Upload a video, trim the moment you want in your browser, and share the selected clip with a simple link.
Tim Lindholm, Java's original JVM maintainer, shares with El Reg some of the grungy build details the doc glosses over
:star: AI-assisted development workflow that produces understanding alongside code. Teach, capture decisions, document, assemble reviews. - baokhang83/fluencyloop
Beginning July 20, Claude Fable 5 will be included in all Max and Team Premium plans, at 50% of limits. Pro and Team Standard users will continue to have access to Fable via usage credits, and will receive a one-time $100 credit. Demand for Fable has been challenging to
But what do I really mean by "free range"? How do we keep kids safe enough?
cat file | grep is abuse. grep can open files by itself. Stop the useless use of cat.
A non-partisan map of American government — every US House and Senate seat and all 50 states' legislative districts, clickable and enriched with public records: votes, committees, campaign finance, disclosures, and election margins. See how much of the electorate actually chose the people who represent you.
A stretched consumer is buying less, and as the pressure spreads across US regions, grocery is turning into a share game.
Buffett’s remarks helped push Google co-founder Larry Page’s net worth above $300 billion as he predicts Google dominating the AI race.
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We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance on the same public benchmark. This protocol raises two fundamental concerns. First, harness evolution is itself an iterative search procedure that repeatedly evaluates and revises candidate harnesses using task feedback. As in agentic test-time scaling, it should therefore be compared with simple task-level search baselines under matched feedback and inference budgets to determine whether its gains arise from improved harness design or from additional search alone. Second, because the search and the final evaluation share the same benchmark, the reported gains risk overfitting to that specific task set. To address these concerns, we conduct an extensive evaluation comparing harness evolution with simple test-time scaling and discovery baselines under comparable feedback and inference budgets, and also evaluate evolved harnesses on held-out tasks to assess whether the discovered improvements generalize. Experiments on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6 show that automatic harness evolution does not consistently outperform simple test-time scaling methods and exhibits limited generalization. Our results raise important questions about the effectiveness of automatic harness evolution and highlight the need for fairer evaluation protocols and benchmarks for automatic harness design. Our code is available at https://github.com/rethinking-harness-evolution.
A 32-bit float pipeline with color management, eleven new nodes across color, compositing, lighting, and texturing, and a one-time commercial license.
Beautifully designed, copy-paste UI components for AI agent conversations: thinking states, tool calls, streaming text, citations, tables and more. Ships in React, Vue and Svelte with plain CSS and no Tailwind.