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The European Union is taking steps to reduce its reliance on the major credit card networks Visa and Mastercard, with the goal of creating a homegrown alternative payment system that would give the EU more control over cross-border transactions and reduce fees charged to merchants and consumers.

The article explores the concept of the technological singularity, a hypothetical point in time when technological growth becomes so rapid that it leads to unforeseeable changes in human civilization. It discusses the potential impacts and implications of the singularity, including the possibility of superintelligent artificial intelligence and the transformation of the human condition.

The article explores the author's emotional response to changes in a cherished hobby, reflecting on the challenges of adapting to evolving interests and the bittersweet nature of personal growth and transformation.

A student journalist at the University of California, Los Angeles (UCLA) received a subpoena from Google, demanding information about their reporting on a contract between Google and U.S. Immigration and Customs Enforcement (ICE). The article explores the implications of this subpoena and the potential chilling effects it could have on press freedom and academic research.

The article discusses the Half-Life 2 (HL2) game engine and its significance in the gaming industry. It highlights HL2's advanced graphics, physics-based gameplay, and its influence on the development of other game engines.

The article discusses the Khronos Group's efforts to simplify the Vulkan API by breaking it down into smaller, more manageable subsystems. This approach aims to make Vulkan more accessible to developers by reducing the complexity and learning curve associated with the full Vulkan API.

The article introduces Entire, a new platform that aims to revolutionize the way people interact with the digital world by providing a seamless and personalized experience. It highlights Entire's vision to create a more connected and empowered digital ecosystem.

The article explores the fundamental properties and applications of complex numbers, a crucial concept in mathematics that extends the real number system to include imaginary units. It delves into the algebraic structure, geometric interpretation, and practical uses of complex numbers in various fields such as physics and engineering.

The article discusses the similarities and differences between the programming practices of 'showboating' and 'rodneying', with the former focusing on impressive displays of coding prowess and the latter emphasizing pragmatic problem-solving and maintainable code.

Hi HN,

AI agents that can run tools on your machine are powerful for knowledge work, but they’re only as useful as the context they have. Rowboat is an open-source, local-first app that turns your work into a living knowledge graph (stored as plain Markdown with backlinks) and uses it to accomplish tasks on your computer.

For example, you can say "Build me a deck about our next quarter roadmap." Rowboat pulls priorities and commitments from your graph, loads a presentation skill, and exports a PDF.

Our repo is https://github.com/rowboatlabs/rowboat, and there’s a demo video here: https://www.youtube.com/watch?v=5AWoGo-L16I

Rowboat has two parts:

(1) A living context graph: Rowboat connects to sources like Gmail and meeting notes like Granola and Fireflies, extracts decisions, commitments, deadlines, and relationships, and writes them locally as linked and editable Markdown files (Obsidian-style), organized around people, projects, and topics. As new conversations happen (including voice memos), related notes update automatically. If a deadline changes in a standup, it links back to the original commitment and updates it.

(2) A local assistant: On top of that graph, Rowboat includes an agent with local shell access and MCP support, so it can use your existing context to actually do work on your machine. It can act on demand or run scheduled background tasks. Example: “Prep me for my meeting with John and create a short voice brief.” It pulls relevant context from your graph and can generate an audio note via an MCP tool like ElevenLabs.

Why not just search transcripts? Passing gigabytes of email, docs, and calls directly to an AI agent is slow and lossy. And search only answers the questions you think to ask. A system that accumulates context over time can track decisions, commitments, and relationships across conversations, and surface patterns you didn't know to look for.

Rowboat is Apache-2.0 licensed, works with any LLM (including local ones), and stores all data locally as Markdown you can read, edit, or delete at any time.

Our previous startup was acquired by Coinbase, where part of my work involved graph neural networks. We're excited to be working with graph-based systems again. Work memory feels like the missing layer for agents.

We’d love to hear your thoughts and welcome contributions!

The article discusses the potential impact of climate change on the future of coffee production, highlighting concerns about the shrinking areas suitable for growing high-quality coffee and the need for adaptation strategies to ensure the sustainability of the coffee industry.

The article explores the case of a London-based TikTok content creator who has been accused of spreading hateful and misleading content targeting immigrants. It examines the potential impact of such misinformation on social media and the challenges in addressing the spread of hate speech online.

The article provides an overview of the current status of GitHub, including any ongoing incidents or outages that may be affecting the platform's services. It offers updates on the investigation and resolution efforts by the GitHub team.

This article chronicles the author's journey into self-hosting various services on a Linux system, including a personal website, email, and cloud storage. It highlights the technical challenges and learnings involved in setting up and maintaining a self-hosted infrastructure.

This article provides a step-by-step guide on how to build a Reinforcement Learning with Human Feedback (RLHF) system from scratch. It covers the key components, such as the base model, reward model, and training process, to create an AI system that can learn from human feedback.

A year ago, we launched Distr here to help software vendors manage customer deployments remotely. We had agents that pulled updates, a hub with a GUI, and a lot of assumptions about what on-prem deployment needed.

It turned out things get messy when your software is running in places you can't simply SSH into.

Over the last year, we’ve also helped modernize a lot of home-baked solutions: bash scripts that email when updates fail, Excel sheets nobody trusts to track customer versions, engineers driving to customer sites to fix things in person, debug sessions over email (“can you take a screenshot of the logs and send it to me?”), customers with access to internal AWS or GCP registries because there was no better option, and deployments two major versions behind that nobody wants to touch.

