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dboles99/README.md

Daniel Boles

Applied AI developer. LLM systems, agents, evaluation, language technology.

Portfolio: dboles99.github.io

简体中文:应用型 AI 开发者,专注于大模型系统、智能体、评测与语言技术。

繁體中文:應用型 AI 開發者,專注於大型語言模型系統、代理、評測與語言技術。

Selected work

Project What it is Result
agent-gateway Admission control, budgets, watchdogs, and checkpoints for unattended coding agents 241 tests passed, 1 skipped
tm-rag-copilot Retrieval-augmented translation over a synthetic translation memory, with deterministic QA 40 of 41 segments passed QA with a local Qwen model, against 33 of 41 for retrieval alone
docx-mcp Read-only MCP server that lets an agent read Word documents with tracked changes accepted 67 tests passed; six tools, none of which can write
rosetta-bench Harness for comparing verbose and compact prompt formats across language-model tools 38 tests passed; no model results published yet
llm-watermark-lab Research lab for measuring statistical watermarks in language-model text A watermark kept a mean 88% of its clean score through print-scan-OCR in 4 of 4 scorable cells; not a calibrated claim
pseudocode-ai-workflows Pseudocode specifications compiled to Python and PowerShell with contracts and a human approval gate Eight defects found by execution and logged
papers Four papers on pseudocode as a guardrail and on language-model text watermarks A citation audit found 9 of 14 identifiers wrong in an earlier review
bachelorpluslite A Rust text editor with Notepad's features and a local note layer that never touches the network 1,104 test functions in the source; 94 recorded decisions
bachelorpadplus_rust The larger Rust text-processing application for Windows and Linux 2,026 test functions in the source; 68 recorded decisions
amplified-futures-vcv C++ DSP modules for VCV Rack 2 Not measured

Method

The work proceeds through small experiments rather than large claims, because a change small enough to measure is also small enough to undo. Each one faces tests or an evaluation; what works stays, and what fails goes into the record with its numbers, since a negative result on paper saves more time than a success nobody measured. These repositories ask for that reading, limits included.

Pinned Loading

  1. amplified-futures-vcv amplified-futures-vcv Public

    Amplified Futures — no-wave VCV Rack 2 modules for drone, feedback, and detuned oscillator systems.

    C++ 4

  2. docx-reviewer-remap docx-reviewer-remap Public

    Rename reviewers on Word tracked changes without touching the document's content.

    Python 1

  3. SlideBridge SlideBridge Public

    Convert PowerPoint decks into review-ready Word documents with slide images and tables.

    Python 1

  4. rosetta rosetta Public

    Dual-representation prompt specs: one source, a human form and a machine form, with schema validation and lockfiles.

    Python 1

  5. rosetta-bench rosetta-bench Public

    Benchmark harness comparing human-readable and machine-readable prompts across model providers, with an LLM judge.

    Python 1

  6. docx-mcp docx-mcp Public

    A read-only MCP server that lets an AI agent read and search a Word document as it would stand with every tracked change accepted.

    Python 1