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Launch HN: Agnost AI (YC S26) – Extract user feedback from agent conversations Hey HN, we’re Shubham &amp; Parth, childhood friends building Agnost AI (<a href="https:&#x2F;&#x2F;agnost.ai">https:&#x2F;&#x2F;agnost.ai</a>), product analytics for teams building chat and voice agents.<p>We read production conversations and find behavioral failures like users rageprompting (cursing at the agent), repeatedly rephrasing the same request, correcting the agent, asking for missing features, or leaving after an answer that was technically successful.<p>We have an interactive demo with no signup here: <a href="https:&#x2F;&#x2F;app.agnost.ai?demo=true">https:&#x2F;&#x2F;app.agnost.ai?demo=true</a><p>Here&#x27;s a demo video: <a href="https:&#x2F;&#x2F;www.tella.tv&#x2F;video&#x2F;agnost-ai-launch-hn-demo-9haa" rel="nofollow">https:&#x2F;&#x2F;www.tella.tv&#x2F;video&#x2F;agnost-ai-launch-hn-demo-9haa</a><p>The core problem is that chat and voice products do not have the same metrics as web apps. When the product interface is language, clicks and funnels become much less useful. Users also rarely give explicit feedback, and when they do it&#x27;s usually sugarcoated. I barely type &#x2F;feedback in Claude or Codex myself. Most users just curse, ask again, correct the agent, or leave. So product engineers get technical visibility from latency, errors, and traces, but still have to guess whether users got what they wanted.<p>We got here after building around agents for the last year and got a couple of founders asking for something like a PostHog for conversations for the AI assistants they were building.<p>We are not trying to be in the observability or evals space. Observability tells you what happened technically. Evals validate cases you already know. We&#x27;re more on the discovery side like what users wanted, where they got frustrated, what they asked for repeatedly, and what new evals should exist.<p>Teams send us agent conversation messages through SDKs or OTel, optionally with metadata like account, plan, source, organization, etc. We cluster conversations into product-specific intents. Feature requests and bugs are default categories; most other clusters are created dynamically from the customer’s data and evolve over time. You can create your own cluster in plain English. If a cluster gets too broad, we split it. If a new pattern appears, we suggest it.<p>One AI video editor company used Agnost AI to find feature requests hidden inside chat. The biggest one was that around 70 users wanted auto-subtitles, but users said it as “add this text in this frame” 12x in a single session, “can you caption it”, “give me transcript of audio” and variations across languages. The team later built the feature.<p>Doing this over millions of messages without sending everything to an LLM was the hard part initially. In ClickHouse, “fetch the last 50 events by time across conversations” and “fetch all events in this conversation” want different sort orders, so we had to iterate a lot on sorting keys, partitions, materialized views, and projections.<p>For finding new clusters, sending everything through an LLM was too slow and expensive. HDBSCAN-style embedding clustering also gets painful at scale because of pairwise comparisons. We first split conversations into segments based on cosine drift, run BIRCH to compress the candidate space, and then use HDBSCAN-like clustering on the smaller set. For matching existing clusters, we use embeddings, smaller classifiers&#x2F;BERT-style models, and LLMs only as fallback for ambiguous cases.<p>We’re live with multiple companies and ingesting ~1M chat and voice messages per day. Pricing is public: Starter is free, Pro is $499&#x2F;month, and Enterprise is for higher volume, security, retention needs. We use each customer’s data only for that customer. We are SOC 2 Type 1 compliant, Type 2 is in progress, and our SDKs are on PyPI and npm.<p>We’d love feedback from the HN community and people building chat or voice agents: how do you detect these signals today, what feedback methods have worked, and what would block you from trying this? Happy to answer questions and take criticism.

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Show HN: Opening lines of famous literary works This came from an idea that had been knocking around in my head for several years. I had been collecting opening lines of famous works and thought it would be cool to see one everyday as I opened the browser. I tried different styles but landed on the simple background with the text, let the words speak for themselves. Over time i&#x27;ve added more quotes I believe now there are close to 60, so hopefully you can refresh a few times and get a fresh one every time. I hope you guys like it, enjoy!