We waited a year before making our first breaking change, which led to a major SemVer update—but it was eventually necessary. We needed to completely rewrite how we manage customer organizations. In Distr, we differentiate between vendors and customers. A vendor is typically the author of a software / AI application that wants to distribute it to customers. Previously, we had taken a shortcut where every customer was just a single user who owned a deployment. We’ve now introduced customer organizations. Vendors onboard customer organizations onto the platform, and customers own their internal user management, including RBAC. This change obviously broke our API, and although the migration for our cloud customers was smooth, custom solutions built on top of our APIs needed updates.

Other notable features we’ve implemented since our first launch:

- An OCI container registry built on an adapted version of https://github.com/google/go-containerregistry/, directly embedded into our codebase and served via a separate port from a single Docker image. This allows vendors to distribute Docker images and other OCI artifacts if customers want to self-manage deployments.

- License Management to restrict which customers can access which applications or artifact versions. Although “license management” is a broadly used term, the main purpose here is to codify contractual agreements between vendors and customers. In its simplest form, this is time-based access to specific software versions, which vendors can now manage with Distr.

- Container logs and metrics you can actually see without SSH access. Internally, we debated whether to use a time-series database or store all logs in Postgres. Although we had to tinker quite a bit with Postgres indexes, it now runs stably.

- Secret Management, so database passwords don’t show up in configuration steps or logs.

Distr is now used by 200+ vendors, including Fortune 500 companies, across on-prem, GovCloud, AWS, and GCP, spanning health tech, fintech, security, and AI companies. We’ve also started working on our first air-gapped environment.

For Distr 3.0, we’re working on native Terraform / OpenTofu and Zarf support to provision and update infrastructure in customers’ cloud accounts and physical environments—empowering vendors to offer BYOC and air-gapped use cases, all from a single platform.

Distr is fully open source and self-hostable: https://github.com/distr-sh/distr

Docs: https://distr.sh/docs

We’re YC S24. Happy to answer questions about on-prem deployments and would love to hear about your experience with complex customer deployments.

The article discusses the polarizing figure of a London cyclist who has garnered both admiration and criticism for his advocacy of cyclists' rights and confrontational approach to challenging motorists. The piece explores the debate surrounding his methods and the broader tensions between cyclists and drivers in the city.

Hi HN, I'm Arsalan, founder of LiveDocs (https://livedocs.com). We're building an AI-native data workspace that lets teams ask questions of their real data and have the system plan, execute, and maintain the analysis end-to-end.

We previously posted about LiveDocs four years ago (https://news.ycombinator.com/item?id=30735058). Back then, LiveDocs was a no-code analytics tool for stitching together metrics from tools like Stripe and Google Analytics. It worked for basic reporting, but over time we ran into the same ceiling our users did. Dashboards are fine until the questions get messy, and notebooks slowly turn into hard-to-maintain piles of glue.

Over the last few years, we rebuilt LiveDocs almost entirely around a different idea. Data work should behave like a living system, not a static document or a chat transcript.

Today, LiveDocs is a reactive notebook environment backed by real execution engines. Notebooks are not linear. Each cell participates in a dependency graph, so when data or logic changes, only the affected parts recompute. You can freely mix SQL, Python, charts, tables, and text in the same document and everything stays in sync. Locally we run on DuckDB and Polars, and when you connect a warehouse like Snowflake, BigQuery, or Postgres, queries are pushed down instead of copying data out. Every result is inspectable and reproducible.

On top of this environment sits an AI agent, but it is not "chat with your data." The agent works inside the notebook itself. It can plan multi-step analyses, write and debug SQL or Python, spawn specialized sub-agents for different tasks, run code in a terminal, and browse documentation or the web when it lacks context. Because it operates inside the same execution graph as humans, you can see exactly what it ran, edit it, or take over at any point.

We also support a canvas mode where the agent can build custom UI for your analysis, not just charts. This includes tables with controls, comparisons, and derived views that stay wired to the underlying data. When a notebook is not the right interface, you can publish parts of it as an interactive app. These behave more like lightweight internal tools, similar in spirit to Retool, but backed by the same analysis logic.

Everything in LiveDocs is fully real-time collaborative. Multiple people can edit the same notebook, see results update live, comment inline, and share documents or apps without exposing raw code unless they want to.

Teams use LiveDocs to investigate questions that do not fit cleanly into dashboards, build analyses that evolve over time without constant rewrites, and automate recurring questions without turning them into brittle pipelines.

Pricing is pay-as-you-go, starting at $15 per month, with a free tier so people can try it without talking to us. You'll have to sign up, as it requires us to provision a sandbox for your to run your notebook. Here's a video demo: https://youtu.be/Hl12su9Jn_I

We are still learning where this breaks. Long-running agent workflows on production data surface a lot of sharp edges. We would love feedback from people who have built or lived with analytics systems, notebooks, or "chat with your data" tools and felt their limits. Happy to go deep on technical details and trade notes.

The article discusses the 'mdvi' tool, which is a command-line interface for rendering and manipulating Markdown documents. It highlights mdvi's ability to preview Markdown files and convert them to various output formats, such as PDF and HTML, making it a useful tool for Markdown-based content creation and publishing.

Clawhub is an AI-powered platform that enables developers to build applications that interact with real-world objects and environments. The platform provides a suite of tools and services to help developers create innovative solutions using computer vision, robotics, and other cutting-edge technologies.