I build systems that turn AI from a chat box into an operating layer.
My GitHub is a public research map of the tools, frameworks, and patterns I study: agentic coding systems, local-first LLM workflows, RAG, Salesforce and Agentforce automation, AI-native DevOps, cloud terminals, cybersecurity tooling, trading automation, and founder/operator systems.
The person you call when the AI demo needs to become a secure, observable, repeatable, business-ready system.
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Agentic AI Systems I design prompt systems, coding agents, RAG workflows, model routing, AI memory, and human-in-the-loop automation. |
Salesforce + Enterprise Automation I translate business complexity into CRM architecture, service workflows, integration plans, reporting models, and operational governance. |
Cybersecurity-Minded Delivery I care about secure-by-design systems: IAM, secrets, permissions, auditability, DevSecOps, secure SDLC, AI risk, and observability. |
My stars, forks, and watched projects cluster around several practical research tracks:
mindmap
root((MahaKoala Research Map))
Agentic AI
Claude Code
Codex
OpenCode
Coding Agents
Task Masters
Agent Skills
Multi-Agent Review
AI Engineering
RAG
Page Indexing
Vectorless Retrieval
Prompt Systems
Context Engineering
Memory
Model Routing
Enterprise Automation
Salesforce
Agentforce
Apex
Flow
LWC
SOQL
CRM Intelligence
Cybersecurity
DevSecOps
AppSec
IAM
Secrets
Threat Modeling
AI Safety
Observability
Infrastructure
VPS
Docker
Cloud Init
SSH
SFTP
AI Terminals
Self Hosting
Market Systems
Trading Bots
Backtesting
TradingView
Crypto
Risk Management
Product + Design
Design Systems
UI Extraction
SwiftUI
React
Founder Tools
| Research Track | What the starred repos suggest | How I apply it |
|---|---|---|
| Agentic coding | Strong interest in Claude Code, Codex, OpenCode, statuslines, task managers, autonomous implementation runs, and agent harnesses. | Building structured AI development workflows that are observable, reviewable, and less fragile than vibe coding. |
| Spec-driven development | Repeated signal around OpenSpec, Spec Kit, long-running agent instructions, prompt systems, and context engineering. | Turning vague ideas into requirements, plans, implementation maps, and repeatable delivery systems. |
| RAG and knowledge systems | Interest in PageIndex, vectorless retrieval, document intelligence, memory plugins, and research assistants. | Designing retrieval and reasoning systems for documents, enterprise knowledge, and decision support. |
| AI-native DevOps | Stars around worktrees, containers, terminal agents, browser terminals, SSH/SFTP tools, and AI stack deployment. | Building local/cloud development environments that let agents safely operate inside real systems. |
| Cybersecurity and trust | Signal around secure automation, infrastructure visibility, access control, defensive tooling, monitoring, and AI tool risk. | Embedding permission models, audit trails, secrets hygiene, least privilege, and threat modeling into delivery. |
| Salesforce + AI | Strong alignment with Salesforce skills, Agentforce patterns, Apex, Flow, LWC, SOQL, and CRM automation. | Bringing AI into enterprise service workflows, CX operations, case routing, reporting, and integration architecture. |
| Trading and market automation | Stars around trading bots, backtesting, TradingView workflows, crypto tooling, and risk management. | Exploring AI-assisted financial research, backtesting, market data workflows, and disciplined automation. |
| Product and design systems | Stars around AI design systems, UI extraction, prototype generation, and brand-grade design automation. | Turning product ideas into clean interfaces, design tokens, prototypes, and implementation-ready specs. |
flowchart LR
A[Business Intent] --> B[Prompt + Spec System]
B --> C[Context Engine]
C --> D{Model Router}
D --> E[Local LLM]
D --> F[Hosted LLM]
D --> G[Specialized Agent]
E --> H[Tool Execution]
F --> H
G --> H
H --> I[Memory + Logs]
I --> J[Human Review]
J --> K[Reusable Workflow]
K --> L[Business Outcome]
I care about AI systems that are:
- Useful beyond the demo
- Context-rich
- Observable
- Cost-aware
- Permission-aware
- Safe enough for enterprise workflows
- Composable across tools
- Designed for repeatable execution
flowchart TD
A[AI System] --> B[Identity + Access]
A --> C[Data Exposure]
A --> D[Tool Permissions]
A --> E[Prompt Injection]
A --> F[Secrets Handling]
A --> G[Logging + Audit Trail]
B --> H[Least Privilege]
C --> I[Data Classification]
D --> J[Scoped Tool Access]
E --> K[Input Validation + Guardrails]
