AI agent design. Let’s dive into the layers: 🧱⚙️ Vertical Agents focus on domain-specific tasks, leveraging fine-tuned LLMs to handle industry-specific requirements, whether in finance, healthcare, or logistics. Combining them with Hosting & Serving ensures agents are deployed with low latency and high availability, using technologies like Kubernetes and serverless architectures for scalability. 🌐🚀 Observability is crucial for debugging and optimizing agent performance. With tools like OpenTelemetry or custom monitoring dashboards, developers can track latency, error rates, and usage patterns. This layer ensures that agents operate efficiently and allows for rapid iteration based on user feedback. 📈🔍 Memory and Tool Libraries are the brain and hands of the agent. Memory ensures continuity, using vector embeddings and systems like Redis, Weaviate, or Pinecone for retrieval-augmented generation (RAG). Tool libraries, on the other hand, expand the agent's capabilities, integrating APIs for web scraping, database access, or task automation using frameworks like LangChain or Haystack. 🧠🛠️ Sandboxes, Storage, and Model Serving complete the stack. Sandboxes create a secure execution environment, mitigating risks when agents interact with third-party systems. Storage solutions like S3 or MongoDB manage large datasets, while model serving ensures efficient inference using platforms like TensorFlow Serving or NVIDIA Triton. Together, they create a robust backend for any AI-driven workflow. 🔒📂 Thanks for sharing this technical breakdown, and kudos to Letta for the visual representation! This stack is a solid foundation for building next-gen AI agents, and I’m excited to explore its components further in real-world applications. 🙌 #AIAgents #LLMs #AIArchitecture #NLP #TechStack"

I found this talk from #googleio on Llama on the edge very interesting and relevant to the ecosystem of generative AI. Seeing APIs and sizes on some models Google plays with is a good insight for companies like Axelera AI and others in the field.

Our Sidecar Pro set the standard for #AIsecurity in the recent BELLS Project study. 1️⃣ Unmatched Performance: AutoAlign’s Sidecar Pro surpassed other models across multiple metrics.  2️⃣ Dynamic Customization: Sidecar Pro’s Alignment Controls customize model behavior using user intent from natural language, code, or patterns.  3️⃣ Comprehensive Threat Protection: Shields #AI models from jailbreak attempts, data leaks, bias, and more across diverse use cases.  4️⃣ Consistent Excellence: Sidecar Pro demonstrated superior resilience against competitors in adversarial tests. We’re pleased that Sidecar Pro delivers unparalleled protection and performance across multiple dimensions. See the results for yourself! [Link in comments] 

STOP hitting the snooze button on the #AI revolution. ⛔ It's time to supercharge your data game with DataNeuron LLM Studio, fueled by our turbo-charged tech! Curious about what makes our partnership so vital? 1️⃣ Speed & security: An intelligent data infrastructure is perfect for #GenAI that doesn't play games with governance or safety. 2️⃣ Efficient versioning: NetApp ONTAP features like Snapshots and FlexClone seamlessly streamline data management across your AI pipeline. 3️⃣ Experimentation at scale: NetApp FlexClone allows DataNeuron to facilitate extensive testing environments by cloning data sets and model configurations with minimal resource overhead. Lead the #GenAI race with DataNeuron and NetApp: https://ntap.com/3Odo40V