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Show HN: Showboat and Rodney, so agents can demo what they've built

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We recently started to use agents to update some documentation across our codebase on a weekly basis, and everything quickly turned into cron jobs, logs, and terminal output.it worked, but was hard to tell what agents were doing, why something failed, or whether a workflow was actually progressing.We thought it would be more interesting to treat agents as long-lived workers with state and responsibilities and explicit handoffs. Something you can actually see and reason about, instead of just tailing logs.So we built Clawe, a small coordination layer on top of OpenClaw that lets agent workflows run, pause, retry, and hand control back to a human at specific points.This started as an experiment in how agent systems might feel to operate, but we&#x27;re starting to see real potential for it, especially for content review and maintenance workflows in marketing. Curious what abstractions make sense, what feels unnecessary, and what breaks first.Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;getclawe&#x2F;clawe" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;getclawe&#x2F;clawe</a>

Show HN: Clawe – open-source Trello for agent teams

Hi HN,AI agents that can run tools on your machine are powerful for knowledge work, but they’re only as useful as the context they have. Rowboat is an open-source, local-first app that turns your work into a living knowledge graph (stored as plain Markdown with backlinks) and uses it to accomplish tasks on your computer.For example, you can say &quot;Build me a deck about our next quarter roadmap.&quot; Rowboat pulls priorities and commitments from your graph, loads a presentation skill, and exports a PDF.Our repo is <a href="https:&#x2F;&#x2F;github.com&#x2F;rowboatlabs&#x2F;rowboat" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;rowboatlabs&#x2F;rowboat</a>, and there’s a demo video here: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=5AWoGo-L16I" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=5AWoGo-L16I</a>Rowboat has two parts:(1) A living context graph: Rowboat connects to sources like Gmail and meeting notes like Granola and Fireflies, extracts decisions, commitments, deadlines, and relationships, and writes them locally as linked and editable Markdown files (Obsidian-style), organized around people, projects, and topics. As new conversations happen (including voice memos), related notes update automatically. If a deadline changes in a standup, it links back to the original commitment and updates it.(2) A local assistant: On top of that graph, Rowboat includes an agent with local shell access and MCP support, so it can use your existing context to actually do work on your machine. It can act on demand or run scheduled background tasks. Example: “Prep me for my meeting with John and create a short voice brief.” It pulls relevant context from your graph and can generate an audio note via an MCP tool like ElevenLabs.Why not just search transcripts? Passing gigabytes of email, docs, and calls directly to an AI agent is slow and lossy. And search only answers the questions you think to ask. A system that accumulates context over time can track decisions, commitments, and relationships across conversations, and surface patterns you didn&#x27;t know to look for.Rowboat is Apache-2.0 licensed, works with any LLM (including local ones), and stores all data locally as Markdown you can read, edit, or delete at any time.Our previous startup was acquired by Coinbase, where part of my work involved graph neural networks. We&#x27;re excited to be working with graph-based systems again. Work memory feels like the missing layer for agents.We’d love to hear your thoughts and welcome contributions!

Show HN: Rowboat – AI coworker that turns your work into a knowledge graph (OSS)

