Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.
Palantir’s Q2 2026 results show a 93% revenue jump, but the real story is how its Ontology and AIP platforms turn powerful models into actionable enterprise agents, shifting AI competition from raw model strength to the ability to understand, govern, and execute real business processes.
MemoHarness expands the notion of self‑evolving agents by keeping the language model frozen while continuously adapting the external control system—context assembly, tool interaction, generation settings, workflow orchestration, memory management, and output validation—demonstrating measurable gains on terminal, code‑generation, and finance tasks, yet highlighting limited scalability and transferability.
Amid exploding real‑time data demand, the traditional Lambda architecture suffers from high cost, data inconsistency and operational complexity, prompting a shift to a unified incremental computation engine that delivers minute‑level latency, sub‑hourly cost, and sub‑1% result divergence, as demonstrated by Kuaishou and Xiaohongshu production deployments.
The article analyzes how Freedom Mortgage leveraged Palantir Foundry, AIP, and an Ontology‑driven approach to unify heterogeneous mortgage data, encode regulatory rules as traceable objects, and transform documents and calls into actionable business events, illustrating a path from AI prototypes to end‑to‑end operational systems.
The article critiques uncontrolled AI coding loops that generate massive, unreviewable PRs and proposes a control‑theoretic framework—sensor, controller, actuator, and feedback—to make AI‑driven code changes incremental, safe, and auditable, illustrated with a real Effect‑TS migration case.
This article presents a detailed technical guide on constructing a multimodal product search system that combines text and image queries using embedding vectors, hybrid dense‑sparse models, vector similarity metrics, and Elasticsearch Serverless’s built‑in vector support, while also covering quantization techniques, hybrid search fusion, and best‑practice deployment architectures on Alibaba Cloud.
MemoHarness expands the notion of self‑evolving agents by keeping the language model frozen and continuously improving the surrounding control system—context assembly, tool interaction, generation settings, workflow orchestration, memory management, and output handling—demonstrating measurable gains on terminal, code‑generation, and finance tasks while highlighting limited experimental scale and selective cross‑task transfer.
Facing exploding data volumes and complex business scenarios, Tongcheng Travel’s DataAgent evolved from a traditional BI system to a multi‑agent AI platform, replacing manual SQL queries with NL2DSL and automating marketing tasks, ultimately delivering a 35% conversion lift, 60% higher autonomous operation, and significant cost reductions.
The article analyzes how scaling nuclear‑plant centrifuges from 16 to 11,520 units forces a shift from spreadsheets to a unified ontology‑driven operational model, enabling auditable agents that perform impact analysis, optimize solutions, and require human‑in‑the‑loop approval to keep decision latency low.
Grafana Assistant now queries over 30 data sources, expanding incident clues across monitoring, databases, and ticket systems, but cross‑source access only improves correlation; without unified entity mapping, time alignment, and evidence verification, engineers cannot reliably prove a root cause.
