Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.
The article critiques the uncontrolled “blind rail” AI coding loops that generate massive PRs, explains why control theory‑based feedback loops are essential, and details a concrete Effect‑TS migration case that demonstrates a repeatable, low‑risk engineering pattern for AI‑assisted code evolution.
The article analyzes the 2026‑07‑28 MCP specification update, detailing four production‑grade signals, QCC’s decade‑long shift from simple data retrieval to Agent‑Native services, a five‑layer capability matrix, and a trusted workflow for AI‑driven corporate due‑diligence tasks.
At a closed‑door OpenKG × DataFun session the authors argued that enterprises now lack a unified, computable, evolvable semantic layer—not model capability—and that ontology, re‑imagined as a semantic operating system, can bridge business, data and AI, though organizational and open‑source hurdles remain.
The article traces the evolution from traditional software factories to the “lights‑off” AI‑driven model, exposing a maintainability nightmare, explaining why current LLM‑based coding agents cannot learn good design, reviewing emerging benchmarks, and proposing a pragmatic four‑step process to re‑introduce planning and human oversight.
The article explains how Palantir’s Foundry and AIP combine data integration, ontology‑based business context, and rule management to turn large‑model AI into executable intelligence, illustrated by Freedom Mortgage’s 90‑day rollout of compliance, document, and call‑handling applications that link rules, documents and customer interactions into a unified, actionable system.
PlanRAG adapts decades‑old database query‑planning techniques to Retrieval‑Augmented Generation, turning complex, non‑linear questions into logical query trees that guide retrieval and generation, resulting in smarter search, reduced noise, lower cost, and up to 2.5× faster execution on exploratory reasoning tasks.
The 2026 AI Engineer World Expo revealed that as AI now writes code, calls tools, and self‑optimizes, engineers are shifting from writing code to designing sustainable loops, with new knowledge layers, software‑factory concepts, model routing, security controls, and open‑source advances reshaping the discipline.
The live discussion reveals why stronger LLMs can hide subtle errors, why moving from single‑point chatbots to multi‑agent harnesses requires a cognitive shift, and how enterprises must enforce protocols, permissions, and structured evaluation to safely and cost‑effectively deploy AI agents at scale.
The paper proposes Memory‑Guided Hard Data Augmentation (MGHDA), a closed‑loop pipeline that diagnoses model‑specific hard instances in multimodal named entity recognition, abstracts their error patterns into a Memory Tree, and generates targeted augmentation samples, achieving consistent F1 gains across several backbones while highlighting cost and scalability trade‑offs.
At Palantir AIPCon 9 the US Navy unveiled Ship OS, an AI‑native operating system that injects commercial AI into shipyards, automates change‑notice processing and communication, and compresses multi‑day decision cycles into minutes while preserving existing industrial workflows.
