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.
FUMO Lab’s Fusion Model unifies heterogeneous AI models through a closed‑loop intelligence allocation process, achieving first place on four leading benchmarks, cutting inference cost by 30‑40%, and demonstrating superior stability on scientific QA tasks with concrete case studies.
The article examines how large‑model agents can finally run on phones, PCs, cars and IoT devices by tackling three engineering layers—unlocking compute with Arm SME2, slimming models through ultra‑low‑bit quantization, and deploying with ExecuTorch and Windows on Arm—presented at the free Arm Create 2026 events.
Xinfēi Technology, a fintech data‑intelligence provider, cut its multi‑country data‑warehouse rollout from three months to about two weeks by adopting Alibaba Cloud’s DataWorks Data Agent, which unified data ingestion, task development, quality configuration, lineage analysis and release verification, introduced Skills and an enterprise semantic layer, and achieved up to six‑fold efficiency gains across ODS, DWD, DWS, DQC and reporting.
Palantir’s August 27 update adds Automate tools to AI Forward Deployed Engineer, letting agents configure business automation, manage conditions and effects, and integrate with Ontology, Action, and Function, while introducing governance via branching and approval, yet still with clear capability limits.
Palantir's August 18 update to AIP Analyst introduces Skills and Analysis Lookup, turning reusable analysis procedures into callable commands and using historical analyses as templates, thereby extending agent memory with method reuse tightly integrated with the Ontology framework.
The article analyzes the current Agent hype, explains why autonomous agents often lack business‑level safety and control, and proposes an ontology‑driven Harness Engineering framework that embeds constraints, context management, and feedback loops directly into the business semantics, illustrated with the Knora implementation and real‑world case studies.
The article defines the Agent Harness as the full software infrastructure that turns a stateless LLM into a capable autonomous agent, details its three engineering layers, enumerates twelve production‑grade components, walks through a step‑by‑step execution loop, compares implementations in Anthropic, OpenAI, LangChain, CrewAI and AutoGen, and discusses key design decisions and future trends, emphasizing that harnesses remain essential even as model capabilities improve.
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.
