We waste so much resources in storing/transiting data in human readable forms. Ultimately it's binary, we already use software to display it, requiring the data be in forms that are human readable just wastes resources.
If the data spends more time being executed than read by humans it doesn't deserve to be human readable.
Use binary packed data and have an AI write you an interpreter for the structure.
Traditional data formats often create structural bottlenecks across data and AI pipelines. Instead of relying on them, I wondered what would happen if I kept the readability that both humans and AI can share, while still reaching binary-level speed.
That's why I created a self-contained format that expresses meaning without parsing (Semantic), preserves information in its original state (Raw), and maintains a fully organized structure (Format). This is the Semantic Raw Format (SRF), and I call it BEAT (Behavioral Event Analytics Transcript).
Here is an xPU platform example (1-byte scan).
s = srf == ord('!') # Contextual Space (who)
t = srf == ord('~') # Time (when)
p = srf == ord('^') # Position (where)
a = srf == ord('*') # Action (what)
f = srf == ord('/') # Flow (how)
v = srf == ord(':') # Causal Value (why)
xPU can scan BEAT sequences directly without any additional setup. The rest is just address arithmetic to load and store tokens. In short, it achieves binary-level performance while preserving the human readability of a text sequence. This layout supports byte-wise scanning and simple dispatch, without tokenization or parse trees.
This makes BEAT a natural fit for AI-driven analysis of large-scale event streams in domains such as robotics and autonomous driving. In these environments, its ability to be scanned at binary speed while still remaining directly readable to both engineers and AI models stands out as a clear advantage.
Humans learn the meaning of their actions as they acquire language. AI, by contrast, excels at generating language but struggles to autonomously structure and interpret the full context (5W1H) of its own actions. With BEAT, AI can record its behavior as sequences that read like natural language and analyze that flow in real time (1-byte scan), providing the foundation for feedback loops through which it can monitor its own errors and improve its outcomes.
Writing and reading coexist on the same timeline.
Action → Record → Sense (Latency)
Action ~ Record ~ Sense (Real-time)
---
The Full Score project was created to demonstrate the potential of Semantic Raw Format. You can see how real-time security and AI insights are efficiently implemented through it. Due to the nature of BEAT, most analytics pipelines can be removed, and origin servers are no longer essential for analytics. This design differs fundamentally from traditional analytics.
Traditional Analytics (7 Steps) = Browser → API → Raw Database → Queue (Kafka) → Transformation (Spark) → Refined Database → Archive
Full Score (2 Steps) = Browser ~ Edge → Archive
At its core, BEAT is designed as a true End-to-End format that flows directly from the browser to the xPU without any semantic transformation. Its unique grammar captures the flow of space, time, and depth, enabling AI to reproduce the decision flow underlying human behavior.
Feedback and thoughts are welcome. For more details, please check the FAQ section on the demo site.
We waste so much resources in storing/transiting data in human readable forms. Ultimately it's binary, we already use software to display it, requiring the data be in forms that are human readable just wastes resources.
If the data spends more time being executed than read by humans it doesn't deserve to be human readable.
Use binary packed data and have an AI write you an interpreter for the structure.
Hello HN,
Traditional data formats often create structural bottlenecks across data and AI pipelines. Instead of relying on them, I wondered what would happen if I kept the readability that both humans and AI can share, while still reaching binary-level speed.
That's why I created a self-contained format that expresses meaning without parsing (Semantic), preserves information in its original state (Raw), and maintains a fully organized structure (Format). This is the Semantic Raw Format (SRF), and I call it BEAT (Behavioral Event Analytics Transcript).
Here is an xPU platform example (1-byte scan).
xPU can scan BEAT sequences directly without any additional setup. The rest is just address arithmetic to load and store tokens. In short, it achieves binary-level performance while preserving the human readability of a text sequence. This layout supports byte-wise scanning and simple dispatch, without tokenization or parse trees.This makes BEAT a natural fit for AI-driven analysis of large-scale event streams in domains such as robotics and autonomous driving. In these environments, its ability to be scanned at binary speed while still remaining directly readable to both engineers and AI models stands out as a clear advantage.
Humans learn the meaning of their actions as they acquire language. AI, by contrast, excels at generating language but struggles to autonomously structure and interpret the full context (5W1H) of its own actions. With BEAT, AI can record its behavior as sequences that read like natural language and analyze that flow in real time (1-byte scan), providing the foundation for feedback loops through which it can monitor its own errors and improve its outcomes.
Writing and reading coexist on the same timeline.
---The Full Score project was created to demonstrate the potential of Semantic Raw Format. You can see how real-time security and AI insights are efficiently implemented through it. Due to the nature of BEAT, most analytics pipelines can be removed, and origin servers are no longer essential for analytics. This design differs fundamentally from traditional analytics.
At its core, BEAT is designed as a true End-to-End format that flows directly from the browser to the xPU without any semantic transformation. Its unique grammar captures the flow of space, time, and depth, enabling AI to reproduce the decision flow underlying human behavior.Feedback and thoughts are welcome. For more details, please check the FAQ section on the demo site.
GitHub Repo: https://github.com/aidgncom/beat Full Score Demo: https://fullscore.org Full Score Video: https://www.youtube.com/@aidgn