Production duplex speech model for revenue calls
Hi, HN, we’re building Mia & Leo: AI personalities backed by purpose-built duplex speech models for revenue calls. Teams building real-world agents told us that more than 40% of callers hang up within the first 30 seconds. On a revenue call, that meant a lost booking, an unpaid balance, a cold lead, or a customer who does not come back. This happens because humans interrupt, correct dates, pause halfway through a sentence, or speak over someone in the background. The agent misses it, talks over them, or loses the thread. That’s because of how the current voice systems work. They sequentially listen, stop, think, and then speak. The conversation is actually a walkie-talkie exchange. So, we’re proud to introduce our first iteration of Mia & Leo who keep listening while they speak. They can handle interruptions, overlapping speech, corrections, and background voices without breaking the conversation. They are also built for production. Developers get control over agent behaviour, visibility into calls, failures they can debug, and costs that work at scale. Please try them out at metavoice.io. We'd love to hear any feedback!
Politico
https://littletech.org/ https://static.politico.com/4a/bf/9c4021d8404386b0a311dcccf0...
What's AI's go-to, public or private healthcare?
Show HN: What's AI's go-to, public or private healthcare?
I built a zero-latency developer tool suite in pure Vanilla JavaScript
Show HN: I built a zero-latency developer tool suite in pure Vanilla JavaScript
Unlayer
Hi HN, We’re Adeel and Umair, co-founders of Unlayer (https://unlayer.com/). We let you add content creation to your applications without having to build an entire editor, renderer, template, and export stack yourself. Unlayer lets you create emails, web pages, and documents inside your app, in three different ways: in code, visually, or with AI. Here’s a demo: https://www.youtube.com/watch?v=0HsDtNkdMpM. We started with an embeddable email editor because a lot of products eventually need one: CRMs, marketing tools, customer engagement platforms, marketplaces, internal tools, and vertical SaaS apps all run into this at some point. At first, it sounds like a small feature: "just" add a drag and drop editor. In practice, it turns into a big pain. You end up dealing with email rendering, Outlook quirks, responsive layouts, templates, merge tags, image uploads, exports, permissions, localization, versioning, and a long tail of edge cases that have nothing to do with your core product Over time, we saw the same problem beyond email. Apps also need landing pages, invoices, proposals, reports, contracts, and PDFs. Some of this content is best created visually by end users. Some of it is better generated in code by developers. Increasingly, some of it is also generated by AI agents. Many teams eventually need all three workflows. That is the direction we have been working toward with Unlayer. There are three parts we are showing today: (1) Unlayer Elements. This is our open-source React component library for creating emails, pages, and documents in code (repo: https://github.com/unlayer/elements, more at https://unlayer.com/elements). Instead of hand-writing raw HTML templates, developers can compose content using React components, reuse sections like headers, footers, CTAs, invoice rows, and branded blocks, keep templates in Git, and render them into production output. One newer use case we are seeing is AI-assisted content creation. If an AI agent is asked to create an email, invoice, report, or landing page, the output is usually raw HTML or markdown that becomes hard to maintain. With Elements, the agent can generate structured React components instead. A developer can review the result, refactor it, keep it in Git, and still pass the design into a visual builder later if someone needs to edit it. (2) Visual Builder. This is the drag and drop editor (repo: https://github.com/unlayer/react-email-editor, more at https://unlayer.com/email-builder) that can be embedded inside an app so non-technical users can create or edit content. In the demo, we show the email builder and the AI assistant inside the builder. The goal is not to replace the developer workflow, but to connect it with a visual workflow when marketers, admins, customers, or internal teams need to make changes themselves. (3) Document Builder. This is for structured documents such as proposals, reports, invoices, contracts, and PDFs. We have seen a lot of teams build separate systems for email templates, web pages, and document generation, even though the underlying primitives are similar: layout, content blocks, variables, assets, preview, export, and permissions. More: https://unlayer.com/document-builder The technical challenge is making these workflows share a common foundation. Developers should be able to build templates in code when that makes sense. End users should be able to edit visually when that makes sense. AI agents should be able to generate structured content instead of unmaintainable blobs. The final output should still be usable by the host application. We make money by selling hosted builder, template, export, and platform features to companies embedding this into their products. Elements is open source. The commercial product is the broader hosted platform around builders, collaboration, storage, exports, and production use cases. We were part of W22, so this is a late Launch HN. At the time, Unlayer was an embeddable email editor, and we did not think we had the right broader story for HN yet. Since then, the product has expanded into a more general content creation layer for products, including emails, pages, documents, APIs, open-source developer projects, and AI-assisted workflows. That felt like a better moment to bring it to HN and ask for feedback. We'd really appreciate thoughts from HN. This is one of those areas where a lot of people have strong opinions because they’ve been burned by editors, email HTML, document editing, or “simple” content workflows before. We'd love to hear what resonates, what sounds wrong, and what you think we should be thinking harder about!
