Find local farms near you with raw dairy, pasture eggs, and more
Hey folks, I made a farm-to-door so you can find local farms near you. It allows you to filter by farms that have raw dairy, unwashed eggs, and more “trendy” items. It also lets you filter by who does delivery and pickup, so you can get your goods.
Vibe Coding a $20k /Year Enterprise Logistics Platform
Show HN: Vibe Coding a $20k /Year Enterprise Logistics Platform
I solved my study problems by talking to a goose
I used to study by rereading notes, and then I blanked in the exam hall. Did some research, and found that my experience isn’t isolated, and that passive review doesn’t force retrieval, so nothing sticks, and I knew I had to do something about it. That’s why I built Professor Goose. You pick a topic, explain it out loud to a goose, and he keeps probing until he understands you. Never gives you the answer, just keeps asking follow ups until a sound understanding is reached, which in turn makes you figure stuff out or realise you never understood your topic in the first place. Free to try, no account needed, upload your syllabus for exam board specific questions. Curious whether this approach resonates with others, it sure has for me.
Browse 61 3D Printable Robots
Robotics is advancing really fast lately, with AI inference, different controllers, software, and parts always changing. I wanted a place that supports many device types, Raspberry Pi, NVDA Jetson, Arduino, ESP32, hardware sources, and maximizes for printability. Instructables, Github, and Thingiverse are currently popular but aren't really focused on robotics, So I built orobot.io to try and make printing robots as standardized and accessible as possible. It uses a lot of Agent built content custom to each project, and every project is designed to be used by humans or your agent. Features: - Photos and Estimated Prices for all projects - Links back to source GitHub projects - LLMs write descriptions and tips on how to build - View + Download 3d printable STL files in browser - BOM purchase links are kept up to date with LLMs checking Amazon link health - LLMs write Javascript install and controller wrappers custom to each project so a single one-click install works across many frameworks and controller types - Public skill files, clis, and prompts let your agent do everything it needs to walk you through the complexity. It's still pretty new, so somethings are broken, and there's a lot more I want to build. But I'm very interested to have people try it out let me know if they want to use something like this and give me feedback about where they ran into problems so I can fix it. Thank you HN!
Running the second public ODoH relay
Every privacy-focused DNS service requires an account: NextDNS, Cloudflare for Families, Apple's iCloud Private Relay (paid, iOS-only). The protocol that doesn’t require one - ODoH - had basically one well-known public relay operator (Frank Denis on Fastly Compute, default in dnscrypt-proxy). I built a second one and the client to talk to it.
Tryardent
Hey HN! We’re Vikram and Evan from Ardent (https://tryardent.com). We're building database sandboxes for you and your coding agents. In the last two years coding agents have gotten dramatically more capable at handling complex engineering tasks. But without access to a realistic sandbox at the DB layer for testing, they ship garbage that can take down production databases. I spent over a year building an AI Data Engineer that failed for this exact reason. Evan spent the last 12 years in data engineering and hit this wall building agents at his last company. Ardent was built to make it possible for coding agents to get near instant access to production-like sandboxes so they can test their work. To do this we write a replication stream out of the target DB, scaling with kafka onto a read replica with copy on write enabled and autoscaling compute (we currently prefer neon as a primary branching engine due to their implementation of these properties). Our replication stream uses logical replication + ddl triggers to enable usage on any hosted postgres DB since most platforms do not allow physical replication which is traditionally used for creating replicas. This provides a few primary benefits: 1. Does not require a platform migration to a DB provider like neon, allowing strong separation of production and development concerns. 2. Minimal impact on the production database while allowing clones to spin up in <6s, even at TB scale with copy-on-write Security matters a lot with cloning production so we run a proxy layer to generate custom postgres URLs and route all connections to allow more granular access control to clones, prevent credential leak, and follow a split plane architecture to allow full data residency on your cloud through BYOC. We also support anonymization through the ability to register SQL that runs on branches before they are returned. This has been used for PII redaction and branch modification. Our goal is to make every data infrastructure platform “cloneable” in one place so agents can fully test the impact of their changes on production like data environments without risk. Here's a demo of it: https://youtu.be/5S1kwPtiRU0 We’d love to understand how you work with coding agents on the DB and if you try Ardent (it's free to get started) what worked, what broke and what’s missing.
