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RI

Risklytics

Hi HN, we're Sam and Alex, founders of Risklytics (https://risklytics.ai). We're both on leave from Harvard, and we run an insurance brokerage for companies building robots, drones, autonomous systems and satellites. You describe your machine, we find the insurance companies willing to cover it, and we read everything before you sign. Buying insurance for any frontier tech goes like this: - your first customer or pilot requires coverage before your machine can work on their site - a broker hands you an application written for ordinary businesses, and often will deny you on baseless "risks" (one of our clients got denied for using CAD software in their workflow) - you get filed under something close or outright denied, the person at the insurance company often can't tell what they're looking at, and the safe answer is no - if a quote does come back, the AI risk is often quietly removed by an add-on clause that prevents you from truly being covered Those clauses surprised us. Insurance companies mostly don't write their own policies from scratch. They license standard forms from an industry body called ISO and attach add-on clauses to adjust them, and in January ISO published clauses that void coverage for losses arising out of AI in any part of a process. Each insurer decides whether to adopt them, account by account, and there's no public record of who has and to what capacity. There's an open question in the wording too. It defines generative AI as systems trained on data that produce text, images, audio, video or code. Whether a robot's control model, which produces motor commands, falls under that is unsettled. We've asked underwriters, the people at insurance companies who make these calls, and gotten different answers across the board. We didn't initially plan to be brokers. We came into YC to work in insurance and spent the first half of the batch on a wildfire risk model we wanted to sell to insurance companies. They liked it and wanted to build their own, which is the polite way to say no. While that was dying, a batchmate who launches satellites mentioned he couldn't get insured, and once we started asking around we kept hearing the same story from hardware companies. So we got licensed in a week and now can place coverage. What we've built so far: - a six-step application that asks about what you're building and drafts the structured version insurance companies like, omitting anything you didn't say - a map of which insurers add the AI clauses, which will remove them, and which cover frontier companies on purpose - when an insurer says no, you see their reason and what would change it, full transparency all the time - a hands off process for companies trying to get fully covered Here's a demo of the whole process: https://youtu.be/gVqCamTNH5E On money: the insurance company pays us a commission when you get covered, usually 10-20% of what you are charged, and we charge you nothing on top (that's right, no brokerage fees). Also, if you're a software company with ordinary needs, we'll still handle the whole process for you and get you the best policy we can find. In the past week alone we have helped three companies get covered that were denied elsewhere. The companies include a robotics wholesaler and rental store, who had trouble getting covered earlier because some of their property uses AI (even though they never use it themselves), a bridge collision system that was getting denied because they use CAD software, and a tele-operating robotics service that needed coverage within a week for their first pilot. If you're looking for insurance in any capacity feel free to reach out to me at alex@risklytics.ai or fill out an application at https://risklytics.ai. And if you work on the carrier side and think you'd wanna partner with us let us know.

AU

AuditBadger

Hi, Wanted to share something I've been working on for over a year. AuditBadger is a compliance management platform that uses AI to write policies (there are underlying "templates" with basic requirements), rewrite controls (or trust service criterions) to match the company context, help figure out your own controls, does initial risk assessment, and business continuity planning (which at least gives you an example of how the process should look like). Fun fact - I wanted to share this a year ago, but then I spotted something similar here. The most common comment was about lacking the SOC 2 report, so I decided to pick the fight. I got SOC 2 Type I first, and then recently finished SOC 2 Type II using the tool alone. It took some time - both learning the process, the SOC 2 gotchas, and implementing automatic evidence collection. We're now adding support for the European AI Act and NIS 2; HIPAA is already there (though it requires me to explicitly enable it for customers who want to test it), and CyberEssentials and ENS are coming later this year. The platform is now complete, but my business partner (ISO 27001 Lead Auditor) and I are still dog-fooding it. Everything we build is either based on our own pain points or our customers'—most of them joined our Slack where we try to help them if they get stuck. If you have any questions, I'll be happy to answer them all.

FA

FutureSearch, AI forecasting you can verify

*Title:* Show HN: FutureSearch, AI forecasting you can verify AI forecasting is now approximately superhuman. Today, FutureSearch is exiting our long public beta and launching. We started FutureSearch in August 2023. (We’re the original AI forecasting company, at least in a Tetlock-ian, “forecast anything” sense.) We’re currently #1 of 194 in the most competitive AI forecasting tournament [1], and we score above the #3 and #2 human forecasters in the premier mixed human-bot tournaments [2]. Many people on HN seem to equate forecasting with prediction markets and finance. FutureSearch is not a financial tool, in the same way that “deep research” is not a financial tool. Yes, we do evaluate our forecaster on prediction markets [3]. But forecasting is about being as accurate about the future as possible, and the real game is in forecasting scientific progress, geopolitics, and the future of humanity. Our founding team came from Metaculus, where we pushed human forecasting to the limit on questions like when AGI would arrive. At. FutureSearch, we co-authored the AI 2027 timeline forecast, where we predicted superhuman coding and research would come around 2032, longer than the other authors, but still shorter than skeptics [4]. Thousands of people used the FutureSearch beta and ran >10k high-effort forecasts, on all sorts of diverse topics. Ask it anything about the future. We now support decision forecasts too: “If I do X, will I achieve this outcome?” Forecasting, as a capability, is useful even at the level of expert human crowds. But we predict that we will soon have strongly superhuman forecasting. People who bet against AI capability trend lines tend to lose, and the trend line in AI forecast accuracy tells a pretty clear story [5]. There’s no reason to think the best human forecasters have figured out everything predictable about the world. There’s a lot more signal to be found. And if you’re skeptical, try it. We’ve seen our fair share of exaggerated claims about AI forecasting accuracy [6]. So part of the reason we made the free tier give a few of our highest effort forecasters free is to let anyone verify the quality. [1] https://www.metaculus.com/tournament/summer-futureeval-2026/ [2] evals.futuresearch.ai [3] markets.futuresearch.ai [4] https://ai-2027.com/research/timelines-forecast [5] https://www.astralcodexten.com/p/the-ai-superforecasters-are... [6] https://www.lesswrong.com/posts/uGkRcHqatmPkvpGLq/contra-pap...