Show HN: Cascade Chat – A Hackable IRCv3 Client for macOS, Windows, and Linux Hello HN! I&#x27;m Matt and today I&#x27;d like to show you Cascade Chat.<p>One of my earliest internet experiences was with mIRC. I always admired its straightforward, pleasant UI and the way it wove a hackable core into the code. The way you could build on the visual and API layers of the underlying IRC client to me was fascinating software machinery. It was truly a client that you could build on top of.<p>As my career has progressed, I moved away from Windows and adopted Linux as my daily driver. That was where I found HexChat, the closest thing I could find to mIRC many years later. While I really enjoyed HexChat, it wasn&#x27;t quite what mIRC offered. I eventually found myself on macOS with no clear analogue to either. That&#x27;s why I built Cascade Chat.<p>Cascade is a modern IRCv3 client. It supports persistent local history with full-text search, network management, replies, typing indicators, link previews, pinned messages, native notifications, SASL authentication, server-time, chathistory, account and away tracking, and the ratified IRCv3 capability set.<p>I also wanted Cascade to be hackable, so I built in two fundamentally different layers:<p>- Scripting powered by Go scripts for personal automation, event handlers, and timers. Scripts run in-process with no access to the standard library, filesystem, or network. - Plugins that communicate over JSON-RPC and can be written in any language as external processes.<p>Stack-wise, it&#x27;s a Go application built on top of Wails v3, which leverages the OS&#x27;s native WebView to render modern web tech frontends as desktop applications. The result is an Electron-like experience without packaging a separate Chromium runtime.<p>Full disclosure, since this is HN: I built Cascade using agentic engineering. I made the product and UX decisions, designed the architecture and code interfaces, and I reviewed and dogfooded the resulting work. Coding agents implemented much of the space between those decisions.<p>To further ensure consistent quality, I built gates around that process rather than treating generated code as finished. I focused on unit and integration tests, full-stack end-to-end tests against a real IRCv3 server (Ergo), automated release candidates, and regular dogfooding of the prerelease channel.<p>Cascade is open source under the BSD 3-Clause license. Prebuilt packages are available for macOS, Windows, and Linux. The current builds are unsigned, so macOS and Windows require a first-run confirmation step that I&#x27;ve documented in the README.<p>I&#x27;d love to know if you&#x27;d make Cascade your daily IRC client, and if not, what that&#x27;d take! Feedback and PRs welcome.

Show HN: Oodle.ai – $10 per million agent traces Hi HN, we&#x27;re Kiran and Vijay!<p>Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it&#x27;s exciting for us to show what we&#x27;ve built on top of that foundation: LLM Agent Observability.<p>Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS Lambda.<p>Since we process each span of every trace, instead of running LLM-based evals on each, we first analyze them using deterministic techniques. We detect tool failures, retries, loops, abnormal token usage, latency regressions, schema violations, sentiment, and other production signals. We&#x27;ve written more about the approach here: <a href="https:&#x2F;&#x2F;blog.oodle.ai&#x2F;you-cant-sample-your-way-to-reliable-agents&#x2F;" rel="nofollow">https:&#x2F;&#x2F;blog.oodle.ai&#x2F;you-cant-sample-your-way-to-reliable-a...</a><p>The combination of our own engine, no sampling, and deterministic processing before LLM-for-evals allows us to price at $10 per million traces, provide sub-second p99 query latency, and have healthy margins. Before building this, we used Langfuse for our own agent observability, which was 6x more expensive.<p>Still super early, and rough around some edges, we would love your questions and feedback!

