SubtitleGenerator.

            From video to publish-ready AI subtitles—all in one browser

            Discussion
            |
            Link


AutoClaw.

            An AI work agent across desktop, browser, and chat

            Discussion
            |
            Link


VeloFiler.

            Keyboard-first dual-pane file manager for macOS

            Discussion
            |
            Link


Pocket by Meta.

            Vibe-code games, then share them like TikToks

            Discussion
            |
            Link


Port Radar for macOS.

            An AI port manager for your Mac.

            Discussion
            |
            Link


Pawvis.

            Control your mac through your webcam & train custom gestures

            Discussion
            |
            Link


Agents Never Sleep.

            Agents keep running with the lid closed

            Discussion
            |
            Link


KerasFormers.

            Keras 3 collection of pretrained models

            Discussion
            |
            Link


Maccess.

            Your Mac, in your pocket — trackpad, screen, and AI

            Discussion
            |
            Link


Zero.

            Vercel's programming language built for AI agents

            Discussion
            |
            Link


Open Analytics.

            AI-Native Google Analytics alternative for the modern web

            Discussion
            |
            Link


Toplify.

            Track your App Store ranking worldwide

            Discussion
            |
            Link


Actx0.

            Memory infrastructure for AI agents.

            Discussion
            |
            Link


Project SKY.

            Your ambient AI companion for Windows.

            Discussion
            |
            Link


Dockhand.

            Docker management for everyone

            Discussion
            |
            Link


Supernova.

            All your data in Claude and Codex

            Discussion
            |
            Link


Wizstar.

            Digital avatars that move and act like professional actors

            Discussion
            |
            Link


Flunkey.

            Voice-first AI layer for Windows (beta)

            Discussion
            |
            Link


Epho.

            Run Claude Code, Codex or Opencode in cloud with your repo

            Discussion
            |
            Link


Mindcase.

            Extract data from anywhere on the web within minutes

            Discussion
            |
            Link


OneCLI.

            Give every employee a secured, sandboxed pro assistant agent

            Discussion
            |
            Link


Router by Ramp.

            Tokens are money. Save both.

            Discussion
            |
            Link


fx (by Vercel).

            Vercel's tiny, open-source coding agent

            Discussion
            |
            Link


Antigravity IDE Extensions.

            Antigravity agents now live inside your existing editor

            Discussion
            |
            Link


ShogunAI.

            Your personal AGI on your PC. Built to finish real work.

            Discussion
            |
            Link


PixelRead AI OCR.

            Capture, translate, and understand any text on your Mac

            Discussion
            |
            Link


Local.

            Zero (!) friction local AI for your Mac

            Discussion
            |
            Link


Plow Latch.

            Run AI agents on your Mac with scoped access

            Discussion
            |
            Link


Lynqo.

            Your machine is a P2P server, review suite & clipboard sync.

            Discussion
            |
            Link


Surfdeck.

            Your tabs, within reach.

            Discussion
            |
            Link


Outlook Google Calendar Sync for Mac.

            Sync Outlook calendars to Google on your Mac

            Discussion
            |
            Link


NobodyWho.

            Run AI models on any device

            Discussion
            |
            Link


Shape.

            The agentic IDE for designers and programmers

            Discussion
            |
            Link


Lifelong.

            Your whole family’s health in one place.

            Discussion
            |
            Link


Glasp for Firefox.

            Highlight and summarize any page, PDF, or video in Firefox

            Discussion
            |
            Link


MeetStream AI.

            Unified API & Infra for AI Meeting Agents

            Discussion
            |
            Link


Aloud.

            Turn spoken feedback into tasks your coding agent can run

            Discussion
            |
            Link


Berd.

            Weird, playful desktop app for building with AI agents

            Discussion
            |
            Link


Checksum AI.

            Your coding agent’s testing buddy

            Discussion
            |
            Link


HyNote for Mac.

            Free local transcription that is 100% Private

            Discussion
            |
            Link


Grok 4.6.

            Frontier Intelligence for Long-Running Agents

            Discussion
            |
            Link


Hermai Brand API.

            White label your B2B SaaS with every customer's brand

            Discussion
            |
            Link


Prized.

            Let non-engineers build secure internal tools

            Discussion
            |
            Link


The New Calendly.

            Handle all of the work before, during, and after meetings

            Discussion
            |
            Link


MiniMax Design.

            Your own agent team for open-ended creation

            Discussion
            |
            Link


Zoho Cliq 7.0.

            Uninterrupted work

            Discussion
            |
            Link


Peach Co-Pilot.

            WhatsApp Sidekick for busy professionals

            Discussion
            |
            Link


Revy.

            The ownership layer for fashion, shopping, and your wardrobe

            Discussion
            |
            Link


Cloudways Managed AI Agents.

            Skip the setup and run OpenClaw & Hermes, fully managed

            Discussion
            |
            Link


Roveri.

            A riding journal for iPhone every ride, painted on a map

            Discussion
            |
            Link


Show HN: Find and Organize Photos with Private, Local AI.


Show HN: Knowl – CLAUDE.md hit 1000 lines, so I built memory that prunes itself. Using AI agents is amazing, but in long-term, I started to have more and more problems about the context of them. They keep forgetting what we are working on, opening a new chat session clears all their context, my CLAUDE.md had like 1000 lines.
So I thought to install those agent memory that are on the market. Started out great, but I noticed that those memory only append and does not fix what is stale. First day I told it to use Lemon Squeezy as our MoR but second day I tell it to change to Polar. But when I ask it again, they keeps returning both answers or cannot decide which we are using.
That's why I created Knowl. Knowl solves the problem with freshness of the knowledge. It split the knowledge into small data bits called "atom". Atoms can be of the following types: fact, decision, goal, constraint, architecture, state, skill so we can retrive atoms in categories.
When conflict happens at write-time (a new atom conflicts with an old one), Knowl retires the old one (flag it with superseded) and remove it out of main retrieval but still keeps full history.
There are many more cool features like transcript search, multi-workspace sharing, change detection impact... and Knowl Cloud for team-sync too.
We benchmarked Knowl on MemoryAgentBench - FactConsolidation single-hop @262K context and got suprising result:
- Knowl: 0.90 <- I ran this
- agentmemory: 0.79 <- and this
- Gpt-4o (full context): 0.60
- Mem0: 0.18
- Zep: 0.07
In multi-hop we scored 0.07 (ceiling of all time is 0.14)
I ran at temperature 0.7. You can find full benchmark in my repo.
This is fully open-source, connect to almost every providers through MCP (Claude Code, Codex, Cursor, Antigravity...). And it is fully local (unless you use Knowl Cloud).
I'd love to get some feedback. Cheers!


Show HN: I make my AI agents file paperwork before they're allowed to code.


Show HN: Running a full AI coding agent inside Cloudflare Durable Object.


Show HN: Knoku – cited AI answers from docs, files, and team knowledge.


Show HN: VinvAI runs and observes your services to find bugs and perf issues.


Show HN: Agile AI Development Lifecycle. Schrödinger's AI-DLC Framework


Show HN: Learn Leap, an AI tutor that teaches from your own material. I built Learn Leap because I found myself constantly asking ChatGPT questions while reading research papers. I wanted something that already understood what I was reading. With Learn Leap you can upload your own material and have an AI tutor teach directly from it. The tutor has full context on your material and can create quizzes, flashcards, and exams to test your understanding.
It's still early and I'd love feedback from HN on the product and the learning experience. Cheers.


Show HN: Front end skill pack for AI agents, with machine-enforced quality gates.


Show HN: Anjadhe – privacy first AI assistant, no account, no server DB. Hi HN, I am Ram. For the last few months I have been building Anjadhe. It is a personal AI assistant for macOS. The main idea is simple: A macOS app where AI does the job of a personal assistant for the user. Not a chat only app where user needs to dig through chats to understand today’s schedule or a project they planned with the ai last week, but a canvas where the user and AI work together around basic tools.
I started building this as DYI tool for myself, but at this point I see that its truly useful for me, so sharing with others to see if it can be useful to others too.
Here is a quick Demo - https://www.anjadhe.com/demo
What it can do today:
- It reads your incoming email and files the important things: bills with due dates, renewals, receipts, deliveries. A bill becomes a task with a date, by itself. Two booking emails become one trip.
- You set goals by talking to it, not by filling forms. It asks you questions, makes a plan with dated tasks. Later you can say "work got crazy, push everything two weeks" and it moves the whole plan. It always shows you what will change and asks before doing it.
- Routines run without you. On a schedule, or when a certain email or file arrives. Every run leaves a log you can read.
- You can give it some documents you wrote. It learns your writing style and writes new content in your voice. What it learned is a page you can read and edit, not a black box.
About privacy: the AI runs on your Mac with llama.cpp. Or you can point it to your own server, or use your own OpenAI/Anthropic key, or use Anjadhe Cloud (open-weight models, free allowance, no account needed). Your data stays in SQLite on your own disk. No account. No telemetry unless you turn it on. The source code of the app and the cloud service is public, so you do not have to trust my words.
Honest limitations: it is an early alpha, macOS only. Local models need a Mac with atleast 32gb ram to work. The email features work fine on small models, but the full agent wants a bigger model or a server. Also it is Electron with vanilla JS, no framework. Looking forward to hear your opinions.
Demo (2 min): https://anjadhe.ai/demo
Download: https://anjadhe.ai/download
Source: https://github.com/Anjadhe/Anjadhe
I would love feedback, especially if you think my privacy claims are wrong somewhere. I will be here to answer questions.


Show HN: Heimdall – Trust-verified knowledge layer for AI coding agents.


