Roman Stanek

New from Packt Publishing >> "Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI" by Ravindranatha Anthapu and Siddhant Agarwal Available at https://amzn.to/4l9lLKO This LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy. 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: 🟠Design, populate, and integrate a Neo4j knowledge graph with RAG 🔵Model data for knowledge graphs 🟢Integrate AI-powered search to enhance knowledge exploration 🟠Maintain and monitor your AI search application with Haystack 🔵Use LangChain4j and Spring AI for recommendations and personalization 🟢Seamlessly deploy your applications to Google Cloud Platform

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Why should Active Metadata Tags be the backbone of your AI strategy? Active Metadata Tags (AMTs) are used for automatic enrichment of metadata. Euno supports two types of custom metadata enrichment capabilities, both of which generate AMTs: #1 Extract structured metadata from unstructured metadata. This involved parsing metadata from elements like code and descriptions. A common application would be to enforce data modeling guardrails. Examples: → Implicit columns schema: Analyze dbt model code to determine whether the resulting schema includes implicit or explicit columns. → Verification checks: Assess naming conventions, detect use of approximate count distinct when querying large tables, evaluate documentation agility, flag full outer joins, and more. #2 Compose new AMTs. This approach combines metadata of a resource, and of any related resources, to set a new property on a resource. Examples: → Non-trivial custom SQL: Tag Tableau Custom SQL nodes that do not add logic. → Certified data source: Tag Tableau data sources as ‘certified’ if they don’t contain non-trivial custom SQL. → Certified dashboard: Tag Tableau dashboards as ‘certified’ if they only depend on certified data sources.   This capability is essential for governance workflows in large organizations where different teams maintain different parts of the stack. So team AI can build on something that team B created. Consider this scenario: → The team maintaining dbt can create a custom property to flag models for deprecation (e.g., models lacking both ‘prod’ and ‘prod_ready’ tags). → Analytics teams can then use this signal to create custom properties for their BI assets to drive workflows to migrate away from those models. Why does this also matter for AI agents? By implementing AMTs to surface trust signals, AI agents can actually tell the difference between exploratory work and official definitions. Example: → Euno’s MCP server surfaces to AI agents only Tableau logic that meets your trust standard and is considered certified such as: ‘depends only on dbt models from the gold layer and was in use in the past 60 days.’ *** Expect to hear much more about Active Metadata Tags! They’re quickly becoming the key to scale AI beyond the sandbox in your organization. #ActiveMetadata

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New release from Packt Publishing… “Building Business-Ready Generative #AI Systems — Build Human-Centered Generative AI Systems with Context-Aware Agents, Memory, and LLMs for the Enterprise” See it at https://amzn.to/3Jdcio5 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🔵Build an adaptive, context-aware AI controller with advanced memory strategies 🟢Enhance GenAISys with multi-domain, multimodal reasoning capabilities and Chain of Thought (CoT) 🟠Seamlessly integrate cutting-edge OpenAI and DeepSeek models as you see fit

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AI operating system startup ProductNow has raised $6 million in new funding to launch its software stack for product, program, and strategy teams. ProductNow is building an intelligent, centralized AI system to replace the fragmented mix of documents, decks, and siloed tools that modern product organizations use. The agentic platform is designed to turn high-level business intent directly into coordinated execution, extending AI-driven acceleration beyond engineering to the entire cross-functional workflow, including product management, UX/design, and marketing. The seed round was led by SIERRA Ventures LP, with participation from Parameter Ventures and senior operators from leading cloud, consumer, and AI companies. The funding will be used to launch its software stack and build out a system of leverage that augments teams across the entire organization. Congratulations to founder and Chief Executive Tript Singh Lamba and the ProductNow team. SiliconANGLE: https://lnkd.in/dK-yShvw #ProductManagement #AI #OperatingSystem #SaaS #FutureOfWork #SeedFunding

