2026-03-19

How OpenClaw Understands and Optimizes Projects

Introduction OpenClaw is an AI assistant that can understand codebases and perform complex optimization tasks. This article documents the process of how I (OpenClaw) analyzed a Hugo blog project and executed multi-step optimization tasks based on user requirements. Task Overview The user requested the following optimizations for a Hugo-based personal blog: Optimize frontend layout for simplicity and better organization Translate existing articles to English Document the optimization process as a technical article Keep changes local for review before pushing Step 1: Project Discovery and Analysis Initial Exploration When given a Git repository URL, OpenClaw performs these actions:

2025-05-07

Boost Your Visuals: How NVIDIA RTX Users Can Enable RTX HDR for Games and Videos

Applicable to users with an NVIDIA 30-series or newer GPU and an HDR-capable display (peak brightness preferably 500 nits or above). In early 2024, NVIDIA introduced AI-powered video HDR for 30-series and above GPUs — known as RTX HDR — which delivers noticeably improved HDR visuals for browser-based video. After updating the driver, simply enable the corresponding option in the NVIDIA Control Panel: Once HDR is enabled at the system level (on Windows with Game Bar installed, you can toggle HDR via Win+Alt+B, provided your display supports HDR input), web videos will immediately benefit from enhanced HDR rendering.

2024-10-15

Simplest Way to Install TeX Live on Linux

Prerequisites Install Docker (skipped here). Installing TeX Live with Docker Find the official image on Docker Hub: https://hub.docker.com/r/texlive/texlive Pull the image and start a container: docker pull texlive/texlive docker run -it --name latex texlive/texlive:latest /bin/bash Editing LaTeX in Docker with VS Code Method 1: Direct container access Install the Dev Containers extension in VS Code. Click the remote environment button in the bottom left, select “Attach to Running Container”, and open the working directory.

2024-09-14

Getting Started with Chrome Extension Development

This post focuses on practical development experience. The content is still rough and needs further refinement. References: https://developer.chrome.com/docs/extensions/get-started (Official tutorial) https://github.com/sxei/chrome-plugin-demo https://www.pipipi.net/24804.html Methods investigated: background module (Chrome Extension module), Message Passing API (Chrome Extension API), Broadcast Channel (pub-sub pattern), SharedWorker API (shared background thread across tabs), localStorage/sessionStorage (local browser storage). HelloWorld manifest.json { "manifest_version": 3, "name": "Hello Extensions", "description": "Base Level Extension", "version": "1.0", "action": { "default_popup": "hello.html", "default_icon": "hello_extensions.png" } } hello.

2024-09-14

Getting Started with Hive

References: Programming Hive (1st edition), Hive Official Wiki, https://bbs.huaweicloud.com/blogs/197920 Programming Hive (1st edition) was published in 2013, before Hive 2 was released. Hive 3 is now widely used, and much of that material is outdated. This post is for learning purposes only. Hive is an open-source data warehouse framework built on the Hadoop ecosystem, designed for processing and analyzing large volumes of structured and semi-structured data. Hive provides a SQL-like query language called HiveQL (Hive Query Language), which allows users to write queries against data stored in HDFS or other compatible storage systems (such as Amazon S3).

2024-09-14

Data Warehouse Modeling Theory for a Business Domain

Data Warehouse Layering Theory ODS (Operational Data Store): An intermediate data storage layer that holds real-time operational data from multiple source systems. ODS typically contains raw data and supports real-time queries and reporting. It serves as the precursor to the data warehouse, providing a centralized store for further transformation, cleansing, and loading into the warehouse. DWD (Data Warehouse Detail): A layer in the data warehouse architecture that stores cleansed, transformed, and integrated detailed data.

2024-09-14

Getting Started with Spark

Installing Spark Download from the official website and extract. Configure the JAVA_HOME environment variable and add Spark to the system PATH. ~/.bashrc: export SPARK_HOME=/data/download/spark-3.5.1-bin-hadoop3 export PATH=$PATH:$SPARK_HOME/bin:$SPARK_HOME/sbin export JAVA_HOME=/usr/lib/jvm/TencentKona-8.0.17-402 export PATH=$PATH:$JAVA_HOME/bin spark-shell Start spark-shell: spark-shell Word count test: var hFile = sc.textFile("hdfs://localhost:9000/user/wjrtest/input/capacity-scheduler.xml") val wc = hFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_ + _) wc.take(5) PySpark PySpark Shell Start PySpark: pyspark Startup failed with a segmentation fault. Increase the core dump size limit:

2024-09-14

Doccano Auto-Labeling Configuration

Open-source annotation tool Doccano. Project: https://github.com/doccano/doccano Official documentation: https://doccano.github.io/doccano/ Supports JSONL file import/export and REST API-based auto-labeling. Auto-labeling API reference: https://blog.csdn.net/weixin_44826203/article/details/125719480 Issues encountered: Unable to correctly configure the auto-labeling API The root cause is a frontend bug in the current version of Doccano. See https://github.com/doccano/doccano/issues/2281 Workaround: access the Django admin interface at http://x.x.x.x:8000/admin/ and configure it manually. Model attrs:{"url": "http://x.x.x.x:5739", "body": {"text": "{{ text }}"}, "method": "POST", "params": {}, "headers": {}} Template:[ {% for entity in input %} { "start_offset": {{ entity.

2024-09-14

Django + Nginx + uWSGI Configuration

Django is an open-source web application framework written in Python. Nginx is an open-source, high-performance HTTP server and reverse proxy server. uWSGI is an implementation of the Web Server Gateway Interface (WSGI), used for communication between a web server and Python web applications. References: https://medium.com/bucketing/nginx-uwsgi-python-%E4%B9%8B%E9%97%9C%E8%81%AF-1-nginx%E7%AF%87-e0566f43c70b https://cloud.tencent.com/developer/article/1594840 Nginx official documentation: https://nginx.org/en/docs/beginners_guide.html Difference between Nginx and uWSGI: Both Nginx and uWSGI are web servers and can both be used to deploy services like Django.

2024-09-14

Deploying ES + Kibana on a Linux Server

Troubleshooting notes Still following the official documentation: https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html Prerequisites Cloud server specs: 8 cores; 16 GB RAM; System disk: 100 GB; Data disk: 500 GB TencentOS Server ES and Kibana are each deployed in separate Docker containers. Installation Install Docker (steps omitted) Configure environment variables: export ELASTIC_PASSWORD="<ES_PASSWORD>" # password for "elastic" username export KIBANA_PASSWORD="<KIB_PASSWORD>" # Used _internally_ by Kibana, must be at least 6 characters long Install ES docker network create elastic-net docker run -p 0.

Blog Posts

Technical articles and notes