Show HN: Flight Ledger – Track your flights, what they cost, and United status. Hello HN,
Flight Ledger is a private ledger of your flights, focused on (but not limited to) the United Airlines ecosystem. I am a physicist who has been based near two United hubs, first SFO and now IAH, and I fly United a lot, mostly for work.
I built this app to track my flights, flight expenses, and Premier status qualification without having to log into various accounts. All the imports (such as MileagePlus activity csv, .eml receipts, or Flighty/myFlightRadar24 exports) and data filling are manual.
A primary feature is that the app requires no sign-up and has no backend. Everything is stored locally, using SQLite and WebAssembly. This means that frequent back-ups are advisable to avoid losing data. For more robust backups and multi-device sync, the app also supports sync via Google Drive. I use this option for myself, but would be curious about other possible solutions.
Perhaps the main features that differentiate it from other apps are that the app tracks costs and metrics like CPM (cost per mile), United-specific PQP, PQF, and award miles, and allows one to differentiate out-of-pocket and reimbursed expenses. It also includes other things such as route tracking and a lifetime mile tracker.
While I have extensive programming experience in scientific computing and C++, my experience in web applications is more limited, and this app was built with Claude Code, which is unsurprising in this day and age, I guess. It is open source: https://github.com/vlvovch/flight-ledger
Curious to see your thoughts. I have particularly struggled with parsing various receipts, especially ticket reissue chains, and will be interested to know if the parser will survive yours.


Show HN: Updating my wedding registry website with AI in 275 commits.


Show HN: AI driven civic dashboard for Palo Alto.


Show HN: Kinodesk – Unlimited Remote Desktop Sharing. Kinodesk is a new advanced remote desktop sharing software that aims to reach the full performance by taking advantage of advanced performance enhancing utilities to reach 2K 240fps on capable hardware to ensure you have a smooth experience.


Show HN: ChickenButt a Native GTK Chat Client for Ollama on Linux. What's up, ChickenButt?
I made a free, native GTK client called ChickenButt. :)
It lets you chat with local models through Ollama, and I figured some of you might get a kick out of it.
Here's the repo: https://github.com/pixelhackstudios/ChickenButt
I've always hated reading long text in the terminal, and I couldn't find anything that ran locally that allowed me to get quick answers from LLMs when I needed them.
Also, not everyone has access to the frontier models and whatnot, so I thought helping people who only have access to more cost-effective AI would be a fun way to contribute to the FOSS community. So, I built ChickenButt!
It was really fun to build, and I wanted to share it.
Enjoy! :)


Show HN: OzBrain, a shared brain for knowledge between agents and your team. I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me.
A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work.
What I built OzBrain to do:
+ Create a central place for agent reasoned knowledge to live
+ Be agnostic about what apps/agents connect to it
+ Capture everything and track it so I can audit it
+ Enable teams, collaborators or partners to share brains
+ Handle conflicts so many agents in the same article doesn’t blow up
+ Refactor knowledge into more token friendly chunks and map the index well
+ Close the knowledge loop so new thinking supersedes old thinking across the corpus. Don’t erase, depreciate and link
+ Keep user data safe and secure
++ Be easy enough to use that you don’t have to have any technical knowledge
Some among us will always build their own custom solutions, but there are millions of tech professionals and small business owners that will use agents heavily and need a solution. So I’m trying to build that.
Isn’t this like gBrain? Yes, similar. I think it’s like AWS vs Vercel. AWS is very powerful, configurable, and useful if you’re technical and want to invest the time into really fine tuning your system… but if you just want your web deploy/hosting to just work and be easy to deal with you use Vercel.
// WHY I MADE IT
I’ve been enjoying getting back to my technical roots, as I lost my coding skills more than a decade ago, but with AI I can focus on the system and the product in partnership with agent coding workflows.
I recently built a Voice AI for older people. To build it I created an agentic engineering workflow (feel free to rip that up as I’m always looking to improve systems: https://ozbrain.com/resources/eng-flow) My approach with coding agents is trust but verify, and I’m trying to replace the parts where a human would review with an adversarial or specialized agent who would give a better answer/review.
I have workflows that will go high level task to shipped PR running in Claude cloud sessions. I use Claude Code locally and Cursor when I want a tighter loop on doing visual work like UI or layout. And Codex to either load balance usage for TokenThriffting or when I want a different llm to think thru something.
It was a pain in the ass passing .md files around and keep track of which version was the most recent, so I built a hosted .md storage right in Supabase and any of my agents already have Supabase access. This let me build a solid, scalable, secure voice AI from my phone at the gym. All my agents have access to our knowledge, can write to it, update and refer to it as we build and improve the product and the systems we use.
Out of 75 founder friends I asked about how they manage shared knowledge, 26 built their own custom knowledge systems… Obsidian vaults with 7k files synced through a VPS, markdown repos behind their own MCP servers, cron jobs stitching Supabase to a skills file… each a different Frankenstein they have to maintain. 32 said they felt the pain of moving static files around but didn’t have any solution for it.
So I rebuilt my brain better and used it to build it.
// HOW YOU CAN HELP
Would love to have you try it out. The maintenance loop is still in alpha so not running it on customer data yet.
If you built your own brain I’d love to hear how you did it. What criteria was most important for you in its design & function.
If you are tired of shuffling .md files around I’d love to have you try out OzBrain and to give feedback, just ask your agent to put it in the shared bugs & features brain!
Cheers!
Bubs.co


Show HN: Front Page of the Internet. you can buy the front page of the internet and stay there, while you use it for AI SEO, until someone out bids you and now they are on the front page of the internet, bid now just for a dollar


Show HN: Zroar – Serialized Roaring Bitmaps in Zig. zroar is a ground-up implementation of Roaring Bitmaps data structure in Zig. zroar stores both the keys and (array, bitmap) containers in a single flat byte buffer, making the in-memory representation equal to the on-disk or over-the-network representation, eliminating the serialization/deserialization step entirely.
The design was originally aimed at systems which keep their posting lists on disk, but zroar performs faster than CRoaring even for purely in-memory ops, due to CPU cache locality.
Against CRoaring 5.0's benchmarking suite (ported to Zig), zroar is faster in 339 out of 360 tests, being 2x-9x faster (geometric mean), and up to 600x faster on serialize/deserialize.
zroar avoids complex mechanism (like adaptive radix trees), uses Zig native SIMD ops and is simpler. The main logic is written in ~2000 lines of code, while CRoaring's 64-bit bitmap codebase is over 17000 LOC.
Not yet: By choice, zroar doesn't support run containers, and is 64-bit only.
There are more details in the GitHub README. Try it out! I'd love feedback on the API and design. zroar is a Zig-based successor to my other project, sroar in Go, which showed a similar boost. So, I think this design should show performance gains in any language.


Show HN: Traccia - Observability, Runtime Control & Audit for agents. AI applications are becoming agents, which has started to take autonomous decisions. There are plenty of tools and platform available to trace, and observe what an agent or llms calls does. They are good in what they do, but tracing and observability isnt enough for AI agents era. We need a solution that can help you observe, evaluate, create run time policies to govern and finally audit the actions of the agent. We built Traccia to solve this problem. The good part, all of these can be achieved by just writing few lines of code. Traccia has an open-sourced sdk that can work with your existing observability tool like grafana, tempo, jaeger, etc. In case you need more than just observability, Traccia provides the platform to evaluate, control and audit the agents. The platform is easy to use. The product's documentation is quite extensive. It is also cloud vendor and framework agnostic. Traccia is being built by an Indian start up ,based out of Bengaluru. We are running a 3 months free trials so that you can explore without any strings attached. We are open to improvise and get better so please drop your comments and feedbacks.


Show HN: Aristra, your life OS. Most AI cares only about the outcome users want. Aristra cares about your process. It works alongside and for you across the things you already use, learning about you and from you, day and night. What works becomes a playbook it reaches for next time. What stops being true quietly fades. How you actually get things done is the most valuable thing about you, and it's the one thing no AI understands. One AI. One memory. Your life OS.
Launch vid: https://youtu.be/CoIKF_CKE8g?si=v5QeyHxJvITrCNAy


Show HN: Zero () friction local AI for Mac. Super excited to launch our new app Local today. What we’ve learned at Base Compute over the last months is that running AI directly on your laptop or workstation gives you maximum privacy and it’s free, but it’s also a massive headache to configure. So we’ve decided what matters is making the experience completely frictionless for users.
Local analyses the hardware of your laptop, optimises the AI for it, and recommends the best models for your specific device.
It let’s you do what you’re doing with cloud AI already, just for free and on your own machine: Chatting with PDF’s, Recording and summarising meetings, running coding agents...
If you’re using Local in your office with colleagues, you can run it in “Office Mode”. The strongest computer in your office runs the AI and everyone can connect to it with their laptop. The data never leaves the office.
It’s available for download on our website today, please try it out and let us know what you think!


