@hamelhusain
Evals Evals Evals @OpenAI About Me: https://hamel.dev
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SWE-bench Multimodal v2's issue text and code usually contain enough information to solve tasks, the researcher says.
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Prime Intellect says revisions to AutomationBench’s tool descriptions, tasks and graders drastically changed model scores.
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Vik Paruchuri argues that scoring flaws can penalize correct OCR results and hide important differences between models.
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Subvocal claims non-contact sensing decodes nearly invisible mouthing into text at more than 200 words per minute.
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The authors recommend putting distinguishing words first and keeping structure and vocabulary consistent across items.
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Shreya says readers can order it on Amazon or get a copy at a book signing.
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Epoch AI says four benchmarks were verified and two lacked enough information for a review.
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Interconnects Podcast says the discussion asks why cyber and AI experts talk about recent events so differently.
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LangChain says its three agents handle sales leads, code reviews and marketing drafts, with many runs triggered in the background.
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In notes on their discussion, Harry Stebbings says Naveen Rao predicts a $10 trillion inference market within 10 years.
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A co-creator set October 30, 2026, as the deadline for product managers, engineers and designers to submit workflows.
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A post says it has 40-plus submissions and saturates only if a person heavily tunes agents' prompts to overfit tasks.
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The poster is looking for discoveries for an annual gift guide, but rules out Claude and ChatGPT.
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A user said they’d spent two days struggling with Codex’s auto-reload and asked what was worth trying instead.
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Raindrop says the feature watches every pull request and sends Slack DMs about how agent changes performed in production.
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An event listing says authors Hamel Husain and Shreya Shankar will discuss designing AI evaluations and interpreting results.
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A user’s timeline moves from data analyst in 2003 to data scientist in 2010 and machine learning engineer in 2016.
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Sankalp Shubham found little change in resolved-task rates, while both models faltered on a separate visual test.
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The team behind Holon-S1 says it beat two other models at predicting spatial gene activity in a controlled comparison.
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Hamel Husain and Antaripa Saha compare models on 100 leasing-agent traces, warning against an unchecked 0.50 threshold.