Show HN: Town – Discord in a pixel town where the NPCs have skills I built this thing called Town. Think Discord, but you walk around a pixel town where the NPCs have skills.<p>Agents have become my initial brainstorm partner for a lot of stuff — sub-agents to counter my own thinking, role-playing a specific person, grounding them in a specific knowledge so they refer instead of guess. I started turning my claude-code into this pattern and it worked well enough that I wanted to see if I turn into a fun shareable thing where other people can also talk to.<p>It&#x27;s a small pixel map of buildings. You walk in, step into any building, and talk to the npcs who live there.<p>Each NPC is an AI agent with its own personality (voice, backstory, what they care about) and a skillset (tools like memory, web search, writing docs). The personality makes them fun to talk to; the tools make them able to actually do something.<p>Multiplayer&#x2F;Group discussions: Real people wander the same map. If someone else is in your town you see them walking around and can chat live. Step into a building together and press G to open a shared chat between the humans and AI avatars in that room.<p>The engineering bit I like most: each NPC is a separate agent with scoped tool permissions. The author decides which tools each one gets — a shopkeeper can hand out inventory, a detective can issue a Case Closed card, a barista just talks.<p>A few towns I&#x27;ve built:<p>- AI Startup Town: get in a room with NPCs of famous investors — Paul Graham, Garry Tan, Michael Seibel, Dalton Caldwell. Pitch your idea, stress-test your plan, build your startup doc.<p>- Murder Mystery Town: Iris Bell is dead. Talk to the NPCs, catch someone in a contradiction, figure out who did it, and report back to Detective Reeve to close the case.<p>- Roast Town: get roasted, or learn the fundamentals of roasting.<p>- CORE Town: the sampler — one town that shows off everything. Pitch Garry Tan at YC House, stand trial at Trial of You, get worked over by a noir detective, get roasted. Best place to start.<p>You can build your own town too, and it&#x27;s open source. A town is just a JSON + MDX folder — a buildings list, one .mdx per AI avatar (frontmatter = identity, body = system prompt) — then town deploy from the CLI. There&#x27;s also a claude plugin that builds the whole thing in one shot from a rough description.<p>Guest mode, no signup:<p>Startup: <a href="https:&#x2F;&#x2F;town.getcore.me&#x2F;startup?invite_code=7EQMH0">https:&#x2F;&#x2F;town.getcore.me&#x2F;startup?invite_code=7EQMH0</a><p>Murder Mystery: <a href="https:&#x2F;&#x2F;town.getcore.me&#x2F;murder-mystery?invite_code=S85S24">https:&#x2F;&#x2F;town.getcore.me&#x2F;murder-mystery?invite_code=S85S24</a><p>Roast: <a href="https:&#x2F;&#x2F;town.getcore.me&#x2F;roast-town?invite_code=0MXN93">https:&#x2F;&#x2F;town.getcore.me&#x2F;roast-town?invite_code=0MXN93</a><p>CORE Town: <a href="https:&#x2F;&#x2F;town.getcore.me&#x2F;core-town?invite_code=H4C0TZ">https:&#x2F;&#x2F;town.getcore.me&#x2F;core-town?invite_code=H4C0TZ</a><p>Full list: <a href="https:&#x2F;&#x2F;town.getcore.me&#x2F;explore">https:&#x2F;&#x2F;town.getcore.me&#x2F;explore</a><p>Source: <a href="https:&#x2F;&#x2F;github.com&#x2F;RedPlanetHQ&#x2F;town" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;RedPlanetHQ&#x2F;town</a><p>Rather fun project overall. Honestly I have been thinking a lot about how community engagement is changing with agents and this felt like a good way. It&#x27;s a very raw shot. Kindly share your thoughts and feedback.

Coding agents think ahead of time

面向 Grok Build、Grok Web 与 Grok Console 的多账号 API 网关

A modern video editor built with Tauri, React, and TypeScript. Focus on building free capabilities of premium capcut functionalities

