>_ AI RESEARCH
LLMs • Agents • Evaluation • Data

Technical deep dives into language models, autonomous agents, evaluation methodologies, and synthetic data generation. No fluff, just code and concepts.

READ ARTICLES ABOUT ME

                        # Latest research focus

                        def evaluate_agent(agent, tasks):

                          return {

                            "accuracy": calculate_metrics(),

                            "robustness": stress_test()

                          }

                        >_ Running evaluation...

LATEST ARTICLES

Technical write-ups on current research topics

LLMs Evaluation

Beyond Accuracy: Comprehensive LLM Evaluation

Implementing multi-dimensional evaluation frameworks that go beyond simple accuracy metrics to assess model robustness, bias, and reasoning capabilities.

2023-06-15 • 12 min read

Agents Architecture

Building Stateful AI Agents

Architectural patterns for creating agents with memory and learning capabilities using transformer-based models and reinforcement learning.

2023-05-28 • 15 min read

Data Generation

Synthetic Data at Scale

Techniques for generating high-quality synthetic training data using LLMs, with quality control mechanisms and diversity metrics.

2023-04-12 • 10 min read

Evaluation Metrics

Dynamic Evaluation Frameworks

Creating adaptive evaluation systems that evolve with model capabilities, focusing on edge cases and failure modes.

2023-03-05 • 8 min read

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                        # About the researcher

                        class Researcher:

                          def __init__(self):

                            self.focus = [

                              "LLMs",

                              "Agents",

                              "Evaluation"

                            ]

                            self.experience = "5+ years"

ABOUT

I'm an AI researcher and engineer with a focus on developing and evaluating large language models and autonomous agent systems. My work sits at the intersection of machine learning, software engineering, and empirical research methodology.

Current research interests include:

Novel evaluation methodologies for generative models
Architectures for long-term memory in AI agents
Scalable synthetic data generation techniques
Failure mode analysis in LLMs

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>_ AI RESEARCH LLMs • Agents • Evaluation • Data