A delightful language with friendly error messages, great performance, small assets, and no runtime exceptions.
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A delightful language with friendly error messages, great performance, small assets, and no runtime exceptions.
16BPP.net: Blog / Faster asin() Was Hiding In Plain Sight
Software developer in Boston, MA. I like C++, Qt, UI/UX, graphics, animation and other fun things. 日本語を話します。
The 49MB Web Page
A look at modern news websites. How programmatic ad-tech, huge payloads and hostile architecture destroyed the reading experience.
The platform for news — Newspack
The platform for news Newspack gives news organizations the tools to publish great stories, develop a loyal audience, and drive revenue. All at a surprisingly affordable price. A project of WordPress.com, with support from the Google News Initiative, the Knight Foundation, and The Lenfest Institute. Our product Publishing Fast, flexible publishing We incorporate news industry presentation and […]
Please
Please is a cross-language build system with an emphasis on high performance, portability, extensibility and correctness.
homepages.news — News Homepages documentation
An open-source archive that gathers, saves, shares and analyzes news homepages
How To Argue With An AI Booster
Editor's Note: For those of you reading via email, I recommend opening this in a browser so you can use the Table of Contents. This is my longest newsletter - a 16,000-word-long opus - and if you like it, please subscribe to my premium newsletter. Thanks for reading! In
EYG news: Strong foundations, a blog post and a conference talk.
Updates from the development of the EYG language and structural editor.
fastfadingviolets/new-intelligencer
A claude agent that summarizes your bsky feed to produce a bespoke newspaper
fasterthanli.me
French/Swiss enby making videos about Rust, how computers work, and whatever strikes my fancy. I’m loud about mental health and excellence in software engineering. This year, I’m focused on making ...
🐌 Slow Software for a Burning World 🔥
In a world of “move fast and break things,” we’ve chosen a different tempo — one rooted in care, deep listening, and collective stewardship. Slow software means building for long-term resilience and meaningful participation, rather than chasing novelty, speed, or scale.
Better Than Free
[Translations: Belarusian, Chinese, Estonian, French, German, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Turkish] The internet is a copy machine. At its most foundational level, it copies every action, every character, every thought we make while we ride upon it. In order to … Continue reading →
Google News Is Boosting Garbage AI-Generated Articles
404 Media reviewed multiple examples of AI rip-offs making their way into Google News. Google said it doesn't focus on how an article was produced—by an AI or human—opening the way for more AI-generated articles.
RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
Large Language Models (LLMs) hold significant potential for advancing fact-checking by leveraging their capabilities in reasoning, evidence retrieval, and explanation generation. However, existing benchmarks fail to comprehensively evaluate LLMs and Multimodal Large Language Models (MLLMs) in realistic misinformation scenarios. To bridge this gap, we introduce RealFactBench, a comprehensive benchmark designed to assess the fact-checking capabilities of LLMs and MLLMs across diverse real-world tasks, including Knowledge Validation, Rumor Detection, and Event Verification. RealFactBench consists of 6K high-quality claims drawn from authoritative sources, encompassing multimodal content and diverse domains. Our evaluation framework further introduces the Unknown Rate (UnR) metric, enabling a more nuanced assessment of models' ability to handle uncertainty and balance between over-conservatism and over-confidence. Extensive experiments on 7 representative LLMs and 4 MLLMs reveal their limitations in real-world fact-checking and offer valuable insights for further research. RealFactBench is publicly available at https://github.com/kalendsyang/RealFactBench.git.
RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
Large Language Models (LLMs) hold significant potential for advancing fact-checking by leveraging their capabilities in reasoning, evidence retrieval, and explanation generation. However, existing benchmarks fail to comprehensively evaluate LLMs and Multimodal Large Language Models (MLLMs) in realistic misinformation scenarios. To bridge this gap, we introduce RealFactBench, a comprehensive benchmark designed to assess the fact-checking capabilities of LLMs and MLLMs across diverse real-world tasks, including Knowledge Validation, Rumor Detection, and Event Verification. RealFactBench consists of 6K high-quality claims drawn from authoritative sources, encompassing multimodal content and diverse domains. Our evaluation framework further introduces the Unknown Rate (UnR) metric, enabling a more nuanced assessment of models' ability to handle uncertainty and balance between over-conservatism and over-confidence. Extensive experiments on 7 representative LLMs and 4 MLLMs reveal their limitations in real-world fact-checking and offer valuable insights for further research. RealFactBench is publicly available at https://github.com/kalendsyang/RealFactBench.git.