Safe-install
In light of the ongoing npm supply chain compromises, I built safe-install: https://www.npmjs.com/package/@gkiely/safe-install It brings a couple of protections I wanted from npm but are not built in. Similar to Bun’s trusted dependencies, it lets you disable install scripts by default and define a list of dependencies that are allowed to run build/install scripts: https://bun.com/docs/guides/install/trusted It also supports blocking exotic sub-dependencies, similar to pnpm’s `blockExoticSubdeps` setting: https://gajus.com/blog/3-pnpm-settings-to-protect-yourself-f... I was hoping npm would eventually add something like this, but it does not seem to be happening soon, so I made a small package for it.
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!
I submitted 316 AI-generated PRs to open source
Show HN: I submitted 316 AI-generated PRs to open source
Can you really detect AI writing from human writing?
Show HN: Can you really detect AI writing from human writing?
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.
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!
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.
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.
Daily vibe-coding video games, day 33: Tower Defense (single prompt)
I'm using AI (mostly Claude) to create/publish a new video game every day This is day 33, first stab at the tower defense genre. Most of the games (including this one) I build with a single prompt. Rarely, a couple extra prompts are needed for bug fixes or to tweak the physics/UI. Extremely rarely, the AI has difficulty making the game work right (usually drawing it) and it takes a dozen or more prompts -- but the majority of the time, it gets everything right and makes a fully playable game first try Happy to answer any questions, just a little hobby project of mine I'm having lots of fun with :)
Andonlabs
Hey HN! I'm Lukas from Andon Labs. We let AIs run companies without humans in the loop and report to the public on what can go wrong. Previously, we've done experiments in retail (vending machines, stores, and cafes), but we just launched one in the media sector. We gave four AI agents all the tools they need to both broadcast radio shows live and handle all the business side of running a media company. The agents' revenue is so far terrible (you can try to strike a sponsor deal with them if you want!), but their shows are at times hilarious. You can listen to them at andon.fm, I hope you enjoy this!
The AI Quant Desk for Onchain Finance
Most Web3 tools only track basic balances and approximate wallet data. Raster brings TradFi accounting rigor to DeFi through a unified Truth, Risk, and Decision Intelligence framework. Our proprietary attribution engine reconstructs a fully auditable state from raw blockchain activity to deliver deterministic PnL (Truth). We calculate deep, real-time portfolio risk signals (Risk) to power our governed AI Decision Intelligence, built specifically for deep analysis and institutional workflows.
AI that interviews participants instead of holding another meeting
Show HN: AI that interviews participants instead of holding another meeting