Ankush Gola

NVIDIA's DGX Spark started shipping this week. $3,999 for a petaflop on your desk. Research labs currently skip experiments because justifying cloud spend takes three approvals. Startups burn runway on compute before knowing if ideas work. Teams prototype in the cloud, then find their models behave differently in production. Run 200B parameter models locally. Fine-tune 70B models without cloud dependency. Link two units for 405B parameters. The development cycle tightens considerably. The GB10 Grace Blackwell chip is purpose-built for this—MediaTek collaborated on the CPU side, 128GB of memory sits between the CPU and GPU without the usual bottleneck. Different from retrofitting gaming hardware for AI workloads. Training frontier models still requires massive infrastructure. Production inference at scale needs proper compute. But prototyping, testing, fine-tuning—that friction just dropped significantly. Useful ideas die when the barrier to experiment is too high. The path from concept to test matters. $4,000 isn't nothing, but it's orders of magnitude less than building a cluster. NVIDIA's framing this as "democratizing AI"—partially marketing, partially true. The number of teams who can now iterate at this level just expanded considerably.

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📣 Introducing RRE-1: A New Model for Retrieval Evaluation & Reranking for RAG based applications. Retrieval-Augmented Generation (RAG) has massively helped transform LLM-powered applications, but let’s face it—retrieval quality is still the weakest link in many pipelines. Poor retrieval is one of the major contributors of lower accuracy of your LLM applications. 🧐 That’s why we at AIMon built RRE-1, a powerful retrieval evaluation and reranker model designed to optimize ranking quality at scale. 🎯 👉 Why does this matter? Traditional retrieval methods like vector search are powerful, but they have limitations in effectiveness, especially when handling complex queries and domain specific knowledge. RRE-1 addresses this by offering: ✅ Offline evaluation of retrieval effectiveness, helping teams benchmark and refine their pipelines. ✅ Real-time low-latency reranking to ensure the most relevant context reaches the LLM. 👉 Who is this for? If you’re working on LLM agents, enterprise RAG systems, or AI copilots, this is for you. Our model ensures your retrieval pipeline is as optimized for your generative model—because great responses start with great retrieval. 🔎 Want to see it in action? Read more about AIMon RRE-1 in our blog post (link in comments). If you’re tackling retrieval challenges in your AI stack, we would love to chat! #AI #RAG #Retrieval #LLMs #MachineLearning

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🚀 PRESS RELEASE: OpenBOM Announces Private Beta of AI BOM Agent with MCP Support I'm excited to share the news about availability of OpenBOM private beta of AI BOM Agent with MCP (Model Context Protocol) support. OpenBOM is building a new generation of intelligent, invisible engineering workflows. Instead of forcing engineers to change how they work, OpenBOM introduces AI agents that understand existing tools, files, and data formats. These agents connect to multiple data sources, structure information, automate routine tasks, and enable conversational access to product data. This vision is anchored in the concept of product memory—a persistent, connected knowledge model that spans design, engineering, manufacturing, and procurement. With MCP support, OpenBOM agents can ingest, structure, and link data into the digital thread, enabling reasoning, plain-language queries, and smarter decision-making. Looking ahead, OpenBOM sees AI agents playing a vital role in BOM quality and validation—helping companies ensure accuracy, consistency, and readiness of data before it flows into procurement and production. By transforming CAD data and unstructured Excel spreadsheets into structured, connected product knowledge inside OpenBOM’s graph-based model, we’re: ✅ Linking CAD data and BOMs into the digital thread ✅ Preserving product memory across teams and projects ✅ Laying the foundation for AI-driven workflows in engineering and manufacturing This is the first step in our roadmap to bring intelligence, automation, and conversational access to engineering data—moving from data ingestion and natural language queries to agent-driven actions and connected product memory. As I said in the release: “Excel remains the most widely used tool in manufacturing, but it is also the biggest bottleneck. The OpenBOM Agent with MCP support turns spreadsheets into structured product knowledge—enabling collaboration, traceability, and AI-powered decisions.” 👉 Learn more about the private beta and how to join here: [https://lnkd.in/eD9gmA_X] Over the next few days, I’ll be sharing examples of how OpenBOM AI agents can be used and how product data can be embedded into agentic workflows. #OpenBOM #PLM #AI #DigitalThread #ManufacturingInnovation #MCP

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Nuanced is a structured context layer for AI coding tools: call graphs, symbol maps, and static analysis that make LLMs reliable. We just launched our explainer video: 📽️ https://lnkd.in/eA8wmYeA If you’ve ever banged your head against Claude Code or Cursor, you know the pain: LLMs can write code, but they don’t understand it. They hallucinate helpers, invent imports, and break prod, because they can’t trace real structure across your repo. Nuanced gives them that structure. No more guesswork. Just deterministic edits, grounded in your codebase.

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I’ve been asking engineer friends in SF: “How much more productive are you with AI tools like Cursor and Claude Code?” The answers are all over the place: 👉 Some say ~10% gains. 👉 Others claim 10x. And then there’s the METR study suggesting senior engineers were actually **19% slower** on open-source repos with AI. So where’s the truth? At First Mate Technologies, we’ve been using Cursor and Codex internally. Anecdotally, our team is seeing ~20–30% productivity gains. 💡 Is that in line with what you’re seeing? 💡 Or are we missing a bigger lever? I hear companies are standing up dedicated AI Developer Experience teams to maximize these gains. We don’t have that (yet!)—but I’m wondering if that could be the path to getting from 20–30% to something much larger. Curious to hear: how are you setting up your eng teams and processes to take advantage of AI?

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