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A Farmer Donated Land to Turn into a Park. The City Is Building a Data Center

Show HN: Kronotop – A distributed multi-model database built on FoundationDB After almost three years of development, the first developer preview of Kronotop is out.<p>Kronotop is a distributed multi-model database built on FoundationDB.<p>Our motto is: One transaction, multiple models.<p>Documents, ordered key-value data, and other models can participate in the same strictly serializable transaction, even across namespaces.<p>I&#x27;d love to hear your feedback.

Launch HN: Intuned (YC S22) – Build and run reliable browser automations as code Hey HN, we&#x27;re Faisal and Ahmad from Intuned (<a href="https:&#x2F;&#x2F;intunedhq.com">https:&#x2F;&#x2F;intunedhq.com</a>). We’re building a platform for building, deploying, and maintaining browser automations.<p>Customers primarily use the Intuned AI agent to automate websites that don&#x27;t expose APIs. Common use-cases include scraping data, pulling reports, and submitting forms. As the website changes, our agent also helps automatically heal the automation.<p>On Intuned, browser automations are created by an AI agent and run as <i>code</i>. Our infra captures the context of every run, allowing our agent to debug and maintain the underlying code - to keep the automations working over time. This way, we’re able to offer the predictability, speed, and cost of code, without the painful parts of writing and maintaining it.<p>Here’s a demo of building a scraper on Intuned: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;ruZP73bK4FU" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;ruZP73bK4FU</a><p>Here’s a demo of using AI to maintain a project: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;e4R4hLdHBro" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;e4R4hLdHBro</a><p>Backstory: we were accepted into YC for a completely different idea. During the batch, because of Faisal&#x27;s background at UiPath, several batchmates asked us whether RPA tools could fill API gaps in their products by automating websites without APIs. When it was time to pivot, we went back to those founders to dig deeper. (RPA in this context is referring to using UI automation to do complete non-testing tasks)<p>We discovered that the actual hard problem in browser automation is maintenance. Websites change, selectors break, and failures can be painful to reproduce and fix. So in early 2024, we decided to take a crack at this problem with a handful of customers. It needed a fair number of iterations before we landed on our current code-first approach.<p>How it works: Intuned is infra + agent, deeply integrated.<p>On the infrastructure side, Intuned is a managed runtime for browser automation code. Projects are usually Playwright-based TypeScript or Python. Users can write them directly in our online IDE, or hand the work off to the agent. Either way, once deployed, the platform runs each project in its own isolated machine and handles auth&#x2F;session reuse, scheduling, batch execution, concurrency, observability, and the other plumbing around running browser code.<p>On the agent side, it took us a few iterations to get to the current approach. Our initial attempts were rigid pipelines: collect requirements, inspect the site, generate code, then try to patch whatever broke. It looked reasonable on paper, but real websites are too messy for fixed paths. Late last year, we were planning to ship that version when stronger models landed and harnesses like Claude Code and Codex showed what a more open-ended coding agent could do. We built a prototype on the Claude Agent SDK, it felt much better than what we had, and we scrapped the release and decided to rebuild the agent.<p>The rebuild came down to three pieces around the SDK: an execution environment for running long agent sessions reliably, a CLI that exposes the platform to the agent so it operates Intuned the way engineers do, and a custom plugin (skills + MCP) built around what we&#x27;ve learned building browser automations.<p>The infra-agent integration is where the product gets more interesting. The runtime doesn&#x27;t just run the automation; it captures the context needed to debug it when it fails: params, results, traces, logs. That enables features like Fix with AI, where you can open a failed run and have the agent investigate and prepare a fix.<p>The same integration powers a feature called self-healing. For configured projects, the platform detects failures, starts an agent session with the relevant context, and either proposes a fix for review or deploys it automatically. Demo: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;IVHIXw0lYMs" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;IVHIXw0lYMs</a><p>We recently also packaged the infra and agent as an API called Web Task API, here is a demo: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;1olRn3l95vw" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;1olRn3l95vw</a><p>We strongly believe that browser automations can and should be faster, cheaper and more predictable. Check us out at <a href="https:&#x2F;&#x2F;app.intuned.io&#x2F;" rel="nofollow">https:&#x2F;&#x2F;app.intuned.io&#x2F;</a>, we have a free tier with trial credits for your first few automations. Excited to hear your thoughts, questions, and feedback!

