The Summarizer interface of the Summarizer API contains all the functionality for this API, including checking AI model availability, creating a new Summarizer instance, using it to generate a new summary, and more.
Summarize with built-in AI | AI on Chrome | Chrome for Developers
Distill lengthy articles, complex documents, or even lively chat conversations into concise and insightful summaries.
Introducing the Elicit API - Elicit
Search our 138M+ papers and generate Reports using our API.
Exclusive: New York Times tests out AI-generated search summaries
In recent weeks, the paper has quietly rolled out a new AI-powered search page for a small subset of visitors.
Learning with AI falls short compared to old-fashioned web search
Doing the mental work of connecting the dots across multiple web queries appears to help people understand the material better compared to an AI summary.
Learning with AI falls short compared to old-fashioned web search
Doing the mental work of connecting the dots across multiple web queries appears to help people understand the material better compared to an AI summary.
Google’s AI Overviews Can Scam You. Here’s How to Stay Safe
Beyond mistakes or nonsense, deliberately bad information being injected into AI search summaries is leading people down potentially harmful paths.
Parallel Web Systems | Infrastructure for intelligence on the web
Parallel's new FindAll API turns natural language queries into custom datasets from the web. It finds entities like companies, people, or locations based on your criteria, then enriches them with structured data—all with citations. FindAll Pro achieves 61% recall, 3x better than competitors.
The Best API Documentation Tool
OpenAPI-generated documentation tool with 24k+ stars on Github - make APIs your company's superpower.
FragmentDirective - Web APIs | MDN
The FragmentDirective interface is an object exposed to allow code to check whether or not a browser supports text fragments.
GitHub - pablorevilla-meshtastic/meshview: This project watches a MQTT topic for meshtastic messages, imports them to a database and has a web UI to view them.
This project watches a MQTT topic for meshtastic messages, imports them to a database and has a web UI to view them. - pablorevilla-meshtastic/meshview
Kimi K3 - Kimi API Platform
Kimi K3 is our flagship model for long-horizon coding and end-to-end knowledge work, with a 1M-token context window and industry-leading intelligence. The Kimi API Platform provides K3, K2.7 Code, K2.6 and other large language model APIs, supporting long context, multimodal understanding, and Tool Calling.
IWE - Agent Memory in Plain Markdown
A local-first knowledge graph for you and your AI agents. Query markdown like a database, edit it with guarded operations, enforce structure with schemas.
sosumi.ai - Apple Docs for LLMs
sosumi.ai provides Apple Developer documentation in an AI-readable format by converting JavaScript-rendered pages into Markdown.
Language agents achieve superhuman synthesis of scientific knowledge
Language models are known to hallucinate incorrect information, and it is unclear if they are sufficiently accurate and reliable for use in scientific research. We developed a rigorous human-AI comparison methodology to evaluate language model agents on real-world literature search tasks covering information retrieval, summarization, and contradiction detection tasks. We show that PaperQA2, a frontier language model agent optimized for improved factuality, matches or exceeds subject matter expert performance on three realistic literature research tasks without any restrictions on humans (i.e., full access to internet, search tools, and time). PaperQA2 writes cited, Wikipedia-style summaries of scientific topics that are significantly more accurate than existing, human-written Wikipedia articles. We also introduce a hard benchmark for scientific literature research called LitQA2 that guided design of PaperQA2, leading to it exceeding human performance. Finally, we apply PaperQA2 to identify contradictions within the scientific literature, an important scientific task that is challenging for humans. PaperQA2 identifies 2.34 +/- 1.99 contradictions per paper in a random subset of biology papers, of which 70% are validated by human experts. These results demonstrate that language model agents are now capable of exceeding domain experts across meaningful tasks on scientific literature.
Large Language Models Require Curated Context for Reliable Political Fact-Checking—Even with Reasoning and Web Search
Large language models (LLMs) have raised hopes for automated end-to-end fact-checking, but prior studies report mixed results. As mainstream chatbots increasingly ship with reasoning capabilities and web search tools—and millions of users already rely on them for verification—rigorous evaluation is urgent. We evaluate 15 recent LLMs from OpenAI, Google, Meta, and DeepSeek on more than 6,000 claims fact-checked by PolitiFact, comparing standard models with reasoning- and web-search variants. Standard models perform poorly, reasoning offers minimal benefits, and web search provides only moderate gains, despite fact-checks being available on the web. In contrast, a curated RAG system using PolitiFact summaries improved macro F1 by 233% on average across model variants. These findings suggest that giving models access to curated high-quality context is a promising path for automated fact-checking.