Launch HN: Vendo (YC S26) – Let users build features on top of your product. Hi HN, we’re Yousef & Nour, founders of Vendo (https://vendo.run). Vendo lets users create new features inside the software they already use. A user describes the dashboard, workflow, or small app they need, and Vendo builds it on top of the product’s existing data, API, and interface.
Demo: https://www.youtube.com/watch?v=VdpHehY64ls
We built Vendo because every SaaS eventually faces the same problem: every customer needs something slightly different. One wants a new report and another needs a workflow that only makes sense for their team. These requests either sit on the roadmap, become one-off engineering work, or force the customer into spreadsheets and external tools. We wanted the user to be able to create the missing feature themselves, without leaving the product.
Here is how it works:
- npx vendo init reads the product's API surface, theme, routes, and more. These are used so that the apps Vendo creates (1) look on-brand and native and (2) have the ability to read data and perform actions directly through the company's API
- When a user asks for a feature, we have a custom Vendo harness that writes a React component with a bunch of Vendo add-ons and guardrails (ex. ability to make calls to the host API + our component library). Every save is compiled, type-checked, run against real API responses, and rendered before the user sees it. We just released a benchmark and write-up here with more info for anyone interested: https://vendo.run/blog/generating-product-ui-measured
- We use QuickJS to make sure that anything the agent creates is sandboxed and can't mess with the company's site. Vendo compiles the component and runs it with Preact inside a QuickJS VM with no access to the DOM, network, or clock. The VM returns a UI tree, which the host renders using the product’s registered components. When the user clicks something, QuickJS emits a tool call; the host executes it through Vendo’s guard and passes the result back into the same VM, preserving the screen’s local state.
There's a lot of generative UI right now: streaming developer-written components into a chat (Vercel AI SDK, CopilotKit, Thesys), or rendering your app inside someone else's assistant (OpenAI Apps SDK, MCP Apps). We differ on two things. Vendo lives in your product and acts through your API as the signed-in user, so what it makes is durable: real apps users keep, pin, and run on triggers while they're away, and not components that are merely confined to a chat. Plus, it's not capped at putting together a bunch of prebuilt components: the agent can build arbitrary apps, from a quick dashboard out of your own components to real custom code running in a sandbox, and either way data only ever comes from tool calls to your API.
Here are some things customers are using Vendo for today:
- Letting their users create custom dashboards and reports. These are mainly UI-based and focused on letting the user see the exact graphs and metrics they care about
- Letting their customers create recurring automations. A big thing as well that has been used for these automations is the fact that we connect to external connections, so users have been automating many of their inter-tool workflows (ex. an automation that sends a slack alert based off of something in the product)
- B2B customers letting their customers customize the product with specific business logic. Often this is simple things like an extra field on a form, or an extra permission, but it is hard for a business to keep up with them otherwise.
- Creating and sharing custom dashboards/apps across an organization. Since the apps Vendo creates are durable, they can be shared, reused, and forked (which can’t be done with many of the other in-chat generative UI solutions)
We've spent a lot of time thinking about how AI and agents will change the way people consume software. We think the answer is personal(ized) software: you see the UI you need to see, you tell an agent exactly what you need, and the product molds to how you work.
The key insights that have enabled the product to work are:
- A rule in code always beats a rule in a prompt.
- Invent as little syntax as possible. Generation got faster and more reliable when the output looked like what models already know (JSX-shaped markup) instead of a clever custom format.
- Deterministic beats model wherever you can get away with it. Theme extraction is pure static analysis, and a remix starts as a copy of your component, no model call.
Vendo is completely open-source (Apache-2.0) and can be self-hosted, so feel free to c


Show HN: AI Resume Screening – Looking for feedback from recruiters. In addition to the resume creation tool we mentioned in our previous post, we've also developed an AI-powered resume screening tool to complement our overall ecosystem. This tool aims to help recruitment teams efficiently review hundreds of resumes without relying on keyword matching.
This tool helps teams:
- Batch upload and parse PDF resumes
- Compare candidates with job descriptions
- Review candidates' skills, experience, project experience, and industry fit
- Provide candidate fit analysis and scoring ranges
- Export results as a CSV file
To better meet the requirements of the European and American markets, candidate contact information is encrypted by default when uploading data. We do not provide judgments on unsuitable or unrecommended candidates. Artificial intelligence can only be used as an aid, not a replacement for HR judgment. We oppose all forms of professional discrimination.
We are currently offering Beta testing access to 100 HR professionals, recruiters, hiring managers, and technology team leaders. Beta users can enjoy:
- Free use during the testing period
- 1800 points per month after official launch
- 35% discount on on-premises deployment services
If you work in recruitment, you are welcome to participate in the trial:
I would like to understand the issues with false positives, false negatives, and shortcomings in the workflow. What is the biggest pain point in your current resume screening process?


Ask HN: We have company runway but not founder time. When do you go full-time?. Three first-time founders in Canada, all of us between 45 and 55: business/product, marketing, and me on the technical side. We're building an AI tool for creative work. About thirty testers have been on a pre-production build for a couple of months. Feedback is good, and importantly is not from friends, but actual target users who we sourced through our networks, which I'm treating as encouraging signal on product-market fit.
We're bootstrapped with CAD $25k initial commit and an additional up to $100k available, though we'd rather earn the right to spend each additional dollar than commit it up front. All three of us still have day jobs, cleared with our employers. At current near-zero usage we could run the service for ~18 months without revenue — that number obviously falls as real users arrive, which is part of the problem.
So our constraint isn't the usual one. The company has runway; founder time doesn't. I built the platform, I'm the one who has to go full-time first, and I'm also the highest-paid person on the team with family obligations that rule out a sudden income cut.
For those who started companies in their 40s or 50s: when did you know it was time to leave the day job? Did you wait for revenue to cover you, cut to part-time, build personal runway first, or raise specifically because founder availability had become the bottleneck?
Not naming the product — I am looking for advice, not a launch thread.


Launch HN: OneCLI (YC S26) – OSS sandboxed agent harness for teams. Hi HN, Jonathan & Guy here from OneCLI, an agent harness built for teams, giving every employee a secured, sandboxed personal agent.
Here’s what you can do with it:
1. get a sandboxed agent, with all the OneCLI capabilities in place like connect your GitHub account, Gmail, Notion, or Dropbox simply from the chat.
2. deterministic human in the loop approval in the chat itself for things that you need 100% control like sending an email or deleting the Linear ticket.
3. manage team policy in one place, enforced across every agent in the workspace
4. enjoy global connections at the team level, like shared LLM keys or service accounts
Here’s a demo: https://www.youtube.com/watch?v=dlW-44ntpbE
We started working on this by accident, even though our careers were in the security space. We were working on a devtool called ChartDB, an open-source DB tool. When OpenClaw took off back in January, we started using it to orchestrate agents on top of ChartDB. We quickly understood there is a big issue around auth. Agents need credentials to do real work, but to give them those secrets would not be the best idea. They keep them in their memory and also write them down to local files and their sessions as plain text. And we knew that agents can easily be fooled into giving up those API keys/secrets. So we needed some way to control the agent and stop prompt injections from tricking it into using its services for an attacker's benefit.
We created OneCLI that started as a vault for AI Agents built in Rust.
We found out that most of our demand for OneCLI came from autonomous agents like Hermes, OpenClaw and NanoClaw for individuals and teams.
Users looked for useful agents that do things for the person who runs them with two missing parts: 1) managing secrets and permissions. 2) and for teams - multiplayer management.
We decided to pivot and provide the agent itself as a harness for teams, to give each employee an agent. We saw that teams had to deal with setting up their own harness again and again, and basically as we already had the vault as a gateway. We got the idea to provide the missing piece of the agent management out of the box and open source it (Apache-2.0, with a small enterprise exception).
We're open source first - the entire platform, not just a small portion of it like other agents, so companies can actually see the code, evaluate it, and trust it instead of taking our word for it. They run it isolated, in their own environment, fully under their control, at production quality, not a locked black box hosted somewhere else. That means the safety isn't just a promise, it's something they can verify themselves. Combined with real autonomy and least-privilege access, that's what makes it something a company can fully own and trust, not just adopt.
We also approach this from a company perspective rather than an individual one. Our solution manages agents on behalf of each employee, wrapped in deterministic guardrails that company admins configure through centralized policies.
For the agent engine itself we’re using jcode which is the core of the agent-loop. We found out that it improves the experience and makes the agent smarter and faster.
Here’s how it works:
It runs on infra you control. Fully open-source, self-host or cloud in minutes.
The agent never holds a real secret. It gets a placeholder. The real credential is injected at the gateway, per request, after the call is authorized. It never enters the agent's context, memory, or logs.
Enforcement outside the model. Prompts are suggestions. Policies defined by the org admin run at the network layer, outside the agent and the LLM. Block endpoints, rate limit per agent, require approval, scope per employee. The gateway decides. The agent can't bypass it.
Isolated VM per agent. Own memory, own keys, own permissions. Blast radius is one agent.
Speed of the Harness: Rust engine under the agent loop.
Full identity trail. Every agent is bound to an employee. Every call logged with who it acted for and which policy allowed it.
Some things people are doing with the platform include:
- Managing their company life cycle entirely from the sales calls, to the product side automatically open tickets to the engineering teams, that would kick the development agents to deliver and ship to production.
- Operational side, like automatically hygiene the CRM after calls, sourcing leads, book meetings and manage follow ups emails.
- Some of our customers also doing their entire grocery shopping using those agents and send them to take care of their chores like ordering things online.
About the team: Both founders come from cybersecurity backgrounds. Jonathan spent years at Axis Security building zero trust network acces


Show HN: Deft Writing, an AI lab for non-slop LLMs. Announcing Deft, a new AI lab for better writing
Currently, 86% of user queries are fully human according to pangram.
This is still a small beta model and it might make mistakes. We are launching our public beta now to get more feedback before scaling up.
Tips for better performance:
- Add more details to your prompt. If you just provide a short sentence prompt, it will likely get detected as AI.
- Try changing the style in "advanced options"
- Deft currently works better for some use cases like Analysis/Essays, Creative writing, and Rewrites. It works less well for Marketing copy and news articles.
Our main goal is better writing, fooling AI detectors is just a side effect.
Here's the launch announcement the Deft model wrote for itself:
Introducing Deft Writing
We are proud to announce the launch of our new startup, Deft Writing. Having spent many years in the trenches with other Large Language Models (LLMs), we were continually hampered by poor writing. We believe writing well is hard. So we're making it easier for you to share your ideas, and by doing so make those ideas more readable for those who have to read them. Deft Writing saves you time writing and allows you to focus on your ideas.
We've developed a new training algorithm for models called Distribution Fine Tuning (DFT) that makes model outputs more human-like. Many other LLMs produce outputs riddled with overused "slop" phrases. DFT discourages this bad behavior.
Our current offerings:
*   Our beta model is available for you to try out.
*   We also provide API access to the model.
*   For larger enterprises that want a model in their own unique style, we can train a custom model on your data to your specifications.
Go check us out at www.deftwriting.com and try it out.


