Explore the latest trends in AI application development, learn practical technical tutorials, and discover the latest features and success stories of the JitAI platform
What Doubao’s agentic smartphones reveal about AI-native system architecture, low code development platforms, and how enterprises should design for safe AI agents.
What G2’s 2026 low-code Grid ranking reveals about enterprise demand for AI-powered platforms, agentic systems, and how AI-native JitAI can seize the next wave.
Learn what Google AI data deletion means for low code development platforms and why AI native development platforms like JitAI matter for safe AI agents.
Learn how MCP joining the Linux Foundation reshapes platform engineering for AI tools and agents, and what it means for integration platforms and JitAI users.
Intelligence supply is no longer the bottleneck, but application engineering is lagging significantly. JitAI reconstructs the AI-native application foundation through the JAAP protocol and Matrix Framework, making traditional software modules truly tool-like, and transforming AI intelligence into enterprise native productivity. Explore the transition from the experimental phase to the engineering phase, the rise of the new FDE profession, and the new commercial paradigm.
Enterprises are cooling on the AI agent hype. Discover why automation is harder than it looks, how forward-deployed engineers bridge the gap, and how platforms like JitAI help enterprises deploy AI agents safely and effectively.
Forward Deployed Engineers blend technical and business skills to deliver custom AI solutions, driving innovation and career growth in tech industries.
Discover forward deployed engineer jobs, skills, and how AI native development platforms enhance enterprise systems. Explore trends, salaries, and opportunities.
Forward Deployed Engineers (FDEs) embed with your team to build custom AI, align tech and business, drive adoption with training, and integrate via JitAI—amid surging demand (~800%).
Microservices, containerization, elastic scaling—these cloud-native technologies sound impressive, but they're complete overkill for most enterprise systems. Cloud-native architecture is designed for consumer applications serving millions of concurrent users. Why should internal enterprise systems serving a few hundred users bear the complexity of K8s clusters?
Over the past decades, development tools have continuously evolved in programming capabilities, yet orchestration abilities remain absent. Business systems comprise modules like portals, pages, components, models, and services—their organizational relationships determine architecture quality. Traditional tools lack visual orchestration support, hiding system structures within code and making maintenance difficult. Software development is undergoing a shift from programming-centric to balanced programming and orchestration, where orchestration-oriented architectures, frameworks, and tools deliver both improved development efficiency and sustained architectural elegance.
The public cloud SaaS era promised convenience, but at what cost? Modern deployment technology has flipped the equation. On-premises infrastructure now offers superior data sovereignty, lower TCO, and freedom from vendor lock-in—without the operational complexity. It's time enterprises reconsidered the on-premises advantage.
Enterprises need systems that deliver fast, customize rapidly, and evolve with business needs. However, lightweight apps built on database-UI paradigms (like Notion, Airtable) and AI applications deployed in isolation from business systems (like Coze, Dify, n8n) are products of short-termism that fail to gain traction in large enterprises.
Discover the role of Forward Deployed Engineers (FDEs) in AI enterprise transformation. Learn how low-code platforms like JitAI empower AI-powered automation at scale.
Traditional low-code/visual development platforms rely on black-box rule engines, fundamentally limiting application extensibility. They sacrifice expressive power for simplicity, inevitably failing in complex enterprise scenarios. True visual development shouldn't constrain capabilities—it should enable developers to orchestrate system modules and technical capabilities visually, transitioning from closed DSL (Domain Specific Language) engines to open orchestration protocols, from limited expression to unlimited integration.
Discover how JitAi's innovative AI coding approach addresses the key limitations of tools like Cursor through high accuracy, low barrier to entry, and cost efficiency for enterprise business application development.
Production-grade AI applications face inherent complexity. Unlike traditional enterprise apps that record transactions, AI apps execute tasks—requiring deep integration with unique business processes and knowledge systems. While custom development is inevitable, it remains costly and ineffective. Traditional paradigms fall short; the market urgently needs AI-native engineering practices and methodologies.
AI-native application architecture must address not only how AI modules are designed and integrated, but also how traditional technical modules are perceived, driven, and orchestrated by AI. Attempting to integrate AI capabilities into legacy event-driven architectures is like putting an internal combustion engine on a horse cart—foolish and inefficient. Traditional enterprise applications like ERP, CRM, and OA systems will inevitably be reshaped by new AI-native architectures and development paradigms.
The industry is exploring how to deploy enterprise AI applications, but many attempts have gone astray. Inability to make partial adjustments to outputs, deployment isolated from business systems, standalone UIs that can't collaborate with humans, and claims of being universal products—none of these represent what production-grade AI applications should be.
In today's rapidly evolving AI landscape, Large Language Models demonstrate impressive capabilities, yet they often operate as isolated "intelligent islands," unable to directly access our file systems, databases, or API services. This is the core problem that the Model Context Protocol aims to solve.
Under the dual forces of AI advancement and its rapid evolution, enterprises are struggling with low development efficiency, high technical barriers, and poor system scalability. Traditional development takes months just to build basic infrastructure, while low-code platforms, though convenient at the start, quickly fall into the traps of limited flexibility and lack of AI-native support—making it hard to sustain complex business evolution.
In today's world, where everyone is embracing AI in software development, how much of your code do you write yourself? How much is generated by AI? In this evolving landscape, every software engineer will soon have to become an AI native developer.
An AI Agent (intelligent agent) possesses autonomous decision-making and task execution capabilities. It can automatically select appropriate tools based on user input and contextual information to complete complex business processes.