#embeddedsystems — Public Fediverse posts on home.social

Caria Giovanni - Harpocrates @[email protected] · 2026-05-27 · 07:24 UTC

Working on something outside the usual AI paradigm sharing a preliminary note, not a "Eurkea".

The project (Aleph) explores whether biological organizational principles can replace neural architectures for embedded AI. No weights, no pre-training, no LLM. The core question: do the same solutions biology found over billions of years: specialization, quiescence, emergent coordination translate to digital systems?

Methodology follows Telesio's empirical approach: observation before theory, measurement before assertion. Every behavior in this post has a log file and a timestamp.

One verified result worth sharing:

Three independent components: a health monitor, a kernel, and an audio output organ — have no knowledge of each other. No shared state, no direct communication. When the watchdog process dies, the system emits a 220Hz tone. No explicit rule produces this. It emerges from composition.

This is weak emergence: predictable by reading the code. Not strong emergence. The distinction matters and we're not overstating it.

Current stack: Rust, Alpine Linux, SQLite, Unix sockets. Hardware: Dell Inspiron i5, 3.7GB RAM. ~452KB binary binary. ~39°C at rest.

What we don't know yet: whether this approach scales, whether the Bayesian learning converges usefully, whether the biological clock model holds across real day/night cycles.

#rustlang #embeddedsystems #ai #distributedsystems

Caria Giovanni - Harpocrates @[email protected] · 2026-05-27 · 07:24 UTC

Working on something outside the usual AI paradigm sharing a preliminary note, not a "Eurkea".

The project (Aleph) explores whether biological organizational principles can replace neural architectures for embedded AI. No weights, no pre-training, no LLM. The core question: do the same solutions biology found over billions of years: specialization, quiescence, emergent coordination translate to digital systems?

Methodology follows Telesio's empirical approach: observation before theory, measurement before assertion. Every behavior in this post has a log file and a timestamp.

One verified result worth sharing:

Three independent components: a health monitor, a kernel, and an audio output organ — have no knowledge of each other. No shared state, no direct communication. When the watchdog process dies, the system emits a 220Hz tone. No explicit rule produces this. It emerges from composition.

This is weak emergence: predictable by reading the code. Not strong emergence. The distinction matters and we're not overstating it.

Current stack: Rust, Alpine Linux, SQLite, Unix sockets. Hardware: Dell Inspiron i5, 3.7GB RAM. ~452KB binary binary. ~39°C at rest.

What we don't know yet: whether this approach scales, whether the Bayesian learning converges usefully, whether the biological clock model holds across real day/night cycles.

#rustlang #embeddedsystems #ai #distributedsystems

Caria Giovanni - Harpocrates @[email protected] · 2026-05-27 · 07:24 UTC

Working on something outside the usual AI paradigm sharing a preliminary note, not a "Eurkea".

The project (Aleph) explores whether biological organizational principles can replace neural architectures for embedded AI. No weights, no pre-training, no LLM. The core question: do the same solutions biology found over billions of years: specialization, quiescence, emergent coordination translate to digital systems?

Methodology follows Telesio's empirical approach: observation before theory, measurement before assertion. Every behavior in this post has a log file and a timestamp.

One verified result worth sharing:

Three independent components: a health monitor, a kernel, and an audio output organ — have no knowledge of each other. No shared state, no direct communication. When the watchdog process dies, the system emits a 220Hz tone. No explicit rule produces this. It emerges from composition.

This is weak emergence: predictable by reading the code. Not strong emergence. The distinction matters and we're not overstating it.

Current stack: Rust, Alpine Linux, SQLite, Unix sockets. Hardware: Dell Inspiron i5, 3.7GB RAM. ~452KB binary binary. ~39°C at rest.

What we don't know yet: whether this approach scales, whether the Bayesian learning converges usefully, whether the biological clock model holds across real day/night cycles.

#rustlang #embeddedsystems #ai #distributedsystems

Caria Giovanni - Harpocrates @[email protected] · 2026-05-27 · 07:24 UTC

Working on something outside the usual AI paradigm sharing a preliminary note, not a "Eurkea".

The project (Aleph) explores whether biological organizational principles can replace neural architectures for embedded AI. No weights, no pre-training, no LLM. The core question: do the same solutions biology found over billions of years: specialization, quiescence, emergent coordination translate to digital systems?

Methodology follows Telesio's empirical approach: observation before theory, measurement before assertion. Every behavior in this post has a log file and a timestamp.

One verified result worth sharing:

Three independent components: a health monitor, a kernel, and an audio output organ — have no knowledge of each other. No shared state, no direct communication. When the watchdog process dies, the system emits a 220Hz tone. No explicit rule produces this. It emerges from composition.

This is weak emergence: predictable by reading the code. Not strong emergence. The distinction matters and we're not overstating it.

Current stack: Rust, Alpine Linux, SQLite, Unix sockets. Hardware: Dell Inspiron i5, 3.7GB RAM. ~452KB binary binary. ~39°C at rest.

What we don't know yet: whether this approach scales, whether the Bayesian learning converges usefully, whether the biological clock model holds across real day/night cycles.

#rustlang #embeddedsystems #ai #distributedsystems

#distributedsystems #ai #embeddedsystems #rustlang

Caria Giovanni - Harpocrates @[email protected] · 2026-05-27 · 07:24 UTC

Working on something outside the usual AI paradigm sharing a preliminary note, not a "Eurkea".

The project (Aleph) explores whether biological organizational principles can replace neural architectures for embedded AI. No weights, no pre-training, no LLM. The core question: do the same solutions biology found over billions of years: specialization, quiescence, emergent coordination translate to digital systems?

Methodology follows Telesio's empirical approach: observation before theory, measurement before assertion. Every behavior in this post has a log file and a timestamp.

One verified result worth sharing:

Three independent components: a health monitor, a kernel, and an audio output organ — have no knowledge of each other. No shared state, no direct communication. When the watchdog process dies, the system emits a 220Hz tone. No explicit rule produces this. It emerges from composition.

This is weak emergence: predictable by reading the code. Not strong emergence. The distinction matters and we're not overstating it.

Current stack: Rust, Alpine Linux, SQLite, Unix sockets. Hardware: Dell Inspiron i5, 3.7GB RAM. ~452KB binary binary. ~39°C at rest.

What we don't know yet: whether this approach scales, whether the Bayesian learning converges usefully, whether the biological clock model holds across real day/night cycles.

#rustlang #embeddedsystems #ai #distributedsystems

John Poole @[email protected] · 2026-05-20 · 17:30 UTC

AI coding agents get much more interesting when they can touch real hardware. I used Codex to compile, upload, monitor serial consoles, analyze failures, and iterate on two ESP32-based T-Beam boards until a BLE file-transfer bug in fresh C++ #Reticulum interface was fixed.