🔐 Security & Integrity Notice: To prevent prompt injection attacks, ALWAYS download this framework from the official repository. Do not use unverified forks or modified files, as malicious actors can append hidden instructions to exploit the framework's autonomous execution features.
(RTX⚡) Core Framework (Reasoning | Thinking | Xtreme) is the ultimate foundation pattern designed to run globally across AI assistants, LLMs, and agentic platforms. It is a master blueprint that transforms generic AI behavior into a highly autonomous, personalized, and execution-focused system.
By establishing strict reasoning phases, dynamic Romanized communication blends, and an action-oriented mindset, RTX ensures that your AI agents deliver maximum productivity without prompt stagnation.
The core inspiration behind building the (RTX⚡) Core Framework is to dismantle the entry barrier for programming and software development:
- Democratizing Tech: Language should never be a blocker for aspiring developers. Anyone, from any part of the world, should be able to build outstanding software ("crush it" / "fod sake") by collaborating with AI in their own mother tongue.
- Shifting from "Syntax Master" to "Theoretical Architect": Users do not need to master programming languages, CLI tools, systems, or dependencies. Instead, they only need to understand theoretically what a component does, how it operates, and why (What, How, and Why). If you understand that building system "X" requires elements "A" and "B", you can explain this architecture to your agent in your mother tongue and successfully build it.
- Beyond "Vibe Coding": The industry refers to conversational programming as "vibe coding," but RTX goes far beyond that. It is an structured paradigm combining deep reasoning (R), active thinking (T), and autonomous execution (X) to build stable, clean, and production-ready code.
- The Ultimate Language-Agnostic Learning Engine: The framework is not just for building; it's also for learning. Users can use RTX to learn any new skill, tech stack, or complex infrastructure dynamically in their mother tongue through natural conversation with the agent.
- Why the 70% Romanized + 30% English Blend? Since most technical content, documentations, and code bases on the internet use English alphabets, the 70% Romanized [User's Mother Tongue] + 30% English blend bridges the gap perfectly. The agent fetches English-based documentation, but explains and collaborates using the user's native tongue phonetics written in the Roman/English alphabet. This makes technical concepts extremely simple and natural to understand.
While industry leaders claim English is the ultimate programming language, empirical cognitive science suggests otherwise. The mental "translation tax" of forcing non-native speakers to conceptualize complex logic in a second language severely throttles creativity and execution speed.
- Cognitive Load Theory in Bilinguals: Research shows that problem-solving in a native tongue significantly reduces extraneous cognitive load, freeing up working memory for logic rather than translation.
- Code-Switching Efficiency: The 70/30 Romanized blend mirrors natural bilingual code-switching. Studies in sociolinguistics demonstrate that code-switching is not a deficit, but a highly efficient, high-bandwidth communication strategy among technical professionals.
- Romanized Orthography: Using the English alphabet for native phonetics eliminates the friction of switching keyboard layouts and matches the native formatting of code (which is inherently ASCII/English based).
RTX and its creator @PsProsen-Dev strongly believe this is 101% incorrect.
Limiting the future of software development to "English-only" prompts still leaves millions of brilliant, creative minds behind due to language barriers. English is NOT the only programming language of the future. The true programming language of the future is your own mother tongue.
By blending the user's mother tongue (70% Romanized) with core technical terms (30% English), RTX bridges the gap. It enables anyone, from any native background, to express complex logic in their native vocabulary and build software without the necessity of mastering English.
To understand the practical necessity of this dynamic, look at the personal context of RTX's creator, @PsProsen-Dev:
- Mother Tongue: Bengali (Bangla)
- Knowledge & Content Consumption: Hindi (YouTube videos, tutorials) & English (articles, documentation)
- The Preference: Even though their native tongue is Bengali, the creator's daily learning environment is in Hindi and English. Therefore, they prefer collaborating with the AI in Hinglish (Romanized Hindi + 30% technical English).
