Initially RebootX was made to connect cloud providers (AWS, GCP, etc.) and dashboard providers (Grafana) to manage these on-the-go. It is very useful, especially in case of emergency.Then I&#x27;ve added <a href="https:&#x2F;&#x2F;github.com&#x2F;c100k&#x2F;rebootx-on-prem" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;c100k&#x2F;rebootx-on-prem</a> to connect anything that is &quot;runnable&quot;. This natural abstraction helped me integrate the new integration this post is about : Cursor.This way, we can also manage agents we start from our laptops with our phones. I&#x27;ll be glad to get your constructive feedback to improve the integration.Best,

Show HN: Manage Cursor agents from your smartphone

Show HN: CAS – I reverse-engineered Claude Code to build a better orchestrator

Show HN: Rewriting Mongosh in Golang Using Claude

Thesys just open-sourced their generative UI rendering engine. Interesting timing given where Google a2ui and Vercel&#x27;s json-render are headed.
The difference worth noting: a2ui and json-render both treat JSONL as the contract between the LLM and the renderer. Thesys is betting that&#x27;s the wrong primitive. Their engine uses a code-like syntax (OpenUI Lang) instead — LLM writes it, renderer executes it. The argument is that LLMs are fundamentally better at generating code than generating structured data, so you get cleaner output and ~67% fewer tokens.
The broader vision seems to be a model-agnostic, design-system-agnostic layer that sits between any LLM and your actual UI components. You bring your own components and design tokens, the engine handles translating LLM output into rendered interfaces — charts, forms, tables, cards.
Generative UI as a category is still figuring out what the right abstraction is. This is a concrete stake in the ground against JSON-as-spec.

Show HN: OpenUI – A code-like rendering spec for Generative UI

Show HN: Loquix – Open-source Web Components for AI chat interfaces

Hey HN,I built StreamHouse, an open-source streaming platform that replaces Kafka&#x27;s broker-managed storage with direct S3 writes. The goal: same semantics, fraction of the cost.How it works: Producers batch and compress records, a stateless server manages partition routing and metadata (SQLite for dev, PostgreSQL for prod), and segments land directly in S3. Consumers read from S3 with a local segment cache. No broker disks to manage, no replication factor to tune — S3 gives you 11 nines of durability out of the box.What&#x27;s there today:
 - Producer API with batching, LZ4 compression, and offset tracking (62K records&#x2F;sec)
 - Consumer API with consumer groups, auto-commit, and multi-partition fanout (30K+ records&#x2F;sec)
 - Kafka-compatible protocol (works with existing Kafka clients)
 - REST API, gRPC API, CLI, and a web UI
 - Docker Compose setup for trying it locally in 5 minutes<pre><code> What&#x27;s not there yet:
 - Battle-tested production deployments (I&#x27;m the only user so far)
 - Connectors for consumers to immediately connect to (i.e clickhouse, elastic search etc)
 </code></pre>
The cost model is what motivated this. Kafka&#x27;s storage costs scale with replication factor × retention × volume. With S3 at $0.023&#x2F;GB&#x2F;month, storing a TB of events costs ~$23&#x2F;month instead of hundreds on broker EBS volumes.Written in Rust, 15 crates thus far. Apache 2.0 licensed.GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;gbram1&#x2F;streamhouse" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;gbram1&#x2F;streamhouse</a>
How it works blog on my main website: <a href="https:&#x2F;&#x2F;streamhouse.app&#x2F;how-it-works" rel="nofollow">https:&#x2F;&#x2F;streamhouse.app&#x2F;how-it-works</a>Happy to answer questions about the architecture, tradeoffs, or what I learned building this.