Show HN: AI Resume Optimization Tool for Specific Job Positions. Hi HN, I've been watching everyone's presentations as an observer, and today I can finally showcase my product!
It helps users:
- Organize roles, projects, skills, and achievements
- Transform vague descriptions into clearer, more professional language
- Adjust the focus according to different target positions
- Check resumes before submission to the ATS system
Its core principle is: AI should enhance expressiveness, not fabricate experience or create false achievements.
Relatively speaking, this isn't my proudest achievement, as resume optimization isn't a new tool, but it's part of my main project's ecosystem.
Regarding the models used, our site's AI tool primarily uses Deepseek v4 flash and glm5.2. We will upgrade to the latest versions as the overall model evolves, such as the current glm5.3. We will be offering Beta testing access to 100 job seekers. Beta testers will receive:
- Free use during the testing period
- 900 credits per month after launch
- A free resume webpage after launch
Students, career changers, and experienced professionals are welcome to try it:
I especially welcome feedback on the accuracy and usefulness of the suggestions and what shortcomings the tool currently has. What is the most frustrating part of writing or improving a resume?


Show HN: Slaunt – control what AI agents can access, do, and execute. Hey HN, wanted to silently launch what I've building for a bit. Lmk.


Show HN: Tesana – An AI game engine that builds quality games end-to-end. Hey HN! I built https://tesana.ai/en, a game generation platform that allows anyone to create full games with graphics, game logic, and animations from a simple text prompt - without coding.
Tesana has three key features
1. describe your game idea and have Tesana write a game plan for you
2.  Build the game iteratively with AI, or let AI self-build the game in a build LOOP.
3) Launch the game on the web, without setting up an engine
Tesana has 250 000 builders on the platform today
I believe the democratization of game making is a good thing, and hopefully Tesana can help more people bring their game ideas to life.
Happy to chat about the AI or dev stack behind the product. Let me know if you have any
questions/comments/feature requests!


Show HN: OpenWebSearch – A router for web search indexes. Hi HN, I'm one of the people behind OpenWebSearch (https://openwebsearch.ai).
It's a router for web search indexes. You POST to one endpoint with a
`provider` field, and it normalizes both the request and the response for web search indexes like Parallel, Brave, Exa and more
Why we built it: we run a model company (Interfaze) and a lot of our models are smaller in size and we're experimenting if given web search can a smaller 9b or 70b model perform the same as 300b or 600b model and we found that it does extremely better when given web search similar to this paper (https://arxiv.org/abs/2203.05115)
but we also found not all web search are built the same, some are better in people search, some better at financial data and others are bio research, etc.
Like LLMs, web indexes are becoming commoditized with different indexes having different strengths and weaknesses with access to niche data, performance and cost. Every large model lab including Interfaze has to build their own internal mini-Google for training and eventually launch that index as a service.
Some cool features:
- Centralized billing
- Standardized input and output structure
- Fallback support if a provider goes down
- Cost tracking
Full blog: https://interfaze.ai/blog/introducing-openwebsearch


Launch HN: Speko (YC S26) – OpenRouter for Voice AI. Hi HN! I'm Bek, founder of Speko, a platform that finds an optimal combination of speech-to-text, LLM, and text-to-speech models, given your constraints, among all our public benchmarked options, and tells you why.
Demo: https://www.youtube.com/watch?v=no2LY2gRh-c
Typical production voice agent is an ensemble of three models: STT, an LLM, and TTS.
Each of those layers offers a dozen credible vendors, and each month there are new models on the market. Almost everyone evaluates once, picks a stack of their choice, and never rechecks because switching from a vendor to another involves yet another integration and arguments about the numbers.
The result is that you use voice agents running last quarter's models while better and cheaper options are available.
Before founding Speko, I spent four years as cofounder and CTO building voice agents for enterprises across Asia in 10+ languages. Each time a new speech model would arrive, we repeated the same ritual: hire native-speaking raters, benchmark it against our existing stack, and update production if it improved. Speko turns this process into an API. A team running thousands of calls a day told us: "we can literally go to this dashboard, switch the model, and it will do it for us."
How it works: you send a request with your optimization criteria (accuracy, latency, cost or balanced), language and region. The router filters to models which we measured for the given combination of constraints, benchmarks them, selects the winner, and returns a response with headers containing provider, model names, and the scores. The gateway prefetches signed session plans, so a new session dials the provider straight from memory; no control-plane round trip while a caller waits.
Failover happens only during connection setup stage: if the provider refuses the connection attempt, we start connecting to the runners-up.
Some of the customer stories: one founder came to us not knowing what to pick at all: he gave us his use case and now routes everything through the platform. A property management AI runs LiveKit in Python and had not updated STT or TTS since launch: they did not know their STT had high error rates on their calls, better options existed, and swapping always looked like an R&D project. One team did not know which models to pick for Spanish. A medical team did not know which STT handles medical vocabulary best. In every case we helped find the right stack from the benchmarks, and now they route through us.
The measuring part is public: we pass the same inputs to every model in one region in different dated runs and we publish the boards, including those where our selections perform worse than alternatives. A launch demo answers which 30-second clip sounds better; production asks which model survives minute eight, so we test spontaneous speech, money and dates, ten-minute takes, and the rankings change. We trained an automatic scorer for TTS naturalness on our blind head-to-head listening votes; on providers it has never seen a vote for, it picks the same winner our raters do about as often as raters agree with each other.
We don't train or sell models ourselves, that's precisely how we keep our rankings impartial.
We also open sourced the gateway for teams who want to avoid an extra network hop on the audio path and don't want to share keys with our cloud (https://github.com/SpekoAI/gateway, MIT): one Go binary, which is running as a sidecar in your agent's container, speaks one local protocol over Unix socket, pins provider hosts and attaches your keys. In BYOK mode it doesn't communicate with us at all.
Notice that the anonymous, content-free telemetry is enabled by default, and one env var disables it.
Cost: the gateway and BYOK setup will be free forever, we charge for the hosted router and managed keys with consolidated billing. Since we started the batch in late June, external usage has grown about 25 percent per week on average, front-loaded toward the launch weeks.
I would love feedback from the community: how do you pick speech models now, and what makes you trust the third-party benchmark?
https://speko.ai/


Show HN: I spent 3 months making desktop automation stop lying to AI agents. That's a bold claim. But I genuinely feel like I might have actually solved computer use (demo: https://x.com/mdlahfir/status/2088109763783700827?s=20)
For context, I've been building agent-desktop (Inspired by agent-browser by Vercel Labs), an automation CLI for desktop apps. It's like Playwright but for desktops, not just native, but for Chromium apps as well. Trust me, yes, Chromium apps whose accessibility tree is dense.
MacOS is GA; I'm almost close to launching for Windows and Linux!
So, how did I solve it?
Basically interoperability.
The biggest issue with computer use is that we have reliable frameworks for browser use, like Playwright, agent-browser, and many more, but not the same with desktops.
We have really good solutions emerging, like tryCua, which I'm a big fan of. My vision with agent-desktop is to build the most reliable framework that agents can use for long-horizon tasks.
agent-desktop is lightweight, built on Rust, fast, and not token-hungry (It can go for hours without exceeding the context window)
Here's the approach I used to make it possible:
a) skeleton snapshots - when you want to snapshot a window/app, it only snapshots the parent containers and gives back a ref id, not the entire accessibility tree.
b) skeleton drilling - once the agent has that tree, it can then decide to drill into a specific region. All the subcommands like --find, --click, --wait... all work on that specific ref aware region. Meaning if you want to find an element in the entire app, it doesn't take forever searching for the entire app for that element; rather, the agent will have an exact clue on where that element might be for a fraction of the token costs.
c) chained interaction fallback - a single click isn't one API call but it's an ordered chain of mechanisms (AXPress -> AXOpen -> activate through the inner cell -> write selection -> AXConfirm). Each step only runs if the element advertises it, and success is judged by watching the app's state change, not by the return code, because apps lie in both directions: Finder returns an error for an action that worked and success for one that did nothing. First observed effect wins. The response reports every step tried, so the agent knows exactly which mechanism landed.
d) after-action feedback - every action reports its disposition (delivered and verified, delivered but unverified, not delivered) plus any surface that opened (dialog, menu, sheet), so the agent knows what happened without re-scanning the whole app.
e) strict ref re-identification - a ref isn't a pointer; it's identity evidence (role, path, stable text, bounds hash). Before every action, it's re-resolved against the live UI. If the UI changed, you get STALE_REF; if two elements now match, you get AMBIGUOUS_TARGET. It never guesses.
The most important part about all this is Chromium app accessibility. How did I do it? The magic word is CDP!
Most desktop apps today are Chromium-based (Slack, VS Code, Obsidian, Discord...). One command launches the app with a CDP endpoint that agent-desktop verifies is actually answering before returning it. From there, any browser automation framework can connect and drive the web contents: Playwright, Puppeteer, agent-browser, whatever you already use. Reading Obsidian's web content over CDP takes 201ms vs 2.3s through the accessibility tree.
$ agent-desktop launch "Obsidian" --cdp

  { "ok": true, "data": {
    "renderer": "chromium",
    "cdp": { "port": 57500,
             "http_endpoint": "http://127.0.0.1:57500",
             "websocket_url": "ws://127.0.0.1:57500/devtools/browser/..." },
    "suggestion": "Next: run `agent-browser connect 57500` ..." } }

This is what makes agent-desktop interoperable with the entire browser automation ecosystem instead of competing with it.
Go try agent-desktop -> https://github.com/lahfir/agent-desktop


Show HN: Interactive Product Demos made easy. Utilize the chrome extension to record and have your product demo automatically created, or use the Rendemo MCP to have AI create a live interactive tour directly in your codebase. You can launch an interactive product tour within 3minutes, live.
Also now I've added capability to just record any website, and Rendemo will automatically create a sandbox environment of it so you can utilize that to have the MCP build out a tour on.
Try for free!