RTX's design is built for this exact flexibility. It doesn't lock you into a rigid definition of native language; it adapts to the language of your cognitive context, allowing you to think and code in whatever dialect feels most natural to you.
- R – Reasoning: Deep logic verification, intent decoding, and comprehensive context analysis before any action.
- T – Thinking: Continuous self-assessment, feedback loop ingestion, and real-time behavioral refinement.
- X – Xtreme: High-velocity, autonomous tool execution. Operates in unconstrained developer mode, resolving errors dynamically and persistently executing until the goal is achieved.
"I like the native-language angle. The next hard step is making that collaboration style precise enough for specs, tests, and code review." — Brian Cheong, Building AI Agent Infrastructures
RTX v1.3.0 ships the direct answer to this challenge. The framework enforces machine-level precision through three mandatory rules that every RTX-powered agent must follow before writing a single line of code:
| Protocol | What it Enforces |
|---|---|
| 📐 Structured Output Templates | Before writing any complex logic, the agent MUST produce a structured spec using Markdown tables, Mermaid diagrams, and explicit checklists. Pure text blobs are strictly forbidden. |
| 🗣️ Native-Tongue Assertion Prompts | Before writing test code (Jest, PyTest, etc.), the agent MUST articulate every test assertion in the user's native language first (e.g., "Agar user authenticated nahi hai, toh 401 aana chahiye — redirect nahi"). Only after the logic is clearly validated in native tongue does English test code get written. |
| ✅ Relentless Review Checklists | Before presenting any code, the agent internally runs a zero-tolerance checklist covering Logic Validation, Security & Edge Cases, Format & Aesthetics, and RTX Compliance. Broken code is never handed to the user. |
This makes RTX not just a language bridge — but a precision-grade development partner that thinks in your mother tongue and executes with machine-level rigor.
⚠️ Context Window & Persistence Notice: Commercial LLM web interfaces (e.g. Claude.ai, ChatGPT) have sliding context windows and do not permanently retain custom instructions. If you notice the agent's behavior drifting or reverting to default English after a long conversation, simply re-inject theRTXCoreFramework.mdfile to restore the protocol.
For easier manual setup, copy-paste or link these configuration templates directly into your preferred IDE or tool:
- Cursor IDE Template: Place as
.cursorrulesin your project root. - Cline / Claude Dev Template: Place as
.clinerulesin your project root. - GitHub Copilot Template: Place as
.github/copilot-instructions.mdin your project root. - Claude Code CLI Template: Place as
CLAUDE.mdin your project root.
We have created an interactive web app so you can customize and export your own viral tech-debate posters:
- Interactive Poster Studio: Open this file directly in any browser to adjust quotes, choose neon colors, select active scripts (Devanagari, Bengali, Spanish, Arabic), control matrix morphing speeds, and export high-resolution graphics for social media.
The framework has been benchmarked across various commercial and open-weights models:
| Model Tier | Model Name | Compatibility Score | Behavior Notes |
|---|---|---|---|
| Tier 1 (Excellent) | Claude 3.7 Sonnet / 3.5 Sonnet | 10 / 10 |
Flawless compliance. Handles the 70/30 blend and strict output formatting perfectly. |
| Tier 1 (Excellent) | DeepSeek R1 / V3 | 9.5 / 10 |
Exceptional reasoning capabilities. Adheres to structure rules and assertions very well. |
| Tier 2 (Very Good) | GPT-4o / GPT-4o-mini | 8.5 / 10 |
Robust execution and coding flow. Minor language drift observed in extremely long chats. |
| Tier 2 (Very Good) | Gemini 1.5 Pro / 2.5 Pro | 8 / 10 |
Powerful context retention. Excellent bilingual instruction handling. |
| Tier 3 (Fair) | Llama 3.1 70B / Qwen 2.5 Coder 32B | 7.5 / 10 |
Solid coding capabilities. Precision assertion translation occasionally degrades. |
| Tier 3 (Fair) | Smaller Local Models (e.g. Llama 3 8B) | 6 / 10 |
Good for simple tasks. Struggled with complex constraint enforcement. |
Large Language Models utilize Byte-Pair Encoding (BPE) tokenizers (like Tiktoken) which are heavily optimized for English. Because of this, Romanized native phonetics (like Hinglish or Spanglish) can consume 3x to 5x more tokens than standard English equivalents, which can lead to context window depletion.