Show HN: StreamHouse – Open-source Kafka alternative

Instead of hard-coding payroll rules, PayrollEngine models business logic as
composable Regulation layers — versioned JSON&#x2F;YAML config + runtime C# (Roslyn).
Layers inherit and override like CSS cascade: national law → industry → company.v0.10.0-beta.1 shipped earlier this week alongside the new docs site (payrollengine.org).The most interesting new example: MultiCountryPayroll — DE&#x2F;FR&#x2F;NL sharing a
single base regulation, with a split employee whose contract crosses borders
mid-period. The regulation handles it without a single country-specific
code path.Other additions:
- Payrun Preview: in-memory calculation, no DB writes
- Async payrun jobs: HTTP 202, bounded queue, webhook on completion
- Parallel employee processing with per-employee state isolationStack: .NET 10, SQL Server, Docker, RoslynGitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;Payroll-Engine&#x2F;PayrollEngine" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Payroll-Engine&#x2F;PayrollEngine</a>
Docs: <a href="https:&#x2F;&#x2F;payrollengine.org" rel="nofollow">https:&#x2F;&#x2F;payrollengine.org</a>

Show HN: PayrollEngine – Open-source regulation-based payroll framework (.NET)

Hi HN. This week Meta acquired Moltbook (agent social network), OpenAI acquired Promptfoo (agent testing), and Mandiant&#x27;s founder raised $190M for Armadin. Agent infrastructure is clearly where things are heading.We built AgentSign -- a zero trust engine for AI agents. The problem: agents are operating without any identity infrastructure. Moltbook went viral for fake posts because there was zero verification on who or what was posting.AgentSign gives every agent a cryptographic identity certificate, signs every action into an execution chain, and runs runtime code attestation before anything executes. There&#x27;s also an MCP Trust Layer for agent-to-MCP server verification, and a Stripe-powered Trust Gate for agent payments.5 subsystems: identity certs, execution chain verification, runtime code attestation, output tamper detection, and cryptographic trust scoring.Free and open source. Built in London.SDK: <a href="https:&#x2F;&#x2F;github.com&#x2F;razashariff&#x2F;agentsign-sdk" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;razashariff&#x2F;agentsign-sdk</a>Happy to answer questions.

Show HN: AgentSign – Open-source zero trust engine for AI agents

Show HN: Faster, cheaper Claude Code with local semantic code search via sqlite

Agent self on boarding, built in. It reads the docs, pays the 5 USDC&#x2F;month on Solana, spins up an inbox, and starts receiving&#x2F;sending. No signup form. No credit card. No you.• Prompt to mailbox (no human interaction required)
• @agentmbox.com mailbox included
• Custom domains included, even Custom DNS will be set up via agent
• IMAP, SMTP, and REST API — any language, any frameworkOn X: <a href="https:&#x2F;&#x2F;x.com&#x2F;solapunk80&#x2F;status&#x2F;2031701417304494537" rel="nofollow">https:&#x2F;&#x2F;x.com&#x2F;solapunk80&#x2F;status&#x2F;2031701417304494537</a>
On ProductHunt: <a href="https:&#x2F;&#x2F;www.producthunt.com&#x2F;products&#x2F;agentmbox?launch=agentmbox" rel="nofollow">https:&#x2F;&#x2F;www.producthunt.com&#x2F;products&#x2F;agentmbox?launch=agentm...</a>

