Daniel Kliewer
AI Developer & Full-Stack Technologist

Operating at the critical intersection of human alignment, decentralized AI architectures, and high-integrity data systems.

Austin, TX • danielkliewer@gmail.com • danielkliewer.com • GitHub: kliewerdaniel

I. Executive Summary: AI Development and Data Integrity

Daniel Kliewer is a multifaceted AI Developer and Full-Stack Technologist operating at the critical intersection of human alignment, decentralized AI architectures, and high-integrity data systems. With a professional background spanning over a decade in rigorous data annotation methodologies and a self-taught mastery of modern software engineering, he specializes in transforming complex human data—such as psychological traits and writing styles—into quantifiable, machine-readable formats.

His work is driven by a commitment to local-first computing (Loco LLMs), prioritizing data privacy, computational sovereignty, and cost-effective deployment over reliance on proprietary cloud services.

Daniel Kliewer Profile

II. Core Technical Expertise

Mr. Kliewer possesses comprehensive full-stack capability across the development lifecycle, specializing in architectures designed for AI integration, data persistence, and scalable deployment.

DomainKey Skills & Technologies
Languages & FrameworksPython, JavaScript, TypeScript, React, Next.js (App Router), Django, Django REST Framework, FastAPI, Tailwind CSS
AI & LLM ToolingOllama (Local Inference), RLHF (Reinforcement Learning from Human Feedback), ChromaDB (Vector Store), LangChain, SmolAgents, llama.cpp, OpenAI function calling, Pydantic AI
Data & DatabasesPostgreSQL (Relational Data), SQLite, MongoDB (NoSQL option), Universal Data Tool (UDT), JSON/JSONL pipelines, R (for NLP/data cleaning)
Architecture & DevOpsDocker (Containerization), Git/GitHub, Netlify (Cost-effective deployment), Continuous Integration/Deployment (CI/CD) practices, VSCode + Cline (AI-assisted workflow)
Specialized SkillsAgent Orchestration (NetworkX, LangGraph), Dynamic Prompt Generation (using f-strings and metadata), Persona Modeling, SEO Optimization

III. Key Projects and Achievements

1. PersonaGen (Quantified Persona Generator)

A proprietary full-stack application leveraging local LLMs (Ollama) to analyze writing samples, generate a highly detailed 50-metric JSON persona profile, and then use this structured data to generate tailored, style-cloned content.

  • Technical Depth: The system utilizes recursive chain construction via LangChain/LangGraph to generate and store reasoning metadata in a vector database (ChromaDB) alongside structured JSON/SQLite for persistence, ensuring the persona evolves and maintains coherence ("round character" behavior).
  • Demonstration: Showcases advanced prompt engineering, data parsing via regex, and API integration (tested with XAi, Anthropic, and readily adaptable for OpenAI).

2. AI Agent Orchestration and Workflow Design

  • Tech Company Orchestrator & Workflow Repository: Authored a highly-starred open-source guide (workflow.git) detailing a documentation-driven development workflow for creating technology companies using AI agents. This framework uses graph structures (NetworkX) to define agent roles, data flow, and iterative prompt chains (stored in ai_guidelines.md and prompts.md).
  • Decentralized Intelligence: Implemented integrations of the Model Context Protocol (MCP) with OpenAI's Agents SDK and Ollama, establishing a framework for decentralized intelligence architectures that prioritize computational sovereignty.

3. AI-Integrated Personal Platforms

  • Insight Journal Platform: Developed a privacy-focused journaling system integrating locally hosted LLMs (Ollama) to provide personalized feedback and insights on user entries, aiming to enhance self-reflection and cognitive engagement outside of commercial cloud platforms.
  • Multimodal Story Generation System: Built a system that transforms visual inputs into structured, multi-chapter narratives, employing RAG and LLM calls to summarize content into metadata to overcome the inherent limitations of context windows for long-form generation.

IV. Professional Experience and Data Expertise

Independent Developer & Annotator

Austin, TX • 2022 – Present

  • Reinforcement Learning from Human Feedback (RLHF) Expert: Possesses over a decade of experience in high-integrity data annotation, including foundational work on platforms like Amazon Mechanical Turk (pre-iPhone era) and subsequent contracts for major tech companies.
  • Model Alignment & QA: Contributed directly to RLHF pipelines by providing human ranking and feedback on model outputs, training reward models to align LLM behavior toward specific goals (e.g., helpfulness, harmlessness, truthfulness). Expertise includes understanding annotator behavior and developing robust QA strategies to prevent circumvention.
  • Multimodal Data Processing: Annotated complex datasets, including video feeds collected from AR-integrated devices, transforming raw, real-world perception into structured linguistic intelligence for model training.

Fine Arts Professional and Web Designer (Freelance)

January 2010 – Present

  • Developed and maintained multiple digital presences and web applications (e.g., danielkliewer.com, kadaligogh.com, eastsidechess.org).
  • Produced experimental film and digital art displayed at venues such as the Austin Film Society's Avant Cinema program and Co-Lab Projects (2012), demonstrating creative coding and multimedia expertise.

V. Education and Community Leadership

Self-Taught Mastery & Continuous Learning

  • Computer Science & Mathematics: Developed a strong theoretical foundation in linear algebra, advanced probability/statistics, data structures, and algorithms through rigorous self-study, leveraging resources from MIT OpenCourseWare and Harvard edX.
  • Community Advocacy: Founder of the Loco LLM Community and organizer of the Loco LLM Hackathons, promoting open-source AI development and community collaboration.

B.A. in History

University of Mary Hardin-Baylor • Belton, TX • 2003 – 2007

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