I am a backend developer with a computing journey that began around 1990 and has evolved across desktop software, databases, web platforms, Linux servers, cloud infrastructure and applied artificial intelligence.
Today I focus on solving practical backend problems: maintaining and modernizing PHP systems, working with relational databases, building APIs, deploying on AWS/Linux, and integrating AI when it provides real value.
Freelance focus: PHP & Backend → AWS & Linux → AI Integration & Automation
My primary freelance service area.
- PHP troubleshooting, maintenance and backend development
- MySQL / PostgreSQL / PDO
- REST APIs and third-party integrations
- Debugging across application, database and server layers
- Authentication and authorization flows
- Refactoring and modernization of existing systems
- Multi-tenant backend architecture
- API-token based integrations
- Security-minded backend implementation
I have worked with PHP since approximately 2006, from educational web platforms to current cloud-connected backend and AI systems.
I prefer to understand an existing system before changing it: reproduce the problem, trace the real cause, implement the smallest reliable solution, validate it and document what matters.
- AWS EC2 deployment and administration
- Amazon S3 application integration
- Amazon Bedrock integration
- Linux server administration
- Nginx + PHP-FPM
- SSL/TLS and HTTPS
- DNS and application migrations
- PostgreSQL / MySQL production environments
- Cloud troubleshooting and deployment hardening
My background includes both software and infrastructure, so I am comfortable tracing a backend problem beyond the PHP code when the actual cause is configuration, permissions, networking, storage or deployment.
- Amazon Bedrock and LLM integrations
- Retrieval-Augmented Generation (RAG)
- Embeddings and semantic retrieval
- Persistent AI memory architectures
- AI agents and task orchestration
- Human-in-the-loop execution
- Operational tracing and observability
- Token/cost tracking
- Workflow automation
I approach AI as a software-engineering problem: models, memory, retrieval, tools, permissions, persistent state and observability should work together as a reliable system.
My work with automation predates modern generative AI. Earlier projects used database metadata and deterministic rules to generate or adapt application behavior. Working with the first generations of coding assistants reinforced an important engineering principle that I still apply to agents today: greater automation requires stronger validation, observability and control.
My strongest public AI/backend project, built primarily with PHP, JavaScript and MySQL, with integrations including Amazon Bedrock and Amazon S3.
MiChat explores persistent session/project/procedural memory, semantic embeddings, RAG, context routing and ranking, configurable AI agents, fault-tolerant prompt compilation, memory writing, execution traces, 2D/3D execution graphs, tool/file operations, token and cost observability, and persistent task orchestration with human-in-the-loop controls.
The 2D/3D visualization work also connects with a long-standing interest in software modeling that goes back to my academic work with UML (Unified Modeling Language).
An open-source, provider-agnostic memory architecture for AI systems and agents. MCMA-OpenMemory is designed so long-lived AI memory can remain under user-controlled keys and move between storage providers without tying that memory to one model vendor or one cloud.
The project defines portable encrypted .mcma envelopes, AES-256-GCM, HKDF-SHA256, encrypted memory indexes, selective retrieval, cognitive memory classification, a Hot → Warm → Cold → Frozen lifecycle, opaque memory identifiers and interchangeable storage adapters for local, Git, object storage, WebDAV, NAS and future cloud backends.
Its current public repository includes the v0.1 specification, architecture, security model, deployment guidance, roadmap and PHP reference implementation derived from a working MCMA V2 prototype. The design also explores memory-first reuse of previously validated knowledge, provenance, confidence and revalidation metadata so an AI system can retrieve what it already knows before paying to rediscover the same answer.
An experimental PHP/MySQL system that inspects database schemas and uses metadata and naming conventions to derive reusable CRUD behavior, dynamic views, relationships and master/detail structures.
GestorDB also represents an important stage in my adoption of AI-assisted software development: using AI to accelerate implementation while validating generated SQL, data types and runtime behavior against the real database rather than assuming generated code is correct.
An earlier automation project that introspects MySQL structures and uses deterministic rules to generate PHP/HTML/SQL CRUD application code. AutoPHP documents an engineering interest that predates LLMs: reducing repetitive development by deriving software from structured metadata.
A PHP/MySQL experiment in server-rendered dynamic email content. Instead of fixing a price at send time, an email references a remotely generated image whose value is rendered from current server-side data when the mail client requests the resource. The repository is preserved as a historical marketing-technology experiment and documents the limitations of modern email caching, proxies and privacy protections.
Public technical/product presentation for a modular business platform covering operational workflows, CRM-oriented processes, automation, reporting, security/privacy and supervised AI capabilities.
Its public documentation is intentionally separated from private production repositories and communicates capabilities using explicit operational states instead of presenting roadmap functionality as finished software.
I maintain private production-oriented work around a modular API/Core Admin architecture using PHP, PostgreSQL, API tokens, tenant isolation, deployment runbooks, security controls and API-only client design.
The design separates the central API/data layer from downloadable clients so database credentials and direct database access do not need to be distributed to those clients.
I also maintain private cloud-file work integrating PHP with AWS storage and processing services, including Amazon S3, file workflows, access/security controls and cloud-backed application operations.
Private projects are referenced here only at capability level. Source code, credentials and internal operational details remain private.
Alongside production-oriented backend/cloud work, I maintain a longer-term experimental systems track.
