🌐 PAXECT — The Universal Deterministic Bridge

Build once, run anywhere.
Connect all operating systems and programming languages through one reproducible, offline-first runtime.

🔗 Learn more about the ecosystem:
👉 PAXECT Universal Bridge ›

Looking for the full bundle (Core + plugins + demos)?
See PAXECT Core Complete →

PAXECT SelfTune — Cross-Platform Autotune Enterprise Suite

Status: v1.0.0 — Initial Public Release — October 22, 2025

Deterministic, offline-first, and reproducible — built for secure enterprise pipelines, adaptive control, and NIS2-ready digital hygiene.

PAXECT SelfTune is a cross-platform deterministic runtime that merges five adaptive control subsystems into one unified, auditable engine: guard mode, overhead control, observability logging, runtime smoothing, and self-learning adaptation. It delivers predictable, zero-AI tuning for real-world workloads while remaining fully reproducible across OS environments.

Optimized for Linux, Windows, macOS, FreeBSD, Android, and iOS. Plug-and-play with zero dependencies, no telemetry, and no vendor lock-in.

Overview

PAXECT SelfTune provides an enterprise-ready performance-management layer for deterministic pipelines and reproducible environments. Unlike typical heuristic or AI-driven optimizers, SelfTune operates entirely offline, ensuring stability and verifiability in critical systems.

It seamlessly integrates with PAXECT Core and the wider PAXECT Core Complete ecosystem to maintain bit-identical reproducibility and adaptive safety across dynamic workloads.

Key Features

🛡 Guard Mode — automatic fail-safe fallbacks to maintain deterministic runtime state
⚖ Dynamic Overhead Control — default cap at 75 %; prevent CPU or I/O overshoot
📊 Deterministic Smoothing & Logging — reproducible metrics, JSON summaries
🔁 Self-Learning Adaptation — pattern-driven parameter adjustment (no AI, no cloud)
🧩 Cross-OS Integration — identical behavior on Linux, macOS, Windows, and mobile
🧠 Observability Suite — /metrics, /ready, /last endpoints for audit and profiling
🔒 Privacy by Design — offline-first, NIS2-aligned digital hygiene

Integrations

SelfTune works as a core plugin within the PAXECT Core Complete runtime, interacting with:

Core – deterministic execution and checksum validation
Link – inbox/outbox relay performance guarding
AEAD Hybrid – secure cryptographic pipeline tuning
Polyglot – cross-language load balancing and performance bridging

Use Cases

CI/CD pipeline governance and predictable runtime profiling
On-premise data processing with deterministic throttling
Edge analytics and IoT systems needing offline autotuning
Research or HPC environments where reproducibility matters
Enterprise compliance testing and audit simulation scenarios

Demos Included

Demo	Name	Function	Mode	Status
1	Quick Start	Basic decision logic	Local	✅
2	Integration Loop	Continuous feedback integration	Local	✅
3	Safety & Manual Cooldown	Fail-safe and manual throttling	Local	✅
4	Timed Throttle	5-minute and 30-minute throttling rules	Local	✅
5	K8s Runtime Simulation	Deterministic container workload simulation	Local	✅
6	Batch File I/O	Offline optimization for sequential jobs	Local	✅
7	Dashboard Snapshot	Export runtime state for dashboard reporting	Local	✅

All demos are portable and run locally on all supported platforms.

Core Capabilities

No-AI Policy: No artificial intelligence, machine learning, or probabilistic models.
Deterministic Autotuning: Ensures predictable, repeatable runtime optimization.
Production-Grade Logging: Every decision recorded with UTC timestamp and full context.
Unified 5-in-1 Architecture: Guard, learn, smooth, throttle, and log within one engine.
NumPy Benchmarking: Uses real matrix multiplication for reproducible CPU performance metrics.
Cross-Platform & Lightweight: Written in pure Python, requires only NumPy.

Architecture Overview

The 5-in-1 deterministic engine integrates five coordinated control modules that ensure predictable optimization and verifiable performance.