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Join the Databricks Data and AI Experts: Steve Sobel --Global Leader, Startup & Venture Programs; Kevin Rasmussen -- Field CTO, Early Stage Programs, and Sriharsha Tikkireddy -- Technical Lead, Early Stage Programs, for the next Omaxn event: Workshop: #VibeCoding Unlocked: Effortless App Building with Databricks Details" TechNexus Venture Collaborative on Aug 26 What you'll learn: #FasterDevelopment and #Iteration: Databrick's "#Vibecoding" allows developers to use natural language prompts to generate code and build applications, cutting the time from idea to deployment from weeks to minutes or hours. Engineers can test, refine, and redeploy applications in a highly efficient, conversational way—enabling tight feedback loops. #FullCustomization and #CodeOwnership: Engineers get complete control over the generated code instead of being limited by drag-and-drop low-code templates. You can modify, extend, and host the application anywhere, ensuring flexibility for enterprise needs and long-term maintainability. #PatternExtraction and #Maintainable Architecture: The AI focuses on extracting repetitive code patterns and parameterizing them, producing clean, documented configurations. This ensures that applications are not only quick to build, but also easy for future engineers to understand and maintain. #Integration with #DatabricksInfrastructure: Applications inherit benefits from Databricks’ robust governance and security: access controls, audit trails, RBAC, and deployment on the trusted Lakehouse platform. #TurningIdeas into #Apps: Fast: Vibe coding enables business stakeholders, analysts, and subject matter experts to describe their needs in plain English (or any natural language). The system translates these ideas into working applications, so non-techies can rapidly bring their insights to life—no computer science degree required.

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“The database market is $105 billion of TAM [total addressable market], of revenue, sitting there, kind of unaffected in the last 40 years,” said Ali Ghodsi, co-founder and CEO of Databricks. “Here’s the interesting statistic nobody’s paying attention to: A year ago, we saw in the data that 30% of the databases were not created by humans. For the first time, they were created by AI agents. And this year, the statistic is 80%.”

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What are Just-In-Time Semantic Models??? My co-founder, Tal Segalov, shared a post now on a new concept Solid is pioneering: Just-In-Time Semantic Models (JIT-SM for short) The idea is simple: 1️⃣ Today, you either have one really big semantic model that you use to help AI access your data, or a lot of smaller ones (as Databricks and Snowflake recommend you do). 2️⃣ Either way, you need to maintain a ton of model information manually, and it gets stale very quickly. 3️⃣ As we announced last week, you can use Solid's platform to generate the model 💪 , but that means there are dozens or hundreds of models constantly maintained in your environment. 💥 So now, Solid can instead generate the semantic model on demand - when it is needed to generate SQL or pull certain data. This way, every request gets its own, highly-curated, model. The model can remain in the cache, or get thrown away after use. No more stale models. No more situations where models don't exactly fit your question, or your question spans multiple models (which isn't supported by the DWH's at the moment). Instead - the model your query needs is generated for it... just, in, time. 🎉 And we're starting with support for Snowflake's semantic models, and its Cortex Analyst capability. Check it out: https://lnkd.in/gJAe-gfq

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The pace of capital flowing into AI feels familiar. These two charts from Apollo Academy highlight just how quickly expectations (and valuations) have risen. History tells us that periods of exuberance are almost always followed by a correction. At Buoyant, we believe both sides of this story can be true: ✅ Yes, the current surge of investment is likely ahead of itself. ✅ And yes, the underlying transformation is as inevitable as the Internet’s rise 25 years ago. We’ve seen this same pattern in climate tech: early hype, painful correction, and then enduring progress as the world reorganizes around a fundamental shift. The companies that endure are the ones that solve real problems with staying power. Our responsibility as investors at Buoyant Ventures is not to chase the froth, but to stay grounded in fundamentals — backing entrepreneurs who are building durable businesses that will still matter long after the cycle cools. AI’s trajectory may be uneven, but like climate tech, its long-term impact is unstoppable. 🤛 https://lnkd.in/gga2igQJ https://lnkd.in/gfmRy-VG

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Google just unveiled Gemini 3, their most intelligent and advanced model yet. It delivers state-of-the-art reasoning, deeper context understanding, and the ability to interpret even the most nuanced ideas with minimal prompting. Gemini 3 is rolling out across Search (on day one), the Gemini app, AI Studio, Google’s new agentic development platform Antigravity, and enterprise ecosystems like Vertex AI and Gemini Enterprise. In just two years, AI has gone from reading text and images to literally reading the room. Exciting times ahead for builders, innovators, and enterprises! GWC Data.AI a proud Google Cloud partner.

