Hamel Husain
PulseAugur coverage of Hamel Husain — every cluster mentioning Hamel Husain across labs, papers, and developer communities, ranked by signal.
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AI model grading: Four-tier system offers actionable insights over binary
The author discusses a discrepancy in grading methodologies for AI models, contrasting their four-tier system (FATAL, RISKY, MISSED, HARMLESS) with Hamel Husain's recommendation for binary (good/bad) grading. While Husa…
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AI author Sonnet 5 generates flawless exam questions but lacks human creativity
An experiment revealed that an AI author, Sonnet 5, was flawless in generating exam questions based on provided data and instructions, unlike its human counterpart who made multiple errors. However, the AI's output, whi…
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LLM identifies errors in automated exam grader
An individual used an LLM, specifically Sonnet 5, to re-grade an exam after their automated code grader made errors. The LLM was tasked with grading 29 answer sheets based on a rulebook, comparing its results against th…
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OpenAI's Jalapeño chip claims efficiency gains; agent systems evolve
OpenAI has released benchmark details for its custom inference chip, Jalapeño, claiming significant improvements in efficiency and latency over NVIDIA's GB200 and GB300 systems. The chip reportedly offers better perform…
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AI Engineer Roadmap: 12 YouTube Videos for World-Class Skills
A list of 12 YouTube videos has been curated for aspiring AI engineers aiming for world-class status by 2026. The selection covers fundamental concepts like Large Language Models (LLMs), transformer architecture, and to…
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AI 'engineering' terms like loop and graph engineering spark debate
The terms "loop engineering" and "graph engineering" have recently gained traction in AI discussions, largely due to viral social media posts. These terms, however, are seen by some as evolving or renaming of existing c…
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AI product evaluation difficulty signals design flaw, says expert
Hamel Husain argues that difficulty in evaluating AI products is a significant product flaw. He suggests that if a product is hard for its creators to verify, it will likely be difficult for users as well. Husain propos…
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Data scientists' core skills are essential for AI harness engineering and evaluation
The role of data scientists is evolving with the rise of large language models, shifting from direct model training to a focus on the "harness" that guides AI systems. While foundation model APIs reduce the need for tra…
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AI coding tools disrupt nbdev workflow, prompting developer shift
Hamel Husain, a former proponent of the literate programming tool nbdev, has stopped using it due to the rise of AI coding assistants. He found that nbdev's unique workflow, which combines code, documentation, and tests…
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Technical writer shares strategies for building an audience through AI content
Technical writer Hamel Husain shares strategies for building an audience, emphasizing authentic engagement with others' work and consistent content creation. He advises developers to add value to existing discussions an…
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ML Engineer Hamel Husain Shares LLM Building Insights and Evals Focus
Hamel Husain, an experienced machine learning engineer, shares insights from a year of building with large language models. He emphasizes the importance of "evals" for debugging, analyzing, and measuring AI systems, dra…
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Hamel Husain proposes Adversarial Validation for AI drift detection
Hamel Husain proposes a technique called Adversarial Validation for detecting drift in AI model inputs and training data. This method involves training a binary classifier to distinguish between two datasets, such as tr…
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AI Product Success Hinges on Robust Evaluation Systems, Says Expert
Building successful AI products requires a robust evaluation system, according to Hamel Husain, who led the team that created CodeSearchNet. He emphasizes that rapid iteration, encompassing quality evaluation, debugging…
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Hamel Husain advises AI product teams on selecting evaluation tools and building robust systems.
Hamel Husain, an AI consultant, emphasizes the critical need for robust evaluation systems in developing successful AI products, drawing from his experience with projects like CodeSearchNet and Rechat's AI assistant, Lu…
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Fine-tuning LLMs still valuable for niche syntax, style, and rules
Fine-tuning large language models remains a valuable technique, particularly for tasks requiring specific syntax, style, or rules, according to Hamel Husain. While prompt engineering is a crucial first step for testing …
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Hamel Husain shows how to intercept LLM API calls and prompts
Hamel Husain's blog post argues for the importance of understanding the exact prompts sent to large language models, even when using abstraction frameworks. He criticizes some tools for obscuring the prompts, which hind…
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Hamel Dev offers Axolotl debugging tips for LLM fine-tuning
Hamel Husain has published a guide on debugging the Axolotl project, a tool for fine-tuning large language models. The guide offers practical tips such as simplifying test scenarios, using smaller datasets and models, a…