Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.
Amid rapidly commoditized large‑model capabilities, Palantir achieved an 85% YoY revenue surge and zero churn by replacing generic RAG approaches with a deep enterprise ontology that unifies business semantics, creating a durable infrastructure moat while other AI firms see valuation collapse.
The article presents Li‑Net, a 0.5 MB lightweight time‑series model co‑developed by SF Technology and universities, accepted at ICDE 2026, which overcomes multi‑channel, non‑stationary, multimodal forecasting difficulties, achieves state‑of‑the‑art accuracy with low latency, and is deployed across SF’s global logistics to improve demand, inventory and capacity planning while cutting costs.
This article presents a comprehensive, end‑to‑end solution for multimodal product search, detailing how embedding, vector retrieval, and Elasticsearch Serverless combine to enable text, image, and natural‑language queries with high relevance and low operational overhead.
The article analyzes why large language models and RAG alone cannot meet enterprise AI needs, argues that a business ontology provides essential semantic grounding for agents, outlines ontology construction methods, demonstrates hybrid search improvements, and shares real‑world case studies showing dramatic efficiency gains.
The article analyzes why 74% of AI projects fail due to feedback gaps and data silos, explains how the open‑source Lance format addresses these issues with unified multimodal storage, outlines a layered Lance‑on‑Ray architecture, and details three real‑world practices—implicit feedback loops, GPU‑accelerated self‑evolution, and semantic knowledge‑graph evolution—to boost R&D efficiency.
This article analyses why memory has become the critical bottleneck for AI agents, explains the five‑layer MemOS framework that fuses model‑driven and application‑driven approaches, presents performance gains such as 45‑72% token savings and 30% response‑quality improvement, and showcases real‑world deployments and security mechanisms that turn memory from a pain point into a scalable infrastructure.
The Hermes AI Agent, despite its hype and one‑click deployment, suffers from four critical issues—cognitive gaps after deployment, uncontrolled self‑evolution, limited memory applicability, and finite security rules—each of which DTClaw addresses with professional skill bundles, a deterministic Skill‑Tune engine, pluggable memory architecture, and the CARLI five‑dimensional security model, backed by benchmark improvements.
The article analyzes the critical role of memory in AI agents, compares model‑driven and application‑driven approaches, details the five‑layer MemOS architecture with three‑level memory coordination, and presents performance gains such as 100‑200% monthly cloud‑service growth, up to 72% token savings, and a 30% improvement in answer quality.
Lance File Format v2.2 tackles the AI data explosion by delivering hundred‑fold random‑read performance, advanced two‑layer compression, zero‑cost schema evolution, Git‑style versioning, external blob handling, and a roadmap toward native media support and intelligent encoding, positioning it as a core infrastructure for large‑scale multimodal workloads.
The article examines costly failures of conventional data middle platforms—such as a $40 million payroll system flop and a chemical firm’s data‑cleaning bottleneck—then shows how Palantir’s ontology‑driven approach delivers triple‑digit ROI for BP, 98% R&D efficiency for Novartis, and $14 million annual savings for General Mills, highlighting the three‑layer semantic, dynamics, and decision architecture that turns data into actionable decisions.