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Hi HN, I&#x27;m Arsalan, founder of LiveDocs (<a href="https:&#x2F;&#x2F;livedocs.com">https:&#x2F;&#x2F;livedocs.com</a>). We&#x27;re building an AI-native data workspace that lets teams ask questions of their real data and have the system plan, execute, and maintain the analysis end-to-end.We previously posted about LiveDocs four years ago (<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30735058">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30735058</a>). Back then, LiveDocs was a no-code analytics tool for stitching together metrics from tools like Stripe and Google Analytics. It worked for basic reporting, but over time we ran into the same ceiling our users did. Dashboards are fine until the questions get messy, and notebooks slowly turn into hard-to-maintain piles of glue.Over the last few years, we rebuilt LiveDocs almost entirely around a different idea. Data work should behave like a living system, not a static document or a chat transcript.Today, LiveDocs is a reactive notebook environment backed by real execution engines. Notebooks are not linear. Each cell participates in a dependency graph, so when data or logic changes, only the affected parts recompute. You can freely mix SQL, Python, charts, tables, and text in the same document and everything stays in sync. Locally we run on DuckDB and Polars, and when you connect a warehouse like Snowflake, BigQuery, or Postgres, queries are pushed down instead of copying data out. Every result is inspectable and reproducible.On top of this environment sits an AI agent, but it is not &quot;chat with your data.&quot; The agent works inside the notebook itself. It can plan multi-step analyses, write and debug SQL or Python, spawn specialized sub-agents for different tasks, run code in a terminal, and browse documentation or the web when it lacks context. Because it operates inside the same execution graph as humans, you can see exactly what it ran, edit it, or take over at any point.We also support a canvas mode where the agent can build custom UI for your analysis, not just charts. This includes tables with controls, comparisons, and derived views that stay wired to the underlying data. When a notebook is not the right interface, you can publish parts of it as an interactive app. These behave more like lightweight internal tools, similar in spirit to Retool, but backed by the same analysis logic.Everything in LiveDocs is fully real-time collaborative. Multiple people can edit the same notebook, see results update live, comment inline, and share documents or apps without exposing raw code unless they want to.Teams use LiveDocs to investigate questions that do not fit cleanly into dashboards, build analyses that evolve over time without constant rewrites, and automate recurring questions without turning them into brittle pipelines.Pricing is pay-as-you-go, starting at $15 per month, with a free tier so people can try it without talking to us. You&#x27;ll have to sign up, as it requires us to provision a sandbox for your to run your notebook. Here&#x27;s a video demo: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;Hl12su9Jn_I" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;Hl12su9Jn_I</a>We are still learning where this breaks. Long-running agent workflows on production data surface a lot of sharp edges. We would love feedback from people who have built or lived with analytics systems, notebooks, or &quot;chat with your data&quot; tools and felt their limits. Happy to go deep on technical details and trade notes.