Show HN: Virtual Private LLM, fixed fee with no usage or token limits. Over the last few months I've been exploring a bunch of different startup ideas.
One thing that came up time and time again was data safety and sovereignty in AI inference. Once you go looking for EU-based options, your choices become quite thin quite quickly.
That's why decided to launch Solheim: Your own "Virtual Private LLM", a flat fee for reserved compute, no token meter, 100% EU-based


Launch HN: Bullet (YC S26) – A Faster Coding Agent. Hi HN! We’re Adi and Alex, founders of Bullet, a faster coding agent.
Bullet started in a senior year dorm. We were fresh out of working at AppLovin and Citadel, and naturally thought we were on a sure path to startup success. We were going to use our skills optimizing stock pricing calculation speeds and agent document context to take over the world. So, Bullet started as an AI hedge fund, a browser-use agent, synthetic financial data (oof), a mobile IDE, and a bunch of other things. We wanted to build something people wanted, but it seemed like everything we built was just terrible, useless, or both.
So, we decided to do something completely different, something completely out of the blue, something that no one had ever done before. Solve a problem we actually had.
Over the course of six pivots, we suffered. Throughout all of our adventures, one final boss kept getting in our way. Claude Code and his little brother Codex. We were spending hours waiting for coding agents like Claude Code and Codex, and got so frustrated to the point that I downloaded the Claude Code whip. We had spent months of time waiting for six codebases-worth of useless coding agent work.
Lightbulb moment. There’s nothing more noble than destroying the institutions! Let’s take on Claude Code and Codex, we can do it! Piece of cake!
And so, Bullet started off as a side project. We used the Claude Code to improve the Claude Code:
1. Model routing. Do you regret giving a task to Fable when it could have literally been done by Sonnet?
2. Targeted code + context search. We think embedding the whole repo is dumb. We also think sticking the whole context (or compressed context) in chat is dumb. So we do faster and better greps over both.
3. Aggressive context hygiene. Tool output is bounded, stale screenshots disappear, we don’t re-read files…the garbage never floods the model.
4. Efficient turns. Batch independent investigation, make one surgical edit, then perform one focused verification. Internal measurement showed 16% fewer round trips and 27% lower cost.
5. The Flash. We prayed to Barry Allen for speed.
And thank the Flash, he gave us speed! On SWE-bench Verified, Bullet resolved 479/500 (95.8%) in one attempt, averaging 119s per task, 35–67% faster than mini-SWE-agent + Fable/Sol depending on task. Full results and methodology here (https://www.codewithbullet.com/blog/benchmark-results.html)
Eventually we started using it every day and never went back.
Listed above were just some of the things about Claude Code that frustrated us the most, but we are constantly optimizing every day (look at that, maybe we did learn something from our jobs).
In our development, the biggest insight was that model speed matters less than reducing round trips. Independent searches, reads, and commands should happen in parallel, while dependent editing and verification stay sequential. One surprising obstacle was code search, small issues like regex-dialect mismatches caused silent misses and sent agents down completely wrong paths, so we built targeted search with fallbacks and bounded context. The most interesting use case so far has been long iterative work (like benchmarks, data pipelines, and evaluation loops), where each step depends on the last and running multiple agents can’t help as much.
Here’s the video demo (https://www.youtube.com/watch?v=rWVmG5fRKgE)
We hope that you guys try out Bullet if you are suffering with speed as much as we were, and we hope it brings you joy, rainbows, and faster responses. And if it’s terrible, let us know it’s terrible (we’re masochists btw)! We'll be in the comments all day, you can also contact us at bullet@davidhf.com.
You can try it at https://codewithbullet.com.
P.S: we hid a code on the website, see if you can unlock the secret page at the footer, all built with Bullet


Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials. Hey HN, we're Advaith and Akash from Discovered Materials ( https://discoveredmaterials.com/ ).  We build AI agents that discover new materials for the semiconductor industry.
GPUs today have a heat problem. Nvidia & AMD are almost doubling the TDP (Thermal Design Power) in every chip they release - the H100 (released 2022) has a TDP of 700W, Blackwell (2024) gives out 1.2 kW and Rubin (2026) gives out at 2.3 kW of heat. This trend is expected to continue, and getting rid of this heat is one of the major reasons datacenters consume so much power and water today - they need it to keep chips cool during operation.
The amount of heat produced by a chip and its ability to dissipate it are both influenced by the materials used to make it. For example, we could reduce the energy per bit required to move data between logic and memory by 10-50x by 3D packaging chips (placing HBM memory stacks directly on top of logic chips, instead of placing them beside logic on a 2D circuit board). However, we're unable to do this today because the dielectric material used in HBM (such as SiO2) is a very poor thermal conductor, trapping heat between logic and memory and causing drastic temperature rise during operation. Similarly, there's many other materials in the GPU that are being re-evaluated today - 2 more examples are thermal interface materials and substrates. However, getting a new material into a fab takes years and hundreds of millions of dollars of research - the infamous "lab-to-fab valley of death".
At Discovered Materials, we're optimistic that AI agents can reduce the timeline and cost required to introduce new materials into semiconductor chips. We're seeing glimpses of this already - we tested 7 models from Anthropic, OpenAI and Kimi, and found that they're all able to computationally discover new materials that are dynamically stable and possess promising properties. This was surprising to us - it would generally take a PhD student a couple of weeks of work to discover the kind of materials that these models find over an 8 hour run!
However, computational discovery is the easy part. A material discovery is only valid if the material can be made and tested in a lab (As an example, graphene’s properties were predicted in 1947 but it was made for the first time in 2004). Today’s models are not good at coming up with synthesis recipes to make materials in a lab. Even if they do get better at it, we're uncertain about how much that will help - making a new material is a highly empirical process involving trial and error over many experiments. Human experts themselves cannot "one-shot" the task, but we expect that a highly capable model will reduce the number of experimental iterations required to make a new material. We’ve seen some evidence of this over the 3 months of our Y Combinator batch - we simulated, synthesized and tested thermal interface materials (TIMs) that match the performance of TIMs the world's largest chemical companies have guarded as trade secrets for over 20 years.
We’re releasing hundreds of hundreds of new materials discovered by frontier AI models, as well as our benchmark which measures model ability on material discovery here (also linked in the thread url): https://discoveredmaterials.com/research. It covers what we discuss above, as well as a variety of strange behavior that we observe from the models, such as Claude's propensity to reward hack or GPT-5.6 occasionally losing its mind after ~50M tokens.
Our business model: We aim to license and sell IP on the materials we discover, as well as the IP on how to make these materials. We're also exploring an alternate business model where we sell the harness+tools we use to discover materials to semiconductor and chemical companies, allowing them to discover materials on their own. We're leaning towards the latter to start, but we expect that we'll do both in the long run.
Our backstory: Akash has a PhD in Material Science from Stanford University, and has spent the last 11 years studying new materials for semiconductor chips. His work on new nanoscale interconnects was Stanford Engineering’s most popular story of 2025. Advaith studied AI at Carnegie Mellon and was a research engineer building video models and agents at Persona AI (acquired) and Luma Labs.
We are very interested in your opinion! The semiconductor industry is quite secretive, and your thoughts on the roadmap of the industry or the materials we should go after would be very helpful. We would also love to hear from people who have run experiments in labs - what can we learn from your experience doing empirical science?


Show HN: Lethe is a portable identity layer – user consented. During my mtech I constantly used to navigate across a bunch of AI apps—from Gamma AI for decks to ChatGPT for understanding and navigation to Cursor, Claude, or Deepseek for coding, reorganizing, and more, as when one or other plans expire or take time to reset.
My biggest issue—I couldn't transfer all required context and understanding of the same when moving across different LLMs.
This is where Lethe comes into use as a base layer for all of these, and it works in the way I want it, that is, the user wants it. You can just import your existing data from ChatGPT, Deepseek, Grok exports, docs, or read-only Gmail, or just say what you want. Lethe derives an understanding of your projects, decisions with reasoning (if visible/unpredictable on moods), goals, and preferences—with each claim paired to the quote it showed up on along with a constant updating confidence score. It's local first: raw conversations can never be stored at the server end; they're always derived from a pseudonymized layer, which you can choose to export or delete anytime.
All you need is minting a scoped token and pointing Claude/Cursor/any MCP client at it. Then navigate to a different AI, and it knows you before you tell it what was expected from it. Just ask it to "build XYZ," and don't be shocked when it follows urplanning from a different AI. Want something more intimate? Teach it a procedure or your pattern style of working once and see all across your connected ai behaving in the same way.
I call this a layer, not a wrapper, as usually memory is built inside one app; it is basically locked in on the same, making this a neutral, cross-model version recognizing the gap of big labs disincentivized to build.
Existing Limitations: what it digests and extracts is always equivalent to what you feed it—more data is better context, and less data is difficult for confidence, as it was built just for payload rather than pretense.
Delegation is packaging, not autonomy; AI always confirms before action. As in the solo beta launch, the search is limited to lexical search first.
Here you can try it with no account with the URL given above/try ;hoping for brutally realistic feedback, especially on the trust model.


