Michael Vandi

When we hear about AI breakthroughs, the word GPU is never far behind. But what is a GPU really, and why is it so critical for training and running LLMs like GPT? A Graphics Processing Unit (GPU) was originally designed for rendering graphics in video games. Unlike a CPU, which has only a few powerful cores optimized for sequential tasks, a GPU has thousands of smaller cores designed to handle tasks in parallel. This architecture is incredibly helpful for LLMs because training these models requires performing billions (sometimes trillions) of matrix multiplications and vector operations. CPUs are not efficient at this scale, whereas GPUs, with their massive parallelism, can perform these operations simultaneously. This makes model training that would take years on CPUs possible within weeks or even days on GPUs. #AZ #AZPremium 

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Introducing native tool support in Vellum for complex agentic workflows. Today, we’re launching 𝗧𝗼𝗼𝗹 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 𝗡𝗼𝗱𝗲: a built-in component that handles tool use out of the box and helps AI teams move 100x faster. (i) It handles OpenAPI schemas, looping, and output parsing automatically. (ii) Supports multiple tools. (iii) And runs end-to-end from one component. This is by far the easiest component to set up in Vellum! And it still gives you plenty of flexibility. You can define tools in 2 ways: with raw Python or TypeScript using third party packages, or by reusing nested workflows that you’ve built with code/UI. Coming next is: more granular debugging, and a Tool Directory that lets you drop in common tools like Search, Extract, or SaaS integrations with a single click. 👋 to painful function calling setup once and for all 👋 Sign up to Vellum to try it today: https://lnkd.in/d44ejUGc Read how it works: https://lnkd.in/d-PHdx5t

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🚀 VACE: All-in-One Video Creation and Editing 🎥 VACE is an all-in-one model for video creation and editing that unifies multiple video tasks, including reference-to-video generation, video-to-video editing, and masked video editing within a single framework. 🔹 Why is this groundbreaking? VACE, built on the Diffusion Transformer (DiT) architecture, integrates various input types (videos, images, masks, references) using a Video Condition Unit (VCU). This standardization allows for seamless video manipulation across tasks, reducing deployment costs and expanding creative possibilities. 🔹 How does it work? - The Concept Decoupling Strategy differentiates reference information from editable elements - The Context Adapter translates task-specific requirements into structured representations across time and space, enabling flexible video synthesis - The Compositional Approach allows users to combine multiple editing capabilities, such as subject swapping with inpainting or frame references with pose control 🔹 Results Speak for Themselves VACE was tested across 12 tasks on 480 samples, showing performance comparable to specialized models with the advantage of a unified, adaptable system. This is a major step forward for AI-driven video editing. The future of efficient, flexible, and creative video generation is here. #AI #VideoEditing #GenerativeAI #VACE

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🚀 Launch Alert: Pype is Now Open to All AI Builders! The biggest cost of AI agents isn't the LLM bills - it's the cost of their mistakes. We learned this working with pioneering companies who were burning countless engineering hours managing their AI agents. What started as a hypothesis became hard reality. Working with amazing beta partners globally, we saw teams struggling with: - AI agents making expensive API calls without limits - Engineers pulling all-nighters to debug agent behavior - Production mistakes costing real money and time The result? We helped our partners save $2M in their AI automation processes. What surprised us most was seeing how creatively companies are using AI agents. It made one thing clear - the AI agents are here to stay. And tooling around them is a necessity. 🎯 What Pype Does: - Debug agents faster with intelligent tracing (not just dumb logs!) - Provides robust guardrails for AI agents - Helps you battle-test your agents - Cuts engineering bandwidth waste by 40% Are you an engineer who's already built an AI agent? Let's take your journey from 1 → 100 together. Drop a comment or DM - I'd love to hear your story. Or, try Pype for free - link in comments below 👇 #AIAgents #TechLaunch #Engineering #StartupLife #LLM #Evals

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👉 Read here: https://lnkd.in/gAsk6N6G 🚀 ANNOUNCEMENT - Amidst all the hype around “AI breakthroughs,” one thing often gets overlooked — rigorous evaluation. It’s easy to build another model or agent and harder to measure ROI. We laser focussed on 2 specific metrics, Precision and Recall. Today we are releasing the complete implementation of the Semantic Model generation framework and evals of one of our core IP. While we are unable to disclose the ground-truth and evals in customer implementations, be rest assured we have achieved higher values for both the metrics across 5 enterprise customers who are live in Production. Hope the community will benefit from this! Author Credits - Jaskaran Singh Narotsit Karki Qamar Ali Ankit Baliyan Raphael Tony #Opensource #DataEngineering #DataScience #GenAI #SemanticModel

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Betula AI - Platform to Create Voice Agents for Business or Personal Use Betula AI allows users to set up voice agents in minutes. Betula AI uses top-tier LLMs fine-tuned for each task, with voice capabilities powered by Vapi, Deepgram, and ElevenLabs. Betula AI addresses address the key challenges of a production-grade multi-agent voice system: - minimizing agent response and action latency - optimizing for sequential speech constraints - ensuring seamless conversation flow between agents - blocking spam - recovering from dropped calls Betula.ai Refer to the comments for full details regarding Betula AI.

