SOFTWARE • AI • SYSTEMS • PRODUCTS • INFRASTRUCTURE
I’m Arun Tito, a founder, software engineer, growth builder, and product engineer from Hyderabad, India.
My journey started with digital marketing and social media marketing, working with social growth, content, lead generation, SEO, digital PR, monetization, and growth systems. I later moved deeper into software engineering and now combine both sides: understanding how businesses grow and building the technology that makes those systems scalable.
Today I build AI-powered products, software infrastructure, automation systems, growth systems, knowledge platforms, and digital products. I work across the path from product idea to production system: architecture, domain design, APIs, databases, queues, authentication, AI workflows, interfaces, infrastructure, deployment, and iteration.
What interests me most is the part underneath the interface: how data moves, how systems execute, how failures are handled, how components communicate, and how a product can keep evolving without collapsing under its own complexity.
Build systems, not just features.
| Area | Focus |
|---|---|
| Software Engineering | Full-stack applications, APIs, architecture, databases, async jobs |
| AI Engineering | LLM integration, agents, local models, AI workflows, automation |
| Product Engineering | Zero-to-one products, product systems, UX-aware architecture |
| Systems Engineering | Infrastructure, queues, execution engines, observability, Android, kernel work |
| Knowledge Systems | Entities, relationships, editorial pipelines, semantic search, explanation systems |
| Growth Engineering | Growth infrastructure, integrations, automation, digital execution |
| Digital Marketing | Digital strategy, social media marketing, SEO, lead generation, digital PR |
| Social Media Growth | Social platforms, content workflows, growth operations, monetization |
Think in systems Design before scaling Automate repetition Keep boundaries clear Ship real software Learn from production
Digital Marketing · Social Media Marketing · Software Engineering · AI / ML Engineering · Product Engineering · Infrastructure · Developer Tooling · Open Source · Technical Partnerships · Zero-to-One Products
PHP Python TypeScript JavaScript SQL HTML CSS Bash Kotlin C/C++
React Next.js Tailwind CSS Vite
Laravel Node.js Python PostgreSQL Redis Supabase REST APIs
AWS Docker Linux Git GitHub GitHub Actions Vercel Railway
Ollama Qwen OpenRouter Gemini CLI Antigravity OpenCode AI-assisted Development
| Domain | Working Area |
|---|---|
| AI Product Engineering | Practical AI-powered product workflows and intelligent application features |
| LLM Integration | Hosted and local language models inside real application workflows |
| AI Agents | Tool-driven agents, orchestration, execution workflows, and automation |
| Local AI | Ollama-based inference and model-assisted development |
| AI-assisted Development | Architecture, implementation, debugging, research, and iteration with coding models |
| Knowledge Systems | Structured entities, semantic relationships, editorial systems, and knowledge architecture |
| AI Content Pipelines | Extraction, structured data, drafting, validation, and human approval |
| Automation | Queues, background jobs, integrations, asynchronous execution, and orchestration |
| ML Foundations | Model capabilities, inference, evaluation, and AI system design |
01 · DOOB — Growth Intelligence & Execution Platform
DOOB is being built as a growth intelligence platform, not a traditional SMM panel. Its architecture connects goals, signals, intelligence, execution, providers, and automation into one product system.
| Dimension | Details |
|---|---|
| Stack | Laravel, PHP 8.x, Next.js, React, TypeScript, PostgreSQL, Redis, Docker |
| Architecture | Product, API, administration, provider, and execution surfaces |
| Performance | Redis caching, asynchronous queues, Horizon, provider abstraction, background execution |
| Security | Clerk JWT integration, application boundaries, role-aware architecture, transactional workflows |
| Impact | Growth intelligence, digital execution, automation, and infrastructure orchestration |
| Repository | doob-v2 — private |
- Goal Intelligence Engine
- Signal Pipeline
- Platform Connections
- Execution Engine
- Provider Resolver / Provider Manager
- Process execution jobs
- Wallet persistence and transactional workflows
- Redis queues and Laravel Horizon
- PostgreSQL schema and migration management
- Clerk JWT API integration
- Next.js application surfaces
- Domain-driven backend architecture
- Production deployment and infrastructure
02 · KARADAVI — Deep Knowledge Forest
KARADAVI is an explanation-first knowledge platform built around a Deep Knowledge Forest: structured entities, connected concepts, editorial articles, and relationships designed to help people understand what they search for.
AI may assist the process. AI does not publish.
AI can help with extraction, structuring, research, and drafting. Human editorial approval remains the publishing gate.
Companies People Technology Science Space Concepts History Places Nature & Earth Society & Culture
| Dimension | Details |
|---|---|
| Stack | Next.js, React, TypeScript, Supabase, PostgreSQL, Python, Ollama, Qwen |
| Architecture | Canonical entities, relations, editorial CMS, extraction pipeline, knowledge graph foundations |
| Performance | Structured retrieval, canonical models, optimized rendering, PWA architecture |
| Security | Supabase RLS, controlled administration, role-aware editorial workflows |
| Impact | Explanation, context, connected knowledge, and search-oriented understanding |
| Repository | enterkaradavi — public entry · karadavi — private core |
- Canonical Knowledge Entity Definition
- Entity type normalization and aliases
- Entity relationship architecture
- Deep Knowledge Forest content model
- Editorial CMS architecture
- Python extraction pipeline
- Canonical extraction JSON
- Ollama + Qwen drafting workflow
- Human editorial approval
- Supabase PostgreSQL architecture
- Row Level Security design
- Directus CMS architecture research
- Public knowledge website
- Admin interfaces
- PWA installation and service worker support
- Knowledge graph foundations
- Search-oriented explanation architecture
03 · DOOB Architecture & Infrastructure Labs
Public engineering work documenting pieces of the DOOB architecture and infrastructure research.
