Today, neural networks are used everywhere. Programmers use them to generate code, explain other solutions, automate routine tasks, and even create entire applications from scratch. It would seem that this should lead to an increase in efficiency, reducing errors and acceleration of development. But reality is much more prosaic: many still do not succeed. The neural networks do not solve key problems – they only illuminate the depth of ignorance.
full dependence on LLM instead of understanding
The main reason is that many developers are completely relying on LLM, ignoring the need for a deep understanding of the tools with which they work. Instead of studying documentation – a chat request. Instead of analyzing the reasons for the error – copying the decision. Instead of architectural solutions – the generation of components according to the description. All this can work at a superficial level, but as soon as a non -standard task arises, integration with a real project or the need for fine tuning is required, everything is crumbling.
Lack of context and outdated practices
The neural networks generate the code generalized. They do not take into account the specifics of your platform, version of libraries, environmental restrictions or architectural solutions of the project. What is generated often looks plausible, but has nothing to do with the real, supported code. Even simple recommendations may not work if they belong to the outdated version of the framework or use approaches that have long been recognized as ineffective or unsafe. Models do not understand the context – they rely on statistics. This means that errors and antipattterns, popular in open code, will be reproduced again and again.
redundancy, inefficiency and lack of profiling
The code generated AI is often redundant. It includes unnecessary dependencies, duplicates logic, adds abstractions unnecessarily. It turns out an ineffective, heavy structure that is difficult to support. This is especially acute in mobile development, where the size of the gang, response time and energy consumption are critical.
The neural network does not conduct profiling, does not take into account the restrictions of the CPU and GPU, does not care about the leaks of memory. It does not analyze how effective the code is in practice. Optimization is still handmade, requiring analysis and examination. Without it, the application becomes slow, unstable and resource -intensive, even if they look “right” from the point of view of structure.
Vulnerability and a threat to security
Do not forget about safety. There are already known cases when projects partially or fully created using LLM were successfully hacked. The reasons are typical: the use of unsafe functions, lack of verification of input data, errors in the logic of authorization, leakage through external dependencies. The neural network can generate a vulnerable code simply because it was found in open repositories. Without the participation of security specialists and a full -fledged revision, such errors easily become input points for attacks.
The law is pareto and the essence of the flaws
Pareto law works clearly with neural networks: 80% of the result is achieved due to 20% of effort. The model can generate a large amount of code, create the basis of the project, spread the structure, arrange types, connect modules. However, all this can be outdated, incompatible with current versions of libraries or frameworks, and require significant manual revision. Automation here works rather as a draft that needs to be checked, processed and adapted to specific realities of the project.
Caution optimism
Nevertheless, the future looks encouraging. Constant updating of training datasets, integration with current documentation, automated architecture checks, compliance with design and security patterns – all this can radically change the rules of the game. Perhaps in a few years we can really write the code faster, safer and more efficiently, relying on LLM as a real technical co -author. But for now – alas – a lot has to be checked, rewritten and modified manually.
Neural networks are a powerful tool. But in order for him to work for you, and not against you, you need a base, critical thinking and willingness to take control at any time.
