AI-Powered Development,
Entirely Local
Portal IDE and TinyCPUDevAI are built for developers who want real AI capability without giving up control. Everything is local-first by default: your code stays on your machine, your workflow runs without mandatory cloud dependency, and the runtime adapts from lower-end hardware to high-end workstations using the same integrated AI stack.
No forced cloud inference path for core coding workflows. Local execution keeps sensitive code and reasoning in your environment.
Hardware-adaptive tuning lets the platform stay usable on modest machines while scaling up aggressively on stronger systems.
Built-in reasoning, retrieval, planner workflows, and training visibility are integrated into the IDE rather than fragmented across separate services.
Explore Portal IDEThe Entire Experience Runs on TinyCPUDevAI
TinyCPUDevAI is the intelligence layer behind the local-first stack: inference, retrieval, planner flows, repair context, and training visibility are all tied back to one coherent runtime rather than scattered across unrelated services.
Run TinyCPUDevAI-Flavored Python In Your Browser
Test the core philosophy instantly: local execution, no server round-trips, no hidden cloud compute, and no data export.
Click "Run in Browser" to execute.
- Runs fully in the browser with no server-side code execution.
- Supports a focused Python-like subset tuned for instant local demos.
- Capability helpers mirror Portal IDE themes like retrieval, planning, and hardware-aware execution.
What TinyCPUDevAI Enables
Every Mythlogic product on this site inherits something concrete from the same stack: local reasoning, retrieval-aware workflows, transparent runtime design, and a bias toward hardware-conscious execution.
Artificial Intelligence
TinyCPUDevAI runs entirely on your hardware: no cloud, no subscriptions, no data leakage. Its custom MinGRU architecture delivers O(1) memory context behavior and self-improving local intelligence.
Developer Tools
Portal IDE is directly powered by TinyCPUDevAI with staged reasoning roles, shared blackboard coordination, workspace retrieval, persistent history and memory, and a training monitor built for real iteration.
Games
Our game systems inherit the same AI-first engineering discipline as TinyCPUDevAI: strategic depth, adaptive behavior, and mechanics designed for long-term mastery.
Releasing Soon
Different products, one shared engineering philosophy: local-first execution, observable architecture, and AI that is attached to real tools instead of floating above them.
Portal IDE
What’s NewA local-first desktop IDE with a Rust kernel, Tauri workbench, staged AI reasoning, retrieval, planner flows, and a training monitor that all share one runtime story.
- ✓ Rust sovereign kernel with traceable IPC and kernel-owned state
- ✓ Tauri + Svelte workbench with editor, terminal, Git, planner, monitor, and chat surfaces
- ✓ Two-stage reasoning flow with Jr. Developer and Project Manager model roles
- ✓ Workspace RAG, knowledge RAG, persistent history, and memory retrieval
- ✓ Planner board plus Goal Loop for long-running, workspace-scoped task execution
- ✓ Training monitor with checkpoint controls, watchdog behavior, and variant-local artifact isolation
- ✓ Transport-backed LSP/DAP, repair previews, and validation-aware debugging loops
- ✓ Local-first core workflow with no mandatory cloud hop for primary IDE use
TinyCPUDevAI “Tiny”
A custom local MinGRU runtime and training stack that powers Portal IDE and can also be commissioned as a secure, offline deployment for higher-control environments.
- ✓ Custom MinGRU architecture with recurrent hidden-state inference
- ✓ Exportable runtime paths across PyTorch, ONNX Runtime, and OpenVINO-style deployment targets
- ✓ INT4 quantization support with safer fallback behavior
- ✓ Workspace, knowledge, history, and memory retrieval in the reasoning loop
- ✓ Pretokenization, cache reuse, checkpointing, and monitor-visible training operations
- ✓ Secure offline deployment path for custom environments
- ✓ Shared blackboard coordination across staged reasoning passes
Marketplace, Certification, and Community Signals
Even before the ecosystem is full, the site can signal where it is going: official extensions, community demand, and credentials that qualify serious leads.
Python Toolchain
Built-in — zero configurationFull-stack Python support: formatter, type checker, linter, debugger, and REPL integration. Pre-installed and tuned for TinyCPUDevAI’s development workflow.
