Every operating system you've ever used does the same thing: it takes your intent and compiles it down into hardware signals.
What happens if you reverse that?
The idea
Take raw sensor data — video, audio, accelerometer readings — and compile it upward into structured knowledge about the world. Not raw pixels. Not audio waveforms. Structured, anonymized semantic metadata.
We call these units Sparks. A Spark might contain "hand raised to 45 degrees, facial expression: surprise" — but never the actual photo. Raw data exists only in volatile memory during processing and is deleted immediately.
This is AisthOS (from Greek aisthesis — perception). A Perception Operating System.
Why build this?
Because the AI industry is hitting four walls simultaneously:
Wall 1: Training data is running out. The web corpus that fed GPT-3/4 and LLaMA is exhausted. Epoch AI estimates high-quality public text will be fully consumed between 2026 and 2032.
Wall 2: Synthetic data causes model collapse. Shumailov et al. proved in Nature (2024) that training on AI-generated data causes irreversible degradation. Even mixing real and synthetic data doesn't fix it.
Wall 3: Annotation is manual and expensive. Tesla pays operators $24–48/hr to collect training data for Optimus — people in helmets with five cameras. The tools for continuous streaming annotation from live sensors don't exist.
Wall 4: GPUs and electricity are in shortage. H100 costs $25–40K with a 4–8 month waitlist. Data centers consumed 415 TWh in 2024; the IEA projects 945 TWh by 2030. Several U.S. states have imposed moratoriums on new data center construction.
Three formalisms
AisthOS rests on three concepts:
Template — what to extract. A multimodal schema: T = (M, E, F, R) where M = modalities, E = entities, F = format, R = cross-modal relationships. Unlike Avro or Protobuf, Template fields are "which knowledge to extract," not "which bytes to save."
Filter — when to extract. Semantic triggers, not numerical thresholds. Not "temperature > 30°C" but "the mother said 'time to feed.'"
Spark — the result. A unit of anonymized knowledge (~200 bytes). Contains semantics, not data. Privacy-by-design as an architectural decision, not a policy checkbox.
Together they form the Perception Compiler.
Does it actually work on real hardware?
Yes. Today.
| Device | Chip | FPS | Power |
|---|---|---|---|
| Smart glasses | GAP9 RISC-V | 18 fps | 62.9 mW (9.3h battery) |
| Dashcam | Ambarella CV72S | 4×5MP + AI | <3 W |
| RPi5 + Hailo-8L | 13 TOPS | ~120 fps (batch=8) | 4–5 W |
Full pipeline on RPi5:
capture(5ms) → detect(8ms) → classify(3ms) → filter(1ms) → spark(2ms) = 19ms → 52 fps
The compression ratio: 1 second of 4K video (H.265) ≈ 2–3 MB. One Spark ≈ 200 bytes. That's over 10,000× reduction.
A terabyte drive would hold Sparks from 16 years of continuous operation.
Why not just use the cloud?
Because the math doesn't work anymore:
| Centralized GPU | AisthOS (Edge) | |
|---|---|---|
| Node cost | H100: $25–40K | Device: $70–200 (already purchased) |
| Shortage | HBM +20%, 4–8 month wait | Billions of devices already exist |
| Energy | Data centers: 415 → 945 TWh by 2030 | 60 mW – 30 W per device |
| Privacy | Data goes to cloud | Data never leaves device |
| Scaling | Linear cost increase | +1 user = +1 free processor |
A million AisthOS devices = a million processors working for free. Each already paid for, deployed, and powered. Research shows 80% edge / 20% cloud delivers >75% cost savings.
And the energy crisis is real: moratoriums on new data centers in Virginia, Georgia, Vermont. Dublin banned new grid connections. Companies are planning nuclear reactors for AI. AisthOS uses compute that society already manufactured.
AisthOS Inside™: proving privacy, not promising it
Any manufacturer can claim "we respect your privacy." AisthOS Inside™ is an open certification standard — like Wi-Fi Certified — that makes privacy verifiable.
Seven principles: no raw data storage, Sparks-only output, no PII, user sovereignty, visible indicator, no hidden modes, open audit.
The code is MIT (free). The certification mark requires passing tests. Four levels from free self-certification to enterprise.
We identified 6 security threat types (4 specific to Perception OS):
- Template Injection — fixed ontology schemas, max 8 fields, no free text
- Filter Surveillance — max 3 attributes, person-specific banned, entropy check
- Physical Prompt Injection — text quarantine, dual PII detection, 95% fail-safe
- Adversarial PII Bypass — cascade detection across multiple architectures
Full security analysis: Security Annex
Where this is going
Near term: companion AI robots, dashcam training data, retail behavior analytics, smart glasses (solving the Google Glass privacy problem).
Long term: automated scientific discovery. Systems like AI-Newton (2025) can derive physical laws from structured data. AisthOS provides the missing perception layer — automatic conversion of real experiments into structured input.
Imagine a thousand devices observing physical phenomena and generating Sparks from which AI extracts patterns. That's the direction.
Try it / contribute
AisthOS is in early development. We're looking for:
- Privacy/security researchers to review our threat model
- Edge AI engineers to test on new hardware
- Community members to discuss the certification standard
- Anyone to comment, critique, and challenge our assumptions
GitHub: github.com/aisthos/aisthos
Website: aisthos.dev
License: MIT
Built by Vladimir Desyatov with AI-assisted development. The collaborative process itself demonstrates the AisthOS philosophy: AI as a transparent tool that amplifies human capability.
If you're an arXiv author in cs.AI and willing to endorse a new submission, I'd be grateful — reach out via GitHub Issues.
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