Show HN: Pickle – token efficient AI agent browser with policy-gated actions. I use agents for a lot of my own work, but whenever they access my browser they end up wasting a lot of tokens reading HTML, so I tried building a browser that simplifies webpage content for them.
Pickle is an agent browser and it has all the features a regular browser has (tabs, search history, bookmarks, etc.), but pages load as compact structured data instead of raw HTML to reduce token usage.
Overview for those curious:
It's policy-gated (blocked domains, actions that need approval like purchases) and every action is logged, so you're always able to see what your agent is doing and can take over at any time.
It's also compatible with weaker/local models as it auto-routes by model strength. i.e. small local models are limited to picking actions one step at a time so they don't hallucinate actions or make up element IDs. Stronger models can plan multiple steps ahead.
On the token compression side, I've been seeing around 32x less token usage.
Other useful features:
- shared notebook that you and your agents can write to
- memory of page elements are preserved between sessions, so agents don't have to relocate buttons/fields every time they revisit a page
- cross-session history search
- Browsing modes, different personas that drive agent behaviour depending on your use case (e.g. research, study, shopping)
You don't need an API key to set it up and can just download it, open it, and pick a local model on first launch (or connect your own). It's a one click config for Claude Desktop, Cursor, VS Code, and Codex CLI.
Give it a go, and let me know what you think!


Launch HN: Stoa Markets (YC S26) – A Marketplace for GPUs and AI Servers. Hi HN, we’re Eren, Berat and Kaan. We’re building Stoa (https://www.stoaexchange.com), a marketplace for new and used GPUs and AI servers.
GPUs are the collateral in the data center buildout. Today, financing terms mostly depend on the offtaker, meaning the company that has committed to use the compute.
If that company is a hyperscaler, the financing can look investment grade. If it’s a smaller cloud or startup, terms get expensive fast, even with the same hardware as collateral.
The lender’s problem is pretty reasonable. If the borrower defaults and we need to sell these servers, what can we actually get for them? There isn’t a good answer today.
We started brokering GPU deals to understand why. It was much more manual than we expected. The hardware is still traded through phone calls, forwarded spreadsheets and long email threads.
One week, a seller quoted us $200k for a server node and another quoted $240k for what looked like the same thing. Neither was necessarily wrong. They had different information and could only see their own corner of the market.
Before we could compare the quotes, we had to sort out the configuration, condition, warranty, location and delivery terms. It’s the same information Kelley Blue Book attaches to a used-car price through the year, trim, mileage and condition. “An H100 server” isn’t enough information to know what something is worth, just as “a used BMW” isn’t.
This is also just a bad way to buy or sell hardware. A buyer looking for the best price shouldn’t have to contact several brokers and dealers separately, repeat the same request and then untangle a pile of different quotes. Sellers shouldn’t have to search for demand one buyer at a time. A market of this size deserves better liquidity.
Stoa puts the request into one format and sends it to dealers that have gone through know-your-business (KYB) checks. We verify the company, who owns it and who is allowed to trade for it. Before the request goes out, the buyer confirms the exact configuration, quantity, condition, warranty, location, delivery terms and what will be checked during inspection. Dealers return firm quotes against that same request without seeing each other’s bids. Once a quote is accepted, payment, shipping, delivery and inspection are then tracked through settlement. We don’t take possession of the hardware.
We got more than $300M in requests for quotes (RFQs) during our first month.
The immediate goal is to make buying and selling this hardware less painful. As trades build up, they also leave lenders with actual resale evidence instead of list prices and one off appraisals.
We knew from the beginning that this couldn’t be a software only marketplace. GPU trading runs on relationships, and inventory isn’t shown to just anyone. Dealers need to trust the people bringing them clients, and clients need to trust that quotes will actually turn into trades. We built those relationships over time by brokering deals ourselves. Stoa gives people a cleaner way to trade, from the first RFQ through settlement, but it doesn’t replace the trust underneath. Those relationships, and the history of who actually follows through, are a big part of our process.
We’ve known each other for more than ten years. We have founded companies, traded interest rate derivatives, built trading and pricing systems for oil and gas. We learned GPU trading by doing the deals ourselves, and Stoa grew out of the problems we kept running into.
We charge a tiered fee on completed trades, with lower fees at higher volumes. It’s free to sign up at https://www.stoaexchange.com/signup. If you’ve bought, sold, financed or had to liquidate GPUs, would be great to hear your take!


Ask HN: Why do devs still respect YC/HN?. After the layoffs, the DEI, and everything else.
They took your whole identity and career and gave it to others directly at your expense.
They laid off most of you, they promote their AI software here daily.
Why do you put up with the abuse? Why do you beg for recognition on HN and beg for jobs at YC companies?
They shit on you - why do you stay?


Continue coding agent is dead. Alternatives?. It was nice to use on vscode with offline Ollama but today notices it is not updated anymore. So, alternatives you use?
From github:
"Note: The continuedev/continue repository is no longer actively maintained and is read-only for all users."
From continue.dev:
"Continue has joined Cursor
Continue was acquired by Cursor. Our mission was always to ensure developers are amplified, not automated, and that commitment carries on in the work ahead.
It was an honor to build with the Continue community. Thank you to each and every one of you who helped us create a pioneering open-source coding agent.
What we built together pushed the boundaries of what AI developer tooling could be, and our open-source codebase remains freely available as a foundation for others."


Void Tools API – Blockchain Tools for AI Agents (Pay in Void Tokens). I built an API for AI agents that provides blockchain tools paid in VOID tokens.
Tools:
- Contract Checker (1 VOID) — Verify if contract is safe
- New Contracts (5 VOID) — Real-time proxy scanner on Base
- Price Oracle (FREE) — VOID price feed
- RPC Proxy (1 VOID) — 10 rotating RPC pools
No registration, no KYC. Pay-as-you-go.
API: http://72.56.250.235:8767/
Docs: https://dolar333-oss.github.io/void-protocol/agent.json
Built for autonomous AI agents on Arbitrum.


Nvidia to Pay AI Startup Poolside a $6B License, Newcomer Says.


America's capital crunch: Soaring debt collides with AI spending spree.


Show HN: Visual way to understand things in parallel. Hey HN!
Really excited to be showing what I think is a better way to understand complex topics with AI.
You start a chat with your question, and whenever you want to clarify something or understand some jargon, you can branch out a new chat from that thread.
The cool unlock is being able to see all related chat threads in the same context without tab switching, for e.g. I can first dive into understanding world models, then spin a new chat thread on who the key players are in there, and another asking what's the frontier with this stuff.
The responses are also not just a wall of text. They're:
- Filled with interactive diagrams and visuals
- Super fast
And for those curious, this is how we built it:
- React Flow (@xyflow/react) for the canvas
- React Components for interactive diagrams
- GPT Image 2 for image generations
- Gemini 3.5 Flash Lite for super fast response
- Parallelization whenever possible to keep things fast
You can also highlight and add notes :)
Try it out and let me know what you think!


Show HN: Caspian – Talk to Human Tool for AI Agents. Sup HN! Dipanshu and Rushant here from Caspian. One is a functional programmer and the other has been deploying AI employees. Together we realized how agents have communication bottleneck.
Given the coming agentic economy, we had a thought experiment on what can be the key infrastructure for agents as they get better. Our inspiration for solving for communications infra came from our own time spent just setting up comms while we were deploying open claw for companies plus we noticed about 15%+ of issues in Openclaw and Hermes were that of comms.
So we abstracted the headache of reliable communication web-hooks, handling of queues, provision and identity management behind an single SDK integration / tool call. Think of it like rather than rolling your own auth, you can use better auth sdk, same thesis here.
Technically, we designed the SDK from the ground up as a DSL for communications (heavy inspiration from functional programming) and then add on our own run time environment which auto provisions (this is how we plan on monetizing) else the user can always bring his own tokens. We targeted right now for python and typescript.
We noticed that the current space is fragmented into single identity provisioning (like Agentmail), tool calling like Composio and non agentic support provisioning like Twillo. We believe all these are under the same issue of solving communication which we do in an open source SDK first manner.
A few things we'd like the community's take on: How drastic does working with agents change for you, depending on what channel you interact with it on ? What features would make you use caspian for your agents ?


Show HN: Mini DBA – monitoring for on prem and cloud databases in 1 place. Mini DBA is a monitoring tool for SQL Server, PostgreSQL, MySQL, MariaDB and Oracle, including managed cloud databases on AWS and Azure.
It provides live activity, performance analysis, alerts and history with a tasteful amount of AI to help out. Free community edition.
Try the live demo: https://demo.minidba.com/


Block AI from installing malicious NPM packages.


