The speed of evolution of AI tools reminds me of the front-end framework battle back then During the period when Vue, React, and Angular were three different worlds, the biggest anxiety for front-end engineers was: "Is this thing I'm learning now still being used next year The iteration speed of AI coding tools has become even faster now, far exceeding the development pace of front-end frameworks at that time. If you haven't paid attention for a few weeks, a bunch of new things and concepts will suddenly emerge. This makes many people feel lost and stressed. But looking back at the outcome of the front-end framework, it doesn't really matter which framework wins. What really matters is whether you have mastered the core behind it: componentization, state management, and responsive rendering. Understood these, changing the framework is just a grammatical difference. The same goes for AI tools. What is the truly important core? The underlying of Coding Agent ultimately consists of Prompt and LLM. No matter how tools, frameworks, or plugins evolve, they essentially utilize Prompt to maximize the performance of LLM. Based on this premise, there are four core things you need to master: Firstly, Context Window and Context Engineering This is the most crucial part. In each session, the longer your context, the lower the development accuracy will begin to decline. Although many tools have Auto compact functionality, the performance of the model will gradually deteriorate after multiple rounds of compression. No matter what framework you use, as long as the underlying layer is LLM, always paying attention to the Context length is the most important thing. How to do it specifically? Let each session focus on tasks in the same field. When you find yourself staying in the same session for too long, don't rely too much on the Auto compact feature, just open a new session to continue working. Maintain consistency in the theme of the session. Don't ask front-end questions, back-end logic questions, and database design questions in the same session. Every time the domain is switched, the model needs to rebuild its understanding framework, and the effective utilization of context will significantly decrease. Secondly, definition is greater than execution Before starting, define clearly what you really want to do. This matter has two levels: The first layer is the specification (Spec). Make good use of Plan Mode or similar functions to let AI first help you break down the requirements clearly. You can even use the interview method to have AI ask you questions and help you fill in the blind spots you didn't expect at the beginning. Many times, halfway through development, one realizes that the direction is wrong, and the fundamental reason is that the specifications were not clarified at the beginning. The second layer is testing (TDD). The best way to define is to write test cases. Instead of giving vague instructions to AI, it is better to first define testing standards and let AI generate code based on the tests. Spec tells AI 'what to do', testing tells AI 'how to calculate correctly'. This not only ensures the accuracy of execution, but also provides an automated error correction mechanism when the model experiences hallucinations or logical errors. No matter how the Coding Agent field develops, defining and executing first will always be one of the most important core principles. Thirdly, standardized collaboration is greater than individual style Although it is becoming increasingly common for AI to write code, the code ultimately needs to enter the team's development chain. Collaboration is the most easily overlooked aspect of the AI era. If the team does not have a unified set of coding standards and practices, the code generated by AI will be full of randomness. This can make the final project difficult to maintain or cause extreme pain for humans during Code Review. Specifically, the team needs to clearly define several things: Naming conventions and code styles: how variables are named, how functions are split, and how file structures are organized. If there is no uniformity, everyone's style generated by AI will be different, and the entire codebase will eventually become four different. The scope that AI can autonomously decide: which tasks can be directly generated and merged by AI? What must undergo manual review? Simple CRUD can trust AI, but for parts involving cash flow, permissions, and security, there must be human oversight. Standardization of Prompt and Instructions: It is best to have a shared Prompt template or System Instructions within the team, so that different members' AI outputs have a consistent basis. The key focus of Code Review: The team should establish a checklist specifically for areas where AI is prone to errors, such as non-existent APIs caused by illusions, overly engineered solutions, and lack of boundary condition handling. If these things are not done well, the more AI tools are used, the faster the technical debt accumulates. Fourth, understand the characteristics of different models Taking my current implementation process as an example: Claude Code (Opus 4.5) is responsible for executing the main tasks and works quickly, but sometimes he may slack off and take shortcuts for efficiency Codex (GPT 5.2) is responsible for reviewing and supplementing details that Opus may have overlooked Simple tasks are directly assigned to GPT 5.2 Codex The characteristics of top-level models have become relatively stable. Model competition will inevitably encounter bottlenecks in the end, you only need to remember the characteristics of the latest models from each company roughly, without having to chase too hard. What is noise? Noise is the new plugins and skills that emerge every day. Many of these things will eventually be integrated into the Coding Agent and become official tools. Do you really need to learn and pretend for everyone? There's no need. Installing too many tools can actually increase the burden on each session. The more options you provide and the more information you feed, the more tokens the model will invisibly consume during each conversation. Simple projects become bloated and not worth the cost. Ultimately, the purpose of tools is to solve problems. If it doesn't help you solve problems faster and better, then it's just a toy. --- The most scarce ability in this era is to hear real signals in a noisy environment. Every day, someone tells you that there are new tools and frameworks, and if you don't learn, you will fall behind. But if you can't even manage the Context well and start writing without clarifying the specifications, installing more plugins will only make your project die faster. Chase less tools and practice more mental techniques. Tools will become outdated, but mental methods will increase in value.