Langy, an automated AI engineer (we gave it a robot body) [video]
Founder here. Langy is an AI engineer that lives inside our platform, LangWatch. It reads your production traces, writes Scenario tests and evaluations for the problems it finds, opens a pull request on your repo, and proves the fix by running those simulations in CI. A human still merges. Reachy robot from Hugging Face arrived on the same week we were planing on launching it, so we wanted to use for the launch. So we it wired to our Langy which actually lives on the platform, and asked it to test our own customer-support voice agent. On the video you can see it writing the agent tests reading traces and everything. LangWatch is an open source platform (github.com/langwatch/langwatch) and Scenario, the simulation-testing library it drives, is open source too if you want to run that part yourself: https://github.com/langwatch/scenario. A full write-up on how Langy works under the hood is coming later this week. Happy to get into how it drives the robot, what is real vs staged, latency, or how the whole harness around it actually works.
Maptoolkit.org
We built Maptoolkit.org - a free, production-grade vector maps service built on OpenStreetMap and MapLibre. Map rendering and tile hosting have historically been either expensive (Google, Mapbox) or complex to maintain yourself. (Plus: Their maps are car-focused.) Our maps feature max data zoom 15, global hill-shading, 3D terrain, contour lines, water depths, and specialized styles (like Hiking, Cycling, Winter). No sign up or API keys required. You can use it like this: ``` const map = new maplibregl.Map({ container: 'map', style: 'https://styles.maptoolkit.org/summer.json', // <- that's all you need to add center: [11.40, 47.27], zoom: 12 }); ``` And you can create custom styles with our MapMaker. Maybe a Elden Ring map style for your website? We hope you like it.
Agent in 9 Lines Python
I asked myself: what would a minimal implementation of an agent look like? Something that works out of the box, is a real agent with tool calling, but without 1000s of lines of code, without dozens or hundreds of npm or pypi dependencies. Something with just a few 'essential' features (not a whole kitchen sink that most agent harnesses come with nowadays). An implementation close to pseudocode that you can look at in one page, everything there at a glance, no scrolling. This is the agent.py I ended up with so far: import json,sys;from subprocess import getoutput as sh;from urllib.request import Request as R,urlopen url=sys.argv[1];h=[];b=dict(model="gpt-5.6",input=h,tools=[dict(type="custom",name="sh")]) while p:=input("> "): h+=[dict(role="user",content=p)];H={"Content-Type":"application/json"} while True: o=(r:=json.load(urlopen(R(url,json.dumps(b).encode(),H))))["output"] h+=o;c=[i for i in o if i["type"]=="custom_tool_call"];z=r["usage"]["total_tokens"]/10500 if not c:print(o[-1]["content"][0]["text"],f'\n[{z:06.3f}%]');break h+=[dict(type="custom_tool_call_output",call_id=i["call_id"],output=sh(i["input"])) for i in c] It is a bit code golfed but I think it is fairly readable - imports are all from stdlib (0 external dependencies!) - assumes there is an inference api endpoint running somewhere - assumes the inference api endpoint is openai-like - model hardcoded to "gpt 5.6" (=> Sol), can easily be changed to e.g. open weight (kimi k3, glm 5.2 etc) - api endpoint url is passed as arg to the python script - configures only 1 custom tool: 'sh' - 'sh' is sufficient for interacting with the environment in an open ended way - new api output gets added to history ("h") - if api output contains tool calls the tool calls get executed - agent gives control back to user when the last model response is without tool calls - agent message to user shows % of context window used Noteworthy: no dependencies other than python stdlib (!) - less startup time - less dependency churn - less supply chain attack vector surface - less code to verify and understand no mcp, no plugins, no security theater - if you want to add something specific: add it explicitly - adapt the environment to give the agent access or restrict access to tools, resources, network etc (the env is the security boundary, not the