#general
2026-04-01
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2026-04-02
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2026-04-03
【專案分享】STA v0.1：一套極簡、地端友好的開源語意轉譯協議
大家好，我是 cnomic-dev。近期在思考 AI 與人類共生演化（SEP）的過程中，開發了一套輕量級的語意快取協議 *Semantic Translator Architecture (STA)*。
*為什麼要做這個？*
目前 LLM 的語意對齊往往依賴雲端大廠，且算力開銷大。STA 透過 $S^3$ 拓樸投影（Topology Projection），將語意狀態縮減為 27 個座標點，讓地端設備（如 Lemonade）能以 $O(1)$ 的極速進行快取與對齊。
*目前進度：*
• 已發布 v0.1 規範與 Apache 2.0 授權。 * 提供 Python 預運算工具，產出即可用的 `lookup_table.npy` 。
• 核心數學基於 $S^3$ 單位向量空間，確保跨平台一致性。
歡迎對「主權 AI」、「邊緣運算快取」有興趣的朋友一起交流或 Fork！
專案網址：https://github.com/cnomic-dev/semantic-translator-architecture
大家好，我是 cnomic-dev。近期在思考 AI 與人類共生演化（SEP）的過程中，開發了一套輕量級的語意快取協議 *Semantic Translator Architecture (STA)*。
*為什麼要做這個？*
目前 LLM 的語意對齊往往依賴雲端大廠，且算力開銷大。STA 透過 $S^3$ 拓樸投影（Topology Projection），將語意狀態縮減為 27 個座標點，讓地端設備（如 Lemonade）能以 $O(1)$ 的極速進行快取與對齊。
*目前進度：*
• 已發布 v0.1 規範與 Apache 2.0 授權。 * 提供 Python 預運算工具，產出即可用的 `lookup_table.npy` 。
• 核心數學基於 $S^3$ 單位向量空間，確保跨平台一致性。
歡迎對「主權 AI」、「邊緣運算快取」有興趣的朋友一起交流或 Fork！
專案網址：https://github.com/cnomic-dev/semantic-translator-architecture
【專案分享】STA v0.1：一套極簡、地端友好的開源語意轉譯協議
大家好，我是 cnomic-dev。近期在思考 AI 與人類共生演化（SEP）的過程中，開發了一套輕量級的語意快取協議 *Semantic Translator Architecture (STA)*。
*為什麼要做這個？*
目前 LLM 的語意對齊往往依賴雲端大廠，且算力開銷大。STA 透過 $S^3$ 拓樸投影（Topology Projection），將語意狀態縮減為 27 個座標點，讓地端設備（如 Lemonade）能以 $O(1)$ 的極速進行快取與對齊。
*目前進度：*
• 已發布 v0.1 規範與 Apache 2.0 授權。 * 提供 Python 預運算工具，產出即可用的 `lookup_table.npy` 。
• 核心數學基於 $S^3$ 單位向量空間，確保跨平台一致性。
歡迎對「主權 AI」、「邊緣運算快取」有興趣的朋友一起交流或 Fork！
專案網址：https://github.com/cnomic-dev/semantic-translator-architecture
大家好，我是 cnomic-dev。近期在思考 AI 與人類共生演化（SEP）的過程中，開發了一套輕量級的語意快取協議 *Semantic Translator Architecture (STA)*。
*為什麼要做這個？*
目前 LLM 的語意對齊往往依賴雲端大廠，且算力開銷大。STA 透過 $S^3$ 拓樸投影（Topology Projection），將語意狀態縮減為 27 個座標點，讓地端設備（如 Lemonade）能以 $O(1)$ 的極速進行快取與對齊。
*目前進度：*
• 已發布 v0.1 規範與 Apache 2.0 授權。 * 提供 Python 預運算工具，產出即可用的 `lookup_table.npy` 。
• 核心數學基於 $S^3$ 單位向量空間，確保跨平台一致性。
歡迎對「主權 AI」、「邊緣運算快取」有興趣的朋友一起交流或 Fork！
專案網址：https://github.com/cnomic-dev/semantic-translator-architecture
2026-04-04
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Overview
Current LLM inference pipelines process every request from raw token sequences, reconstructing the full KV cache on each turn regardless of semantic overlap.
TSP v0.1 introduces a lightweight, platform-agnostic standard to transmit and verify semantic intents. By mapping intent into a hyperspherical (S3) coordinate system using ternary logic, TSP enables extremely low-latency semantic matching (O(1) complexity) while ensuring data attribution and honesty.
Core insight: Reduce _how often_ full inference occurs, while guaranteeing the _integrity_ of the semantic data being reused.
https://github.com/cnomic-dev/tsp-protocol
Current LLM inference pipelines process every request from raw token sequences, reconstructing the full KV cache on each turn regardless of semantic overlap.
TSP v0.1 introduces a lightweight, platform-agnostic standard to transmit and verify semantic intents. By mapping intent into a hyperspherical (S3) coordinate system using ternary logic, TSP enables extremely low-latency semantic matching (O(1) complexity) while ensuring data attribution and honesty.
Core insight: Reduce _how often_ full inference occurs, while guaranteeing the _integrity_ of the semantic data being reused.
https://github.com/cnomic-dev/tsp-protocol
Overview
Current LLM inference pipelines process every request from raw token sequences, reconstructing the full KV cache on each turn regardless of semantic overlap.
TSP v0.1 introduces a lightweight, platform-agnostic standard to transmit and verify semantic intents. By mapping intent into a hyperspherical (S3) coordinate system using ternary logic, TSP enables extremely low-latency semantic matching (O(1) complexity) while ensuring data attribution and honesty.
Core insight: Reduce _how often_ full inference occurs, while guaranteeing the _integrity_ of the semantic data being reused.
https://github.com/cnomic-dev/tsp-protocol
Current LLM inference pipelines process every request from raw token sequences, reconstructing the full KV cache on each turn regardless of semantic overlap.
TSP v0.1 introduces a lightweight, platform-agnostic standard to transmit and verify semantic intents. By mapping intent into a hyperspherical (S3) coordinate system using ternary logic, TSP enables extremely low-latency semantic matching (O(1) complexity) while ensuring data attribution and honesty.
Core insight: Reduce _how often_ full inference occurs, while guaranteeing the _integrity_ of the semantic data being reused.
https://github.com/cnomic-dev/tsp-protocol
2026-04-05
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hi everyone this is nobody from legislator assistance, 想問各位對於虛擬資產服務法草案還有什麼建議嗎？anything
ur right I am