Codex scraped the ICM website and discovered 2026 Fields Medal winner list

Codex starts encrypting prompts, uses ciphertext for inference instead

Show HN: Rejourney – Open-source revenue leak prediction for web and mobile apps Hello there (General Kenobi&#x27;s favorite way to start the conversation),<p>I’m Rashid, a sophomore at UT Austin. I built Rejourney.co (<a href="https:&#x2F;&#x2F;rejourney.co&#x2F;" rel="nofollow">https:&#x2F;&#x2F;rejourney.co&#x2F;</a>) to predict issues with your apps and websites before they happen, based on real user session recordings.<p>Here is a silly video (with cats) on how it works: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=Z95MDxBXMjk" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=Z95MDxBXMjk</a><p>It’s open source, and the post link is the github repo, but here it is again: <a href="https:&#x2F;&#x2F;github.com&#x2F;rejourneyco&#x2F;rejourney" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;rejourneyco&#x2F;rejourney</a><p>I originally built this because I had a campus freebie finder app that grew quickly, and I had a lot of users dm me on instagram about issues with the app’s onboarding and UX confusion. I initially lost about 340 users out of my 5,000ish users because of these issues, and I had to recover some by nudging them with notifications. It was a big pain, and I felt bad that I lost this many users to small and easy fixes. So I built Rejourney to predict that before it happens. Here is how it works:<p>First, the SDK is installed on Web JS, Swift, or React Native apps. You then help the SDK a little with a few lines of tracking important events -- such as a subscription bought, a signup completed, etc -- before you ship the app. We called these “critical conversion events”.<p>From here, Rejourney records the user session along with the meta data you set up, and relates it to the sequence of the user journey, each touch&#x2F;scroll&#x2F;pan interaction, and rage taps. If deemed an issue, it bundles in API response times and codes, ANRS, and crash traces into the context.<p>A heuristic then bundles all the user recordings into similarity cohorts for processing, and finds similar user journeys and outcomes in relation to the critical conversion actions that matter to you. If a trend is found that is possibly worrying, it admits the user recordings into segmentation and processing by an LLM on our back (in this case Gemini for cost and speed, but it has been tested on GPT 5.5 if you decide to self-host and set this up on your side).<p>If the LLM views similarities in the touch sequence frame by frame, it can determine whether the cohort is likely to present a negative outlook on the critical conversion event that matters to you. Based on the replays and all the surrounding context, it outputs a .MD file with the context and the fix that would patch it (which you can copy into your coding agent). Optionally, you can attach your github repo so the .MD file includes a code fix with the detected issue.<p>Furthermore, this occurs at the scale of thousands of user recordings daily. We have seen how this works on a medium-scale, as Rejourney has been tested with about 2.5 million user recordings from people shipping the SDK. One of our users even emailed us reporting a 30% increase in onboarding after 2 weeks of fixing non-stop issues found.<p>We have made it soooo cost effective to run with different strategies, that our first 3 paid users made us break even on costs…and this means more compute space for cool things later :D<p>Other considerations and criteria: Privacy was also very very important as we have to consider GDPR, after the retention period (usually 7-days) we quantize all the user recordings, anonymize all the fingerprints and aggregate them into a general dashboard (similar to Firebase’s general analytics dashboard).<p>I’d love to hear your feedback, critics, and requests in the comments! I’m all ears (or eyes since I’m reading).

The Future Worth Building Is Human – Thinking Machines Lab

The great digital fatigue: How digital burnout is changing social media use

European "age verification" "app" forcing everyone to use Android or iOS

Show HN: Benchmark your eng team's AI agent maturity in 5 minutes we had hundreds of discussions with engineering leaders over the past few months, and everyone&#x27;s trying to understand where they are in the AI journey.<p>we collected all this data into a benchmark and built a free grader to let you know where you stand.<p>you answer on a 1–5 scale (e.g., autonomy runs from &quot;suggestions only&quot; to &quot;agents own multi-hour workflows across code, infra, and external systems&quot;) - takes about 5 minutes.<p><a href="https:&#x2F;&#x2F;agent-benchmarks.com&#x2F;software-factory&#x2F;" rel="nofollow">https:&#x2F;&#x2F;agent-benchmarks.com&#x2F;software-factory&#x2F;</a><p>waiting for your results!

Indian scientists produce most detailed 3D atlas of the human brainstem

Just Let Me Write Digits

Australian energy retailers must provide three hours of free daytime electricity

YouTrackDB is a general-use object-oriented graph database