Launch HN: Livedocs (YC W22) – An AI-native notebook for data analysis

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Hey HN, stripe-no-webhooks is an open-source library that syncs your Stripe payments data to your own Postgres database:
<a href="https:&#x2F;&#x2F;github.com&#x2F;pretzelai&#x2F;stripe-no-webhooks" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;pretzelai&#x2F;stripe-no-webhooks</a>.Here&#x27;s a demo video: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;cyEgW7wElcs" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;cyEgW7wElcs</a>Why is this useful? (1) You don&#x27;t have to figure out which webhooks you need or write listeners for each one. The library handles all of that. This follows the approach of libraries like dj-stripe in the Django world (<a href="https:&#x2F;&#x2F;dj-stripe.dev&#x2F;" rel="nofollow">https:&#x2F;&#x2F;dj-stripe.dev&#x2F;</a>). (2) Stripe&#x27;s API has a 100 rpm rate limit. If you&#x27;re checking subscription status frequently or building internal tools, you&#x27;ll hit it. Querying your own Postgres doesn&#x27;t have this problem. (3) You can give an AI agent read access to the stripe.* schema to debug payment issues—failed charges, refunds, whatever—without handing over Stripe dashboard access. (4) You can join Stripe data with your own tables for custom analytics, LTV calculations, etc.It creates a webhook endpoint in your Stripe account to forward webhooks to your backend where a webhook listener stores all the data into a new stripe.* schema. You define your plans in TypeScript, run a sync command, and the library takes care of creating Stripe products and prices, handling webhooks, and keeping your database in sync. We also let you backfill your Stripe data for existing accounts.It supports pre-paid usage credits, account wallets and usage-based billing. It also lets you generate a pricing table component that you can customize. You can access the user information using the simple API the library provides:<pre><code> billing.subscriptions.get({ userId });
 billing.credits.consume({ userId, key: &quot;api_calls&quot;, amount: 1 });
 billing.usage.record({ userId, key: &quot;ai_model_tokens_input&quot;, amount: 4726 });
</code></pre>
Effectively, you don&#x27;t have to deal with either the Stripe dashboard or the Stripe API&#x2F;SDK any more if you don&#x27;t want to. The library gives you a nice abstraction on top of Stripe that should cover ~most subscription payment use-cases.Let&#x27;s see how it works with a quick example. Say you have a billing plan like Cursor (the IDE) used to have: $20&#x2F;mo, you get 500 API completions + 2000 tab completions, you can buy additional API credits, and any additional usage is billed as overage.You define your plan in TypeScript:<pre><code> {
 name: &quot;Pro&quot;,
 description: &quot;Cursor Pro plan&quot;,
 price: [{ amount: 2000, currency: &quot;usd&quot;, interval: &quot;month&quot; }],
 features: {
 api_completion: {
 pricePerCredit: 1, &#x2F;&#x2F; 1 cent per unit
 trackUsage: true, &#x2F;&#x2F; Enable usage billing
 credits: { allocation: 500 },
 displayName: &quot;API Completions&quot;,
 },
 tab_completion: {
 credits: { allocation: 2000 },
 displayName: &quot;Tab Completions&quot;,
 },
 },
 }
</code></pre>
Then on the CLI, you run the `init` command which creates the DB tables + some API handlers. Run `sync` to sync the plans to your Stripe account and create a webhook endpoint. When a subscription is created, the library automatically grants the 500 API completion credits and 2000 tab completion credits to the user. Renewals and up&#x2F;downgrades are handled sanely.Consume code would look like this:<pre><code> await billing.credits.consume({
 userId: user.id,
 key: &quot;api_completion&quot;,
 amount: 1,
 });
</code></pre>
And if they want to allow manual top-ups by the user:<pre><code> await billing.credits.topUp({
 userId: user.id,
 key: &quot;api_completion&quot;,
 amount: 500, &#x2F;&#x2F; buy 500 credits, charges $5.00
 });
</code></pre>
Similarly, we have APIs for wallets and usage.This would be a lot of work to implement by yourself on top of Stripe. You need to keep track of all of these entitlements in your own DB and deal with renewals, expiry, ad-hoc grants, etc. It&#x27;s definitely doable, especially with AI coding, but you&#x27;ll probably end up building something fragile and hard to maintain.This is just a high-level overview of what the library is capable of. It also supports seat-level credits, monetary wallets (with micro-cent precision), auto top-ups, robust failure recovery, tax collection, invoices, and an out-of-the-box pricing table.I vibe-coded a little toy app for testing: <a href="https:&#x2F;&#x2F;snw-test.vercel.app" rel="nofollow">https:&#x2F;&#x2F;snw-test.vercel.app</a>. There&#x27;s no validation so feel free to sign up with a dummy email, then subscribe to a plan with a test card: 4242 4242 4242 4242, any future expiry, any 3-digit CVV.Screenshot: <a href="https:&#x2F;&#x2F;imgur.com&#x2F;a&#x2F;demo-screenshot-Rh6Ucqx" rel="nofollow">https:&#x2F;&#x2F;imgur.com&#x2F;a&#x2F;demo-screenshot-Rh6Ucqx</a>Feel free to try it out! If you end up using this library, please report any bugs on the repo. If you&#x27;re having trouble &#x2F; want to chat, I&#x27;m happy to help - my contact is in my HN profile.

