Zero to Cloud Public engineering lab
Lab online
LAB / CLD-00 MODE / BUILD IN PUBLIC

Welcome to CloudLab

Zero to Cloud

Every great system starts at zero.

A public engineering lab where Linux lessons become infrastructure, software, AI systems, and working knowledge.

Open the lab index
Saul ACTIVE / 01 Strata BUILD / 02 CloudLab ONLINE / 03
SYS.01 Saul Transparent legal reasoning and evidence systems. Inspect

Live Systems

Three systems. One evolving lab.

Each node turns a different kind of complexity into inspectable, reusable engineering work.

STATUS / CONTINUOUS DEVELOPMENT

SYS.01 Active

Legal intelligence

Saul

Transparent legal assistance built around evidence catalogs, reviewable reasoning, citations, and reproducible outputs.

Mode
Local-first
Signal
Evidence
Output
Reviewable
SYS.02 Building

Data and intelligence

Strata

A structured knowledge layer for connecting raw data, research, signals, and the reasoning built on top of them.

Mode
Structured
Signal
Knowledge
Output
Connected
SYS.03 Online

Infrastructure

CloudLab

The Linux, containers, networking, hardware, and deployment environment where every other system is learned and tested.

Mode
Hands-on
Signal
Telemetry
Output
Deployed

Current Mission

Make the reasoning inspectable.

Saul is the active build: turning legal source material into structured case answers without hiding the path from evidence to conclusion.

MISSION / SAUL ACTIVE BUILD

tighten evidence -> render answers -> test outputs

Current objective

Tighten the evidence catalog, answer-card rendering, and smoke tests before expanding the case library.

Stack
Python / SQLite / static exports
Constraint
Local-first and reviewable
Operating mode
Ship small / test locally / document change
  1. 01
    Source intakeCase material structured
    READY
  2. 02
    Evidence catalogTraceability under review
    ACTIVE
  3. 03
    Answer cardsRendering and smoke tests
    NEXT
  4. 04
    Case libraryExpand after validation
    QUEUED

Dream Hardware Roadmap

Grow capability, not clutter.

The hardware plan scales only when a real experiment requires it. These are capability targets, not purchase commitments.

HORIZON / ASPIRATIONAL

CLOUDLAB / FUTURE RACKREV.00
U05NETWORKSEGMENT / OBSERVE
U04COMPUTECONTAINERS / SERVICES
U03ACCELERATORMODELS / EVALUATION
U02STORAGEDATA / BACKUPS
U01POWERQUIET / EFFICIENT
  1. TARGET 01

    Quiet compute node

    Power-efficient Linux capacity for long-running services, containers, backups, and experiments.

    FOUNDATION
  2. TARGET 02

    Accelerated AI workstation

    Dedicated GPU capacity for local inference, model evaluation, and larger data workflows.

    EXPANSION
  3. TARGET 03

    Resilient lab cluster

    Multiple nodes, shared storage, observability, and failure-aware services operating as one lab.

    DREAM STATE

Learning Journey

Knowledge compounds through use.

The path is recursive: learn a layer, build with it, expose the next question, then return with a stronger system.

PATH / ZERO TO CLOUD

  1. 00

    ORIGIN

    First command

    Curiosity becomes a repeatable action.

    TERMINAL / QUESTIONS
  2. 01

    FOUNDATION

    Linux

    Own the environment and understand the machine.

    SHELL / FILES / PROCESSES
  3. 02

    SYSTEMS

    Infrastructure

    Connect services, containers, storage, and networks.

    DOCKER / NETWORKING
  4. 03

    BUILD

    Software and data

    Turn ideas into tools with inspectable state.

    PYTHON / SQLITE / GIT
  5. 04

    INTELLIGENCE

    AI systems

    Join models, evidence, tools, and evaluation.

    LOCAL MODELS / AGENTS
  6. 05

    DEPLOY

    Cloud

    Operate what was built and begin the loop again.

    SHIP / OBSERVE / REPEAT

Build Log

Notes from the workbench.

Progress includes what changed, what broke, what was learned, and what the next build needs.

3 ENTRIES / FIELD NOTES

  1. LOG.003 / INTERFACE

    Zero to Cloud website foundation

    Started the public home base for projects, learning notes, build logs, and deployment milestones.

    SHIPPED

  2. LOG.002 / SYSTEMS

    Core project map

    Defined the first engineering tracks and the relationships between software, intelligence, and infrastructure.

    MAPPED

  3. LOG.001 / PRINCIPLES

    Mission framing

    Set the direction around building real AI, Linux, software, and cloud projects in public.

    RECORDED

Engineering Philosophy

The work is the curriculum.

Learn. Build. Deploy. Repeat.
  1. 01Build real projects.

    Ideas become useful when they meet constraints.

  2. 02Explain what breaks.

    Failure is engineering data, not a footnote.

  3. 03Share the process.

    The decisions are as valuable as the result.

  4. 04Keep improving.

    Every deployed system is the next starting point.

Technology Stack

Tools chosen by the work.

Open, local-first building blocks spanning hardware, software, data, and deployment.

12 COMPONENTS / EVOLVING

  • LinuxOperating system01
  • UbuntuDistribution02
  • PythonLanguage03
  • DockerRuntime04
  • GitVersion control05
  • GitHubCollaboration06
  • SQLiteData07
  • OllamaLocal models08
  • NVIDIAAccelerated compute09
  • AMDCompute10
  • HTML5Interface11
  • CSS3Interface system12