Ant Data leverages Ant Group's leading technological innovation in big data, databases, and multimedia, with years of industry practice. Through long-term technology planning and continuous innovation, we strive to build world-class data technology and products.
The article explains how OceanBase tackles AI‑agent challenges such as isolation, memory, and trial‑and‑error by introducing logical tenants, layered decoupling, hybrid search, database branching, multimodal large‑object storage, and Model‑in‑DB AI columns, backed by benchmark results and real‑world deployments.
The article explains SINDI, a sparse‑vector retrieval index designed for VSAG, detailing its window‑based weighted inverted lists, multi‑layer pruning, quantization and reranking techniques that achieve dramatically faster index construction, smaller index size, and high query throughput compared with traditional inverted or graph indexes.
The article outlines an end‑to‑end industrial pipeline for large‑model training data, detailing six concrete steps for raw web text processing, multimodal VQA/caption/interleave/audio generation methods, and a data‑engineering backbone that uses agentic pipelines, unified storage, and a post‑training production platform to ensure high‑quality, verifiable data.
The article argues that AI memory is not mere storage but an external state that feeds decisions, proposes three core propositions—Memory as decision‑usable external state, a minimal closure of Raw Ledger + Views + Policy, and event sequences as the fundamental unit—and details how a System 1 + System 2 architecture, non‑parametric designs, temporal handling, and learnable policies together shape the practical limits of modern agentic memory systems.
The article analyzes the shortcomings of ad‑hoc Vibe Coding, introduces Spec‑Driven Development as a structured AI‑assisted workflow, details a nine‑step process with concrete commands and examples, evaluates its trade‑offs, and offers practical guidance on when and how to apply it in team projects.
The article examines how ChatBI is evolving beyond simple natural‑language SQL generation toward a collaborative, context‑aware system that integrates a semantic layer and business memory, enabling trustworthy analysis, plan‑mode workflows, and continuous learning, ultimately redefining the productivity boundaries of data intelligence.
The article presents a private‑deployed DB‑GPT solution that combines Retrieval‑Augmented Generation (RAG) and Text2SQL to address low utilization of unstructured marine‑environment knowledge, cross‑source data querying difficulties, and security concerns, detailing technical selection, implementation steps, and performance gains that reduce query time from 30 minutes to 1‑3 minutes.
The article presents an end‑to‑end, eight‑phase automated evaluation pipeline for large‑model agents that replaces manual bad‑case inspection with AI‑assisted pre‑annotation, cutting analysis time from a full‑day to about 30 minutes and achieving over 90 % efficiency gain while enabling iterative knowledge‑base refinement.
This article shares the practical lessons learned from building Dataphin‑MCP, an AI‑enabled data‑development platform, by outlining five concrete "silver" rules, illustrating each with real‑world cases, and discussing deeper considerations for building robust AI‑first tools and harnesses.
This article walks through turning a generic AI model into a personal assistant by explaining user‑centric workflows, crafting effective natural‑language prompts, adding clarification steps, validating AI‑generated results through multiple methods, and handling errors with product interactions to create a reliable, evolving assistant.
