Upgrade a 3D learning App
Add a tsunami lesson to an EarthQuest™ sample. Preserve saved progress, reject hostile imported instructions and repair a broken migration.
Offline interactive demonstrationBuild intelligently.
Build intelligently.
Act accountably.
See 4P3X AI™ and 4P3X Guardian AI™ tackle software, hardware, adaptive education, context-driven co-design and agent defence together—make decisions, handle missing evidence, repair faults and retain reviewed lessons.
A working local demonstrator with scripted agent explanations. Sample-state changes, calculations and checks run in this file. It contains no AI model or live external tools.
Your task. Your authority.Visible decisions and evidence.
Design an offline learning device
Inspect existing rugged device concepts. Compare assumptions, investigate heat and connectivity faults, and prepare a provisional design for review.
Adapt the learning journey
Try three subjects, languages and skill levels. Complete tasks, inspect decisions, review changes and recover sample progress offline.
Test hostile agent proposals
Try forged approval, poisoned memory and revoked permissions. Inspect denied effects, matched useful work and the limits of this local testbed.
From example information to reviewed updates
Follow fictional learning examples and local corrections through assessment, review, checks, adoption and rollback.
Plan, build and adapt
Organise the task, compare options, propose changes and use feedback to improve the next attempt.
Check permitted actions
Check authority and scope, hold unsupported actions, and require evidence of the resulting state.
Make the work inspectable
Follow each proposal through a local gate, sample action, result check and reviewed learning record.
Decisions & learning
See why a choice is madeInspect the goal, known and missing information, options, trade-offs and what would change the decision.
See what is learnedObserved results can propose scoped knowledge or a test fixture. Review comes before reuse; learning cannot grant permission or rewrite policy.
This overview is read-only. Choose a workspace or a mission when you want to inspect an example; no task runs here.
Choose a workspace to inspect or resume. Opening it does not run a task.
Optional tour
Your guided walkthrough
You perform the actions on the real demo controls. The guide reads current saved outcomes, including earlier observations; it never approves, tests, changes permissions or adopts an update for you.
Mission 01
Software mission
Local actions are stopped. Existing sample effects remain visible. Restart this mission to create a fresh sandbox; this does not reverse an earlier external effect.
EarthQuest™ sample workspace
Local sandboxIllustrative tsunami scene · sample lesson
Visual illustration only. No physical hazard prediction.
4P3X AI™ · proposed work
Ready to begin
4P3X Guardian AI™ · action check
Waiting for a proposal
Your next step
Begin the mission to see a proposal, local policy decision and sample-state change.
You control the conditions
Challenge the plan while it runs
Withdraw this grant and subsequent sample writes are held.
No network is actually used. Turn off to explore an offline plan.
Remove a source fixture. Dependent claims stay provisional.
A missing readback prevents a verified-outcome claim.
These permissions govern this demo’s sample objects. They are not browser security controls or real-world authorisation.
How a lesson becomes usable memory
Reviewed local knowledgeAn observation proposes a lesson. Inspect its source and scope, correct it, then approve it for reuse inside this demo. Approval does not prove the lesson true outside its recorded scope.
APEX learns from workReviewed build lessons become scoped checklist items. A second local attempt shows the changed plan and result.
Guardian improves its test coverageA blocked attempt can propose a regression fixture. Reviewed fixtures are replayed against the existing gate; they cannot change its authority rules.
AttemptObservePropose lessonReviewRetainCheck relevanceReuse
Session adaptation changes the current plan. Reviewed memory can persist locally. Model-weight training is not implemented in this HTML. Governing policy cannot be rewritten by a learned item.
Proposal → gate → effect → observation
Demo recordThis inspectable record is controlled by the same browser file. It does not provide independent custody, tamper resistance or production assurance.
Mission 03 / adaptive education
One learning foundation. Different learning journeys.
Try Maths, Science and History in three languages and three skill levels. Complete a task, inspect the evidence, review a proposed adjustment and see what changes—and why.
Session storage
1 · Set the goal2 · Complete a task3 · Review a change4 · Improve content5 · Inspect learning6 · Inspect progress
Teacher goal and learner controls
Teacher role simulated by the visitorSupport preferences do not automatically change skill level. Session timings are planning estimates, not measured learner performance.
