Unverified50% confidenceOpinionExact time
Model improvements that skip hands-on source code work are not convincing.
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Diffusion Models from Zero: Principles, Tools, and Innovation Paths Explained
bilibili论文不秃头指南10/15/2026
Related Entities
Related Claims
UnverifiedAdding data drift metrics cannot solve model failure problems, because the issue lies in the absence of accountability rather than the absence of signals73% similarUnverifiedWithout an attempt ledger, developers cannot distinguish between "the model is too weak" and "the harness is broken," nor compare two models without guessing.72% similarUnverified循环本身不会提升模型的推理能力,模型不是因为循环而变聪明的71% similarUnverifiedThe root cause of coding agents performing poorly in real projects is primarily the lack of context, rather than insufficient model reasoning capability.70% similarUnverifiedBeginners are advised not to start by fine-tuning local models, as full-parameter fine-tuning has high hardware and data requirements and is not suitable as a starting point.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1101417API
curl https://kongchang.com/api/v1/knowledge/claims/1101417MCP
get_claim(id=1101417)