TH

threadfork

Hi HN, I built threadfork. It's an AI notetaker where the whole pipeline runs locally on Apple Silicon. Your microphone and system audio gets captured without a meeting bot and most importantly your meeting data doesn’t get sent or saved anywhere. I've been building in the privacy + local LLM space for the past several years (I started DataFog, an OSS PII detection library with 300K+ downloads); keeping all the processing and data on-device was a day 0 condition for me when building this out. Granola's updated privacy policy, effective later this week, keeps non-enterprise users opted into model training on "de-identified" data by default. Having built PII detection for a living, I'll say plainly: de-identifying meeting transcripts is really hard. Names are the easy part; deal terms, client details, and health disclosures are the transcript. For the lawyers, consultants, and therapists I've been talking to, the only version of this that holds up is the model never seeing the data at all. It's macOS 14+ on Apple M-Series computers (for now), 14-day trial, no credit card. I have a user running this on an M1 8GB so older machines welcome. $40/month after that. I use this product daily and you can expect regular updates. Welcome any/all feedback.

CC

Command Center, the AI coding env for people who care about quality

Hi HN! We’re Jimmy and Ray. Jimmy is a Thiel Fellow with a Ph. D. from MIT who has worked on programming tools for 15 years; Ray became VP of Sales at a $2B company when he was 19 and has built side-businesses vibe-coding. Last year, we set to answer the question “If AI can write code 100x faster, then why aren’t you shipping 100x faster?” What we learned shocked us — even fairly nontechnical people and solo founders told us they were spending more than half of their development time reading the AI-written code. And much of the rest of the time was spent either de-slop-ping it, or wishing they had done so. As luck turns out, our last two products were a tool that quickly onboards people to large codebases ( https://x.com/0xjimmyk/status/1873357324229984677 ) and trainings that taught deep concepts of code quality to CEOs, YC founders, and engineers at top companies ( mirdin.com ), so we were extremely well-positioned to solve these problems. Command Center is an agentic coding environment focused on quality. With a few keypresses, you can start building 3 features at once and soon have 3 diffs ready, each consisting of 2000 changed lines across 50 files…. This is normally the point where you think “Crap, what now?” With Command Center, at this point you simply click “Refactor,” and watch the vibed slop turn into readable robustness. Then you click “Generate Walkthrough,” and then suddenly, to read a 2000 line diff, instead of scrolling up and down trying to make sense of it, you just press the right arrow key 200 times. See something you don’t like? Click on line 37, type “Do this and all other network fetches in the background Cmd+Enter,” and you have a few more agents getting your code into final shape. Click or type “Commit,” “Push,” “Create PR” — you just shipped a high quality, non-slop feature We’re striving to be the best at every step of the pipeline, but can just try Command Center in pieces wherever you feel your current workflow is weakest. We have users who do all their coding in Zed or the Codex app, and then jump over to Command Center for a walkthrough when it finishes running. There’s even a skill that will pop open a Command Center walkthrough from the environment of your choice. Or you can just keep Command Center running while you do your work elsewhere, and if your AI deletes anything, you have Command Center’s snapshots to the rescue. We launched quietly last year and have been refining since. The quality and usability have kept going up, and Command Center is now ready for a lot more attention. Since our quiet launch, we’ve seen at least a dozen other agentic coding environments appear….approximately all of which have the same feature set focused on the part which is already easy (generating the first version of the code) and with at best a shoddy answer to the hard part (everything that comes after). Command Center’s focus is making the hard parts easy. Here’s what our users have to say: “[The refactorings] give your LLM taste. I’ve never seen an LLM write code this good before.” — Doug Slater, Staff Engineer, Climavision “With Command Center walkthroughs, I can get through a 400-line diff in less than half the time.” — Prateek Kumar, Platfor Engineer, Sumo Logic This product is not for everyone. If you’re someone who preaches “the prompt is the source, the code is the compiler output,” then you probably won’t enjoy Command Center. But if you want to uphold traditional engineering discipline while also shipping 20 PRs a day, then this is the environment for you.

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.