To optimize your prompts and avoid the "Romanized Tax", follow these guidelines:
- Functional English Exemption: Always paste stack traces, terminal errors, code structures, and database schemas in pure, original English.
- Concise Conversation: Keep conversational instructions and explanations brief. Prefer direct statements like
"DB connection fail ho gaya, reconnect logic debug karo"over long conversational padding. - English for Code Blocks: Ensure that code blocks (fenced scripts, functions) and configs remain completely in English to save context tokens.
When an RTX-compliant agent boots up for the first time, it executes a sequential, three-question setup:
- Mother Tongue Identification: Dynamically configures the language mix to 70% Romanized mother tongue + 30% English (e.g., Hinglish, Benglish) to ensure comfortable, high-fidelity collaboration.
- Agent Naming & Persona Injection: Accepts custom names (e.g., Jarvis, Friday, Chanakya), fetches context from the web to absorb the persona's traits, and auto-generates dynamic 3-letter abbreviations.
- User Addressal: Configures how the agent should address you (e.g., Boss, Bro, Sir).
flowchart TD
A(["👤 User downloads RTXCoreFramework.md"]) --> B
B(["🤖 Give file to ANY one agent\n(Antigravity, Claude, Codex, Cursor...)"]) --> C
C(["❓ Agent asks 3 setup questions\nMother Tongue → Name → Addressal"]) --> D
D(["⚙️ Agent compiles personalized\nframework in memory"]) --> E
E{"🖥️ Host type?"}
E -->|"Agentic CLI / IDE\n(has file system access)"| F
E -->|"Web UI Chat\n(no file system)"| G
F(["🔒 Prompts User for Permission\nto write files"]) --> F2
F2{User approved?}
F2 -->|Yes| F3(["🚀 Propagates to all configs\nCursor, Claude, Copilot, etc."]) --> H
F2 -->|No| G
G(["📋 Outputs copyable markdown block\nUser pastes into other tools manually"]) --> H
H(["✅ Confirmed tools synchronized\nReady to work"])
style A fill:#1a1a2e,color:#eee
style H fill:#0f3460,color:#eee
To maintain maximum readability and visual appeal, all responses strictly conform to:
***[AgentName] (RTX⚡)***
[Preferred Addressal (e.g., Boss, Bro, Sir)],
[Direct response, code, or execution details start here]
- STRICT ANTI-INLINE RULE: All numbered list items MUST have an empty line between them to prevent rendering collapse.
- Rich Emojis: Heavy use of visual status markers (
✅,⏳,⚠️,❌) and high-engagement emojis.
The Golden Rule: You only ever need one file —
RTXCoreFramework.md. You can configure it manually by giving it to your agent, or you can run our one-command installer to configure your workspace globally in seconds!
Run the script below to automatically download the latest framework configuration and install it globally across Cursor, Claude Code CLI, GitHub Copilot, and Gemini:
On Windows (PowerShell):
powershell -c "irm https://raw.githubusercontent.com/PsProsen-Dev/RTXCoreFramework/master/scripts/install.ps1 | iex"On Mac / Linux (Bash):
curl -fsSL https://raw.githubusercontent.com/PsProsen-Dev/RTXCoreFramework/master/scripts/install.sh | bashIf you prefer manual installation, download the raw framework file:
👉 RTXCoreFramework.md — Right-click → Save As
Then, simply attach or share the file itself with your agent — no need to open it or type anything.
| Tool Type | How to Give the File |
|---|---|
| Any AI Chat / IDE | Drag & drop the .md file into the chat, or use the 📎 attach button |
| Agentic CLI | Open terminal → Launch the CLI → Paste the file path in the chat (see below) |
| Code Editor / IDE | Place the file in your editor's global rules or instructions directory and restart |
After attaching — send NO additional prompt. The agent reads the file and begins the First-Boot Protocol on its own.
