Add per-model provider + data-retention/prompt-training disclosure
還沒有人認領這個 Issue。
評估
研究方向
首先檢視網站上的模型/提供者清單,以及 issue 中所參照的現有 Data Retention Policy。定義每個模型的提供者、提示詞訓練政策和保留政策應如何表示,並評估停用由提供者進行訓練的模型這項要求的選項。完成的標準是網站針對每個模型揭露這些政策,並在可行的情況下支援此 opt-out。
由索引模型根據 Issue 內容生成。
描述
Summary
It should be clear what's the prompt training and data retention policy on each model/provider. I don't care if Command Code doesn't train on my prompts and doesn't retain my data, that's pointless if the underlying provider could be doing that.. You're essentially saying today like "I don't use your data dude, just chill", but omitting that the underlying providers might be doing it.. Transparency is pretty important and that's why I cancelled my sub instead of upgrading it... Thought of upgrading but gave up after realizing that. It's not enough as well that you say that 98% of your models are zdr or no prompt training... It's only enough when you at least disclosure in your website the prompt training and retention policy for each model. And if possible, provide a way of disabling models whose provider might train on prompts...
Expected Behavior
Disclosure in your website the prompt training and retention policy for each model. And if possible, provide a way of disabling models whose provider might train on prompts...
Actual Behavior
Data Retention and Prompt Training policies at PROVIDER level are not disclosure. Privacy Policy is misleading as it talks about you, not the providers.
Steps to reproduce the issue
Try finding any disclosure on the prompt training or data retention policies per model... It doesn't even disclosure the providers per model..
Command Code Version
1.26.0
Operating System
macOS
Terminal/IDE
kitty
Shell
⌘ Data Retention Policy
Session file (optional)
No response
Fix prompt (optional)
No response
Additional context
OS: macOS Tahoe 26.5.2
- 主要語言
- 沒有語言資料
- 星號
- 4k
- 分支
- 350
- PR 合併指標
- 30 天內沒有已合併 PR
貢獻指南
這個儲存庫沒有索引到貢獻指南
從這裡開始
- 先讀完整個 Issue,再讀專案的貢獻指南。
- 在 Issue 下留言說明你要接手 —— 這能避免兩個人做同樣的事。
- Fork 儲存庫,在一個分支上完成修改。
- 送出 Pull Request,並在描述裡引用這個 Issue 編號。
CommandCodeAI/command-code 的其他 Issue
-
難度 2/5 1-3 小時 新手友好度 68/100
CommandCodeAI/command-code#855 ·
-
難度 2/5 1-3 小時 新手友好度 78/100
CommandCodeAI/command-code#841 · 1 則留言 ·
-
難度 2/5 1-3 小時 新手友好度 68/100
CommandCodeAI/command-code#655 · 1 則留言 ·
-
難度 2/5 1-3 小時 新手友好度 68/100
CommandCodeAI/command-code#608 ·
-
難度 3/5 1-2 天 新手友好度 70/100
CommandCodeAI/command-code#893 ·
查看 CommandCodeAI/command-code 的全部 Issue
相似的 Issue
-
enhancement
難度 2/5 1-3 小時 新手友好度 68/100
JuliusBrussee/caveman#1102 · 1 則留言 ·
-
難度 2/5 1-3 小時 新手友好度 78/100
use-agent-os/agent-os#3263 ·
-
[Bug]: context-limit error parsing has no pattern for llama.cpp's "context size (N tokens)" phrasing 未關閉area/compression area/local-models area/sessions comp/agent duplicate P2 sweeper:risk-session-state type/bug
難度 2/5 1-3 小時 新手友好度 82/100
NousResearch/hermes-agent#117793 · 1 則留言 ·
-
possible bug
難度 2/5 1-3 小時 新手友好度 88/100
Mintplex-Labs/anything-llm#6415 · 1 則留言 ·
-
難度 2/5 1-3 小時 新手友好度 82/100