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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.

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 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.

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 discusses the decline of the Telnet protocol, which was once widely used for remote access and administration but has since been largely replaced by more secure alternatives like SSH. It explores the reasons behind Telnet's gradual disappearance and the implications for network security and connectivity.

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 importance of maintaining user trust and privacy when developing software, highlighting the need to balance user needs with ethical considerations and avoid potential misuse of data or features.

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!

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.

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.

The article explores why the trucks used by ISIS are perceived as better than those used by the American military, highlighting the importance of reliability, maintenance, and adaptability in battlefield equipment.

The article argues that competition should not be seen as the primary validation for a business or project. It emphasizes that focusing too much on competition can lead to a narrow perspective and distract from creating unique value for customers.

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.

We recently started to use agents to update some documentation across our codebase on a weekly basis, and everything quickly turned into cron jobs, logs, and terminal output.

it worked, but was hard to tell what agents were doing, why something failed, or whether a workflow was actually progressing.

We thought it would be more interesting to treat agents as long-lived workers with state and responsibilities and explicit handoffs. Something you can actually see and reason about, instead of just tailing logs.

So we built Clawe, a small coordination layer on top of OpenClaw that lets agent workflows run, pause, retry, and hand control back to a human at specific points.

This started as an experiment in how agent systems might feel to operate, but we're starting to see real potential for it, especially for content review and maintenance workflows in marketing. Curious what abstractions make sense, what feels unnecessary, and what breaks first.

Repo: https://github.com/getclawe/clawe

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.

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.

Hi HN — cofounder Michael here.

We've been building Tambo for about a year, and just released our 1.0.

We make are making it easier to register React components with Zod schemas, a build an agent picks the right one and renders the right props.

We handle many of the complications with building generative user interfaces like: managing state between the user, the agent, and react component, rendering partial props, and we handle auth between your user, and MCP. We also support adding MCP servers and most of the spec.

We are 100% open-source and currently have 8k+ GitHub stars, thousands of developers, and over half-millions messages processed by our hosted service.

If you're building AI agents with generative UI, we'd like to hear from you.

Hey HN, stripe-no-webhooks is an open-source library that syncs your Stripe payments data to your own Postgres database: https://github.com/pretzelai/stripe-no-webhooks.

Here's a demo video: https://youtu.be/cyEgW7wElcs

Why is this useful? (1) You don't have to figure out which webhooks you need or write listeners for each one. The library handles all of that. This follows the approach of libraries like dj-stripe in the Django world (https://dj-stripe.dev/). (2) Stripe's API has a 100 rpm rate limit. If you're checking subscription status frequently or building internal tools, you'll hit it. Querying your own Postgres doesn't have this problem. (3) You can give an AI agent read access to the stripe.* schema to debug payment issues—failed charges, refunds, whatever—without handing over Stripe dashboard access. (4) You can join Stripe data with your own tables for custom analytics, LTV calculations, etc.

It creates a webhook endpoint in your Stripe account to forward webhooks to your backend where a webhook listener stores all the data into a new stripe.* schema. You define your plans in TypeScript, run a sync command, and the library takes care of creating Stripe products and prices, handling webhooks, and keeping your database in sync. We also let you backfill your Stripe data for existing accounts.

It supports pre-paid usage credits, account wallets and usage-based billing. It also lets you generate a pricing table component that you can customize. You can access the user information using the simple API the library provides:

  billing.subscriptions.get({ userId });
  billing.credits.consume({ userId, key: "api_calls", amount: 1 });
  billing.usage.record({ userId, key: "ai_model_tokens_input", amount: 4726 });

Let's see how it works with a quick example. Say you have a billing plan like Cursor (the IDE) used to have: $20/mo, you get 500 API completions + 2000 tab completions, you can buy additional API credits, and any additional usage is billed as overage.

You define your plan in TypeScript:

  {
    name: "Pro",
    description: "Cursor Pro plan",
    price: [{ amount: 2000, currency: "usd", interval: "month" }],
    features: {
      api_completion: {
        pricePerCredit: 1,              // 1 cent per unit
        trackUsage: true,               // Enable usage billing
        credits: { allocation: 500 },
        displayName: "API Completions",
      },
      tab_completion: {
        credits: { allocation: 2000 },
        displayName: "Tab Completions",
      },
    },
  }

Consume code would look like this:

  await billing.credits.consume({
    userId: user.id,
    key: "api_completion",
    amount: 1,
  });

  await billing.credits.topUp({
    userId: user.id,
    key: "api_completion",
    amount: 500,     // buy 500 credits, charges $5.00
  });

This would be a lot of work to implement by yourself on top of Stripe. You need to keep track of all of these entitlements in your own DB and deal with renewals, expiry, ad-hoc grants, etc. It's definitely doable, especially with AI coding, but you'll probably end up building something fragile and hard to maintain.

This is just a high-level overview of what the library is capable of. It also supports seat-level credits, monetary wallets (with micro-cent precision), auto top-ups, robust failure recovery, tax collection, invoices, and an out-of-the-box pricing table.

I vibe-coded a little toy app for testing: https://snw-test.vercel.app. There's no validation so feel free to sign up with a dummy email, then subscribe to a plan with a test card: 4242 4242 4242 4242, any future expiry, any 3-digit CVV.

Screenshot: https://imgur.com/a/demo-screenshot-Rh6Ucqx

Feel free to try it out! If you end up using this library, please report any bugs on the repo. If you're having trouble / want to chat, I'm happy to help - my contact is in my HN profile.

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.