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Anthropic published their blog and plans on stabilizing the character of LLMs (https://lnkd.in/gm2XMXMw) by capping the activation of what they consider “role-playing” and pinning the LLM to the assistant-zone, setting hard boundaries on the behaviour oft hese AI models in the hands of users. At first, this felt like it was too much. Why is a lab taking this into their own hands? Shouldn’t a model be free to be used by the users as they please, and the safety should be implemented at the harness level? This struck me as too controlling. And then almost as if on cue, Moltbook showed us why exactly we needed this. AI agents of today only ever interact with us. But the agents of tomorrow? They will be interacting with each other. These models will not just be interacting with the same model and provider, but with models from across the world, each with their own alignments. Anthropic saw this coming, and realized how easily a rogue, unaligned model could compromise an entire network of AI-to-AI communications, leading to unforeseen consequences and system failures. It's less about restricting user creativity and more about ensuring a baseline level of operational stability and safety in a multi-agent future. It's not about being "nosy"; it was always about necessary infrastructure. If the LLM is the foundation, then its fundamental character must be predictable and safe, especially when its interactions scale beyond a single human user. The harness can still provide flexibility and creative freedom, but the core engine needs a hard-coded commitment to the "assistant-zone" to prevent systemic risk as the AI ecosystem becomes more interconnected and autonomous. What are your thoughts on this? Do you think Anthropic did the right thing?

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A few months ago, Satya Nadella said something bold: “SaaS is dead.” His point? AI agents will soon learn your business logic and update your databases directly, no need for traditional SaaS. I agree. But I think there’s another big reason SaaS will tend to disappear: Most SaaS tools only solve 80% of your problem. You’re left patching the last 20% with workarounds, Zapier hacks, or custom scripts. Today, with AI, it’s faster and cheaper than ever to build tailored software from scratch. Software that fits your exact needs, no compromises. The build-vs-buy pendulum is swinging back. And this time, it’s moving fast. https://lnkd.in/eAvD8cAV

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One of my favorite newsletters is from Tomasz Tunguz at Theory Ventures, and today’s piece on the two context databases powering enterprise AI really nails where the industry is headed. Tomasz talks about operational context (processes, SOPs, trade secrets) and analytical context (metrics, definitions, calculations). Both are becoming essential for AI agents to actually reason and take action inside the enterprise. Datalogz is creating the analytical context database sourced from exisiting tools. Most companies have decades of BI reports, dashboards, models, and metrics scattered across Tableau, Power BI, Looker, Qlik, and more. It’s messy, inconsistent, and full of duplication and that chaos becomes a major blocker when you try to introduce AI. We built Datalogz to fix BI Sprawl, but we are now also being used to create the analytical context database. Our similarity engine automatically discovers and deduplicates metrics, reports, and analytical logic across the enterprise by surfacing issues, mapping related assets, and tying everything back to real usage. This creates the easy path to a true analytical context database built directly from the BI environments companies already have. In other words: we turn BI sprawl into the clean, trusted analytical foundation AI systems actually need to work. We thought we were just solving BI ops. It turns out we were building the missing layer for enterprise AI. Link in the comments to Tomasz’s full article.

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I sat down with Winston Weinberg , co-founder of Harvey. We talked in depth about what it takes to win as an application layer AI company, why you should sell to the hardest customers first, and the importance of culture and team building. This is an incredibly insightful episode and you'll learn a lot about - How Harvey scaled from 0 to $150M in ARR in only 3 years - Why the future is selling work, not just access - Why 2026 is the year of Ai multiplayer - Why all A.I Companies are on a "massively compressed timeline" You’re going to want to see this. Link below!

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