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Show HN: Real-time AI news aggregator with daily digest
Show HN: I built my first MCP to manage Google Ads
My co-founder and I tried running Google Ads for our Shopify store, but we could not get a positive ROAS (we spent 150USD on a single conversion).<p>As we also could not afford an agency, we turned to AI (IMO Codex >>> Claude Code).<p>We found that Google’s Ads MCP could only read data, but it could not make changes. So we built our own hosted MCP with read and write access.<p>It can inspect performance, find wasted spend, create or update campaigns, and work with Google Analytics and Tag Manager.
Show HN: The cheapest GPU cloud – H100s at $2.04/HR, H200s at $3/HR
Show HN: Aura – a Rust agent that investigates and fixes production incidents
We run a SaaS that handles petabytes of data. Our SRE team experimented with using claude, openclaw, langchain, etc. within our incident response workflows. We struggled with overflowing context, lethal trifecta vectors, hallucinations, and burned a lot of frontier tokens mostly on easy work. Approval fatigue was a challenge, and we drew a hard line at relaxing permissions in production.<p>Long story short, we built and open-sourced AURA, a Rust-based harness specifically designed for the type of operations work which routinely involves large volumes of telemetry data and coordinated investigations across many domains of knowledge / state. We have found even on open-weights models, root cause accuracy has been very good, and remediation actions are guarded with human-in-the-loop.<p>AURA runs from a centralized configuration file where workers are defined and scoped to task domains, (e.g. logging review, metrics analysis, and git/scm related queries). All permissions, tools access, LLM backend(s), worker prompts, and the main coordinator prompt are also defined in code. Permitted tool use is enforced deterministically outside the agent's context, so the agent cannot grant itself capabilities through prompting. AURA also handles the rest of the execution layer, such as human approval for sensitive actions and fault tolerance. A lot of thought went into context window management: large tool outputs and worker responses are persisted to disk and agents are given tools to slice/read as needed.<p>The project is Apache 2.0. You can install rpm/deb/brew natively or pull the source and build your own. It runs either as an assistant on your workstation, or as a daemon that you can connect to via any OpenAI-compatible frontend. The GitHub repo is here: <a href="https://github.com/mezmo/aura/" rel="nofollow">https://github.com/mezmo/aura/</a><p>The whole thing is free/Free. No signups or open core trapdoor.<p>Demo: We put together an 8-minute video showing a checkout outage resolved by following evidence from several different systems: <a href="https://www.youtube.com/watch?v=TERHoRzT8cE" rel="nofollow">https://www.youtube.com/watch?v=TERHoRzT8cE</a><p>tl;dw: 502s from a checkout service cause Grafana to trigger an incident via PagerDuty. AURA (using DeepSeek-V4-Flash) uses its workers and tools to correctly determine root cause as a memory leak in a downstream service, and attributes the incident to a defect in a recently merged PR. The demo then concludes with a human-gated tool call to GitHub, where AURA documents the exact lines of code that are causing the problem, and recommends a fix. Once the PR is deployed, AURA validates that the errors are clearing and transactions are no longer failing. The whole time, AURA’s leaving an audit trail of OTEL events into Phoenix.<p>Technical details:<p>AURA uses an agent coordinator that drafts, executes, and supervises DAG flows through user-defined worker agents to solve complex investigations in parallel. Worker agents are bound to strongly typed durable artifacts used as evidence packets, and the system discourages wasteful recalculation of remote data. AURA also has a powerful header routing system that doesn’t allow the agent to actually touch your keys. HITL interrupts over webhook are first class citizens and have a schema that is easy to adapt to your own workflow. HA deployment options are coming soon.<p>What’s still rocky:<p>- We’re currently working on the async input system for when you’re running AURA as a service. Right now the API just accepts a request, and streams messages until the main loop completes. It’s easy to integrate into a workflow, but the logic required winds up being heavier than it ought to be.<p>- Because there’s no inbound webhook interrupt mechanism, automating AURA for IR requires middleware to invoke it or it needs to poll an MCP for alert escalations.<p>We’re looking for users, contributors, guidance on where to take this next, or just GitHub stars. Looking forward to reading and responding to feedback here.