F --> L[Secret Scanning + Rotation]
G --> M[SIEM / Observability]
H --> N[Trusted AI Operations]
I --> N
J --> N
K --> N
L --> N
M --> N
| Domain | Practical capability |
|---|---|
| Application Security | OWASP-aware design, secure API patterns, input validation, auth flows, permission boundaries, dependency risk awareness. |
| AI Security | Prompt injection awareness, tool-use risk, MCP/agent permission scoping, data leakage prevention, model output review patterns. |
| Cloud Security | IAM boundaries, least privilege, environment isolation, key rotation, cloud logs, and secure deployment posture. |
| DevSecOps | CI/CD hygiene, secret scanning, dependency scanning, GitHub security posture, release governance, branch protection thinking. |
| Security Operations | Monitoring, alerting, log forwarding, incident visibility, SOC/vendor handoffs, and operational runbooks. |
| Salesforce Security | Profiles, permission sets, connected apps, OAuth scopes, field/object access, audit trails, automation user governance. |
| Data Protection | Data classification, retention thinking, access reviews, customer data sensitivity, privacy-aware integrations. |
flowchart TD
A[Business Request] --> B[Discovery + Process Mapping]
B --> C[Salesforce Data Model]
C --> D[Automation Design]
D --> E[Integration Architecture]
E --> F[Security + Permissions]
F --> G[Reporting + Observability]
G --> H[UAT + Enablement]
H --> I[Production Rollout]
flowchart LR
A[Market Data] --> B[Research Agent]
B --> C[Strategy Hypothesis]
C --> D[Backtesting]
D --> E[Risk Rules]
E --> F[Paper Trading]
F --> G[Execution Review]
G --> H[Automation Candidate]
| Role Signal | Skill Set |
|---|---|
| AI Systems Consultant | LLM strategy, agent workflows, tool orchestration, AI adoption plans, AI governance, executive communication. |
| Solutions Architect | System design, integration mapping, API architecture, data flow modeling, platform selection, implementation planning. |
| Salesforce Architect / Product Owner | CRM automation, case workflows, service operations, object/data modeling, Flow/Apex/LWC collaboration, stakeholder delivery. |
| AI Engineer / Agent Builder | RAG, prompt systems, coding agents, local LLM routing, memory, eval loops, context engineering. |
| DevSecOps-Minded Builder | CI/CD thinking, secrets hygiene, security posture, IAM, monitoring, deployment safety, production readiness. |
| Business Analyst / Operator | Requirements gathering, process maps, Jira-ready stories, acceptance criteria, documentation, UAT, executive updates. |
| Technical Product Consultant | MVP planning, product architecture, monetization paths, design systems, roadmap creation, launch readiness. |
| Automation Strategist | Workflow automation, scripting, n8n/Zapier-style orchestration, AI-assisted operations, repeatable playbooks. |
journey
title How I Turn Ideas Into Systems
section Discover
Clarify business outcome: 5: MahaKoala
Map users, systems, data, risks: 5: MahaKoala
section Architect
Design workflow and integrations: 5: MahaKoala
Define security and observability: 5: MahaKoala
section Build
Prototype with AI-assisted delivery: 5: MahaKoala
Create reusable automation: 5: MahaKoala
section Harden
Review permissions and data exposure: 4: MahaKoala
Add monitoring, logging, and runbooks: 4: MahaKoala
section Scale
Document operating model: 5: MahaKoala
Train users and iterate: 4: MahaKoala
quadrantChart
title Where I Create Leverage
x-axis Tactical Execution --> Strategic Architecture
y-axis Low Automation --> High Automation
quadrant-1 AI Operating Systems
quadrant-2 Enterprise Transformation
quadrant-3 Manual Support
quadrant-4 Automation With Strategy
"Salesforce Admin Work": [0.45, 0.48]
"Agentic Coding": [0.72, 0.78]
"DevSecOps Guardrails": [0.66, 0.70]
"RAG + Knowledge Systems": [0.78, 0.82]
"Executive AI Roadmaps": [0.88, 0.73]
"Secure AI Operations": [0.91, 0.86]
AI is not just about better models.
It is about:
- better workflows
- better context
- better tools
- better memory
- better routing
- better observability
- better permissions
- better business outcomes
The winning teams will not simply “use AI.”
They will build repeatable operating systems around it.
Private enterprise and client implementation work stays private.
Public GitHub shows the research trail:
what I’m evaluating, forking, testing, learning from, and combining into consultant-grade systems.
The next great consultant will not just recommend software.
They will design the AI-assisted operating system that lets the business move faster, think clearer, execute safely, and scale with less friction.
That is what I’m building toward.