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Investment is moving from pre-training to post-training. Early LLM budgets focused on training ever-larger base models. Post-deployment, models were mostly fixed... improvements came via scaling next-gen models. Now labs split resources between pre-training and RL fine-tuning with careful inference optimization. In post-training, models learn reasoning, follow prefs and refine outputs for specific tasks. Models update more frequently, weekly or monthly vs yearly. Each iteration collects new data, extends RL training and adjusts rewards to improve reasoning. New economic models charge for updated reasoning engines vs static models. Serving costs and latency are key so providers favor efficient pro models with most accuracy at lower compute. 3 more takeaways from ICML: 1️⃣ RL is only beginning to cross into “unverifiable” domains. Traditional RL used tasks with clear auto checks (like code compilers or math calcs) for rewards. Domains like Math Olympiad or legal arguments have complex solutions that can't be auto-verified. These need more complex reward models scoring reasoning steps, argument clarity or persuasiveness vs just correctness. With solid reward models, RL could teach coherent proofs, experiment design or legal strategies. This is still early work - current systems often break tasks into verifiable parts or use human prefs, but broader paths are emerging. 2️⃣ Personalization is a safer near-term goal than true continual learning. Interest in adapting models to users is growing with 2 key approaches. Personalization tailors outputs by learning user-specific rewards from few feedbacks or adding curiosity rewards that prompt questions about user tastes. These adjust behavior without changing model weights, improving perceived helpfulness and empathy. Continual learning updates parameters in real time from all user input. But continual learning poses safety and privacy risks like overfitting, bias amp & data leakage. Personalization and context windows (remembering interactions without weight changes) are more practical and responsible. 3️⃣ RL scaling will follow multiple paths. → One path applies current RL methods (PPO, guided reward models, diffusion reg) to more domains. This incremental path needs no new algos, just better rewards, bigger varied datasets & optimization tweaks. → The second tackles sparse-reward probs with long feedback delays. Success depends on credit assignment in long seqs and off-policy learning using varied experiences. Off-policy RL supports multi-datacenter training with clusters handling acting, collection and learning. → The third involves continual learning with frequent updates using user feedback and new data. Each has trade-offs: incremental and safe, risky but domain-expanding, adaptive but complex w/ safety issues. What'd I miss?

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💬 Launching our newest speech-to-speech model "gpt-realtime" & full GA for the Realtime API The Realtime API is now generally available, along with our most advanced speech-to-speech model yet: gpt-realtime. This launch makes it easier than ever to build production-ready, reliable voice agents. ✨ What’s new: • Remote MCP server connections - extend your agents with more external tools and systems • Image inputs - ground conversations in what users see (photos, screenshots, etc.) • Session Initiation Protocol (SIP) - connect directly to phone networks, PBX systems, desk phones, and SIP endpoints • Reusable prompts - save and reapply prompts, tools, and variables across sessions • New voices (Cedar & Marin) + updated existing voices → more natural, expressive, and higher-quality audio • Improved function calling & instruction following → better at using the right tools at the right time 📖 Explore docs, test in Playground, and dive into our prompting guide to get started. 👉 Check out the full announcement and customer use cases on our blog, all links in the comments.

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Grok 3 is officially out. The xAI team showcased some of the benchmark scores and the technical details for their latest model. Grok 3 was trained first on 100,000, and then on total of 200,000 H100s - the first LLM to be trained at such a scale. It pretty resoundingly confirmed that the pertaining is still very much a big factor, albeit to squeeze it to the next level it is now required to get at least an order of magnitude of compute more than the previous generation.

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We launched a major upgrade to our open source project last week and are delighted to see the community response to it. ❤️ It also made our open source repo trend on Github 😯 📢 Introducing SigNoz Single Binary 🚀 We have combined some previously separate components (query-service, frontend, and alertmanager) into a single binary called SigNoz. This means fewer moving parts, simpler maintenance, and a much smoother installation experience. This architectural improvement significantly simplifies the deployment and maintenance of SigNoz, making it more accessible to a broader range of users. Some of the key benefits include: 👉 Allows installation on bare metal servers 👉 Faster & Easier Installation 👉 Simplified Maintenance 👉 Reduced Infrastructure Overhead Check out all the details in our blog(link in comments)

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Now more than ever we need to not only invest in AI, but determine how to get the most out of it. I am excited to share that Jellyfish AI Impact now gives engineering leaders the power to measure ROI across GitHub Copilot, Cursor, Google Gemini Code Assist and Sourcegraph. With AI Impact, you can: Measure AI coding tool impact on delivery speed, workflow efficiency and resource allocation Optimize your AI investments by identifying how much and what type of work is effectively AI-assisted
 Compare AI coding tools to determine which perform best for specific functions throughout the SDLC Scale AI adoption across your organization by applying best practices from high-performing teams Check it out here: https://lnkd.in/e_NZq4p2

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Tired of heavyweight AI frameworks? We've built a powerful, yet surprisingly simple solution using Node-RED's flow-based approach. Our 5-block architecture (General Query Classifier, Agent DEMUX, LLM Query, RAG engine and Curator) enables flexible routing, LLM agnosticism and easy scalability. Benefits include low-code development, visual debugging, multi-level nesting and one-click deployment using a Directed Acyclic Graph (DAG) apporach. #AI #NodeRED #LLM #RAG #lowcode #AIframework #innovation #GenAI #agentic #dag IndustryApps

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