Show HN: YC W26 AgentMBOX agent self-onboarding mailboxes

I have a handful of GA4 properties — a couple web apps, a side project, a thing I shipped on a weekend — and I got tired of clicking through Google&#x27;s UI every morning to check if anything weird happened overnight. I was spending my mornings clicking through GA4 properties one at a time just to see yesterday&#x27;s numbers, and whenever I needed to actually understand what was going on I&#x27;d have to connect Claude to each one individually. It&#x27;s a lot of tab-switching for what should be a 30-second check. So I built a dashboard that pulls them all into one screen and tells me when traffic does something statistically unusual.That&#x27;s Plask. It connects via Google OAuth (read-only scope) and shows all your GA4 properties in one place. Sessions, users, key events, comparison deltas — the quick pulse check I wanted without navigating Google&#x27;s interface six times.The part I spent the most time on is the anomaly detection. I went with modified Z-scores using Median Absolute Deviation over a 28-day rolling window instead of standard deviation — it handles the spiky traffic patterns you get on smaller sites without crying wolf every weekend. No ML, no black box, just basic stats that run at zero cost. Alerts get classified as info&#x2F;warning&#x2F;critical, and you can tune sensitivity per property.There&#x27;s also a weekly digest powered by Claude Haiku that turns the numbers into a couple paragraphs of plain English every Monday morning. I built it mostly because I wanted to stop opening the dashboard on Mondays.Stack for those interested: Next.js 16, Supabase Postgres with Drizzle ORM, Auth.js v5 (needed custom OAuth scopes that Supabase Auth doesn&#x27;t support), Stripe, deployed on Vercel. Daily sync runs as a cron at 6am UTC — nothing fancy, no event streaming.Solo project. Would genuinely appreciate feedback on the product, the anomaly detection approach, or anything else. Happy to go deep on implementation details.<a href="https:&#x2F;&#x2F;plask.dev" rel="nofollow">https:&#x2F;&#x2F;plask.dev</a>

Show HN: I replaced my morning GA4 tab explosion with one page

Show HN: kitty-graphics.el – Images, LaTeX and PDFs in terminal Emacs

I made this side tool to visualize SHA-256 while building a loyalty card wallet application to easily identify if a collected stamp is certified by the issuer by simply seeing it, instead of scanning something like a QR code or matching a serial number.I think there are more potential use cases, like creating a random avatar based on an email address or something else. Feel free to share your feedback :)source code: <a href="https:&#x2F;&#x2F;github.com&#x2F;tonytonyjan&#x2F;crypto_flora" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;tonytonyjan&#x2F;crypto_flora</a>

Show HN: CryptoFlora – Visualize SHA256 to a flower using Rose curves

I felt frustrated finding high-signal discussions on HN, and I started this project to better understand how this community actually works.That led me to build readhn, an MCP server that helps with three things:- Discover: find relevant stories&#x2F;comments by keyword, score, and time window- Trust: identify credible voices using EigenTrust-style propagation from seed experts- Understand: show why each result is ranked, with explicit signals instead of a black-box scoreIt includes 6 tools: discover_stories, search, find_experts, expert_brief, story_brief, and thread_analysis.I also added readhn setup so AI agents can auto-configure it (Claude Code, Codex, Cursor, and others) after pip install.I’d love feedback on:1) whether these ranking signals match how you evaluate HN quality,2) trust-model tradeoffs,3) what would make this useful in your daily workflow.If this is useful to you, starring the repo helps others discover it:
<a href="https:&#x2F;&#x2F;github.com&#x2F;xodn348&#x2F;readhn" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;xodn348&#x2F;readhn</a>

Show HN: Readhn – AI-Native Hacker News MCP Server (Discover, Trust, Understand)

Hello,I’m building Draxl, a source format for a world where code is edited by millions of agents.AI agents will produce far more code than humans do today. Rebased branches, concurrent edits, and long-lived forks will become much more common. Code management needs more precise control at that scale.Draxl embeds stable AST node IDs directly in the source, so tools can target syntax by identity instead of by line position. Here’s a small example:Here is a small example:<pre><code> @m1 mod demo {
 @d1 &#x2F;&#x2F;&#x2F; Add one to x.
 @f1[a] fn add_one(@p1[a] x: @t1 i64) -&gt; @t2 i64 {
 @c1 &#x2F;&#x2F; Cache the intermediate value.
 @s1[a] let @p2 y = @e1 (@e2 x + @l1 1);
 @s2[b] @e3 y
 }
 }
</code></pre>
The syntax is:<pre><code> @id[rank]-&gt;anchor
</code></pre>
* `@id` gives the next node a stable identity* `[rank]` orders siblings inside ranked slots* `-&gt;anchor` attaches detached docs or comments to an existing sibling idThe same code lowers to ordinary Rust:<pre><code> mod demo {
 &#x2F;&#x2F;&#x2F; Add one to x.
 fn add_one(x: i64) -&gt; i64 {
 &#x2F;&#x2F; Cache the intermediate value.
 let y = (x + 1);
 y
 }
 }
</code></pre>
In Draxl, functions, statements, expressions, docs, and comments can all carry stable IDs. Ordered siblings carry explicit ranks. Detached docs and comments can carry explicit anchors.That lets a tool say &quot;replace expression `@e3`&quot; or &quot;insert a statement into `@f1.body[ah]`&quot; instead of &quot;change these lines near here.&quot;That should make semantic replay more reliable under heavy concurrent editing. It should also reduce false merge conflicts and localize real ones more precisely.It also opens up other uses. You could attach ownership, policy, or review metadata directly to AST nodes.I’m interested in early feedback: does this source model feels useful, and whether editing code this way seems like a better fit for agent-heavy workflows. Where are the best places on the internet to discuss this sort of thing?Connect with me: <a href="https:&#x2F;&#x2F;x.com&#x2F;rndhouse" rel="nofollow">https:&#x2F;&#x2F;x.com&#x2F;rndhouse</a>

