AAGMAALDEV (pronounced “ahg‑mah‑l‑dev”) is a next‑generation development platform that integrates Artificial General Intelligence (AGI), Machine‑Learning‑Assisted Automation (MLAA), and Low‑Code/No‑Code (LCNC) environments into a single, cohesive ecosystem. Its core promise is to let developers—and even non‑technical stakeholders—create, test, and deploy complex software systems with dramatically reduced manual effort, while preserving the flexibility needed for enterprise‑grade solutions.
TL;DR: AAGMAALDEV is a unified framework that lets you “talk” to an AGI to design, code, and maintain applications, turning high‑level business intent into production‑ready software.
| Step | Action | Details |
|------|--------|---------|
| 1 | Install Node.js (≥ 18) | Required for the CLI and package manager. |
| 2 | Install the aagmaaldev CLI | npm i -g aagmaaldev |
| 3 | Verify installation | aagmaaldev --version should output the current version (e.g., 1.4.2). |
| 4 | Optional: IDE plugins | VS Code extension “Aagmaaldev Helper” provides syntax highlighting and snippets. | aagmaaldev
Tests live in tests/ and use the built‑in AagmaalTest runner:
test "counter increments"
let c = Counter()
c.increment()
assert(c.count == 1)
Run them with:
aagmaaldev test
| Scenario | Office electricity (MWh/yr) | Remote‑home electricity (MWh/yr) | Net reduction | |----------|----------------------------|-----------------------------------|--------------| | Current (100 % onsite) | 12.5 | 0 | – | | Hybrid 40 % remote | 7.5 | 0.8 | ‑5.2 MWh (≈ 42 % drop) | | Hybrid 60 % remote | 5.0 | 1.2 | ‑7.3 MWh |
Corresponding CO₂e reduction (40 % remote) = 1,200 t yr⁻¹. TL;DR: AAGMAALDEV is a unified framework that lets
| Item | Convention |
|------|-------------|
| Branches | main (stable), dev (integration), feature branches: feat/<short‑desc>, bugfixes: bug/<short‑desc>, releases: release/vX.Y.Z |
| Commits | Conventional Commits (feat:, fix:, chore:, refactor:) – enables automated changelog |
| Docker Tags | registry.company.com/aagmaaldev:<git‑sha> + latest for dev |
| Env Vars | Upper‑snake‑case, prefixed: AAGMAAL_DB_URL, AAGMAAL_REDIS_URL |
| Tests | *_test.js,py,go for unit; integration tests under tests/integration/ with a *_it.js,py suffix |
| Component | Data source | Tool/approach | |-----------|-------------|---------------| | Energy usage | Building management system (BMS) – 2022‑2024 kWh | Regression analysis to isolate office‑only consumption | | Cost modeling | Finance ledger, IT procurement records | Excel financial model (NPV, ROI, payback) | | Employee sentiment | Online survey (n = 1,845) + focus groups (4 groups) | Likert‑scale analysis; thematic coding (NVivo) | | Carbon conversion | EPA Emission Factors (2023) | kWh → CO₂e conversion | | Step | Action | Details | |------|--------|---------|
Assumptions
Tip: Start with a small, bounded project (e.g., a CRUD API) to get comfortable with the AI’s suggestion patterns before tackling mission‑critical systems.