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Replies to comments on my "LLMs are eroding my career" post <a href="https:&#x2F;&#x2F;web.archive.org&#x2F;web&#x2F;20260608100644&#x2F;https:&#x2F;&#x2F;human-in-the-loop.bearblog.dev&#x2F;replies-to-comments-on-my-llms-are-eroding-my-career-post&#x2F;" rel="nofollow">https:&#x2F;&#x2F;web.archive.org&#x2F;web&#x2F;20260608100644&#x2F;https:&#x2F;&#x2F;human-in-...</a>

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Do agents.md files help coding agents? <a href="https:&#x2F;&#x2F;xcancel.com&#x2F;rasbt&#x2F;status&#x2F;2063649136323252397" rel="nofollow">https:&#x2F;&#x2F;xcancel.com&#x2F;rasbt&#x2F;status&#x2F;2063649136323252397</a><p><a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2602.11988" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2602.11988</a>

Show HN: Web Speed – A shared web-map registry for AI agents (MCP, open source) Hey everyone, I&#x27;ve been working on a tool called Web Speed. It takes web pages full of HTML and parses them into an easily readable sitemap for an AI agent. By ensuring the AI doesn&#x27;t have to analyze the full HTML from the page or screenshots of the page, it makes the agent a lot cheaper and faster. It&#x27;s MCP-native, so any AI that supports MCP can run it and take control of your browser for you. There are both “normal” and post-auth versions live on GitHub. What I think is the coolest thing about the project is the global cache of sitemaps. When a user visits a site, it takes the sitemap and sends it up to the server so that later, a person can get that sitemap from the cache, further speeding up the agent. Right now, only the paid API version can access the cache. I would love your thoughts on the project, any ideas, criticisms, or concerns. I really just want to see if people think that this would be a useful tool. Please reach out if you have any questions about Web Speed. Repo (OSS): <a href="https:&#x2F;&#x2F;github.com&#x2F;Dominic-Pi-Sunyer&#x2F;web-speed-oss" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Dominic-Pi-Sunyer&#x2F;web-speed-oss</a> Repo (Post-auth): <a href="https:&#x2F;&#x2F;github.com&#x2F;Dominic-Pi-Sunyer" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Dominic-Pi-Sunyer</a> API and Docs: <a href="https:&#x2F;&#x2F;getwebspeed.io" rel="nofollow">https:&#x2F;&#x2F;getwebspeed.io</a><p>Thanks, Dominic

Show HN: I put my Claude Code rate-limit burndown in the status line

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Show HN: Nightwatch, The open-source, read-only AI SRE nightwatch is a local-first, read-only layer on top of your monitoring. it groups alert storm into incidents, flags noisy checks and has an agent that can investigate for you live systems. You can e.g. jump from the incident into the agent directly.<p>the reason for this weekend project is that we had a kubernetes upgrade that went wrong, and at some point a rollback wasn&#x27;t possible anymore, so it had to be fixed live during the night while several problems came together. We run a lot of different systems, on-prem and several Kubernetes clusters, and in a situation like that you spend most of the time just figuring out what is actually broken and where.<p>So i thought that it would be pretty cool to have eyes in the dark in each system that can talk to your &quot;brain&quot;.<p>so the idea is to put a baby owl into each environment. Each owl runs where the systems live, keeps that environment&#x27;s credentials local, and only dials outbound to a central brain, so there is no inbound hole into prod. It exposes a set of read-only skills, and the agent uses them to gather evidence and form a root-cause hypothesis, so the on-call engineer starts with a head start instead of from zero.<p>read-only for now, i don&#x27;t trust it near prod yet and honestly neither should you.<p>llocal-first for easy self-hosting and to keep credentials on your side. the clustering and recommendations run fully offline with no llm at all. the agent needs a tool-calling llm, you can point it at a remote one, or self-host one (ollama etc.) if you want to stay fully offline.<p>for non selfhosters: before every remote llm call, nightwatch strips real secrets (unrestorable) and swaps identifiers like ips, hostnames and paths for reversible placeholders, so the model only sees masked data while real values are restored only in the proposed commands and tool calls<p>Would love if you try it in your Systems

Thi.ng – open-source building blocks for computational design and art

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