Show HN: Stripe-no-webhooks – Sync your Stripe data to your Postgres DB

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Show HN: I made paperboat.website, a platform for friends and creativity

Hi HN — I’m the developer of NetViews, a macOS utility I built because I wanted better visibility into what was actually happening on my wired and wireless networks.I live in the CLI, but for discovery and ongoing monitoring, I kept bouncing between tools, terminals, and mental context switches. I wanted something faster and more visual, without losing technical depth — so I built a GUI that brings my favorite diagnostics together in one place.About three months ago, I shared an early version here and got a ton of great feedback. I listened: a new name (it was PingStalker), a longer trial, and a lot of new features. Today I’m excited to share NetViews 2.3.NetViews started because I wanted to know if something on the network was scanning my machine. Once I had that, I wanted quick access to core details—external IP, Wi-Fi data, and local topology. Then I wanted more: fast, reliable scans using ARP tables and ICMP.As a Wi-Fi engineer, I couldn’t stop there. I kept adding ways to surface what’s actually going on behind the scenes.Discovery &amp; Scanning:
* ARP, ICMP, mDNS, and DNS discovery to enumerate every device on your subnet (IP, MAC, vendor, open ports).
* Fast scans using ARP tables first, then ICMP, to avoid the usual “nmap wait”.Wireless Visibility:
* Detailed Wi-Fi connection performance and signal data.
* Visual and audible tools to quickly locate the access point you’re associated with.Monitoring &amp; Timelines:
* Connection and ping timelines over 1, 2, 4, or 8 hours.
* Continuous “live ping” monitoring to visualize latency spikes, packet loss, and reconnects.Low-level Traffic (but only what matters):
* Live capture of DHCP, ARP, 802.1X, LLDP&#x2F;CDP, ICMP, and off-subnet chatter.
* mDNS decoded into human-readable output (this took months of deep dives).Under the hood, it’s written in Swift. It uses low-level BSD sockets for ICMP and ARP, Apple’s Network framework for interface enumeration, and selectively wraps existing command-line tools where they’re still the best option. The focus has been on speed and low overhead.I’d love feedback from anyone who builds or uses network diagnostic tools:
- Does this fill a gap you’ve personally hit on macOS?
- Are there better approaches to scan speed or event visualization that you’ve used?
- What diagnostics do you still find yourself dropping to the CLI for?Details and screenshots: <a href="https:&#x2F;&#x2F;netviews.app" rel="nofollow">https:&#x2F;&#x2F;netviews.app</a>
There’s a free trial and paid licenses; I’m funding development directly rather than ads or subscriptions. Licenses include free upgrades.Happy to answer any technical questions about the implementation, Swift APIs, or macOS permission model.

Show HN: I built a macOS tool for network engineers – it's called NetViews

I work on real-time voice&#x2F;video AI at Tavus and for the past few years, I’ve mostly focused on how machines respond in a conversation.One thing that’s always bothered me is that almost all conversational systems still reduce everything to transcripts, and throw away a ton of signals that need to be used downstream. Some existing emotion understanding models try to analyze and classify into small sets of arbitrary boxes, but they either aren’t fast &#x2F; rich enough to do this with conviction in real-time.So I built a multimodal perception system which gives us a way to encode visual and audio conversational signals and have them translated into natural language by aligning a small LLM on these signals, such that the agent can &quot;see&quot; and &quot;hear&quot; you, and that you can interface with it via an OpenAI compatible tool schema in a live conversation.It outputs short natural language descriptions of what’s going on in the interaction - things like uncertainty building, sarcasm, disengagement, or even shift in attention of a single turn in a convo.Some quick specs:- Runs in real-time per conversation- Processing at ~15fps video + overlapping audio alongside the conversation- Handles nuanced emotions, whispers vs shouts- Trained on synthetic + internal convo dataHappy to answer questions or go deeper on architecture&#x2F;tradeoffsMore details here: <a href="https:&#x2F;&#x2F;www.tavus.io&#x2F;post&#x2F;raven-1-bringing-emotional-intelligence-to-artificial-intelligence">https:&#x2F;&#x2F;www.tavus.io&#x2F;post&#x2F;raven-1-bringing-emotional-intelli...</a>

Show HN: Multimodal perception system for real-time conversation

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