Visualize LLM outputs against datasets, manually annotate results, and run automated evaluations to algorithmically optimize prompts.
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Updated
Nov 22, 2025 - TypeScript
Visualize LLM outputs against datasets, manually annotate results, and run automated evaluations to algorithmically optimize prompts.
An implementation of the Anthropic's paper and essay on "A statistical approach to model evaluations"
Create an evaluation framework for your LLM based app. Incorporate it into your test suite. Lay the monitoring foundation.
A framework for evaluating large language models (LLMs) across a variety of tasks.
Squeeze your model with pressure prompts to see if its behavior leaks.
Codex-native autoresearch harness with structured worker/judge turns for optimizing anything you can measure.
Detecting Relational Boundary Erosion in AI systems. A framework for testing whether models maintain honest, calibrated, and appropriate boundaries.
Disposable Daytona sandboxes for LLM evals and isolated command execution
Evaluation patterns, release gates, and anti-hallucination techniques for developer-focused AI workflows.
In this we evaluate the LLM responses and find accuracy
Local-first LLM evaluation runner with baselines, caching, markdown reports, and CI-friendly quality, latency, and cost gates.
This project demonstrates a production-grade Evaluation (Evals) Framework used to benchmark multiple Large Language Models (LLMs) against a "Source of Truth" NBA dataset.
Evaluation and reliability harness for agentic LLM systems, with task success, latency, cost, retries, fallback routing, and failure taxonomy.
Synthetic marketplace benchmark harness with deterministic demo and Codex subagent pilot
Sovereign Adversarial Simulation & Interdiction Engine for the 0.05V Standard.
Portfolio site for AI-assisted software experiments, prototypes, and polished demos.
CLI release gate for structured AI changes.
Germany-based AI/backend engineer. Shipping production quality loops, build logs, and practical engineering notes.
Agentic research pipeline with local retrieval, structured evaluation, conditional revision, and traceable outputs using Groq.
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