DAGraph
Show HN: DAGraph – local-first reactive graph from arithmetic to OLAP SQL
Headless Cloud Security
The cloud security company I work for, Sysdig, launched “Headless Cloud Security” last week. The short version: as attacks get faster and more automated, security tooling is going to need to evolve beyond dashboards and humans clicking through workflows all day. We’ve already seen “headless” models emerge in other categories, and engineering teams are rapidly adopting agentic and CLI-first workflows with tools like Claude Code, Cursor, and MCP servers. Security teams, historically, tend to lag engineering adoption curves by 6–18 months, but I don’t think that gap will hold much longer. The idea behind headless security is that security capabilities should be consumable programmatically — through APIs, AI agents, IDEs, CI/CD pipelines, and automated workflows — not just through a UI. This post covers it in more detail: https://www.sysdig.com/learn-cloud-native/what-is-headless-c... Curious whether others here are seeing similar shifts inside their orgs, especially around AI-assisted development and security operations.
I made a screen recording app to make demos like an Apple commercial
Hey HN! I made ShotGlass, a Mac app for screenshots, screen recordings, and cinematic demos. I was tired of jumping between four apps to make one product demo. Screenshots, recordings, annotations, and After Effects for anything cinematic. I also saw the MacBook Neo commercials (recording playing on a 3D MacBook in a scene) and wondered why no screen recorder just did that. So ShotGlass does all of it: multi-window capture, annotations, zooms, transitions, and dropping a recording onto a 3D MacBook with a simulated lens. It's free to try, a one time purchase and everything stays local. I tried to make it simple to use and, for fun, themed like a glass of whisky. I'm updating it quite a lot, so I'd love your feedback.
Can you really detect AI writing from human writing?
Show HN: Can you really detect AI writing from human writing?
I submitted 316 AI-generated PRs to open source
Show HN: I submitted 316 AI-generated PRs to open source
Voker
Hey HN, we're Alex and Tyler, co-founders of Voker.ai (https://voker.ai/), an agent analytics platform for AI product teams. Voker gives full visibility into what users are asking of your agents, and whether your agents are delivering, without having to dig through logs. Our main product is a lightweight SDK that is LLM stack agnostic and purpose-built for agent products. (https://app.voker.ai/docs) Agent Engineers and AI product teams don’t have the right level of visibility into agent performance in production, which results in bad user experiences, churn, and hundreds of hours wasted with spot checks to find and debug issues with agent configurations. Demo: https://www.tella.tv/video/vid_cmoukcsk1000i07jgb4j65u67/vie... We recently conducted a survey of YC Founders and 90%+ of respondents said that the only way they know if their Agents are failing users in production is by hearing complaints from customers. They push a prompt change hoping that it fixes the problem and doesn’t break something somewhere else, and the cycle repeats. We saw tons of observability and evals products popping up to try to address these problems, but we still felt like something was missing in the agent monitoring stack. Obs is good for individual trace debugging but is only accessible to engineers. Evals are good for testing known issues, but don't give insights into trends that teams don’t expect, so engineers are always playing catch up. Traditional product analytics tools do a good job tracking clicks and pageviews across your product surface but weren’t built ground up for agent products. Knowing what users want out of agents, and whether the agent delivered requires specific conversational intelligence / unstructured data processing techniques. We came up with the agent analytics primitives of Intents, Corrections, and Resolutions to describe something pretty much all conversational agents had in common: a user will always come to an agent with an intent, the user might have to correct this agent on the way to getting their intent resolved, and hopefully every intent a user has is eventually resolved by the agent. Voker processes LLM calls by automatically annotating individual conversations and picking out user intent and corrections. Voker takes these and uses LLMs and hierarchical text classification to create dynamic categories that give higher level insights so you don’t have to read individual conversations to know what are the main usage patterns across your users. The most common substitute solution we’ve seen is uploading obs logs to Claude or ChatGPT and asking for summary insights. There are a few problems with this - mainly that LLMs aren’t good at math or data science, so you don’t get accurate or consistent statistics. Its highly likely that the LLM overfits to some insights and underfits to others. The LLM isn’t programmatically reading and classifying each individual session or interaction. This is why we don’t use LLMs for any of our core data engineering (processing events, calculating statistics) so the analytics we produce are consistent, reproducible, and accurate. We have a publicly available, lightweight SDK that wraps LLM calls to OpenAI, Anthropic and Gemini in Python and Typescript. Voker handles the data engineering to turn raw data into usable analytics primitives and higher level insights. Free tier: 2,000 events / mo, requires email signup. Paid plans start at $80/mo with a 30 day free trial. We'd love to hear how you're currently detecting trends, and if you try Voker, tell us what part of our analysis is valuable, and what still feels missing. Thanks for reading, and we’re looking forward to your thoughts in the comments!