Jithu Joijoide

Tuning Spark, Ray, or Dask for ML isn’t one-size-fits-all. In a new Domino Blueprint, Principal Solution Architect Sameer Wadkar shows how API-driven scaling complements autoscaling to right-size clusters on demand—cutting idle spend and speeding bursts for GPU/I/O-heavy work. Practical steps, a lightweight Python client, and Helm install included. If you care about FinOps and MLOps, this is for you. Read the blueprint: https://lnkd.in/ga_qJkhC

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Did you hear it? … the song of the AI Agents dominating #awsreinvent2025 Nearly every other booth you walk to is demoing an #aiagent platform. In a nutshell, we’re talking about simple ai agents. I’d call them baby agents. A prompt, a RAG, and a few MCPs here and there to collect data, all governed by an agent studio to design agent workflows. Most platforms are promoting an open model framework where you can pick and choose your preferred LLM. Bedrock is one of the key options available. What’s missing? The business application. A lot of those platforms are in their very early stages, many are not even GA yet. What’s common: build from scratch mentality, and my ai agent platform can do everything, everywhere, all at once. IT buyers? Torn down between all of these platforms not knowing if it’s the right time to start choosing one or many. The potential? It’s going to be insanely huge, with businesses and organizations reaching incredible levels of automation, while also providing superior quality and experience to their customers. Get ready as 2026 will be exciting! [sounds surreal to say so, nearly have to apologize that my post did not use any single AI, not even for brainstorming or improving my style. WDYT: Better with or without AI?]

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If your streaming workflow needs precise business logic, custom modules are the answer. This Thursday, Ian Zenoni and I are digging into how to extend Wowza Streaming Engine through scalable, real-world implementations. We’ll cover when modules make sense, walk through a practical example of custom metadata injection, and share hard-earned tips on testing, debugging, and running modules in production. If you’ve ever said “I just need Engine to do one more thing,” this session is for you. I will, of course, be dressed in my finest sports coat and strongly encourage audience participation. Bring your questions.

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Claude just released a memory import tool: https://lnkd.in/eM8c7Gf5 You paste a prompt into ChatGPT (or whatever you're using), it spits out your context — preferences, projects, how you work — and you drop it into Claude's settings. That's it. Claude picks it up from there. The switching cost has always been the real barrier. Not the AI itself, just the fact that you've spent months building context somewhere else. This is Anthropic admitting that out loud and actually doing something about it. Quiet release. Worth knowing about.

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Just wrapped the 7th Perplexity AI Business Fellowship session featuring Ali Ghodsi, CEO of Databricks, in conversation with Aravind Srinivas. Ali's refreshing candor about AI's enterprise impact, dropping some uncomfortable truths about enterprise AI that every engineering leader needs to hear: - "The only reason the AI bubble hasn't burst is inference-time compute" - not actual breakthroughs - When Databricks plugged advanced reasoning models into enterprise applications? "Barely any boost." The problem isn't intelligence but context - is "C" California or Celsius? - Success formula: unique data + distribution channels. Nothing else matters. - AI revolutionizes coding, which represents just 20% of engineering time. The other 80%? Human meetings and alignment work which AI can't touch. - Biggest blocker? Not capabilities but evaluation frameworks. "We have no way to measure reliability" in specialized enterprise contexts. This mirrors what we're seeing in healthcare AI - where reliable assessment frameworks are critical when patient outcomes are on the line. As I noted in my analysis of BVP's Healthcare AI Index (https://lnkd.in/gy3P7zdU), success isn't about algorithms but workflow integration and evaluation rigor. For engineering teams, the path forward isn't chasing fancier models but solving the semantic understanding and evaluation challenges that remain stubbornly human. #EnterpriseAI #EngineeringLeadership #HealthcareAI

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Dramatic framing aside, this episode of the MLST podcast (one of my favorites) raises interesting points about the future of AI. I've used Evolutionary Algorithms and gradient descent LLMs together, and found that they complement each other very well. LLMs offer world knowledge and efficient local search. Evolutionary methods, especially with Stanley's serendipity approach, bring simpler and more novel solutions. I believe in a hybrid future, but often there is more than one path to reach the same destination... #genai #ai https://lnkd.in/gnbp7wWn

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Most AI memory systems fail because they treat schema as an afterthought. I have seen dozens of RAG implementations break down in production - not because of embedding quality or retrieval accuracy, but because teams skip the foundational work of defining clear data schemas. Without structured memory models, your AI system becomes a black box that degrades over time. We recently worked with a fintech client whose LLM was hallucinating financial data. The root cause? Their memory system had no schema validation. Documents, user interactions, and context were stored as unstructured blobs. When we implemented strict schema definitions for their knowledge base, accuracy improved by 40% overnight. Start with schema design before you build your RAG pipeline. Define your data structures, validation rules, and memory hierarchies upfront. Your future self will thank you when you are debugging at 2 AM. What is your biggest challenge with AI memory reliability in production? #AIInfrastructure #RAG #MachineLearning #DataEngineering

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