Каталог продуктов

AN

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!

VO

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!

CY

CyberWriter

Apple has quietly shipped a pretty complete on-device AI stack into macOS, with these features first getting API access in MacOS 26. There are multiple components in the foundation model, but the skills it shipped with actually make this ~3b parameter model useful. The API to hit the model is super easy, and no one is really wiring them together yet. - Foundation Models (macOS 26) - a ~3B-parameter LLM with an API. Streaming, structured output, tool use. No API key, no cloud call, no per-token cost. - NLContextualEmbedding (Natural Language framework, macOS 14+) -- a BERT-style 512-dim text embedder. Exactly what OpenAI and Cohere sell, sitting in Apple's SDKs since iOS 17. - SFSpeechRecognizer / SpeechAnalyzer - on-device speech-to-text including live dictation. Solid accuracy on Apple Silicon. I built cyberWriter, a Markdown editor, on top of all three, mostly as a test and showcase to see what it can do. I actually integrated local and cloud AI first, and then Apple shipped the foundation model, it stacked on super easy, and now users with no local or API AI knowledge can use it with just a click or two. Well the real reason is because most markdown editors need plugins that run with full system access, and I work on health data and can't have that. Vault chat / semantic search. The app indexes your Markdown folder via NLContextualEmbedding (around 50 seconds for 1000 chunks on an M1). The search bar gets a "Related Ideas" section that matches by meaning - typing "orbital mechanics" surfaces notes about rockets and launch windows even when those exact words never appear. Ask the AI a question and it retrieves the top 5 chunks as context. Plain RAG, but the embedder, retrieval, chat model, and search all run locally. AI Workspace. Command+Shift+A opens a chat panel, Command+J triggers inline quick actions (rewrite, summarize, change tone, fix grammar, continue). Apple Intelligence is the default; Claude, OpenAI, Ollama, and LM Studio all work if you prefer. The same context layer - document selection, attached files, retrieved vault chunks - feeds every provider through the same system-message path. Because the vault context is file and filename aware, it can create backlinks to the referenced file if it writes or edits a doc for you. Voice notes and dictation. Record a voice note directly into your doc, transcribe it with SpeechAnalyzer, or just dictate into the editor while you think. Audio never leaves the Mac. The privacy story is straightforward because the primitives are already private. Vectors live in a `.vault.embeddings.json` file next to your vault, never sent anywhere. If you use Apple Intelligence, even the retrieved text stays on-device. For cloud models there is a clear toggle and an inline warning before any filenames or snippets leave the machine. Honest limitations: - 512-dim embeddings are solid mid-tier. A GPT-4-class embedder catches subtler relationships this will miss. - 256-token chunks can split long paragraphs mid-argument. - Foundation Models caps its context window around 6K characters, so vault context is budgeted to 3K with truncation markers on the rest. - Multilingual support is English-only right now. NLContextualEmbedding has Latin, Cyrillic, and CJK model variants; wiring the language detector across chunks is Phase 2. The developer experience for these APIs is genuinely good. Foundation Models streams cleanly, NLContextualEmbedding downloads assets on demand and gives you mean-poolable token vectors in a handful of lines. Curious what others here are building on this stack - feels like low-hanging fruit that has been sitting there for a while. https://imgur.com/a/HyhHLv2 The Apple AI embedding feature is going live today. I'm honestly surprised it even works out of the box.