Show HN: Aura – a Rust agent that investigates and fixes production incidents
We run a SaaS that handles petabytes of data. Our SRE team experimented with using claude, openclaw, langchain, etc. within our incident response workflows. We struggled with overflowing context, lethal trifecta vectors, hallucinations, and burned a lot of frontier tokens mostly on easy work. Approval fatigue was a challenge, and we drew a hard line at relaxing permissions in production.<p>Long story short, we built and open-sourced AURA, a Rust-based harness specifically designed for the type of operations work which routinely involves large volumes of telemetry data and coordinated investigations across many domains of knowledge / state. We have found even on open-weights models, root cause accuracy has been very good, and remediation actions are guarded with human-in-the-loop.<p>AURA runs from a centralized configuration file where workers are defined and scoped to task domains, (e.g. logging review, metrics analysis, and git/scm related queries). All permissions, tools access, LLM backend(s), worker prompts, and the main coordinator prompt are also defined in code. Permitted tool use is enforced deterministically outside the agent's context, so the agent cannot grant itself capabilities through prompting. AURA also handles the rest of the execution layer, such as human approval for sensitive actions and fault tolerance. A lot of thought went into context window management: large tool outputs and worker responses are persisted to disk and agents are given tools to slice/read as needed.<p>The project is Apache 2.0. You can install rpm/deb/brew natively or pull the source and build your own. It runs either as an assistant on your workstation, or as a daemon that you can connect to via any OpenAI-compatible frontend. The GitHub repo is here: <a href="https://github.com/mezmo/aura/" rel="nofollow">https://github.com/mezmo/aura/</a><p>The whole thing is free/Free. No signups or open core trapdoor.<p>Demo: We put together an 8-minute video showing a checkout outage resolved by following evidence from several different systems: <a href="https://www.youtube.com/watch?v=TERHoRzT8cE" rel="nofollow">https://www.youtube.com/watch?v=TERHoRzT8cE</a><p>tl;dw: 502s from a checkout service cause Grafana to trigger an incident via PagerDuty. AURA (using DeepSeek-V4-Flash) uses its workers and tools to correctly determine root cause as a memory leak in a downstream service, and attributes the incident to a defect in a recently merged PR. The demo then concludes with a human-gated tool call to GitHub, where AURA documents the exact lines of code that are causing the problem, and recommends a fix. Once the PR is deployed, AURA validates that the errors are clearing and transactions are no longer failing. The whole time, AURA’s leaving an audit trail of OTEL events into Phoenix.<p>Technical details:<p>AURA uses an agent coordinator that drafts, executes, and supervises DAG flows through user-defined worker agents to solve complex investigations in parallel. Worker agents are bound to strongly typed durable artifacts used as evidence packets, and the system discourages wasteful recalculation of remote data. AURA also has a powerful header routing system that doesn’t allow the agent to actually touch your keys. HITL interrupts over webhook are first class citizens and have a schema that is easy to adapt to your own workflow. HA deployment options are coming soon.<p>What’s still rocky:<p>- We’re currently working on the async input system for when you’re running AURA as a service. Right now the API just accepts a request, and streams messages until the main loop completes. It’s easy to integrate into a workflow, but the logic required winds up being heavier than it ought to be.<p>- Because there’s no inbound webhook interrupt mechanism, automating AURA for IR requires middleware to invoke it or it needs to poll an MCP for alert escalations.<p>We’re looking for users, contributors, guidance on where to take this next, or just GitHub stars. Looking forward to reading and responding to feedback here.