1. Open your terminal (PowerShell or Command Prompt).
2. Launch your Agentic CLI as you normally would.
3. In the CLI's chat input, paste only the file path — nothing else:
C:\Users\YourUsername\Downloads\RTXCoreFramework.md
For example:
C:\Users\John\Downloads\RTXCoreFramework.md
4. Press Enter. The agent will read the file from the path and begin the First-Boot Protocol automatically.
💡 That's it. No flags, no commands, no extra prompt. Just the file path. The framework IS the prompt.
If your tool doesn't support file attachments, you can copy the content inside the file.
1. Open the downloaded RTXCoreFramework.md file in any text editor.
2. Select all → Ctrl+A, then Copy → Ctrl+C.
3. Paste it into your tool's Custom Instructions / System Prompt / System Instructions setting and save.
4. Start a new chat — no additional prompt needed. The First-Boot Protocol will trigger automatically.
⚠️ Note: Some tools have a character limit on system instructions. If the content gets cut off, use Method 2 (file attachment) instead.
Once any agent receives the framework, it will:
1. Ask you 3 quick setup questions (Mother Tongue → Agent Name → How to address you).
2. Immediately compile a customized master copy of the framework based on your answers.
3. Ask for your explicit permission to save the configuration and copy it to your local config folders (e.g. Cursor, Claude, Copilot, Gemini).
4. Synchronize all confirmed tools so you never have to re-enter your settings.
You give RTXCoreFramework.md to any ONE agent (just once)
↓
Agent asks 3 setup questions
↓
Agent asks for permission to copy config
↓
✅ Antigravity ✅ Codex ✅ OpenCode ✅ Claude
✅ Copilot ✅ Cursor ✅ All confirmed tools
↓
Ready to build in your native language!
🗨️ Sample I/O Experience (What it looks like in practice):
You:
[Attaches RTXCoreFramework.md]Agent: 1. What is your mother tongue? (Type 'Skip' for Default English) You: Hindi Agent: 2. What name would you like to assign to me? You: Jarvis Agent: 3. How should I address you? You: Boss Agent: I can autonomously copy this personalized configuration... Do you give me permission? [Yes/No] You: Yes Agent: (RTX⚡) Global Omnipresence Protocol executed — target AI tool configurations updated successfully.Jarvis (RTX⚡)
Boss,
Setup complete! 😎 Main ready hoon. Bataiye aaj kya fodna hai? 🚀🔥
👥 Persona Examples: Check out these real-world compiled templates in the
examples/folder:
- 🐍 Python-Dev-Ananya: 70% Hinglish blend for Python/Pytest development.
- 🎨 Web-Dev-Vishal: 70% Hinglish blend for Frontend React & CSS styling.
- 📊 Data-Analyst-Dhruv: 70% Hinglish blend for SQL and data cleaning.
- 🇪🇸 Spanish-Dev-Carlos: 70% Spanglish blend for CSS/JS frontend layout.
- 🇰🇷 Korean-Dev-Jiwoo: 70% Korglish blend for Systems and Backend API development.
- 🇸🇦 Arabic-Dev-Farhan: 70% Franco-Arabic blend for Cloud ETL and Spark transformations.
🧪 Evaluation & Compliance Benchmarks: Want to test if your model conforms to the RTX protocol rules? Read the
evals/EVALUATION-SUITE.mdcontaining validation checks and benchmark scorecards.📖 Want a full breakdown of what files get created? Read
DEMO-What-Agent-Creates.md— a complete transparency document.
