Stephen (Steffin) Harris

Defense tech is exploding — and Palantir sits at the center of the AI-driven battlefield. From intelligence platforms to mission-critical analytics, investors are watching defense innovation accelerate faster than ever. Resilient Alpha breaks down where venture capital is flowing, what technologies matter, and how Palantir is reshaping national security investing. #resilientalpha #palantir #defensetech #venturecapital #militaryinnovation #aigovernance #strategicinvesting #marketinsights #VentureInvesting #TechTrends #AIRevolution #DefenseAnalytics #NationalDefense #InvestmentOpportunities #StrategicPartners #TechInvestment #ResilientInvesting #SecurityTech

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Legacy semantic models were never built for the velocity and complexity of AI-driven analytics. They assume fixed schemas, predictable joins, and a handful of BI queries, not hundreds of dynamic, multi-domain requests from copilots and agents. AtScale’s AI-powered modeling engine changes that through an adaptive feedback loop. It continuously analyzes warehouse metadata, query logs, and lineage to infer semantic relationships and hierarchies automatically. When candidate metrics or joins are discovered, they’re surfaced for human validation, accepted, rejected, or refined, and those outcomes feed back into the model’s learning system. All of this runs on AtScale’s composable semantic architecture, governed through Semantic Modeling Language (SML). Every suggestion, update, and approval is version-controlled, reversible, and fully auditable. It’s modeling that scales with your data, not against it. 🔗 Learn more:

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Reinforcement Fine-Tuning (RFT) is a technique that adapts Large Language Models (LLMs) to intricate reasoning tasks. Dive deep into a comprehensive exploration of RFT with GRPO in this course. Learn how to construct reward functions crucial for steering LLMs towards enhanced performance on complex, multi-step reasoning tasks during the GRPO training process.

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I’m moving from theory to practice on my #SyntheticDataJourney, developing benchmarks for synthetic outputs across established ML models and emerging LLM-based approaches. The first barrier we’ve uncovered isn’t model performance. It’s data structure. Data readiness is directly tied to the type of synthetic model you plan to apply. For traditional ML approaches, this is simpler. Structured numeric inputs often work, even when representing categorical variables. LLMs are different. They require context – A LOT of context. Not just metadata, but clarity around what each variable represents, how respondents interpret questions, how scales function, and how elements of the questionnaire connect. We can’t assume the model will “figure it out.” A clear mapping of question intent, response meaning, and structural relationships is table stakes. This has parallels to best practices with data governance and communication - if humans can quickly understand the data as a stand-alone product, LLMs are likely able to do the same. If you haven’t evaluated your organization’s data readiness for synthetic models, that may be the real starting point. Check out Tony Seale's post (link in comments) on AI readiness for another perspective. #MarketResearch #DecisionScience #DataQuality

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