ArcadeDB 26.1.1 Release Notes We're excited to announce ArcadeDB 26.1.1, a significant release that brings 92 closed issues and introduces major new capabilities to the platform. This release marks...

ArcadeDB 25.12.1 Release Notes We're excited to announce the release of ArcadeDB v25.12.1! This release includes significant bug fixes, new features, performance improvements, and dependency update...

Making sense of ~5 years of vendor speak, 10 different architectures that are actually the same, semantic layers finally maturing, and a Bartridge in a B-tree.

Die c’t – magazin für computertechnik, gegründet 1983, ist die auflagenstärkste und einflussreichste deutsche Computerzeitschrift. Die c’t wird...

The Gorman Paradox: Where Are All the AI-Generated Apps? (codemanship.wordpress.com) 12-14  ↑ 104 HN Points

Many people are embracing GenAI. Andy Balaam encourages us to pause and think about why this might not be a good idea.

ArcadeDB v25.11.1 This release introduces significant improvements to vector search capabilities, alongside critical fixes for SQL querying, indexing, and gRPC protocol handling. 🚀 Highlights New J...

DatAasee centralizes and interlinks distributed library/research metadata into an API‑first union catalog.

Verstreute Werke von ((λ()'Dr.ArneBab))

This specification defines the QUERY method for HTTP. A QUERY requests that the request target process the enclosed content in a safe and idempotent manner and then respond with the result of that processing. This is similar to POST requests but can be automatically repeated or restarted without concern for partial state changes.

DatAasee - A Metadata-Lake for Libraries. Contribute to ulbmuenster/dataasee development by creating an account on GitHub.

We're excited to announce the release of ArcadeDB 25.10.1! This version introduces a major new feature: official Python Bindings for embedded use. It also delivers significant query and indexing en...

/dev/null is an ACID compliant database (jyu.dev) 05:28  ↑ 109 HN Points

Launched in 2018, Papers with Code was a community-driven platform for exploring and discovering state-of-the-art research in artificial intelligence and machine learning. Within one year, it became a crucial infrastructure for the computer science community, growing into a resource with more than 18,000 papers and over 1,500 leaderboards (1). The platform aggregated studies from multiple sources and served as a central hub for benchmarked research in the form of leaderboards (see an example of a leaderboard here). It also supported the open science movement by publishing academic papers with their source code. In response to this rapid growth, Papers with Code announced it was joining Facebook AI in 2019. Users were reassured that “Papers with Code [would] remain a neutral, open and free resource” and there would be no changes to their services. Yet, earlier this year, the Papers with Code website suddenly went offline. Without prior notice, users were simply redirected to the Papers with Code Github repository, and the machine learning community was left to wonder about the fate of this key resource. About one month after the platform disappeared, Hugging Face, a private company providing collaborative platforms for machine learning models, released a LinkedIn announcement that it was building a successor platform in partnership with Papers with Code and Meta (formerly Facebook). While the Hugging Face website allows the research community to follow trending papers linked to their source code, it has only recently integrated leaderboard functionality. Unlike Papers with Code, which curated paper-centric leaderboards for a wide range of tasks, Hugging Face leaderboards focus on model-centric, reproducible evaluation pipelines such as the Open LLM Leaderboard, enabling users to compare deployed models under standardized conditions. One reason Papers with Code scaled so well is that it allowed researchers to submit performance results from **any** model (as reported in their papers), regardless of where the model was hosted. In contrast, Hugging Face’s current leaderboard setup requires the model to be publicly hosted on the Hugging Face Hub, loadable via supported APIs. This excludes many works that report results but do not deploy models in that way, limiting visibility into progress across all research. ## The role of public institutions in safeguarding research data The Open Research Knowledge Graph (ORKG) is an open-source and open-data project at the TIB-Leibniz Information Centre for Science and Technology that also enables benchmarked tracking of state-of-the-art research through comparisons (see Fig. 1). As a national library and foundation of public law under the German state of Lower Saxony, the TIB’s mission is to ensure sustainable access to information and digital data of high public value. Figure 1: An ORKG comparison of crowdsourcing and annotation strategies for question answering tasks in natural language processing and vision, accessible on the ORKG platform. In 2021, the ORKG imported data from Papers with Code, capturing benchmarks that would have been lost if the Papers with Code website had gone offline (see Fig. 2). This highlights the importance of redundancy across digital infrastructures. If benchmarks are available only on commercial platforms, they remain vulnerable to corporate decisions, shifting business models, or sudden shutdowns. Public infrastructures, such as the ORKG, ensure that this knowledge remains accessible over the long term. This is a crucial example of the role public institutions play in providing continuity, safeguarding scientific knowledge, and ensuring that resources developed by and for the community do not simply disappear. Figure 2: An ORKG leaderboard for question answering models, accessible on the ORKG platform. ## A call to the community Continuity also requires participation. The strength of public infrastructures, such as the ORKG, depends on the level of community engagement. Keeping leaderboards populated with the latest benchmarks requires researchers to contribute their results. Here is our call to action: if you were disappointed to see Papers with Code discontinued, consider contributing your papers to the ORKG. Your contributions ensure the leaderboards tracking progress in your field remain open and accessible to everyone. ### About the ORKG The Open Research Knowledge Graph (ORKG) is a service that aims to revolutionise the way scientific knowledge is shared and used. By creating a structured, searchable knowledge graph, the ORKG makes scientific information more accessible and usable for the global research community. ### Reference (1) https://medium.com/paperswithcode/papers-with-code-is-joining-facebook-ai-90b51055f694 Schlagwörter: Papers with Code Open Science Open Research Knowledge Graph

ArcadeDB Release v25.9.1 Release Date: October 9, 2025 We're excited to announce the release of ArcadeDB version 25.9.1! This is a significant update that introduces powerful new features for devel...

ArcadeDB Convenience Makefile for Non-Java Developers - Makefile

ArcadeDB v25.8.1 We're excited to announce the release of ArcadeDB v25.8.1! This version introduces a powerful new schema feature, enhances Kubernetes support, and resolves several important bugs t...

mq is a command-line tool that processes Markdown using a syntax similar to jq. It's written in Rust, allowing you to easily slice, filter, map, and transform structured data.