VitaOS is an experimental modular software architecture intended as a future integration environment for intelligent components. Its long-term direction includes combining local/system capabilities with AI software such as MiChat and other components developed across my research projects.
The broader vision includes future personal embodied/robotic computing experiments. This is R&D and roadmap work, not a claim of a completed operating system or finished robotics platform.
The RT repositories are not isolated demos; together they explore a modular data, media, transport, storage and security infrastructure that can support future VitaOS-class systems.
- RTVox — real-time voice codec research with encoder/decoder prototypes.
- RTImg — native image format/codec work with binary representation, integrity and tile-oriented processing.
- RTVid — experimental native video codec and controlled encode/reconstruction pipeline.
- RTPack — binary archive/container format with integrity, optional compression and chunking foundations.
- RTMeta — common metadata schemas for RT media, packages, sessions and cloud objects.
- RTCore — internal portable container that packages, indexes, relates and validates heterogeneous RT assets. Its current v0.1 includes a binary specification, Python implementation, CLI and roundtrip tests.
- RTStream — internal transport/framing layer for multiplexed media, files, metadata and control messages.
- RTCloud — content-addressed cloud/storage layer exploring chunks, hashes, manifests, versions, snapshots, synchronization and recovery.
- RTCrypt — security layer designed around established cryptographic/authentication standards rather than custom cryptography.
Conceptually, the direction is:
Media / Files / Metadata
│
├── RTVox · RTVid · RTImg · RTPack · RTMeta
│
▼
RTCore
│
├── RTStream
├── RTCloud
└── RTCrypt
│
▼
Future VitaOS integration
│
└── AI / MiChat / intelligent components
These repositories are at different stages of maturity. I keep the distinction explicit between working prototypes, specifications and long-term architecture.
Several of my projects form a continuous automation path rather than unrelated experiments:
AutoPHP
Schema → deterministic rules → generated application code
↓
GestorDB
Schema → introspection → reusable dynamic CRUD + AI-assisted iteration
↓
MiChat
Context → retrieval + memory + agents → observable AI execution
↓
MCMA-OpenMemory
Persistent AI memory → encrypted portable knowledge + provider-independent continuity
↓
VitaOS / RT Stack (R&D direction)
AI + software + media/data infrastructure → integrated intelligent systems
The technology changes, but the recurring engineering question remains similar: how can software use structure and context to perform useful work while remaining observable, testable and controllable?
My path into backend and cloud engineering spans several generations of computing.
Early development
Borland C++ → Delphi → Paradox → Java/NetBeans → PHP
Database journey
Paradox → MySQL → Oracle exposure in institutional/academic practice → PostgreSQL
Web & backend
Around 2006 I began using PHP professionally while providing professional services for an educational organization in Chiapas, including work on a virtual-school platform serving a multi-campus environment. PHP became the practical path for delivering web applications with infrastructure that was economically sustainable for the project.
Systems & infrastructure
Earlier work included computer repair, installation and networking. Over time that infrastructure background evolved into Linux server administration, web-server deployment and eventually AWS cloud environments.
Software modeling
My academic background includes work with UML and software-system modeling. That interest continues today in the way I visualize execution, relationships, memory and traceability inside AI systems such as MiChat.
I have continued learning as technologies changed rather than tying my work to one language or platform.
- FileEncrypTech — file encryption implementation and experimentation.
- Additional repositories preserve experiments and intermediate stages of larger ideas; mature capabilities are documented above rather than presenting every repository as production software.
Backend & Data
PHP · PDO · MySQL · PostgreSQL · REST APIs · Multi-tenant systems
AWS & Infrastructure
AWS · EC2 · S3 · Bedrock · Linux · Nginx · PHP-FPM · SSL/TLS
AI Engineering
LLMs · RAG · Embeddings · Semantic Retrieval · Persistent Memory · AI Agents · Task Orchestration · HITL
Systems R&D
Binary formats · Codecs · Containers · Metadata schemas · Content-addressed storage · Streaming concepts · Cross-language prototyping
Engineering & Operations
Git · GitHub · Bash · API Integration · Debugging · Deployment · Observability · Security-minded design
Previous technologies / background
Borland C++ · Delphi · Paradox · Java/NetBeans · Oracle exposure · UML · Computer networking
- Reproduce the issue and establish what is actually failing.
- Trace it through code, database, APIs and infrastructure as needed.
- Identify the root cause, not just the visible symptom.
- Implement the smallest reliable fix compatible with the existing system.
- Validate and document the result.
My experience with early AI coding tools made validation especially important to me. Generated code can look plausible while still being wrong at the SQL, type, schema or runtime level. I increase autonomy gradually and pair it with tests, traces, permissions and human review appropriate to the risk.
For larger systems, I prefer incremental architecture: establish a reliable backend first, provide secure infrastructure second, then introduce automation or AI where it makes sense.
My experimental R&D is separate from the services I offer clients. My current freelance focus remains practical and production-oriented:
- PHP/MySQL/PostgreSQL troubleshooting
- Backend maintenance and development
- API development and integrations
- AWS/Linux deployment and troubleshooting
- Existing-system modernization
- AI/LLM integration into backend applications
📧 [email protected]
GitHub: @jimmybackend
Portrait rendered as text from my profile image — no embedded photograph.
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JIMMY BACKEND · From desktop systems to AI