| Project | Purpose |
|---|---|
| doob-public-architecture | Distributed architecture and orchestration systems |
| doob-architecture-console | Architecture visualization and console work |
| doob-provider-infrastructure | Provider abstraction and infrastructure |
| doob-queue-systems | Queue architecture and distributed execution |
| doob-observability | Observability and operational systems |
| doob-operational-research | Operational and product research |
| doob-topology-lab | System topology exploration |
| doob-infrastructure-diagrams | Infrastructure visualization and topology |
| doob-launch-engine | Launch and execution workflows |
04 · nCleaner — Android System Utility
An Android utility built with Kotlin, Jetpack Compose, and Material 3, focused on storage analysis and controlled device cleanup workflows.
| Dimension | Details |
|---|---|
| Stack | Kotlin, Jetpack Compose, Material 3, Android |
| Architecture | Native Android application |
| Performance | Device-side storage analysis and native processing |
| Security | Cleanup workflows designed around controlled deletion |
| Repository | nCleaner |
05 · Origin-Kernel — Android / Linux Systems
Low-level exploration of Linux kernel work, Android systems, C/C++, kernel build tooling, and mobile software.
| Dimension | Details |
|---|---|
| Stack | Linux Kernel, Android, C/C++, kernel build tooling |
| Focus | Kernel-level software and mobile systems experimentation |
| Repository | Origin-Kernel |
06 · Open Source Experiments
A collection of public repositories used to explore interfaces, software ideas, systems, utilities, and product concepts.
| Project | Repository |
|---|---|
| Time Machine | time-machine |
| Relief OS | relief-os |
| EVAC | evac |
| Firstlight | firstlight |
| Grid | grid |
| Ghost | ghost |
| Wake | wake |
| Recover | recover |
| Blackbox | blackbox |
| Pulse | pulse |
| Trace | trace |
| Cinematic Portfolio | cinematic-portfolio |
| Portfolio | portfolio |
| Links | links |
| SMXM | smxm |
| TITORA | titora |
Independent Product Development · 2024 — Present
Building software products and digital systems across digital marketing, social media growth, AI, growth technology, knowledge infrastructure, automation, and full-stack engineering.
- Architecture: domains, APIs, databases, queues, caching, auth, infrastructure
- Frontend: React, Next.js, TypeScript, reusable product systems
- Backend: Laravel, PHP, PostgreSQL, Redis, REST APIs
- AI: LLM integration, local models, coding agents, AI workflows
- Infrastructure: Docker, cloud deployment, CI/CD, operational tooling
- Product: PRDs, technical roadmaps, UX-aware architecture, iteration
- Systems: asynchronous execution, provider abstractions, Android, low-level experiments
PRODUCT IDEA
│
▼
┌─────────────────────┐
│ Domain & Data Model │
└──────────┬──────────┘
│
┌────────────┴────────────┐
▼ ▼
APPLICATIONS SERVICES
React / Next.js APIs / Jobs / Workers
│ │
└────────────┬────────────┘
▼
DATA & EXECUTION LAYER
PostgreSQL · Redis · Queues
│
▼
AI / INTEGRATION LAYER
LLMs · Providers · Agents
│
▼
INFRASTRUCTURE
Docker · Cloud · CI/CD
│
▼
PRODUCTION
I enjoy working across the layers rather than treating frontend, backend, AI, and infrastructure as isolated disciplines.
| Principle | Meaning |
|---|---|
| Systems over features | Understand the system around a feature before adding complexity |
| Clear boundaries | Keep domains, responsibilities, data ownership, and interfaces explicit |
| Automation first | Remove repetitive manual work where reliable automation is possible |
| Human control | Keep meaningful approval and operational boundaries where automation should not decide alone |
| Production awareness | Design for failures, queues, retries, observability, and operational reality |
| Build for change | Prefer architecture that can evolve without constant rewrites |
| Learn by shipping | Use real behavior, debugging, and iteration as part of the engineering process |
learning:
- advanced AI engineering
- LLM systems and agentic workflows
- distributed application architecture
- cloud and DevOps
- systems engineering
building:
- DOOB
- KARADAVI
- TITORA ecosystem
- AI digital products
- automation and execution infrastructure
- knowledge and explanation systems
exploring:
- Ollama
- Qwen and coding models
- knowledge graphs
- semantic systems
- intelligent search
- developer infrastructure
- Android and low-level systems
open_to:
- digital marketing
- social media marketing
- growth engineering
- software engineering
- AI / ML engineering
- product engineering
- open-source collaboration
- technical partnerships
- zero-to-one product work
 Professional Network |
 Social & Personal |
 Updates & Thoughts |
 Code & Engineering |
 Social Profile |
 Products & Work |
LINKEDIN · INSTAGRAM · X · GITHUB · FACEBOOK · PORTFOLIO · EMAIL · TITORA
BUILD · SHIP · LEARN · REPEAT
Building software, AI systems, and products from idea → architecture → production.
Build systems, not just features.