Research & Innovation/65% confidence

Analysis Version

Summary

This developer specializes in Artificial Intelligence, specifically focusing on LLM benchmarking, agentic environments, and scientific computing. They demonstrate advanced architectural skills using modern Python patterns (asyncio, Pydantic) to build evaluation frameworks, complemented by domain expertise in physics and quantitative finance. Their profile exhibits a strong research orientation, prioritizing novel metric design and experimental frameworks over production packaging.

Score Context

Score reflects GitHub profile completeness rather than research capability. Strong technical innovation (9/10) and domain expertise are evident despite incomplete project packaging and missing tests.

Tech Stack

PrimaryPython7·ProficientC++2Pydantic2asyncio2·FamiliarJulia1Docker1

Repositories

SmallBench

Small, simple agent task environments for training and evaluation

“The developer's most significant work, showcasing modern Python tooling (uv, Pydantic) and advanced agent architecture design.”

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LRCBench

Evals meant to evaluate language models' ability to reason over long contexts.

“Demonstrates scientific rigor in designing benchmarks for LLM reasoning capabilities.”

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Pricing-Exotic-Barrier-Options-with-B.M

(This is a simple proof of concept) I will be using Brownian Motion techniques to develop a procedure for pricing exotic American barrier options

“Highlights quantitative finance expertise and C++ proficiency outside of the primary AI focus.”

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RedAgentExperiments

My work in parallel with PokemonRedExperiments

“Shows continued interest and experimentation in agentic behaviors and reinforcement learning concepts.”

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code_vignettes

code vignettes mostly for processing stochasticities

“Demonstrates polyglot ability (Julia) and interest in stochastic processes.”

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Score History

Persona

Research & Innovation9/10

High density of experimental repositories focusing on cutting-edge LLM benchmarking and agent behaviors.

Production Readiness4/10

Repos contain broken setups (syntax errors in setup.py), unlisted dependencies, and unfinished implementations.

Code Modularity8/10

Strong separation of concerns and type safety in the codebase, even if the deployment artifacts are fragile.

Skills

Python8/10

Demonstrates advanced usage of asyncio, Pydantic, and type hinting in complex evaluation frameworks like SmallBench.

LLM & AI Evaluation8/10

Deep understanding of model reasoning and context, evidenced by the design of specific benchmarks like LRCBench and SmallBench.

Software Architecture7/10

Code analysis reveals clear abstractions (e.g., Agent vs. ACI separation) and modular design, despite packaging issues.

Scientific Computing7/10

Background involves complex math modeling in C++ (Barrier Options) and Julia, indicating strong quantitative fundamentals.

C++6/10

Used effectively for performance-critical domain tasks like financial pricing and particle physics tagging.

Growth

1.Prioritize fixing basic packaging errors (e.g., the syntax error in SmallBench's setup.py) to ensure your tools are actually installable.

2.Implement unit tests for your benchmarks; relying on visual inspection for complex logic like `compare_permutations` risks invalidating your scientific results.

3.Clean up or archive empty placeholder repositories (like obsidian2d3 and DL_Math) to reduce noise and present a more professional portfolio.

4.Explicitly list all dependencies in requirements.txt or pyproject.toml; currently, unlisted imports like 'apropos' make your code impossible for others to run.

joshuapurtell