Dedicated to sharing and discussing big data and AI technology applications, aiming to empower a million data scientists. Regularly hosts live tech talks and curates articles on big data, recommendation/search algorithms, advertising algorithms, NLP, intelligent risk control, autonomous driving, and machine learning/deep learning.
Palantir’s Q2 report shows 93% revenue growth and a 225% profit jump, but the deeper signal is that enterprise AI value now hinges on embedding models into business objects, rules, and workflows through Ontology, a capability that differentiates vendors beyond raw model performance.
Palantir’s August 18 update to AIP Analyst introduces Skills and Analysis Lookup, letting agents store reusable analysis commands and treat past analyses as templates that re‑execute tools on current data, thereby extending Agent Memory from content recall to method reuse within the Ontology‑driven enterprise AI platform.
The article examines Apache Ossie's emergence as an Apache incubating project that aims to provide an open, vendor‑neutral format for sharing semantic models—metrics, dimensions, relationships, and AI context—across BI, data platforms, and AI agents, while outlining its current capabilities, governance model, and remaining challenges such as concept‑level interoperability and query execution.
AWS’s Context Ontology Accelerator (COA) links structured and unstructured data, uses AI to generate and govern Ontology assets, and exposes them via MCP so agents can move beyond simple queries to understand business context, relationships, rules, and permissions.
The article explains how OpenClaw tackles the bottleneck of information overload in enterprise research by replacing static keyword search with an Agentic Search loop that iteratively understands, plans, executes, and learns, while Agentic Memory captures and reuses findings across sessions, creating a self‑reinforcing research flywheel.
Databricks shifts AI coding agents from always using the most powerful model to a task‑aware Smart Routing system that selects both model and harness, cutting per‑task costs by up to 56% while maintaining performance, as shown by internal and public benchmarks.
Palantir’s July 2026 update adds three Agent SDK templates for Claude, OpenAI, and Google, unifying them under a shared Ontology layer and scoped permissions, while keeping each framework’s native loop, and explains how this architecture standardizes business objects, actions, and security boundaries for enterprise AI deployment.
The article outlines MaxCompute’s evolution into a Data+AI platform with a unified model management layer, heterogeneous compute quotas, Blob‑type full‑modal storage, AI Function APIs, and an Agentic interface built on MCP, Skills, and metadata, highlighting real‑world pipelines and performance gains.
Graphwise, a knowledge‑graph and semantic‑data firm with over 200 blue‑chip customers and 30% organic ARR growth, has received a majority‑stake investment from Oakley Capital, highlighting the shift of the Semantic Layer from BI metric unification to a shared AI‑Agent backbone that provides business concepts, relationships, and context for enterprise agents.
Palantir’s new Object Timeline feature aggregates token usage, runtime, waiting time and Agentic Coverage for each business object, turning enterprise AI agents into observable work units and revealing how much work they actually perform, where bottlenecks occur, and what remains human‑driven.
