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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'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: https://github.com/getclawe/clawe

We've been building Tambo for about a year, and just released our 1.0.

We make are making it easier to register React components with Zod schemas, a build an agent picks the right one and renders the right props.

We handle many of the complications with building generative user interfaces like: managing state between the user, the agent, and react component, rendering partial props, and we handle auth between your user, and MCP. We also support adding MCP servers and most of the spec.

We are 100% open-source and currently have 8k+ GitHub stars, thousands of developers, and over half-millions messages processed by our hosted service.

If you're building AI agents with generative UI, we'd like to hear from you.

He wrote instructions and other people executed them, the original prompt engineer!

I bookmarked a project called "Solving Sol" seven years ago and made a repo in 2018. Committed a README. Never pushed anything else.

Fast forward to 2026, I finally built it.

https://intervolz.com/sollewitt/

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 / 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 "see" and "hear" 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 data

Happy to answer questions or go deeper on architecture/tradeoffs

More details here: https://www.tavus.io/post/raven-1-bringing-emotional-intelli...

https://livedocs.com). We'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 (https://news.ycombinator.com/item?id=30735058). 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 "chat with your data." 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'll have to sign up, as it requires us to provision a sandbox for your to run your notebook. Here's a video demo: https://youtu.be/Hl12su9Jn_I

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 "chat with your data" tools and felt their limits. Happy to go deep on technical details and trade notes.

https://github.com/pretzelai/stripe-no-webhooks.

Here's a demo video: https://youtu.be/cyEgW7wElcs

Why is this useful? (1) You don'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 (https://dj-stripe.dev/). (2) Stripe's API has a 100 rpm rate limit. If you're checking subscription status frequently or building internal tools, you'll hit it. Querying your own Postgres doesn'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:

  billing.subscriptions.get({ userId });
  billing.credits.consume({ userId, key: "api_calls", amount: 1 });
  billing.usage.record({ userId, key: "ai_model_tokens_input", amount: 4726 });

Effectively, you don't have to deal with either the Stripe dashboard or the Stripe API/SDK any more if you don't want to. The library gives you a nice abstraction on top of Stripe that should cover ~most subscription payment use-cases.

Let's see how it works with a quick example. Say you have a billing plan like Cursor (the IDE) used to have: $20/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:

  {
    name: "Pro",
    description: "Cursor Pro plan",
    price: [{ amount: 2000, currency: "usd", interval: "month" }],
    features: {
      api_completion: {
        pricePerCredit: 1,              // 1 cent per unit
        trackUsage: true,               // Enable usage billing
        credits: { allocation: 500 },
        displayName: "API Completions",
      },
      tab_completion: {
        credits: { allocation: 2000 },
        displayName: "Tab Completions",
      },
    },
  }

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/downgrades are handled sanely.

Consume code would look like this:

  await billing.credits.consume({
    userId: user.id,
    key: "api_completion",
    amount: 1,
  });

And if they want to allow manual top-ups by the user:

  await billing.credits.topUp({
    userId: user.id,
    key: "api_completion",
    amount: 500,     // buy 500 credits, charges $5.00
  });

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's definitely doable, especially with AI coding, but you'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: https://snw-test.vercel.app. There'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: https://imgur.com/a/demo-screenshot-Rh6Ucqx

Feel free to try it out! If you end up using this library, please report any bugs on the repo. If you're having trouble / want to chat, I'm happy to help - my contact is in my HN profile.

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 "Build me a deck about our next quarter roadmap." Rowboat pulls priorities and commitments from your graph, loads a presentation skill, and exports a PDF.

Our repo is https://github.com/rowboatlabs/rowboat, and there’s a demo video here: https://www.youtube.com/watch?v=5AWoGo-L16I

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'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'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!

It turned out things get messy when your software is running in places you can't simply SSH into.

Over the last year, we’ve also helped modernize a lot of home-baked solutions: bash scripts that email when updates fail, Excel sheets nobody trusts to track customer versions, engineers driving to customer sites to fix things in person, debug sessions over email (“can you take a screenshot of the logs and send it to me?”), customers with access to internal AWS or GCP registries because there was no better option, and deployments two major versions behind that nobody wants to touch.

We waited a year before making our first breaking change, which led to a major SemVer update—but it was eventually necessary. We needed to completely rewrite how we manage customer organizations. In Distr, we differentiate between vendors and customers. A vendor is typically the author of a software / AI application that wants to distribute it to customers. Previously, we had taken a shortcut where every customer was just a single user who owned a deployment. We’ve now introduced customer organizations. Vendors onboard customer organizations onto the platform, and customers own their internal user management, including RBAC. This change obviously broke our API, and although the migration for our cloud customers was smooth, custom solutions built on top of our APIs needed updates.

Other notable features we’ve implemented since our first launch:

- An OCI container registry built on an adapted version of https://github.com/google/go-containerregistry/, directly embedded into our codebase and served via a separate port from a single Docker image. This allows vendors to distribute Docker images and other OCI artifacts if customers want to self-manage deployments.

- License Management to restrict which customers can access which applications or artifact versions. Although “license management” is a broadly used term, the main purpose here is to codify contractual agreements between vendors and customers. In its simplest form, this is time-based access to specific software versions, which vendors can now manage with Distr.

- Container logs and metrics you can actually see without SSH access. Internally, we debated whether to use a time-series database or store all logs in Postgres. Although we had to tinker quite a bit with Postgres indexes, it now runs stably.

- Secret Management, so database passwords don’t show up in configuration steps or logs.

Distr is now used by 200+ vendors, including Fortune 500 companies, across on-prem, GovCloud, AWS, and GCP, spanning health tech, fintech, security, and AI companies. We’ve also started working on our first air-gapped environment.

For Distr 3.0, we’re working on native Terraform / OpenTofu and Zarf support to provision and update infrastructure in customers’ cloud accounts and physical environments—empowering vendors to offer BYOC and air-gapped use cases, all from a single platform.

Distr is fully open source and self-hostable: https://github.com/distr-sh/distr

Docs: https://distr.sh/docs

We’re YC S24. Happy to answer questions about on-prem deployments and would love to hear about your experience with complex customer deployments.

As an experiment, we gave Claude Code the OOXML spec, a concrete editor architecture, and a Playwright-based test suite. The agent iterated in a (Ralph) loop over a few nights and produced a working editor from scratch.

Core text editing works today. Tables and images are functional but still incomplete. MIT licensed.

I built ArtisanForge, a free platform to learn PHP and Laravel through a medieval-fantasy RPG. Instead of traditional tutorials, you progress through kingdoms, solve coding exercises in a browser editor, earn XP, join guilds, and fight boss battles.

Tech stack: Laravel 12, Livewire 3, Tailwind CSS, Alpine.js. Code execution runs sandboxed via php-wasm in the browser.

What's in there:

- 12 courses across 11 kingdoms (PHP basics to deployment)

- 100+ interactive exercises with real-time code validation using AST analysis

- AI companion (Pip the Owlox) that uses Socratic method – never gives direct answers

- Full gamification: XP, levels, streaks, achievements, guilds, leaderboard

- Multilingual (EN/FR/NL)

The idea came from seeing too many beginners drop off traditional courses. Wrapping concepts in quests and progression mechanics keeps motivation high without dumbing down the content.

Everything is free, no paywall, no premium tier. Feedback welcome – especially from Laravel devs and educators.