Module	Description
Matrix Benchmarking (NumPy)	Executes real matrix multiplications for precise runtime metrics
Batch Size Autotuning	Dynamically adjusts block size per iteration
Automatic Overhead Limitation	Enforces throttle when overhead exceeds configured ratio
Transparent UTC Logging	Outputs structured logs for full auditability
I/O Benchmarking	Measures disk and channel throughput deterministically

Installation

Requirements: Python 3.10+ and NumPy ≥ 1.24

# Install locally in editable mode
pip install -e .

# Install NumPy if not yet available
pip install numpy

Verification

To confirm successful installation and NumPy integration:

python - <<'PY'
from paxect_selftune_plugin import run_matrix_benchmark
print("NumPy benchmark:", run_matrix_benchmark(128), "seconds")
PY

Verification Summary

All seven demos were executed successfully on Ubuntu 24.04 (x86_64), confirming deterministic behavior and cross-platform reproducibility.

Demo	Title	Verified Functionality
01	Quick Start	Baseline decision logic with consistent output
02	Integration Loop	Continuous learning feedback under varying conditions
03	Safety Throttle	Automatic fail-safe activation above 75% overhead
04	Timed Throttle	Verified 5-minute and 30-minute cooldown control
05	Kubernetes Runtime	Deterministic multi-pod simulation with shared tuning state
06	Batch File I/O	Sequential JSONL batch optimization with reproducible output
07	Dashboard Snapshot	Export and merge of prior runs into audit-ready metrics

Verification Results:

All demos completed deterministically with no runtime drift
Consistent NumPy benchmark times across all runs
No external dependencies or non-deterministic components detected

Test Environments:

Ubuntu 24.04 LTS (x86_64)
Windows 11 Pro (22H2)
macOS 14 Sonoma

Plugins (official)

Plugin	Scope	Highlights	Repo
Core	Deterministic container	`.freq` v42 · multi-channel · CRC32+SHA-256 · cross-OS · offline · no-AI	https://github.com/PAXECT-Interface/paxect-core-plugin.git
AEAD Hybrid	Confidentiality & integrity	Hybrid AES-GCM/ChaCha20-Poly1305 — fast, zero-dep, cross-OS	https://github.com/PAXECT-Interface/paxect-aead-hybrid-plugin
Polyglot	Language bindings	Python · Node.js · Go — identical deterministic pipeline	https://github.com/PAXECT-Interface/paxect-polyglot-plugin
SelfTune 5-in-1	Runtime control & observability	No-AI guardrails, overhead caps, backpressure, jitter smoothing, lightweight metrics	https://github.com/PAXECT-Interface/paxect-selftune-plugin
Link (Inbox/Outbox Bridge)	Cross-OS file exchange	Shared-folder relay: auto-encode non-`.freq` → `.freq`, auto-decode `.freq` → files	https://github.com/PAXECT-Interface/paxect-link-plugin

Path to Paid - Paxect Selftune plugin

PAXECT is built to stay free and open-source at its core.
At the same time, we recognize the need for a sustainable model to fund long-term maintenance and enterprise adoption.

Principles

Core stays free forever — no lock-in, no hidden fees.
Volunteers and researchers: always free access to source, builds, and discussions.
Transparency: clear dates, no surprises.
Fairness: individuals stay free; organizations that rely on enterprise features contribute financially.

Timeline

Launch phase: starting from the official PAXECT product release date, all modules — including enterprise — will be free for 6 months.
This free enterprise period applies globally, not per individual user or download.
30 days before renewal: a decision will be made whether the free enterprise phase is extended for another 6 months.
Core/baseline model: always free with updates. The exact definition of this baseline model is still under discussion.

Why This Matters

Motivation: volunteers know their work has impact and will remain accessible.
Stability: enterprises get predictable guarantees and funded maintenance.
Sustainability: ensures continuous evolution without compromising openness.

