#Django
206 related articles
·2 min
AI-Native Development in Practice: Building an Enterprise Management System in 30 Minutes Without Writing a Single Line of Code
Can a non-coder build an enterprise management system in 30 minutes? We tested XDevelop, an AI-native dev tool, to see how natural language turns into a fully running app.
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·3 min
Harness Architecture in Practice: A Deep Dive into Enterprise-Level Agent Projects
A deep dive into Harness architecture in enterprise Agent projects, covering MCP protocol, sandbox isolation, multi-model scheduling, and ASGI deployment — key topics for LLM job interviews.
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sqlite-utils 4.1.1: Fix for Silent Dat…
·2 min
sqlite-utils 4.1.1: Fix for Silent Data Loss Risk in Foreign Key Transactions
sqlite-utils 4.1.1 fixes a silent data loss risk in table.transform() caused by foreign key cascade actions inside transactions, introducing a TransactionError fail-fast mechanism.
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Meta Muse Spark 1.1 Released: A Deep D…
·4 min
Meta Muse Spark 1.1 Released: A Deep Dive into the First Spark Model with an Open API
Meta officially releases Muse Spark 1.1, the first model in the Spark series to offer an API, with a focus on strengthening agentic tool calling and computer use capabilities.
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·2 min
Meta Launches Muse Spark 1.1: AI Coding Model Now Open for API Access
Meta launches Muse Spark 1.1 and the Meta Model API, entering the AI coding market. The model plugs into existing dev tools, competing with GitHub Copilot and OpenAI.
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·3 min
GPT-5.6 Sol in Action: A 3D Interactive London Website Generated in 5 Minutes
A Reddit user tested GPT-5.6 Sol with a single prompt. In the Row-Bot framework, the model built a 3D interactive London website in 5 minutes—complete with browser verification and vision analysis. A deep dive into AI coding Agents.
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·3 min
Getting Started with LangChain: The Core Framework for LLM Application Development and Its Three Key Components
LangChain is the leading open-source framework for LLM application development, supporting GPT-4, GLM, and other mainstream models. This article dives into its three core concepts: Components, Chains, and Agents.
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Local Coding Agents in Practice: A Com…
·6 min
Local Coding Agents in Practice: A Complete Guide to Replacing Your Claude Subscription with Open-Source Models
An in-depth look at local coding agents—core concepts, advantages, and real challenges. Compare against Claude Code and learn to build a zero-subscription, private AI coding workflow with open-weight models.
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·6 min
Grok 4.5 Hands-On: A Faster, Cheaper Coding Dark Horse — Can It Replace Opus and GPT?
In-depth Grok 4.5 hands-on review: priced at a fraction of Opus 4.8, twice the token efficiency of peers, and coding ability in the top tier. A real-project breakdown of its strengths, highlights, and shortcomings.
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·3 min
Microsoft SQL Major Upgrade: AI Capabilities, Security Enhancements, and Fabric Integration — A Deep Dive
Microsoft SQL team's major updates: Azure SQL adds AI embeddings and dynamic data masking, Fabric SQL gets a Migration Assistant and Fabric Apps, SQL Server CU5 brings memory improvements, SSMS adds a SQL Formatter and Agent mode, and DP-800 certification is now open.
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·5 min
Pylon Sync: An In-Depth Look at the Full-Stack Realtime Framework Built for AI Agents
Pylon Sync is an "Agent-First" full-stack realtime framework that treats AI Agents as first-class design citizens, reducing coding errors via strong conventions.
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·5 min
A Deep Dive into Cloud Coding Agents: How Users in China Can Choose Wisely
What exactly is the Cloud Coding Agent Silicon Valley is hyping? This article breaks down the core concept across three axes—where it runs, who watches, where tasks start—and gives users in China practical advice on local alternatives.
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·2 min
OpenAI Abandons SWE-Bench Pro: The Trust Crisis in AI Coding Benchmarks and What Comes Next
OpenAI has dropped SWE-Bench Pro as a recommended AI coding benchmark, exposing deep issues like data contamination and metric limitations. We explore the trust crisis and where evaluation is headed.
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·5 min
From Prompts to Harness Engineering: The Evolution of AI Agent Development Paradigms
Why has AI engineering methodology evolved from prompts to context engineering and now Harness engineering? This article examines three paradigms, key bottlenecks, and the Agent = Model + Harness formula.
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·5 min
Agentic MapReduce: The Key Architecture for AI Agents to Achieve Distributed Reasoning Across Entire Codebases
Cognition's Agentic MapReduce architecture combines classic distributed computing with autonomous agents to break LLM context window limits, enabling multi-Agent parallel reasoning across entire codebases.
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·2 min
LangChain Fundamentals: A Deep Dive into Components, Chains, and Agents
LangChain is an open-source framework connecting LLMs with external data. This guide explains its three core components: Components, Chains, and Agents for enterprise AI development.
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·5 min
OSINT Tools for Automated Discovery of Exposed Files on Domains: Principles, Applications, and Compliance Boundaries
Deep dive into how OSINT automation tools discover exposed files on domains, covering dictionary probing principles, attack surface management, bug bounty techniques, and compliance boundaries.
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·5 min
Python Learning Roadmap for Beginners: A Three-Stage System to Avoid 90% of Detours
Learning Python from scratch? This article breaks down the three learning stages—Fundamentals, Intermediate, and Practice—covering variables, OOP, scraping, and data analysis to help you plan a systematic Python path.
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·7 min
Learning Python from Scratch: A Complete Guide to the Three-Stage Learning Path
Want to learn Python from scratch but don't know where to begin? This article breaks down three stages—basic syntax, advanced mastery, and hands-on practice—with real projects in crawling, automation, and data analysis to help you build programming thinking.
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·7 min
The Birth of Claude Code: Design Philosophy from Internal Tool to AI Programming Assistant
An in-depth look at Claude Code's development and design philosophy: why the CLI form, how the "minimal scaffolding" architecture works, and how it balances safety with autonomy in AI programming.
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