Unverified50% confidenceOpinionExact time
General-purpose coding agents struggle to cover growth engineering because they lack modeling of business goals and experimental context.
1
Sources
50%
Confidence
Long-term
Relevance
11/3/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLarge models cannot replace programmers, because complex aspects of software development such as system architecture design, business requirements decomposition, security considerations, and long-term maintenance are difficult for models to complete.77% similarUnverified智能体应用开发入门门槛低,但设计底层框架难度很大75% similarUnverifiedPrompt Engineering的效果存在天花板,只能在模型已有能力范围内进行优化,无法弥补模型对特定业务领域知识的根本性缺失75% similarUnverifiedThe root cause of coding agents performing poorly in real projects is primarily the lack of context, rather than insufficient model reasoning capability.74% similarVerifiedNative large language models have three major shortcomings: a lack of continuous reasoning capability, limitations in knowledge timeliness, and the inability to connect with enterprise systems.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1129733API
curl https://kongchang.com/api/v1/knowledge/claims/1129733MCP
get_claim(id=1129733)