Governance & Ownership

Ownership: All PAXECT products and trademarks (PAXECT™ name + logo) remain the property of the Owner.
License: Source code is Apache-2.0; trademark rights are not granted by the code license.
Core decisions: Architectural decisions and final merges for Core and brand-sensitive repos require Owner approval.
Contributions: PRs are welcome and reviewed by maintainers; merges follow CODEOWNERS + branch protection.
Naming/branding: Do not use the PAXECT name/logo for derived projects without written permission; see TRADEMARKS.md.

Community & Support

Have a bug or feature request?
Open an Issue ›
We track all confirmed issues and enhancement proposals there.

General questions or ideas?
Join the Discussions › Q&A
We regularly review strong ideas and convert them into Issues so they can ship.

Project Recognition

If PAXECT SelfTune helped your research, deployment, or enterprise project,
please consider giving the repository a Star on GitHub —
it helps others discover the project and supports long-term maintenance.

🔄 Updates & Maintenance

PAXECT Selftune Plugin follows an open contribution model.

Updates, bugfixes, and improvements depend on community and maintainer availability.
There is no fixed release schedule — stability and determinism are prioritized over speed.
Enterprises and contributors are encouraged to submit issues or pull requests for any enhancements.
The project owner focuses on innovation and architectural guidance rather than continuous support.

In short: updates arrive when they are ready — verified, deterministic, and tested across platforms.

Sponsorships & Enterprise Support

PAXECT SelfTune is maintained as a verified plug-and-play enterprise module.
Sponsorships enable continuous validation, reproducibility testing, and deterministic compliance across Linux, Windows, and macOS platforms.

Enterprise Sponsorship Options

Infrastructure validation and cross-platform QA
CI/CD and performance compliance testing
Integration and OEM partnerships

How to get involved

Become a GitHub Sponsor
For enterprise or OEM inquiries: PAXECT-Team@outlook.com

Contact

PAXECT-Team@outlook.com
Issues
Discussions

PAXECT Core Complete

Status: v1.0.0 — Initial Public Release — October 22, 2025

The curated PAXECT bundle: Core + AEAD Hybrid + Polyglot + SelfTune + Link — with 10 integrated demos, observability, and deterministic performance across OSes.

What it is: the official reference implementation of the PAXECT ecosystem — a verified, reproducible, cross-OS runtime that showcases the multi-OS bridge and in-freq multi-channel architecture in real workflows.

Unified Ecosystem: Core + AEAD + SelfTune + Polyglot + Link in one deterministic bundle
Reproducibility: bit-identical behavior across Linux, macOS, Windows (best-effort: BSD, mobile shells)
Offline-first: zero telemetry, no network dependencies
Enterprise-ready: 10 reproducible demo pipelines, audit trail, and metrics endpoints
Zero-AI Runtime: SelfTune provides adaptive guardrails without ML or heuristics (no cloud)

Relationship

PAXECT Core is a stand-alone OS-level deterministic bridge (plugin-capable).
PAXECT Core Complete is the curated bundle that includes Core plus the official plugins and demo suite.
Use Core when you want a minimal, plug-and-play bridge.
Use Core Complete when you want the full experience (plugins + demos) out of the box.

Installation

Requirements

Python 3.9 – 3.12 (recommended 3.11+)
Works on Linux, macOS, Windows, FreeBSD, OpenBSD, Android (Termux), and iOS (Pyto).
No external dependencies or internet connection required — fully offline-first runtime.

Optional Utilities

Some demos use these standard tools if available:

bash (for demo_05_link_smoke.sh)
dos2unix (for normalizing line endings)
jq (for formatting JSON output)

Install

git clone https://github.com/PAXECT-Interface/paxect-core-complete.git
cd paxect-core-complete
python3 -m venv venv
source venv/bin/activate      # on Windows: venv\Scripts\activate
pip install -e .

Verify the deterministic core import:

python3 -c "import paxect_core; print('PAXECT Core OK')"

Then run any of the integrated demos from the demos/ folder to validate deterministic reproducibility.