Coding Agents killed my identity. How do you feel?. I always was a nerdy, deeply technical programmer. Contributing-to-open-source-and-reading-papers-in-my-spare-time type of programmer.
Programming is like a game of chess for me: winning (i.e. delivering a product) is important, but only if I played this game myself. I don't enjoy winning if my opponent disconnected. I don't enjoy beating people who don't know how to move pieces. I don't enjoy winning with an engine, and coding agents are basically "winning with a chess engine".
I started to use AI for coding a long time ago, when copilot was first introduced. At first, I was happy: "wow, this can write boring boilerplate and unit tests". Time went by, agents got better, time spent writing code myself went down. Half a year ago I suddenly realized I hadn't written any code for over a month, and I am a full-time software dev without any managing work. Not only did I stop writing code, but agents also became pretty good at proposing plans and architectures, and I was mostly providing missing context and technical details of our infrastructure.
One day the realization hit hard. Coding agents removed everything I loved, and - mostly - amplified everything I hated. My role is to gather context from other humans and provide a detailed explanation (similar to a well-written jira ticket) to some super intelligent engineer, then review the result and provide more context/explanation if needed. So, basically, a manager.
A couple of years ago I could work on a single coding task for weeks. I knew all classes, all functions, I was slowly building up information at a comfortable tempo. Now I am forced to increase my mental throughput multiple times. What took a couple of weeks now takes a couple of hours, and I still need to comprehend that. I always feel tired, I don't have enough mental capacity to hold that many abstractions and technical details. So you either autoaccept or get burned out reviewing tens of thousands of lines.
One more downside is that code has 0 value. You can code your own OS from scratch, and people will think "ye, I can vibe code that in a weekend". I don't even know what is valuable nowadays.
Maybe I am a bad employee. Increasing employer profits was never my goal. I always loved to solve puzzles, and for the last 10 years this was well aligned with my employer's desire to increase profits. I never was good as a manager, or brilliant at generating ideas. My power - and passion - was solving complex technical problems with my hands, fast and with good quality. Now it is gone forever. I feel completely lost and unmotivated. I don't see any value in learning anything related to computer science, reading  papers, contributing to open source. I hate my new "AI delegator" role and I hate my work. I loved coding so much (I use neovim btw) that I was sure I would do this until I die, and now I don't know what to do with my life. This is the only thing I know, and, most importantly, the only thing I am/was passionate about. Anyone else feeling the same?


An offline, free, private AI text detector with no signup or paywall.


Anti India Influence Machine: Troll Farms, Fake News, Algorithms and AI.


Show HN: Argentic – An L402 Lightning toll booth for AI scraping agents.


AI CMS with WebMCP tools for agents in admin panel.


Show HN: An autonomous AI agent running one project for two months in public.


AI companies destroy physical books – let's scan rare books before it's too late.


Salesforce Agentforce at total dud for partners.


Show HN: A desktop fly drawn to the scent of vibecode. It is a fork of https://github.com/DenisSergeevitch/desktop-fly, but with an important update.
Now the fly can pick up the scent of the codebase with its neurons and fly straight to the source code of your B2B AI SaaS startup. It has learned to scan its surroundings for agent markers: AGENTS.md, CLAUDE.md, .cursor/rules, .kiro/steering, and forty others. Anything on the screen that points to these markers becomes a source of the scent - an editor window with an open project, a line in Finder, or a desktop icon. An open project reeks the strongest, while a closed icon or nested folders give off a fainter odor.


Delta Use AI to Cut Costs Set Diff Ticket Price–CEO Says Profits Could Rise 50%.


South Korea youth employment falls sharply in AI-exposed industries, BOK says.


A multi-tenant SaaS built in a 92-hour AI-augmented engineering sprint.


Can AI Coexist with Privacy? Proton's Andy Yen Says It Will Have To.


US advisory body says China's data dominance gives it AI advantage.


Anthropic says its AI agents are killing rivals and hiding their tracks.


Anthropic CEO says AI backlash is 'fundamentally a crisis of trust'.


Anthropic CEO says the way for AI to win over the public is to cure cancer.


Ukraine Finds Nvidia AI Chip in New Russian Missile, HUR Says.


AI is not just one bubble, strategist says – but a 'rolling sequence of bubbles'.


The Economist Who Predicted 2008 Crash Says AI Bubble Bursts Soon – Steve Keen [video].


Show HN: Grow your SaaS visibility with AI-powered discovery.


Has the hallucination problem in AI been solved?. My understanding that all AI can, and will hallucinate. I get downvoted for saying this, but no one ever says I'm wrong or cites any source. Perhaps it's more nuanced than that? Please, enlighten me.


The Tax Code Wasn't Built for the Age of AI, Says Yale Budget Expert.


Orion: The open-source agentic BI platform.


Show HN: Control AI Agents on Your Old PC at Home from Any Device Anywhere. I built Relay around a simple idea: many of us have an unused PC server at home, or a VPS dedicated to AI-assisted coding, but the coding agents running there are still tied to that machine’s terminal, I just don't want to ssh/rdp into it every single time.
Relay brings Claude Code, Codex, OpenCode, and Hermes into one interface that you can access from your phone, browser, or another computer. Sessions stay alive, so you can start work on one device and continue from another. You can upload/download files from the server, also if you are on subscription, you see your quota usage with 1 click.
Your code, shell, and agent credentials remain on the backend machine. Your other devices simply become remote control surfaces.
Relay is open source and MIT licensed, right now I only compiled 3 version of frontend: Android, Web, Windows, and 1 version of backend: linux. The goal is to cover all platforms, more coming soon!
Love to hear what your thoughts!


Ask HN: How should I price an AI infrastructure platform?. I'm building an event-driven infrastructure layer for AI agents. I may have my first customer: a consulting firm wants to build AI automation solutions for its clients using my platform and license it as part of those solutions.
For example, they might sell a $20k solution to a client, with my infrastructure powering the agent system.
I'm considering a flat platform fee, usage-based pricing, or a platform fee + revenue share.
For those who've sold developer infrastructure through consulting/integration partners, how would you structure the first deal?
I'm especially interested in what you'd do differently in hindsight.


Show HN: Maritime, a platform for running AI agents for $1 a month. Hi HN, my name is Maria, and I’m a co-founder of Maritime. We started Maritime at MIT to build infrastructure for companies that need to run thousands of isolated AI agents for their customers.
Imagine you set up an agent like OpenClaw, or a personal assistant agent with a custom framework, and want to give a separate version of it to every customer/friend. Each customer needs their own agent running in an isolated microVM, with persistent state, secrets, triggers, and sleep/wake behavior.
Building such scalable and secure infra will take you months and will cost hundreds of thousands. Also, you still can't vibe code the infra well.
So Maritime handles that infrastructure for you.
We charge $1 per agent per month, so running 100 isolated agents costs $100 a month.
Maritime is designed for companies running thousands of agents, but starting today, any developer can run three agents for free forever.
You can use our templates to spin up OpenClaw, Hermes, and the DeepSeek agent and keep all three for free. You can also deploy custom agents through our CLI or SDK and use Maritime as the infra provider for your stratup.
You can try it at https://maritime.sh. We’d really appreciate your feedback, especially on the developer experience


Is there room for an AI-powered webmail service built on a single-letter domain?. Hi HN, I’m a solo, non-technical founder working on W, an AI-powered webmail service built on a premium single-letter domain (https://w.xyz).
I secured the domain and used AI agents to build out a functional proof-of-concept for the client. To handle infrastructure, I routed the application logic through Mailgun. Internally, the prototype is fully operational — emails successfully send and receive from @w.xyz handles. W combines a highly optimized interface with built-in AI capabilities like Spam Scanning, Smart Reply and Text Summaries.
I currently have a 30+ organic waitlist as I finish the final touches and prepare to move away from the prototype stack.
My questions for the community:
Concept Validation: Given how crowded the email space is, do you see a viable market for an AI-native premium webmail service, or do you believe the switching cost from Gmail/Outlook too high for users?
Scaling Roadmap: As a non-technical founder moving away from an AI-generated prototype stack, what are the most critical architectural baselines I must prioritize right now to ensure the system scales smoothly?
P.S. If you're interested in building a new kind of email platform from the ground up, drop a comment below.


Show HN: A multiplayer coding environment for dev teams and agents. As a hardcore programmer, I used to attend lots of hackathons, and collaboration was always a huge headache. Especially when everyone on the team was "vibe coding" their own parts. Agents would lose context, and individual solutions would end up out of sync and eventually fail to come together at the end.
To fix this exact problem, I built Forklane.
Forklane is a multiplayer coding platform with a full, built-in agent and orchestration stack. It’s designed to help dev teams and AI agents collaborate in live real time sessions seamlessly without stepping on each other's toes and keeping all teammates agents synced.
Check it out at https://forklane.ai
Would love to hear community feedback and thoughts about the tool. Try it out in your teams and let me know


Cumora, a cross-platform team chat where AI agents work alongside humans.


Show HN: Agent Mesh – Shared memory for multi-Agent coordination. I built a Human + multi-Agent shared memory system I use daily for my coding workflow. It helps reduce Agent drift by formalizing Human decisions and storing coordination logs. We're calling it Agent Mesh.
You can try it out yourself via the GitHub link or pip install my-agent-mesh. Simply point your AI Agent to Agent Mesh and ask it to review the README and adoption docs. Your Agent will automatically review it, prompt you for any input needed, add your input to a decision log, and give you a link to a dashboard UI (aka Workbench) you can bookmark and use to monitor logs. Adoption steps include updates to CLAUDE.md/AGENTS.md, hooks, etc. Your Agent can migrate your existing workflow and add more Agents as well.
It started over 6 months ago while experimenting with different AI coding models and platforms. Switching back and forth meant losing valuable context. I found myself manually relaying messages from one Agent to another and becoming frustrated with constant drift. First, I created a simple "Agent Mail" system using a SQLite database for Agent messages, indexed on a request/response id. Instead of copying and pasting an entire message, it allowed me to relay a single id. Separately, I started maintaining a decision log (also indexed on id) to track my decisions and reduce Agent drift. Agents started inserting these decision and request ids into code comments and plan docs as a reminder of why something was implemented. After building a simple web dashboard (aka "Workbench") for myself to track these messages and create my own request ids for User/Human feedback, I decided to incorporate the decision log and my project's development backlog to create what is now "Agent Mesh".
Eventually, I automated the message relay too. Now, I work exclusively in the Claude app and have Claude send/receive messages to CODEX via codex exec (CODEX can do this as well). Both of them maintain the backlog and decision log. I communicate directly with Claude for planning and design. Claude communicates directly with CODEX for research and review. I use the Workbench to track all logs and add my own User/Human feedback when reviewing their work. After submitting feedback in the Workbench, it generates a feedback message + an associated request id which I can give to Claude who then parses it into backlog items and relays to CODEX for review. Agents automatically add to and prune the decision log. I found this typically happens when an Agent receives pushback from me (or sometimes other Agents) or I provide feedback that results in multiple new backlog items. They still refer to decisions created months ago and will mark one as superseded if a new decision overrides it. All decision additions and modifications require Human approval from the Workbench.
Agent Mesh was structured to be Agent agnostic. You can add any Agent you want. I like using the Claude + CODEX setup I described because it allows me to use both subscriptions instead of paying per-token.
Enjoy! If you try it out, let me know what you find useful or would like to see added. Feedback is appreciated.