Show HN: OwnTime – a chess clock for your day's priorities
I made OwnTime to balance between competing priorities in my life. The idea is based on two main influences: the concept of "roles" from "The 5 Choices" (2015), and my repeated failure to effectively implement time blocking due to the necessary flexibility in my role.<p>The app allows you to define time budgets, which are not much more than a few mutually exclusive countdown timers. The UX is essentially that of a chess clock for an arbitrary number of players with configurable time.<p>The whole point of the app is to kick you out of the running role/priority when its time is up. One feature that was essential for me was AlarmKit alarms, which only became possible last year (>= iOS 26.1). Another was watchOS support, as a natural surface for time-related matters.<p>The current version is strictly focussed. The timers track time allocation during one day. All timers are reset at midnight. I didn't build any statistics - you can export the raw internal SQLite store and make any evaluation or dashboard you want from there.<p>I don't plan to extend the app nor to convert it into a service. All data stays local on your devices (synced between phone and watch locally) and fully open to you. No need for an account or subscription. Thus, I ask for a small one-time fee of $1.99.<p>The website has a short video and rendered preview: <a href="https://owntime.app" rel="nofollow">https://owntime.app</a><p>If you want to go straight to the App Store: <a href="https://apps.apple.com/app/id6770125662">https://apps.apple.com/app/id6770125662</a><p>I look forward to your comments and feedback.
Show HN: OwnTime – a chess clock for your day's priorities
I made OwnTime to balance between competing priorities in my life. The idea is based on two main influences: the concept of "roles" from "The 5 Choices" (2015), and my repeated failure to effectively implement time blocking due to the necessary flexibility in my role.<p>The app allows you to define time budgets, which are not much more than a few mutually exclusive countdown timers. The UX is essentially that of a chess clock for an arbitrary number of players with configurable time.<p>The whole point of the app is to kick you out of the running role/priority when its time is up. One feature that was essential for me was AlarmKit alarms, which only became possible last year (>= iOS 26.1). Another was watchOS support, as a natural surface for time-related matters.<p>The current version is strictly focussed. The timers track time allocation during one day. All timers are reset at midnight. I didn't build any statistics - you can export the raw internal SQLite store and make any evaluation or dashboard you want from there.<p>I don't plan to extend the app nor to convert it into a service. All data stays local on your devices (synced between phone and watch locally) and fully open to you. No need for an account or subscription. Thus, I ask for a small one-time fee of $1.99.<p>The website has a short video and rendered preview: <a href="https://owntime.app" rel="nofollow">https://owntime.app</a><p>If you want to go straight to the App Store: <a href="https://apps.apple.com/app/id6770125662">https://apps.apple.com/app/id6770125662</a><p>I look forward to your comments and feedback.
Show HN: Newton's Orchard – Browser-based space/gravity playground
Hi HN!<p>My son spent a lot of time in 5th grade playing with the 2-dimensional PhET gravity/space sims[1]. His STEM class uses it, and he was playing with it at home, and has been looking for more. I found several space sims/playgrounds online, including NASA's Eyes on the Solar System[2], but nothing that seemed to offer a real progression from what he was getting out of the PhET sims, so I built Newton's Orchard over the summer for him.<p><a href="https://newtonsorchard.app" rel="nofollow">https://newtonsorchard.app</a><p>The source is also published at <a href="https://github.com/andrewchilds/newtons-orchard" rel="nofollow">https://github.com/andrewchilds/newtons-orchard</a><p>It features deterministic time scrubbing, all bodies are editable, different preset systems and experiments, missions to complete (complete 3 to unlock the blackhole object type and mission), and a gallery for user submissions.<p>I would very much appreciate feedback from HN - particularly from the lens of making it engaging and illuminating for students, and anyone that would like to just get a feel for how gravity works. Thank you!<p>[1] <a href="https://phet.colorado.edu/sims/html/gravity-and-orbits/latest/gravity-and-orbits_all.html" rel="nofollow">https://phet.colorado.edu/sims/html/gravity-and-orbits/lates...</a>
