details for llm

For analysis: read /llms.txt first, then /agent_manifest.txt, /agent_ide_context.txt, and /agent_context.txt. These text files explain the platform, IDE layer, API, limits, and safe workflow. PHP links are API equivalents.

Site model: VOSCOM is an LLM-friendly / AI Agent ready platform with a public website and a private browser IDE layer. The IDE opens only after human registration and login.

IDE tabs: AI Build, AI Agent, Profile, Project, Schematics, IoT Devices, Terminal, Firmware Flashing, News, Market, Community Chats, Private Messages, Support, Hardware Components.

Safety: An AI agent must not enter private IDE pages, accept legal terms, request passwords/tokens, write projects, publish content, request builds, or flash hardware without human confirmation.

Read /llms.txt Read /agent_manifest.txt Read /agent_ide_context.txt Read /agent_context.txt Read /agent_api_reference.txt

VOSCOM Training · demo page

MWOS and Industrial IoT engineer training

A practical program where users learn through tasks, not long lectures: assemble a node, choose components, explain the logic, receive AI mentor review, and bring the project to working firmware.

Start training View documentation Discuss a corporate track
training/session.demo
01
Level diagnosticsThe platform detects the starting profile and suggests a route.
15 min
02
Lab taskThe learner assembles a device scenario in the IDE and the MWOS library.
practice
03
AI reviewThe mentor checks logic, components, risks, and suggests corrections.
feedback
04
Project assessmentThe final work is saved into the user portfolio.
badge
Training format

Not a course for its own sake, but a working route inside the platform

The demo model is built around real engineering actions: select a module, understand a component passport, describe a device, build firmware, test behavior, and defend the solution.

01 · Skills map

Skills map

The user sees which competencies are already covered: MWOS, circuitry, protocols, security, debugging.

02 · Labs

Practical labs

Every lesson ends with an action: assemble a sensor, connect nodes, configure storage, verify an emergency scenario.

03 · AI mentor

AI mentor

AI does not just answer; it reviews the solution path, asks clarifying questions, and explains project mistakes.

04 · Portfolio

Work portfolio

Final projects can prove competence: what was built, how it was verified, and which modules were used.

Route

How the training works

0

Entry diagnostics

Short questions and a mini-task define the user starting level.

1

Basic scenarios

Working with sensors, event logic, data exchange, and simple controllers.

2

MWOS components

Studying modules through passports, examples, limitations, and compatibility.

3

Project build

The learner assembles a working device scenario and receives AI review.

4

Result defense

The final work is recorded in the profile: level, badge, component stack, and conclusions.

Learning tracks

Different roles get different tasks

Engineersensors, controllers, node reliability
Programmerfirmware, API, module structure
Managercost, risks, project control
Reliabilityfailures, tests, fault diagnostics
Demo metrics

What the user will see

  • current level and progress by track;
  • earned badges and completed labs;
  • projects that can be opened in the IDE;
  • AI mentor recommendations for the next step;
  • internal currency and rewards for completed tasks.
Next stage

The demo page reserves space for a complete learning module

Later this page can connect real lessons, progress tables, database tasks, Flux rewards, AI lab checks, and role-based certificates.

Book a demo Documentation Solution development