TinyCPUDevAI Runtime
Integrated local toolingLocal inference, retrieval, reasoning, patching, validation, and runtime orchestration all live inside the same project stack instead of being outsourced to unrelated hosted services.
UndoFu
AI file recovery for VS CodeAlways-on archive capture for deletions, bulk rewrites, and bad AI edits. UndoFu gives VS Code a real disaster-recovery layer instead of leaving you to reconstruct damage from memory.
Rust Analyzer Port
Community-requested Rust language analysis and toolchain integration for Portal IDE. Submit a request to move this into the active development queue.
Certified TinyCPUDevAI Architect
A free assessment path focused on local-first AI principles, secure deployment assumptions, and system reasoning.
Take the Real AssessmentTransparent Comparison and the Tool Constellation
Trust comes from showing the system honestly: where Portal IDE is structurally different, and how TinyCPUDevAI organizes its capability surface.
Portal IDE vs VS Code
| Dimension | Portal IDE | VS Code |
|---|---|---|
| AI Architecture | TinyCPUDevAI built in | Extension-dependent |
| Default privacy stance | Local-first | Varies by extension |
| State ownership | Kernel-owned memory, history, knowledge, monitor | Split across extensions |
| Reasoning workflow | Two-stage local reasoning with shared blackboard | Not native |
| Planner workflow | Built-in planner board and Goal Loop orchestration | Not native |
| Surface integration | Editor, terminal, problems, monitor, memory, history, web, Git share runtime context | Usually separated by extension boundary |
| Language and debug stack | Transport-backed LSP and DAP lifecycle control | Strong, but editor-centric rather than kernel-centric |
| Training pipeline | Built-in monitor, checkpoints, tokenizer and artifact controls | None built-in |
| Memory system | Persistent RAG core | Session-only |
| Offline capability | 100% offline | Partial / cloud-dependent |
| Persistent context | Workspace RAG + knowledge + history + memory | Varies by extension |
| Validation and repair loop | Problems, terminal, diff preview, AI repair context, explicit apply path | Piecewise through separate tools |
| Autonomous operations | Planner subprocesses, Goal Loop, scrape controller, monitor-managed training | Manual composition required |
| Audience | AI-native workflow | General-purpose editor |
The TinyCPUDevAI Capability Constellation
Authenticity Signals That Compound
Turn the solo-dev story into signals that match the repository: continuity, code ownership, and sustained local-first iteration.
Built by One Developer.
Engineered Like a Team.
Mythlogic Studios is the independent development studio of Christopher Brown — a self-taught developer who builds production-grade AI systems from first principles, without frameworks, wrappers, or compromise. Every architecture choice is anchored to one core idea: TinyCPUDevAI should power real products, not demos.
The studio’s philosophy is simple: build things that are genuinely useful, architecturally sound, and technically ambitious. No shortcuts. No wrappers. No placeholder features. Every product ships when it’s ready — and when it ships, it’s built to last.
Mythlogic Studios operates at the intersection of artificial intelligence, developer tooling, and interactive entertainment. TinyCPUDevAI is the connective tissue across all three domains — a local-first, self-improving intelligence layer that compounds in capability with every build cycle.
Evidence-Backed Reasons This Project Matters
The strongest site copy is the copy that can be checked against the implementation. These are the parts the repository genuinely supports today.
“Portal IDE does not stop at chat. The repo already wires editor state, repair loops, planner surfaces, memory/history retrieval, monitor state, and local inference into one runtime story.”
“TinyCPUDevAI is more than a hosted-wrapper claim. The codebase exposes a custom MinGRU runtime, retrieval-aware prompt assembly, staged reasoning roles, quantization paths, and a training pipeline with monitor-visible controls.”
“The local-first position is backed by real containment choices: loopback-safe IPC, workspace-scoped file actions, kernel-owned state, variant-isolated training artifacts, and recovery-aware monitor flows.”
“The site itself now mirrors the project more honestly: model explainers live beside the AI product, VS Code extension pages are first-class, and the home page is anchored to architecture rather than legacy feature mythology.”
Contact & Support Centralized
All enquiries and bug reports are routed through one dedicated contact path. Tell us your use case and we can scope a TinyCPUDevAI-powered build that matches your security and workflow requirements.
✉ Start Contact & Build InquiryIncludes both Contact Us and Bug Report options routed to MythlogicStudios@outlook.com.