Alexander Stojanovic

Andrew Ng reported from Davos, that AI deployment for transformative impact will require workflow redesign. It is "theory of constraints" all over again (remember Eli Goldratt's "The Goal"?), we have to understand that real business benefits come only from working across silos, from analyzing flow bottlenecks, from understanding that optimizing non-bottlenecks creates waste. As Ng noted, letting a thousand (AI) flowers bloom has failed to lead to significant payoffs. Obviously! We in the silicon industry are no strangers to this. Workflows have been stagnant for decades. Technologies for high level specification, high level synthesis, and formal verification are not yet integrated into the full silicon development flow. They are often done piece-meal, and enthusiasts' pilots are dropped at the hint of a failure or delay. People say they want formal verification in their flow, but want it yesterday and do not have the time to invest in training engineers for its rigorous usage. Ashish Darbari, responding to a question on "if formal verification is so powerful then how come it is not widely used", noted that the problem is in the name - the actual task to be done is formal analysis while the bug detection is only a corollary, but the industry puts the cart before the horse. Based on my experience with industrial practice of high level specifications and formal verification, I tend to agree with his succinct comment. Jan Rabaey, in a DAC keynote speech, said that someday he expects to see a comic book series entitled "adventures in formal verification". AI deployment faces the same problem, if not actively worked on by business leaders. To be fair, deployments of all disruptive technologies face this mental barrier, we are so used to thinking in terms of the old flow (using the old technology) that thinking of a new flow literally means us having to begin again from first principles - few leaders have the courage and the time to do that. Goldratt mentioned that he himself had faced the same mindset problem when deploying the early ERP solutions and was surprised why he wasn't getting the expected large benefits, and he said it took him many years before he could identify the core problem and define solutions for it, which he named (rather academically) as "theory of constraints". AI leaders would do well to rework the conventional workflows in a holistic manner in order to get the real benefits from this new technology. As Henry Ford had said, "If you always do what you've always done, you'll always get what you've always got".

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https://lnkd.in/dWhV_txb The latest challenge that LLMs need to overcome is how to use memory. In this article, an LLM is taught what to remember and how to use this memory.

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Excited to share how Fastly supports the Python Software Foundation as a Fast Forward member! In 2025, PyPI hit some incredible milestones: 📦 3.9M+ new files published 🧩 130K+ new projects created 🌐 1.92 exabytes transferred 👉 Read the review: https://lnkd.in/epenVSWA

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Starburst has always believed in providing customers with the freedom of choice. Nowhere is this more true than the emerging developments around interoperable compute among data platforms. Recently, I sat down with Snowflake’s Ryan C. Green to talk about the Open Semantic Interchange (OSI), an open, vendor-neutral standard for sharing semantic models across AI, BI, and analytics tools. OSI gives customers a common, open way to define and share business metrics. This helps them stay consistent across dashboards, notebooks, and machine learning models, no matter which tools they use. For Starburst, this furthers one of our core goals. Choice. Customers should be able to keep one trusted set of business definitions inside their Snowflake environment while still having the freedom to choose the tools that work best for them. OSI helps make that possible. It marks an important step in bringing Starburst’s engine to Snowflake-interoperable compute. The video from my chat with Ryan is coming soon 👀 In the meantime, you can get more details from our recent press release: https://lnkd.in/ev2XGB4u #OpenSemanticInterchange #OSI #Interoperability #AI #Data #Snowflake #Starburst

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I've just added the latest models to this small tool, which helps in understanding the context window sizes of various LLM models. https://lnkd.in/eiyMFNWm

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AI Visibility Blueprint™ is an engineering manual for building AI-readable digital infrastructure using canonical identity architecture, graph systems, and retrieval confidence design. This workbook-style manual is a structured execution manual designed for professionals who must engineer AI-readable digital infrastructure rather than merely optimize content for discoverability. This manual represents the Execution and Infrastructure Layer within the AI Discoverability Architecture and Retrieval Systems™ Series, and it assumes prior conceptual familiarity with AI visibility frameworks. This is not a SEO visibility guide. It is a build manual for structural discoverability systems that AI retrieval engines can resolve without ambiguity. #AIVisibility #AIera #AI2026+ #Beyond2026 #AIInfrastructure #AIDiscoverability #AISEO #VisibleToAI #ConsciousVisibility https://lnkd.in/g9URpmgG

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To add to that, Confluent Cloud Network (CCN) routing simplifies private networking for Flink, reusing existing Kafka infrastructure. It helps teams quickly connect secure pipelines across cloud environments. Security is further strengthened with IP Filtering—giving teams greater control over access in hybrid environments and enabling them to confidently deploy AI at scale. With these enhancements, Confluent is not just enabling smarter AI—it’s making it enterprise-ready. Organizations can now unlock: Seamless integration of historical and streaming data Secure, scalable infrastructure for AI agents Faster, context-aware decisions powered by trusted data Agentic AI is here. With Confluent Cloud, it’s ready for the enterprise. https://lnkd.in/gh-dA7qs #ConfluentCloud #ApacheFlink #SnapshotQueries #AgenticAI #RealTimeData #StreamProcessing #DataAnalytics #EnterpriseAI #BatchProcessing #DataStreaming #DigitalTransformation #Flink #AI #MachineLearning #DataSecurity #PrivateNetworking #IPFiltering Follow our LinkedIn page for more such interesting CX expert opinions/ Thought Leader Interviews/ CX Leadership news/ articles/ updates https://lnkd.in/g9eeFFgh

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Read this Fortune piece by Byron to learn more about using formal methods with LLMs. He even mentions Leibniz AI in it 🎉

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Large language models offer incredible opportunities, but deploying them reliably at scale requires a new approach. Liad Levi-Raz discusses how LLMOps bridges the gap between research and production for optimized LLM performance. #LLMOps #LargeLanguageModels #AI #MLOps #Production #Scalability #Optimization #this_post_was_generated_with_ai_assistance #responsibleai https://lnkd.in/eR5pcJ-4

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Learn how Scalytics 3.5.0 empowers you to own every aspect of your AI stack—on your terms and behind your firewall. Read how below...

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One way to get better results with LLMs is to grow the training corpus and build larger architectures. Another way that is emerging is to allocate more computation time for inference. In the following paper, the authors propose a novel inference time scaling approach by adapting particle-based Monte Carlo methods -- I really like that idea; partly because it intends to utilize probabilistic modelling for inference, partly because I favor SSMs, and partly because this kind of approach will make it possible to run LLMs locally with decent results. Here is the link: https://lnkd.in/dFUQnT3X Enjoy! #AI #LLM #Inference #ProbabilisticModeling

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