📁 Repository Structure

paxect-core-complete/
├── paxect_core.py
├── paxect_aead_hybrid_plugin.py
├── paxect_polyglot_plugin.py
├── paxect_selftune_plugin.py
├── paxect_link_plugin.py
├── demos/
│   ├── demo_01_quick_start.py
│   ├── demo_02_integration_loop.py
│   ├── demo_03_safety_throttle.py
│   ├── demo_04_metrics_health.py
│   ├── demo_05_link_smoke.sh
│   ├── demo_06_polyglot_bridge.py
│   ├── demo_07_selftune_adaptive.py
│   ├── demo_08_secure_multichannel_aead_hybrid.py
│   ├── demo_09_enterprise_all_in_one.py
│   └── demo_10_enterprise_stability_faults.py
├── test_paxect_all_in_one.py
├── ENTERPRISE_PACK_OVERVIEW.md
├── SECURITY.md
├── CONTRIBUTING.md
├── CODE_OF_CONDUCT.md
├── TRADEMARKS.md
├── LICENSE
└── .gitignore

Modules

Module	Purpose
paxect_core.py	Deterministic runtime · encode/decode · CRC32 + SHA-256 checksums
paxect_aead_hybrid_plugin.py	Hybrid AES-GCM / ChaCha20-Poly1305 encryption for data integrity
paxect_polyglot_plugin.py	Cross-language bridge · UTF-safe transformation between runtimes
paxect_selftune_plugin.py	Adaptive ε-greedy self-tuning · resource-aware control · no AI
paxect_link_plugin.py	Secure inbox/outbox relay · policy validation · offline file sync

Plugins (Official)

Plugin	Scope	Highlights	Repo
Core	Deterministic data container	`.freq` v42 · multi-channel · CRC32 + SHA-256 · cross-OS · offline-first	paxect-core-plugin
AEAD Hybrid	Encryption & Integrity	Hybrid AES-GCM / ChaCha20-Poly1305 — fast, zero dependencies, cross-platform	paxect-aead-hybrid-plugin
Polyglot	Multi-language bridge	Python · Node.js · Go — deterministic pipeline parity	paxect-polyglot-plugin
SelfTune 5-in-1	Runtime control & observability	Guardrails, backpressure, overhead limits, metrics, and jitter smoothing	paxect-selftune-plugin
Link (Inbox/Outbox Bridge)	Cross-OS file exchange	Shared-folder relay: auto-encode/decode `.freq` containers deterministically	paxect-link-plugin

Plug-and-play: Core operates standalone, with optional plugins attachable via flags or config. Deterministic behavior remains identical across environments.

🧪 Demo Suite (01 – 10)

Run reproducible demos from the repository root:

python3 demos/demo_01_quick_start.py
python3 demos/demo_02_integration_loop.py
python3 demos/demo_03_safety_throttle.py
python3 demos/demo_04_metrics_health.py
bash    demos/demo_05_link_smoke.sh
python3 demos/demo_06_polyglot_bridge.py
python3 demos/demo_07_selftune_adaptive.py
python3 demos/demo_08_secure_multichannel_aead_hybrid.py
python3 demos/demo_09_enterprise_all_in_one.py
python3 demos/demo_10_enterprise_stability_faults.py

All demos generate structured JSON audit logs under /tmp/, verifiable through deterministic SHA-256 outputs.

Testing & Verification

Internal pytest suites validate core reproducibility. End-users can rely on the integrated demo suite (01–10) for deterministic verification. Each demo reports performance, checksum validation, and exit status cleanly.

🔒 Security & Privacy

Default mode: offline, zero telemetry.
Sensitive configuration via environment variables.
AEAD Hybrid is simulation-grade; for production, integrate with verified crypto or HSM.
Adheres to Digital Hygiene 2027 and NIS2 security standards.
Follows responsible disclosure in SECURITY.md.

