Building local-first intelligence, verifiable systems, developer tools, and production software that survives contact with the real world.
I am a software engineer focused on applied AI, local-first products, data platforms, and reliability-minded engineering.
My work sits in the uncomfortable but interesting space between research ideas and production constraints: latency, failure modes, privacy, observability, schema drift, platform limits, brittle integrations, and the classic enterprise pastime of asking one system to pretend seven systems are fine.
I care about software that is:
| Private by default | User data should not be treated like confetti at a marketing parade. |
| Fast enough to matter | Performance is not decoration. It is part of the user experience. |
| Explainable enough to debug | If nobody can inspect it, nobody can trust it. |
| Structured enough to scale | Architecture should reduce chaos, not professionally rename it. |
| Tested enough to trust | Hope is not a deployment strategy, despite industry enthusiasm. |
I am currently building through Kairais Tech, where I work across product strategy, architecture, implementation, testing, and release readiness.
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LLM workflows, RAG systems, prompt orchestration, model evaluation, user-controlled memory, and privacy-aware intelligence. |
Developer tools, desktop apps, sync engines, audit trails, local storage, receipts, diagnostics, and platform-native performance. |
Distributed pipelines, EDW systems, Spark/PySpark, BigQuery, cloud migration, CI/CD hardening, and production stabilization. |
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Cross-platform Wi-Fi analyzer and diagnostics suite. A networking diagnostics product with a C++ core and .NET MAUI interface, designed for wireless visibility, explainable analysis, and platform-aware degradation across Windows, Android, iOS, and Linux-oriented tooling.
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Deterministic file sync with receipts and capsules. A local-first desktop sync system built with Rust, Tauri, and Svelte. Focused on scan/plan/run workflows, BLAKE3 integrity, Merkle-root receipts, portable capsules, and audit-friendly transfer evidence.
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Secure, verifiable data destruction. A security-oriented system for controlled erasure workflows, dry-run planning, verification passes, audit receipts, and local-only destruction evidence.
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Local-first API requester and response inspector. A lightweight developer tool for composing, inspecting, validating, and replaying API requests without dragging every workflow into a bloated cloud platform.
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On-device treadmill OCR and private workout logging. A mobile app that uses local OCR to convert treadmill displays into structured workout logs. Built around real-world capture messiness, validation, privacy, and editable machine suggestions.
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Client-side datastore and vector-search direction. A local data engine direction focused on indexed persistence, browser-side storage, interactive apps, and user-controlled retrieval workflows.
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Before building Kairais Tech full-time, I worked on enterprise healthcare data systems where reliability was not decorative.
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Cloud migration cost reduction |
Runtime reduction through modernization |
Production failure reduction |
Transformations in a flagship integration |
I have led backend and data engineering delivery across requirements, design reviews, implementation, deployment gates, rollback readiness, monitoring hooks, production hardening, and post-release stabilization.
That experience shaped how I build now: fewer vague promises, more architecture, more observability, more tests, and fewer heroic debugging rituals at 2 AM. Society may not be saved, but at least the pipeline runs.
| AI / ML | PyTorch · TensorFlow · Hugging Face · Transformers · RAG · NLP · Intent Classification · Model Evaluation · Prompt Orchestration |
| Languages | Python · Java · C · C++ · C# · Rust · TypeScript · JavaScript · Scala · SQL |
| Backend | FastAPI · Spring Boot · Flask · REST APIs · Microservices · Service Contracts · Async Workflows |
| Frontend / Apps | React · Next.js · Svelte · React Native · Tauri · .NET MAUI · Bulma |
| Data Engineering | EDW · ETL · Ab Initio · Hadoop · Hive · Spark · PySpark · Kafka · BigQuery · Airflow |
| Databases | PostgreSQL · MySQL · IBM DB2 · Redis · MongoDB · Hive · BigQuery · SQLite |
| DevOps | Docker · Kubernetes · Git · CI/CD · Release Hardening · Monitoring · Rollback Readiness |
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| Area | Projects | What it shows |
|---|---|---|
| Networking | Vyre | Cross-platform diagnostics, systems boundaries, explainable analysis |
| File Systems | Nodus | Deterministic sync, integrity, receipts, auditability |
| Security | ZeroTrace | Secure deletion, verification, destructive-operation guardrails |
| Developer Tools | Invar, Breakpoint, Ripcord, Aevrix | Local-first tooling, repeatability, diagnostics, engineering workflow design |
| Applied AI | Tempo, WispDB | On-device OCR, local storage, retrieval-oriented architecture |
| Enterprise Data | Healthcare EDW / Cloud Migration Work | Distributed pipelines, modernization, production stabilization |
My strongest interests are not “AI because AI.” That phrase has done enough damage.
I am interested in AI systems that are:
- stateful without being creepy
- private without becoming useless
- local-first where latency and control matter
- grounded through retrieval and structured logic
- evaluated through measurable behavior
- inspectable when they fail, because they will fail, because reality enjoys comedy
Current research and product themes:
Conversational AI
Long-context memory
Retrieval-augmented generation
On-device intelligence
Privacy-preserving user memory
Reliable ML systems
Local-first application architecture
Distributed data processing
System observability
Developer tooling