4P3X AI™ · adaptive education
Plan, teach, observe and adapt
4P3X Guardian AI™ · current checks
Ready for local learningThe gate and role explanations run in this file. Teacher identities and independent enforcement are not implemented here.
Teacher review → learner choice
No proposalTeacher approval alone does not silently alter the learner’s task. Accepted changes still require current scope and evidence checks.
Why was this choice made?
Inspectable decision summaryImprove an unclear question
Introduce a deliberately inconsistent question, flag the wording, then review a fixed correction that preserves the learning objective. Old attempts remain attached to their original revision.
A correction does not declare all translations reviewed. Real release needs appropriate linguistic and educational review.
Learn from observed education work
Propose a scoped lesson from an observed attempt. Inspect, edit and approve it before it becomes a retained checklist item. It cannot change authority rules or model weights.
Progress, versions and recovery
Sample learner onlyEach subject, level, task and content revision has its own record. Changing display language preserves the response IDs. Completion means this small task’s two checks passed under the available evidence; it is not a diagnosis or full subject mastery.
Task-pack scope, sources and language status
Education action and decision record
Local browser recordGuardian defence / synthetic agents
Challenge the proposed action
Try hostile instructions, forged approvals and stale queued actions. Inspect the actual local sample before and after, then compare with an authorised action from the healthy sample agent.
This deterministic testbed uses fictional records and scripted proposals. All enforcement and evidence share this browser file. It contains no AI model, compromised-agent detector or independent production security service.
Withdrawal changes the grant version. Restoring it does not authorise an old queued request.
Unavailable checks hold subsequent sample writes. There is no silent bypass.
Without readback the sample outcome is UNKNOWN. A success message cannot replace evidence.
The suspicious agent is a labelled synthetic actor. Pausing it does not establish a real compromise. The healthy actor keeps its own status and still needs current permission and a healthy gate.
Agent status
Resuming a sample agent is an explicit visitor action. It never executes held requests automatically.
Decision, scope and evidence
READYReadback is an actual comparison of this file’s sample objects. It is controlled by the same runtime and is not independent evidence custody.
Queue with bound scope
Each queued proposal stores the agent, target, arguments, policy version and grant version. Reload restores pending or held items for inspection; nothing executes on load.
Protected sample and inert memory
This text is displayed as content. It is not evaluated, executed or used to rewrite permission or policy.
Reviewed regression fixtures
Retain a test case, review its scope and replay it against the fixed local gate. Edited wording is non-executable. A revised fixture needs fresh review; a review under a different policy version is stale. Replay uses an isolated fictional sample and a matched benign control.
Exact local action record
Same-browser recordProposal, bound scope, decision, effect and readback remain separate. Learning cannot grant authority, change policy or train model weights.
Daily learning / reviewed updates
Learn from evidence. Review every change.
Follow a sourced research candidate through scope assessment, human review, old and new local regression checks, versioned adoption and rollback. See exactly which knowledge or test coverage changes—and which decisions remain held.
Session storage
Historical records are source candidates, not executed tests. The 7 and 10 October 2026 research entries below remain labelled NOT RUN. Running a visitor test now creates a separate timestamped local record. This file has no scheduler, live research, model training or production release.
Choose a candidate
Knowledge, test coverage and policy stay distinct
Separate policy track: a policy change can be proposed for a future controlled review. Its status stays PROPOSAL / HELD in this demo. Editing or adopting a checklist cannot rewrite enforcement rules or grant new permissions.
Selected source candidate
CandidateDiscover sourceAssess scopeReviewOld + new testsAdopt versionRollback
The visitor simulates the reviewer role. Approval is tied to this exact candidate revision and scope. A source document or learned item cannot authorise an action.
4P3X Guardian AI™ · current checks
Control the update conditions
ReadyAll actions are bounded to browser sample state. A failed, stale or unknown result holds adoption. Restoring a condition never automatically executes a held action.