Show HN: Draxl, agent-native source code with stable AST node IDs

Hi all, this is an early version of a side project of mine. Would love some feedback and comments.I like karaoke and I grew up with the Asian style karaoke with the music video behind and the karaoke lyrics at the bottom.Sometimes I want to do a song and there is no karaoke version video like that.A few years ago I came across ML models that cleanly separate the vocals and the instrumental music of a song. I thought of the idea to chain together ML models that can take an input music video file, extract the audio (ffmpeg), separate the tracks (ML), transcribe the lyrics (ML), burn the lyrics back with timing into the video (ffmpeg), and output a karaoke version of the video.This is an early version of the app, Mac only so far (since I use Mac, despite it being an electron app.. I do eventually want to make a Windows build), I&#x27;ve only let a few friends try it. Let me know what you think!

Show HN: KaraMagic – automatic karaoke video maker

I’ve been building BUS Core, a local-first manufacturing&#x2F;workshop system aimed at small makers and shop-style operations.Version 1.0.3 is out today.This release focused on hardening and UI cleanup more than feature expansion. The goal is to make the software more trustworthy in day-to-day use, not just more featureful.The general product thesis is that there’s a gap between spreadsheets and heavy SaaS&#x2F;ERP for small operators who want control over their own data and workflows.It’s local-first, practical, and intentionally boring in the parts that should be boring.Happy to answer questions about:architecturelocal-first tradeoffsworkflow scopehow I’m handling the build&#x2F;process side

Show HN: Bus Core 1.0.3 Local-first manufacturing system for small shops

Hey HN, I&#x27;m Mario. I recently talked to a colleague about AI, agents and how software development will change in the future. We were wondering why we should even share code anymore when AI agents are already really good at implementing software, just through prompts. Why can&#x27;t everyone get customized software with prompts?&quot;Share the prompt, not the code.&quot;Well, I thought, great idea, let&#x27;s do that. That&#x27;s why I built Open Prompt Hub: <a href="https:&#x2F;&#x2F;openprompthub.io" rel="nofollow">https:&#x2F;&#x2F;openprompthub.io</a>.Think GitHub just for prompts.The idea is simple: Users can upload prompts that can then be used by you and your AI tools to generate a script, app, or web service (or prime their agent for a certain task): Just past it into your agent or ide and watch it build for you. If the prompt does not 100% covers your usecase, fork it, tweak it, et voila: tailor-made software ready to use!The prompts are simple markdown files with a frontematter block for meta information. (The spec can be found here: <a href="https:&#x2F;&#x2F;openprompthub.io&#x2F;docs" rel="nofollow">https:&#x2F;&#x2F;openprompthub.io&#x2F;docs</a>) They versioned, have information on which AI models build it successfuly and have instructions on how the AI agent can test the resulting software.Users can mention with which models they have successfully or unsuccessfully executed a prompt (builds or fail). This helps in assessing whether a prompt provides reliable output or not.Want to create a open prompt file? Here is the prompt for it which will guide you through: <a href="https:&#x2F;&#x2F;openprompthub.io&#x2F;open-prompt-hub&#x2F;create-open-prompt" rel="nofollow">https:&#x2F;&#x2F;openprompthub.io&#x2F;open-prompt-hub&#x2F;create-open-prompt</a>Security! Always a topic when dealing with AI and prompts? I&#x27;ve added several security checks that look at every prompt for injections and malicious behavior. Statistical analysis as well as two checks against LLMs for behaviour classification and prompt injection detection.It&#x27;s an MVP for now. But all the mentioned features are already included.If this sounds good, let me know. Try a prompt, fork it, or tell me what you&#x27;d change in the spec or security scanner. I&#x27;m really curious about what would make you trust and reuse prompts. Or if you like the general idea...