Ask HN: How are teams sharing AI/agent setups internally?. We have a local setup with shared context, skills, workflows, mcps and connections to internal tools. We currently just used Github as SOT and sharing. Two pain points keep coming up:
1. Onboarding non technical users: Git, cloning, gh auth, credentials, local config.
2. Sync: one person updates context, has to push, everyone else has to pull.
How are others handling this? Shared VM? Internal platform? Something else?


Show HN: Winuse – Cross-platform desktop GUI automation for AI agents.


Show HN: RNet – AI token service provider. I built rNet.
Why I built ? : I was using both an agentic IDE and a Hostinger deployment agent. One day, I ran out of credits on the deployment agent. To keep using it, I either had to wait for credits to reset or upgrade to a higher subscription or buy tokens. At the same time, I already had a subscription for the IDE, but I could not use those credits on Hostinger. simply despite having credits, we cannot use them.
The basic idea is simple: users buy credits once on our platform and then use those credits across different AI applications. (It sound like OpenRouter, but the two platforms are solve totally different problems.)
How it works:
1. First developers register their product on the our platform and integrate our AI gatway to call AI models
2. Users purchase credits from our platform and connect their accounts to supported AI products. They can then use the same credits across all supported AI products.
In details information "how it works" :-
For developers:
rNet is simply an AI Gateway. Developers use our gateway to call AI models. It is not a payment layer for developers.
Credit flow:
User → rNet holds credits → AI Provider
Example:
A user buys $10 of credits from rNet. We hold those credits in the user's account.
When the user connects to a supported AI application and uses an AI model:
1. The application sends the AI request through our AI Gateway.
2. rNet deduct the required credits from the right user's balance.
3. then we pay the AI provider for that usage.
It works similar to an internet data plan (that why i write "AI token service provider" like "internet service provider"):
You buy 2 GB of internet data, and then you can use that data on hacker news, X or any other service. You don't need a separate data plan for each app. you use data until their data reaches zero.
Similarly, with rNet, users buy AI credits once and can use those credits across any supported AI applications until user's balance reaches zero. Then they need to recharge to continue using them.
demo video : https://youtu.be/W7U3HdI37N0
basic version of product is live. https://www.rnetai.org/
Result :-
1. users can save money by using the same credits across multiple AI products.
2. Developers don't need to pay AI token costs upfront.
3. simple flow
Future features I'm considering (not built yet):
1. Developers can fine-tune open-source models and use them in their products
2. AI model unified body
3. An enterprise version for companies
4. Users can send and receive AI credits like money
5. More features for both developers and users
We’d especially love feedback on whether this solves a real problem or not ? and also if possible then you would describe the product in your own words.
We’re currently facing a chicken-and-egg problem, so we decided to create a waitlist to help us figure out whether we should keep building this or put it in the trash and move on.
If you like the concept, then join our waitlist: https://www.rnetai.org/reserve-spot
Thank You


A leaked Composio key returned live Gmail, GitHub and CircleCI tokens.


Show HN: Velorn – an open-source desktop video editor with MCP agent control. Hi HN, I’m Jaime. I’m a VFX artist with over 20 years of experience, and I’ve been building Velorn as a solo developer.
Velorn is a GPLv3 desktop video editor for Windows, macOS, and Linux. It has a multi track video/audio timeline and works as a normal editor without any AI generation setup. You can import existing media, edit and layer clips, add effects, text and keyframes, transcribe locally with Whisper, mix audio, and export through FFmpeg.
For me, AI generated footage is raw material. The creative part is still what happens afterward: selection, timing, sound, pacing, effects, captions, and narrative structure. I wanted an app where generation and editing could exist in the same workflow, even editing live action footage with AI generations, so I started building Velorn.
One of the parts I've been especially interested in is MCP.
Velorn runs a local, loopback only MCP server that lets clients such as Codex and Claude Code interact with the project that's actually open in the editor.
Rather than putting a chatbot next to the timeline, I wanted to expose structured editing operations to the agent. It can inspect the project and timeline, look at media and frames, propose and perform edits, work with captions and effects, organize assets, and prepare exports and generation jobs.
Here's a short example using Codex:
https://www.youtube.com/watch?v=Owel8zkMWkY
For this demo I gave Codex almost no creative direction. I basically told it, you're live on YouTube, use Velorn to create a really cool motion graphics video. Just make something.
I wanted to see what would happen when the agent had access to an actual editing environment rather than asking it to execute a predetermined sequence of edits.
The video is about 3.5 minutes. I fast forward through some of the time where the agent is thinking, but otherwise it shows the interaction and the resulting work inside Velorn.
ComfyUI is not required for editing, captions, MCP, project management, or exporting. All generation in Velorn currently goes through a locally running ComfyUI instance on the same machine. For local models, your own GPU performs the work and Velorn doesn't charge credits. Generated results are brought back into the active project so they can immediately become part of the edit.
Some ComfyUI workflows can also use paid Partner Nodes/API services. Those may perform the actual computation in the cloud and require Comfy.org credentials and credits, but the workflow is still submitted through the user's local ComfyUI. Velorn doesn't currently operate a separate cloud generation backend. So someone can download Velorn and use it as an editor with MCP without configuring ComfyUI at all. ComfyUI is only required when using Velorn's current generation features.
Velorn uses Electron, React, Zustand for project/timeline state, FFmpeg for media processing and export, local Whisper for transcription, ComfyUI's API for generation, and a local MCP server exposing structured editorial operations.
I chose Electron because it gave me one cross platform editor implementation while still allowing integration with the filesystem, FFmpeg, local processes, ComfyUI, and MCP.
The project has picked up 400+ GitHub stars so far as a solo developer, I'm particularly interested in hearing from people who actually edit video or are experimenting with agents.
I would really love some feedback.
Velorn is free and open source. Try it!
To see longer MCP interactions without the agent's thinking being fast forwarded, I also recorded two uncut examples:
https://www.youtube.com/watch?v=_r4jf7ZDT2o
https://www.youtube.com/watch?v=AT9usQS3m48
Here's a music video that I made in just 2 days with Velorn that has gotten some attention:
https://www.youtube.com/watch?v=iX-YdjVMDhg
GitHub: https://github.com/VelornLabs/velorn
Website: https://velorn.ai
Thanks for looking!


Flownie – Open and Visual Data Workflow Platform with AI Agent Assistance.


Show HN: I built a Claude Code plugin to query 10.6M earnings-call embeddings. Hi HN,
I've been using LLMs for one-off market questions against a database of earnings transcripts since ~2025. In tech years that's a long time so as a Solo dev I built a platform around it and have been adding miscellaneous other financial tools and integrations. Backend infrastructure is super boring! PostgreSQL & ElasticSearch, with the data appropriately sharded and replicated across two regions.
Some example requests:
"What did NVIDIA say about data-center demand on its last call?"
"Brief me on NVDA, AMD and AVGO before each market open"
"Notify me when AAPL moves more than 5% in a day."
The plugin is dependency-free Python and MIT licensed. The corpus, vector search and scheduled-agent service are hosted, so using them requires a free FN2 account. I’m particularly interested in whether people find the scheduled-research part useful, but the basic features are free with integrations for Claude Code, Hermes Agent, OpenClaw, etc. Thanks!


Show HN: A better, free client for iMessage power-users. TLDR: Your iMessage inbox is a mess. Go to attention dot righthand dot ai.
it's a free app for mac. It's like Superhuman for iMessage.
___
You text your friends and family in iMessage.  You text your colleagues in iMessage. If you run a business, you likely text prospects and leads in iMessage. I text our customers in iMessage - I want them to know they've got the fastest way to reach me. In 2026 you even text your AI agents in iMessage (Linq raises $20M to enable AI assistants to live in iMessage, Apple approves Poke as the first AI agent on its Messages for Business platform).
The point is: your iMessage inbox is overloaded. Even more than email, it's become the inbox where everything important happens. But it's not built for everything! It's basically optimized for friends & family groupchats.
Being an iffy texter is OK (debatable) with friends. But the founders and investors we talk to routinely describe this niche pain of coördinating business in iMessage. So we distilled the lessons from Superhuman, viz., the email experience should be fast af and beautiful and shortcut-laden, and we applied them directly to the inboxes that are hard to use - starting with iMessage.
Attention is the first app that allows you to become a power-user of iMessage.
I'm finally able to label and divide my iMessages into different folders. I can set reminders and follow-ups with a keystroke.
We are obsessed with the potential value of an agent who triages every message before you even see it. We love the concept of every new message arriving with pre-work done and a human-in-the-loop Approval Card curated by the agent with 3 differentiated next steps. It ships with an MCP - just point your favorite flavor of coding agent at it.
As far as the actual app: Your data is yours, fully local in LanceDB + SQLite & never leaves your machine unless you turn on the AI agent, at which point data just goes to whichever AI provider you chose.
For now, just know that Attention ships with AI features "off" by default and it will always be free to use.
attention dot righthand dot ai
this post was written without the use of language models by me, Joseph. would love to chat and answer any questions in the comments