[2] <a href="https://eyes.nasa.gov/apps/solar-system/" rel="nofollow">https://eyes.nasa.gov/apps/solar-system/</a>
Show HN: ZSvirt – A lightweight, scalable open source virtualization platform
Show HN: FrontierHarness Eval – 9 harness, same model, cost per pass varies 17x
Show HN: FrontierHarness Eval – 9 harness, same model, cost per pass varies 17x
Show HN: SlideOps – slides from a repo that flag when they drift from the code
I kept generating slide decks about my codebases with an agent, and weeks later they would go stale. Updating them was quite costly (time & tokens), as the agent would have to re-scan the whole repo to re-generate the slides. So I made the slide deck carry its own provenance: SlideOps skill turns a repo into a slide deck, with all references to files carrying exact line range and a hash.<p>Checking is done with standard-library Python: no model calls, no network, and very fast. For example, it distinguishes MOVED (the code shifted) from CHANGED (the content differs). Updating the slides costs tokens, but now the checking part has already given the agent just the relevant context on what exactly needs to be repaired.<p>SlideOps ships as a Claude Code plugin and runs as a plain agent skill in Codex, Copilot CLI and OpenCode.<p>Longer write-up: <a href="https://medium.com/@lukicov/your-documentation-is-a-build-artifact-start-treating-it-like-one-ab48df61b1e0" rel="nofollow">https://medium.com/@lukicov/your-documentation-is-a-build-ar...</a><p>GitHub repo: <a href="https://github.com/glukicov/slideops" rel="nofollow">https://github.com/glukicov/slideops</a>
Show HN: Floe – an open-source plugin for sample libraries – CLAP/VST3/AU
I'm Sam, I make sample libraries (as FrozenPlain). These sample libraries run inside Floe, my audio plugin for Linux, macOS and Windows. It's designed for musicians, composers and producers - typically people involved with film/TV/game scoring or ambient music. I open-sourced it primarily because I'm very fond of this philosophy and I want to open the door for serving a wider audience than just my own libraries.<p>It's totally free, no sign-ups. You'll need a DAW. To try it:
- Download and install Floe: <a href="https://floe.audio" rel="nofollow">https://floe.audio</a>
- Download your choice of free packages: https:floe.audio/packages#community-packages or <a href="https://www.frozenplain.com/product/music-box-suite-free" rel="nofollow">https://www.frozenplain.com/product/music-box-suite-free</a>
- Install the packages using Floe's 'Install Package' button. <a href="https://floe.audio/docs/installation/install-packages" rel="nofollow">https://floe.audio/docs/installation/install-packages</a><p>I'm curious about HN's thoughts regarding the open-source nature, directions I could take for expanding the audience, and the Lua based sample-library it has (<a href="https://floe.audio/docs/develop/develop-libraries" rel="nofollow">https://floe.audio/docs/develop/develop-libraries</a>). Thanks.
Show HN: Markdown Viewer and Editor
I built an online viewer and editor for .md files where the file stays on your machine. Free to use and code is available under MIT License here: <a href="https://github.com/hattray/markdown-editor" rel="nofollow">https://github.com/hattray/markdown-editor</a><p>You can also install it as a Chrome/Edge app so that it works for files on your desktop by double-clicking.<p>Supports standard .md files with formatting, syntax-highlighting for code, table of contents, Mermaid diagrams, LaTeX via KaTex. Also supports exporting to .html, .pdf and .png.<p>Please give it a try and share your feedback for improvements.
Show HN: HN Match Maker – Matching "Who Wants to Be Hired?" With "Who's Hiring?"
Hi!<p>Every month the "Who Wants to Be Hired?" and "Who's Hiring?" Threads come through I would think to myself, "there should be a match maker for these two threads".<p>So I had abacus.ai whip one up. The methodology is pretty simple, data is extracted from posts using an LLM, score matches based on salary, domain experience, remote/onsite, etc., and then eliminate incompatible postings, like 'looking for remote work' and 'onsite only'. The result is two views: jobs-by-user and user-by-jobs.<p>If you've submitted a post to this month's 'Who Wants to Be Hired?", you can find your matching job listings at <a href="https://hnmatchmaker.com/user/:user_name" rel="nofollow">https://hnmatchmaker.com/user/:user_name</a>, for example <a href="https://hnmatchmaker.com/user/G4Vi" rel="nofollow">https://hnmatchmaker.com/user/G4Vi</a><p>---<p>Some observations when looking at the matches pair up right now:<p>Poke around, let me know what you think!<p>Ultimately, I hope this is actually helpful to some folks.