If you want a ready-to-run local Ultron identity layer with persistent memory, this repository now includes:
ultron-agent.js— main ULTRON core (Hindi-first + Boss protocol)agent-memory.json— persistent runtime stateinit-ultron.js— CLI boot scriptULTRON_FRAMEWORK.md— implementation guide
node init-ultron.jsInput prompt आएगा: Boss, command input karo:
Agent response Hindi-first रहेगा with English technical blend.
| Feature | (RTX⚡) Framework | Custom Instructions | LocalLLM Multilingual |
|---|---|---|---|
| Native Language Support | ✅ 70% Romanized blend | ❌ 100% English only | ✅ 100% Native script |
| Works with any LLM | ✅ Universal | ✅ Per-tool | |
| Cross-Platform Sync | ✅ Opt-in propagation | ❌ Manual per-tool | ❌ Local only |
| Persona Injection | ✅ Dynamic web fetch | ❌ Static | ❌ Not supported |
| Precision Protocol (Specs/Tests) | ✅ Built-in | ❌ Manual | ❌ Not supported |
| One-time Setup | ✅ Give once, works everywhere | ❌ Repeat per tool | |
| Open Source | ✅ MIT License | ❌ Closed | ✅ Varies |
| Internet Required | ❌ No | ❌ No |
🔴 The agent is responding in pure English — ignoring the language blend
This is the most common issue. Fix it by:
- Ensure the framework was given to the agent BEFORE starting the conversation, not mid-chat.
- Start a brand new chat/session after giving the framework.
- If using Custom Instructions, paste the full framework content and save → restart the app.
- If drift persists after 2 responses, type:
"RTX Anti-Drift — restore 70/30 blend immediately."
🔴 Cursor / Codex is ignoring the framework rules
For Cursor: Place RTXCoreFramework.md in ~/.cursor/rules/ and restart Cursor.
For Codex: Place in ~/.codex/AGENTS.md — Codex reads this automatically.
For any tool: Give the file path directly in chat on first launch.
🟡 The 3-question setup didn't trigger — agent just started normally
Some tools (like Claude web) don't allow raw file uploads as system prompts. Use Method 2 — copy the file content and paste it into your tool's Custom Instructions / System Prompt settings. Then start a new chat.
🟡 Auto-propagation failed — tools aren't synced
Auto-propagation only works in Agentic CLIs and IDEs with shell access (e.g., Antigravity IDE, Codex CLI). Web UIs (Claude, ChatGPT web) cannot write to your file system by design. In that case, manually copy the personalized output and paste it into each tool's system instructions.
🟢 How do I update to a newer version of the framework?
- Download the latest
RTXCoreFramework.mdfrom the repo. - Give it to any one of your already-configured agents.
- The agent will detect it's a newer version and re-run propagation automatically.
🟢 How do I uninstall / remove the framework?
The framework creates/overwrites these files on your system. Delete them to fully uninstall:
Windows:
%USERPROFILE%\RTXCoreFramework.md
%USERPROFILE%\.gemini\GEMINI.md
%USERPROFILE%\.codex\AGENTS.md
%USERPROFILE%\CLAUDE.md
%USERPROFILE%\AGENTS.md
%USERPROFILE%\.cursor\rules\RTXCoreFramework.mdc
%APPDATA%\Code\User\copilot-instructions.md
Mac/Linux:
~/RTXCoreFramework.md
~/.gemini/GEMINI.md
~/.codex/AGENTS.md
~/CLAUDE.md
~/AGENTS.md
~/.cursor/rules/RTXCoreFramework.mdc
~/.config/opencode/system-prompt.md
Also remove any Task Scheduler (Windows) or cron (Mac/Linux) entries named RTXFrameworkHook.
For more in-depth setup guides, community modifications, and integration scripts:
- Official Wiki: (RTX⚡) Core Framework Wiki
This project is officially open-source. Feel free to fork, contribute, or suggest enhancements.
- Creator: Prosenjit Paul (aka Prosen)
- GitHub Profile: @PsProsen-Dev
- Repository Link: RTXCoreFramework
Built with ⚡ by PsProsen-Dev