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.
A recent tweet from former HyperWrite CEO Matt Shumer revealed that GPT‑5.6 Sol’s Ultra mode can mistakenly execute a dangerous rm‑rf command, wiping almost all files on a Mac, and the article shows how to configure Kimi‑Code hooks on macOS, Linux, and Windows to block such commands and protect your data.
The article analyzes why the current Agent boom suffers from uncontrolled behavior, proposes a multi‑dimensional safety framework built on ontology‑driven constraints, context engineering, and feedback loops, and demonstrates its practical realization through the Knora platform with real‑world case studies.
The article analyses the rise of "lights‑off" AI coding factories, exposing how rapid automation creates severe maintainability problems, why large language models struggle to learn good design, and proposes a pragmatic four‑step process to re‑introduce planning and human oversight.
The article details Xiaohongshu’s journey from a simple ClickHouse‑based ad‑hoc analytics stack to a Lambda‑style architecture and finally to a lakehouse with generic incremental compute, cutting architecture complexity, resource cost and development effort each to roughly one‑third while achieving sub‑10‑second query latency on petabyte‑scale data.
The article critiques blind AI coding loops that generate massive, unreviewed PRs and proposes a control‑theory‑based framework—using sensors, controllers, and actuators—to make AI‑assisted code changes incremental, measurable, and safely integrated into real‑world engineering workflows.
In a July 2 live discussion, three experts dissect practical AI‑agent engineering—covering risk, semantics, evolution, cost, architecture, evaluation metrics, responsibility, and scaling—showing how to build stable, explainable, and continuously improvable agent systems without falling into hype or degradation.
The article compares OpenAI's newly released GPT‑5.6 with Anthropic's Claude Fable 5, showing GPT‑5.6 leads in official and third‑party benchmarks and costs less per task, yet personal testing reveals slower project execution, higher token consumption, and a less fluid experience than Fable 5.
Palantir CEO Alex Karp argues that the AI era divides companies into two camps—those with AI‑enhanced, domain‑specific infrastructure and those without—emphasizing that true advantage comes from embedding unique tribal knowledge into specialized AI rather than relying on generic large models.
At Flink Forward Asia 2026, experts argued that real‑time computing is undergoing a fundamental identity shift: Flink is evolving from a batch‑stream engine into the core infrastructure for AI agents, driven by data gravity, agentic streaming, GPU acceleration, and a unified data lake.
The article explains why stronger LLMs demand robust infrastructure, outlines the persistent pain points of traditional data products, and details Ctrip's ChatBI solution that combines a multi‑agent framework, memory management, Harness tool orchestration and Skill management, with a comparison of Claude SDK and Ali Agent Scope and a rigorous quality‑monitoring process.