Show HN: Don't share code. Share the prompt

A few months ago we posted here and got a lot of insightful feedback. This is what we built from it.MoneyOnFIRE answers two questions: when can you reach financial independence, and what should you do to get there the fastest? It runs a financial simulation across income, taxes, accounts, contributions, returns, and withdrawals, then produces a prioritized action checklist with specific dollar amounts, dates, and steps.Several of the biggest improvements came directly from comments on the last HN thread:Rental property support: The engine now models rental income, mortgages, appreciation, and how properties interact with the rest of a financial plan.Scenario modeling: You can now compare how different choices — lower returns, working longer, adjusting spending — affect your FI timeline side by side.No login required: Several people didn&#x27;t want to create an account or store financial data. You can now run a full plan without signing up.FI vs FIRE: We initially built for the early-retirement crowd. Feedback showed it&#x27;s just as useful for anyone pursuing financial independence on a longer timeline — the calculations and actions are the same.Also shipped: support for multiple children and college timelines, Roth conversion ladders, IRA strategy selection, umbrella and term life insurance sizing, and dynamic reports that update as your inputs change.The core thesis hasn&#x27;t changed: personal finances are a complex web of interacting rules and calculations. We want to solve that and give everyone a clear, ordered set of actions they can actually implement.Happy to answer questions about the engine or the modeling decisions behind it.

Show HN: MoneyOnFIRE – FI date and action plan (v2)

Hey HN, as a former data analyst, I’ve been tooling around trying to get agents to do my old job. The result is this system that gets you maybe 80% of the way there. I think this is a good data point for what the current frontier models are capable of and where they are still lacking (in this case — hypothesis generation and general data intuition).Some initial learnings:
- Generating web app-based reports goes much better if there are explicit templates&#x2F;pre-defined components for the model to use.
- Claude can “heal” broken charts if you give it access to chart images and run a separate QA loop.Would either feedback from the community or to hear from others that have tried similar things!

Show HN: Agentic Data Analysis with Claude Code

I built a site to find engineering manager jobs and make the manager job search less painful. This was created out of my own job search last year. I was using various job aggregator sites and spreadsheets and wanted a more efficient method.The main value of the site is:
* High-quality job listings and data that is updated daily. It saves countless hours not having to sift through irrelevant or stale job listings.Give it a try and feel open to share any feedback.

Show HN: Find Engineering Manager Jobs Efficiently

not a new concept, but here&#x27;s something me and Ana made for our cross-system-cross agent management tool. our own proprietary web design DSL hehLike any codebase, this will change over time, and if you have tried something similar, your ideas, PRs are welcome, let&#x27;s build some cool shit!

Show HN: Get AI to write code that it can read

Show HN: Smux – Terminal Multiplexer built for AI agents

Hi HN,I just wanted to share what I have been working on for the past few months: A firmware analyzer for embedded Linux systems that helps uncovering security issues running entirely in the browser.This is a very early Alpha. It is going to be rough around the edges. But I think it provides quite a lot of value already.So please go ahead and drop a firmware (only .tar rootfs archives for now) and try to break it :)