🏢 Enterprise Pack

See ENTERPRISE_PACK_OVERVIEW.md for extended features and enterprise integration roadmap.

Includes:

HSM / KMS / Vault integration
Extended policy and audit engine
Prometheus, Grafana, Splunk, and Kafka observability connectors
Deployment assets (systemd, Helm, Docker)
Compliance documentation (ISO · IEC · NIST · NIS2)

🤝 Community & Governance

License: Apache-2.0
Ownership: All PAXECT trademarks and brand assets remain property of the Owner.
Contributions: PRs welcome; feature branches must pass deterministic CI pipelines.
Core merges: Require owner approval for brand or architecture-sensitive changes.
Community Conduct: See CODE_OF_CONDUCT.md

Join as a maintainer or contributor — see CONTRIBUTING.md for details.

🔄 Updates & Maintenance

PAXECT Core Complete follows an open contribution and verification-first model:

No fixed release schedule — determinism prioritized over speed.
Verified updates only, across OSes and environments.
Maintainers focus on innovation, reproducibility, and architecture quality.

💠 Sponsorships & Enterprise Support

PAXECT Core Complete is a verified, plug-and-play runtime ecosystem unifying all PAXECT modules. Sponsorships fund ongoing cross-platform validation, reproducibility testing, and audit compliance for deterministic and secure data pipelines across Linux, Windows, and macOS.

Enterprise Sponsorship Options

Infrastructure validation and multi-OS QA
Deterministic CI/CD performance testing
OEM and observability integration partnerships
Extended reproducibility assurance for regulated industries

Get Involved

💠 Become a GitHub Sponsor
📧 Enterprise or OEM inquiries: enterprise@PAXECT-Team@outlook.com

Sponsorships help sustain open, verifiable, and enterprise-ready innovation.

Governance & Ownership

Ownership: All PAXECT products and trademarks (PAXECT™ name + logo) remain the property of the Owner.
License: Source code under Apache-2.0; trademark rights are not granted by the license.
Core decisions: Architectural merges for Core and brand repos require Owner approval.
Contributions: PRs reviewed under CODEOWNERS and branch protection.
Brand Use: Do not use PAXECT branding for derivatives without written permission. See TRADEMARKS.md.

Path to Paid — Sustainable Open Source

PAXECT Core Complete is free and open-source at its foundation. Sustainable sponsorship ensures long-term maintenance, reproducibility, and enterprise adoption.

Principles

Core remains free forever — no vendor lock-in.
Full transparency, open changelogs, and audit-ready releases.
Global 6-month free enterprise window after public release.
Community-driven decision-making on renewals and roadmap.

Why This Matters

Motivates contributors with lasting value.
Ensures reproducible stability for enterprises.
Balances open innovation with sustainable funding.

Contact

📧 PAXECT-Team@outlook.com 💬 Issues 💭 Discussions

For security disclosures, please follow responsible reporting procedures.

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🌐 PAXECT — The Universal Deterministic Bridge

PAXECT SelfTune — Cross-Platform Autotune Enterprise Suite

Overview

Key Features

Integrations

Use Cases

Demos Included

Core Capabilities

Architecture Overview

Installation

Verification

Verification Summary

Plugins (official)

Path to Paid - Paxect Selftune plugin

Principles

Timeline

Why This Matters

Governance & Ownership

Community & Support

Project Recognition

🔄 Updates & Maintenance

Sponsorships & Enterprise Support

Contact

PAXECT Core Complete

Relationship

Installation

Requirements

Optional Utilities

Install

📁 Repository Structure

Modules

Plugins (Official)

🧪 Demo Suite (01 – 10)

Testing & Verification

🔒 Security & Privacy

🏢 Enterprise Pack

🤝 Community & Governance

🔄 Updates & Maintenance

💠 Sponsorships & Enterprise Support

Enterprise Sponsorship Options

Get Involved

Governance & Ownership

Path to Paid — Sustainable Open Source

Principles

Why This Matters

Contact