Expired65% confidenceFactExact time
By 2024, large language models could understand complex code logic, perform multi-step reasoning, and handle large-scale codebases, while mainstream coding tools were still limited to single-line autocomplete or simple Q&A
3
Sources
65%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
The Origin Story of Claude Code: From Internal Experiment to Programming Paradigm Shift
bilibiliClaudeAI6/6/2026
Related Claims
VerifiedLarge language models with strong reasoning capabilities can understand code semantic logic and discover new types of defects that have never appeared in known vulnerability databases.75% similarVerifiedTONGYI Lingma's underlying layer relies on the code understanding and generation capabilities of the Tongyi Qianwen series of large language models.75% similarUnverifiedLarge language models naturally tend to generate code that looks complete, including various defensive programming patterns, abstraction layers, and extension points, which are often unnecessary for current requirements.74% similarUnverifiedMultiple academic papers published in 2024 demonstrated that large language models can autonomously write exploit code when given CVE descriptions.72% similarUnverifiedConversational programming tools like Xiaolongxia are based on Large Language Models (LLMs) and typically comprise three technical layers: a natural language understanding layer, a code generation layer, and an execution layer.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/49814API
curl https://kongchang.com/api/v1/knowledge/claims/49814MCP
get_claim(id=49814)