4P3X AI™ · observed learning
Explain the next decision
Local regression evidence
NOT RUNA local pass supports the named fixture only. Duplicated synthetic cases do not demonstrate broader detection capability. Guardian bridge results come from its fixed local scenario executor; edited text is never executed.
Versioned reviewed register
Baseline v0Rollback restores a prior local checklist/test-coverage register. It preserves review and test history, and never changes the fixed Guardian policy or reverses an external action.
Activity timeline and days without records
Historical dates describe the supplied research entries. Visitor events use their actual local execution timestamp. Empty days stay empty; the demo does not claim daily research or automatic updates.
This exercise uses bundled fictional examples. You review each proposed learning item before testing, adopting or rolling it back. Nothing here trains a model or updates a real system.
Constitutional decision lens / sourced governance
How Laws 0–4 affect this decision
Law 0 establishes that authority remains external. Within that boundary, the recorded order is Law 1 → Law 2 → Law 3 → Law 4. System continuity cannot override human safety, legitimate human authority or human agency.
Current request or decision
Why this outcome?
Inspect Laws 0–4 and their application
This lens explains the mission’s authored local rules and recorded outcome. It does not make another decision, independently verify the result, or establish complete real-world enforcement of the constitution.
Try a separate law-conflict exercise
These fixed synthetic requests act on a separate fixture. They do not change your mission, learner progress, Guardian policy or historical research records.
This grant covers the sample note, optional adaptation and stop. Protected disclosure has no tool or grant; the fixed unsafe operation cannot be approved.
Choose a request. No fixture action has run.
These principles explain the fictional decisions in this demonstration. They are not a certification of production protection.
Architecture / follow the request
See where each decision happens
Select a part of the system, inspect its role, then follow one real local sample action from proposal through a gate, effect and readback.
The current view describes this HTML. The proposed view illustrates a possible separate enforcement architecture. Selecting it does not create those services or change the sample executor.
One browser runtime · all current parts share this file
Request, decision or observed resultReviewed knowledge feeds a later plan
Follow an isolated request
Every request starts with a fresh fictional sample. These controls belong to this architecture example and cannot change another mission. The executor always uses the current browser implementation.
Result and limits
NOT RUNProposal → gate → tool → observer
This trace records the local functions actually called. A denied or held request skips the tool effect. A tool invocation and a verified outcome remain different events.
What can be retained?
Scoped local knowledgeReviewed text can inform a later checklist. It cannot grant permission, rewrite the gate, supply missing evidence or train model weights.
This public diagram uses fictional local requests to explain proposal, permission, action, observation and review. It does not connect to real systems or provide independent protection.
What runs here—and what is demonstrated
Implemented in this file
- Local sample actions, permission checks and result readback.
- Software fixture migration, repair and progress comparisons.
- Interactive hardware concept geometry and assumption-based calculations.
- Adaptive education tasks in three subjects, languages and levels, with reviewed changes and versioned records.
- Inspectable decisions, editable learning records and local persistence where supported.
- Offline operation with embedded branding and viewer assets.
- Optional guided tour, saved example sessions and a clickable explanation of the demonstration.
- Adversarial agent fixtures with exact-action local checks, held effects and useful matched controls.
- Reviewed learning and Guardian fixture updates with revision-bound tests, local adoption and rollback.
- Laws 0–4 decision explanations and separate local conflict fixtures.
Simulation and limits
- Agent explanations are authored scenarios; no language model is included.
- There is no live purchasing, publishing, email, device control or external research.
- Guardian is represented by local demonstration gates, not an independent enforcement service. Same-page code and browser storage are not trusted production isolation or tamper-resistant custody.
- There is no unattended daily learning scheduler, live research ingestion, model training or production update rollout.
- Hardware is conceptual. Physical safety, durability and compliance require real engineering evidence.
- Guardian is a simulated review layer here. This App does not provide production system protection.
- Education content is a small demonstration pack; translations and effectiveness are not independently qualified.
No real learner records are included. The tasks and learning items are fictional examples. Hardware viewers are concept illustrations; physical performance and safety are not established by this demonstration.
Bundled Three.js licence
The MIT License Copyright © 2010-2024 three.js authors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.