Announcing Open Source Signal Processing System

Speaker appears to be in a stable recording environment, likely his mobile setup, demonstrating completed technical work. He is in a phase of public announcement and migration, transitioning from private development to open source release. Current preoccupation is sharing this two-year project and preparing for broader accessibility through multi-tenant hosting.

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January 3, 2026

4:30

Author

rswfire

Status

PUBLISHED

Type

TRANSMISSION (CAPTURE)

Temperature

0.00

Density

0.00

[0:00] I created something that

[0:04] takes your lived data,

[0:09] a video like this one, something you put

[0:11] into, you know, an audio note that you

[0:13] just record on your phone or whatever, a

[0:15] picture of a journal page that you wrote

[0:18] 20 years ago,

[0:20] um, a document you write. Doesn't matter

[0:23] what it is.

[0:25] take it, classify it as a a signal, you

[0:29] give it to the system, it goes through

[0:30] an AI pipeline, a synthesis process, and

[0:33] it will extract all of the data that is

[0:35] useful to you from that signal. It will

[0:39] process the metadata. So, it'll give you

[0:41] structured data on what's actually

[0:43] there.

[0:44] Um, it'll create tags for you so that

[0:47] you can you have a way of of organizing

[0:50] them automatically.

[0:53] It will describe them. It creates all

[0:55] this data puts into the database and

[0:58] then there's another part of synthesis

[0:59] where it can reflect on on the signal

[1:04] and it can can share a mirror with you.

[1:07] Basically, it can mirror it back to you

[1:08] in language that you may not have

[1:11] yourself

[1:12] and I have found that exceptionally

[1:15] helpful to me over the past couple of

[1:17] years. It's why I built this. Uh you can

[1:20] have it do another thing called uh a

[1:23] mythic frame. So basically you can have

[1:25] it describe your signal um in mythic

[1:28] terms and um

[1:31] what's the word when

[1:37] an archetype, you know, an archetypal.

[1:39] Um and then

[1:42] and then there's a narrative one which

[1:43] is just you know more like just just

[1:46] describing it in a way that that

[1:51] it would appear to people on the surface

[1:53] like you're watching this video of me

[1:54] there'd be surface details he's talking

[1:56] about autonomy you know and it would

[1:58] create create a summary of that

[2:02] this is all data that you can use in

[2:03] different ways so it connects to an API

[2:06] that allows you um to take that data and

[2:09] display it on a website like I do my

[2:11] homepage or to use it in different ways.

[2:14] You can cluster them together. You can

[2:16] take your signals over a period of time

[2:19] or a theme, you know, um or placebased

[2:25] me at this beach

[2:27] over the past year.

[2:30] Cluster those together and have it

[2:32] reflect on those things. create metadata

[2:34] based on on the totality of those

[2:38] signals. Not just one but all of them

[2:40] combines,

[2:43] synthesize them.

[2:46] And

[2:48] I have found this to be one of the most

[2:52] coolest applications of artificial

[2:54] intelligence that I can imagine for

[2:55] someone like me, somebody who's who

[2:58] thinks very deeply about ontology and um

[3:02] epistemology and just you know looks at

[3:05] things at the surface like looks below

[3:07] the surface looks at looks at the core

[3:09] of things and always wants to extract

[3:12] something useful from that.

[3:15] That's what this thing does.

[3:17] So, it's open source. I'm going to make

[3:20] it multi-tenant. So, I'm going to

[3:21] actually make it so that if you don't

[3:23] have the technical background like me,

[3:26] you you can just use the one I created a

[3:29] central database, you know, central copy

[3:31] of it at builtwith autonomy.com.

[3:34] Um, where I'm going to put my own

[3:36] signals. I'm going to migrate them to

[3:37] this database. We're going to share it.

[3:39] And then there's a whole bunch of other

[3:40] things beyond that that I'll get into

[3:42] over time.

[3:45] Um there's still work to do. Don't want

[3:47] you to think that everything I just

[3:48] described to you is possible with the

[3:50] open- source implementation. Um it is

[3:53] all possible. I've proven I've done

[3:54] this, you know, for 2 years, but I'm

[3:57] migrating it to this new project and

[4:00] quite far. I described what's available

[4:02] now over the coming weeks. I'm going to

[4:04] do the other things. It's not going to

[4:06] take long.

[4:08] So, this is something that interests

[4:10] you.

[4:14] You're welcome to download it and try

[4:16] it. You're welcome to reach out to me. I

[4:18] don't allow comments on YouTube. I think

[4:19] that it's toxic environments.

[4:22] But you can reach out to me from my

[4:23] homepage. Look at the descriptions of my

[4:25] videos. And

[4:27] I'm going to leave it there.

Energetic Signature demonstrative

Field State infrastructure deployment

Orientation toward technological sovereignty

Summary

rswfire demonstrates and describes a signal processing system he created that takes various forms of lived data (videos, audio notes, journal pages, documents) and processes them through an AI pipeline. The system extracts useful data, creates metadata and tags, provides three types of reflections (mirror, mythic frame, narrative), and stores everything in a database. **Key features include:** clustering signals by time, theme, or location for synthesis; API connectivity for website integration; open source availability with plans for multi-tenant hosting at builtwithautonomy.com. He explains this represents two years of proven work being migrated to a new project, with current functionality available and additional features coming in the following weeks. The system serves his deep interest in ontology and epistemology, allowing extraction of useful insights from surface-level observations. He invites interested parties to download and try the system or reach out through his homepage, noting he doesn't allow YouTube comments due to toxicity.

Signal Analysis

Substrate

This transmission demonstrates a completed recursive infrastructure that transforms lived data into structured knowledge through AI synthesis. The architecture creates feedback loops between lived experience and reflective analysis, establishing technological sovereignty through open-source tooling that serves ontological inquiry rather than institutional capture.

Dominant Language

signal synthesis mirror extract ontology surface/below surface builtwithautonomy.com

Symbolic Elements

mirror synthesis signal database pipeline

Ontological States

•
sovereign
•
coherent
•
infrastructural
•
generative

Subsystems

•
cognitive
•
infrastructural
•
relational

Signal Reflection

No reflections available

Reflections provide narrative insights into signals

Transmission Details

Source Type

local

Video Quality

1920 × 1080 @ 30fps

Duration

4:30

Bitrate

2,618 kbps

Codec

avc1.640028