harness) no system prompt - every token in context window is precious - current strong models do fine without steering via system prompt (or are even harmed by long overly specific system prompts designed for models from months ago) - system prompt or agents.md context can easily be added if needed (agent can also discover it or get prompted to read from environment as is) how to run/deploy the agent - design the environment you want to give the agent (container, docker, sandbox of your choice) - start an inference api endpoint that is openai-like (support the request/response shape used in agent.py above) - inference api endpoint can be as simple as a proxy to openai api that adds credentials/api key - adapt as you want/need it, change the model, remove/alter context window behaviour, add tools, etc etc Looking for any feedback you have to make it more clear or even simpler!
Housecat.com
Hey we are Housecat. I've spend the last 15 years building dev tools at Heroku, Convox (YC S15), and Segment. Dev tools have gotten insanely good over the decades, now we're working to level up productivity tools for everyday work. Housecat is an email inbox running on an single-tenant agent computer with a durable workflow engine and connections to common tools for day-to-day work. My primary workflow with the product is for customer support: get a support email, trigger a triage process, open a GitHub issue, spin up a coding agent to fix it, and update the customer along the way. All from the same UI and sandbox. We've experimented with lots of different approaches to new workspace tools, and learned that many people still need a great GUI. We've landed on the email inbox as a very familiar and permanent surface and are building tools and automations directly inside it. Under the hood every user gets their own single-tenant sandbox VM and persistent SSD on exe.dev. User connections to Google, Slack, GitHub, etc. are managed in an external service so the VM and agent are subject to external governance and don’t have direct access to secrets. Durable workflows are built on DBOS.dev. Running interactive and deterministic workflows directly inside an email couldn’t feel more different than chatting about emails in Claude.app. The app is built on HTMX, Unix, Go, and SQLite. The app is source available to users and the Linux VM is fully open for a user and their agent to poke around. You can try it out at https://home.housecat.com/beta. It requires a Google / Gmail connection for the email app, which you can revoke any time. We'd love to share more and learn how the community here is managing email, agentic chat, and custom workflows for their work.
HN Hall of Fame
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Bento
Over the past few months, our team has been building more and more slidedecks using web frontend technologies with coding harnesses like Claude Code, but a common complaint is to make even small edits we need to edit the code either manually or via the harness. To avoid this loop, I ended up creating Bento, a single HTML file with everything you need in a slide tool including animations and shared editing. There's no install or cloud login, everything works offline. The default deck is around 560 KB and it doesn't need to fetch anything once you got it. Open it in a browser and then you can edit, present, print and save. Share it via email or via Airdrop and all they need is a browser to edit, present and also do live collab on the slides. Drop it in to Claude or ChatGPT to transform existing pptx files into Bento slides. There is no cloud involved, only an encrypted blind relay to allow for shared editing. The relay doesn't see any of the data. Check it out at https://bento.page/slides/ which takes you straight to the editor. Go to https://bento.page/guestbook/ to try out the live guestbook to experience share editing / collab. There is also a gallery with some sample decks on the website - https://bento.page/ All the code is MIT licensed and you can find it here - https://github.com/nyblnet/bento . I used reveal.js with several other libraries (including some homegrown ones), and Claude Code.
Reachpad
Show HN: Reachpad – Run all your coding agents from anywhere from the browser