Show HN: HN Match Maker – Matching "Who Wants to Be Hired?" With "Who's Hiring?"
Hi!<p>Every month the "Who Wants to Be Hired?" and "Who's Hiring?" Threads come through I would think to myself, "there should be a match maker for these two threads".<p>So I had abacus.ai whip one up. The methodology is pretty simple, data is extracted from posts using an LLM, score matches based on salary, domain experience, remote/onsite, etc., and then eliminate incompatible postings, like 'looking for remote work' and 'onsite only'. The result is two views: jobs-by-user and user-by-jobs.<p>If you've submitted a post to this month's 'Who Wants to Be Hired?", you can find your matching job listings at <a href="https://hnmatchmaker.com/user/:user_name" rel="nofollow">https://hnmatchmaker.com/user/:user_name</a>, for example <a href="https://hnmatchmaker.com/user/G4Vi" rel="nofollow">https://hnmatchmaker.com/user/G4Vi</a><p>---<p>Some observations when looking at the matches pair up right now:<p>Poke around, let me know what you think!<p>Ultimately, I hope this is actually helpful to some folks.
Show HN: HN Match Maker – Matching "Who Wants to Be Hired?" With "Who's Hiring?"
Hi!<p>Every month the "Who Wants to Be Hired?" and "Who's Hiring?" Threads come through I would think to myself, "there should be a match maker for these two threads".<p>So I had abacus.ai whip one up. The methodology is pretty simple, data is extracted from posts using an LLM, score matches based on salary, domain experience, remote/onsite, etc., and then eliminate incompatible postings, like 'looking for remote work' and 'onsite only'. The result is two views: jobs-by-user and user-by-jobs.<p>If you've submitted a post to this month's 'Who Wants to Be Hired?", you can find your matching job listings at <a href="https://hnmatchmaker.com/user/:user_name" rel="nofollow">https://hnmatchmaker.com/user/:user_name</a>, for example <a href="https://hnmatchmaker.com/user/G4Vi" rel="nofollow">https://hnmatchmaker.com/user/G4Vi</a><p>---<p>Some observations when looking at the matches pair up right now:<p>Poke around, let me know what you think!<p>Ultimately, I hope this is actually helpful to some folks.
Show HN: Weedout – Safari extension that hides YouTube AI-labeled videos
I'm the developer. I built this because AI-generated conspiracy videos were getting out of control in my own YouTube feed. Weedout is a $1.99 Safari extension for macOS that removes videos YouTube labels “Made with AI” from feed, search, related videos, playlists and Shorts. It uses YouTube's label rather than AI detection, runs locally, and does not catch unlabeled videos.<p>Source for developers who want to fork or build: <a href="https://github.com/masteranza/weedout-for-youtube" rel="nofollow">https://github.com/masteranza/weedout-for-youtube</a><p>Pull requests are not accepted; the repository is for independent forks.
Show HN: Weedout – Safari extension that hides YouTube AI-labeled videos
I'm the developer. I built this because AI-generated conspiracy videos were getting out of control in my own YouTube feed. Weedout is a $1.99 Safari extension for macOS that removes videos YouTube labels “Made with AI” from feed, search, related videos, playlists and Shorts. It uses YouTube's label rather than AI detection, runs locally, and does not catch unlabeled videos.<p>Source for developers who want to fork or build: <a href="https://github.com/masteranza/weedout-for-youtube" rel="nofollow">https://github.com/masteranza/weedout-for-youtube</a><p>Pull requests are not accepted; the repository is for independent forks.