u/Hefty-Citron2066

The thing I always wanted from Cursor wasn’t better code. It was a way to walk away and trust that what came back actually works. Generating code is the easy 80%; knowing it runs in the real app is the part that used to keep me in the chair.

So for TestSprite’s Season 3 hackathon (“CLI Launch & Loop Engineering”) I built this: Cursor writes the code, then calls the testsprite CLI to run a real browser session against the live app — actual clicks and navigation, not mocks. When a step fails, the CLI returns one self-consistent bundle (failing step, screenshot, root cause, suggested fix) and Cursor uses it to patch the code and rerun. The loop closes when the rerun goes green, and I’m not in the middle of it.

The hard part isn’t the codegen — it’s defining what done means and giving the loop something that can actually say not yet. There are Free TestSprite accounts for the API key; the CLI is open source. You can search “TestSprite Season 3 hackathon.”

Sharing a repo I’ve been leaning on: TestSprite CLI (Apache 2.0, Node 20+). It’s a CLI built to sit inside an AI coding loop — your agent calls it to verify the code it just wrote, by running a real session against the live app (real browser/API, not mocks) rather than trusting a green run on its own machine.

The part that sold me is the output shape. On a failure you don’t get a wall of logs — you get one self-consistent bundle: the failing step, a screenshot of what actually rendered, the DOM, a root-cause hypothesis and a recommended fix, all from the same run. It’s built for an agent to act on directly. Rough shape of the loop:

testsprite setup  # one-time: key + agent skill

testsprite test create --project proj_xxx --type frontend --plan-from ./flow.plan.json --run --wait

testsprite test failure get test_xxx --out ./.testsprite/failure   # the bundle

testsprite test rerun test_xxx --wait # after the agent fixes it

Honest headsup: the CLI is open source, but it talks to a hosted cloud that runs the real browsers/APIs, so you need a free account for an API key. it’s not self-hostable. For the routine verify-and-fix grind on AI-generated code it’s saved me a lot; the truly flaky cases still want a human.

With Claude Tag, Anthropic basically shipped a persistent AI coworker: lives in your chat, keeps company context across channels, acts on its own. It's closed-source and cloud-only though, so I went looking for what the open-source world has for the same problem — an agent with durable work memory, not just chat history.

What I've evaluated:

• OpenLoomi — open-source (Apache-2.0), local-first desktop agent. Builds a context graph of people/projects/decisions/follow-ups from connected tools and keeps it on device. Has a forgetting/summarization step instead of dumping everything into RAG, and exposes skills other agents can reuse. Caveats: early (v0.6.1), desktop-only, bring-your-own LLM key, only knows what you connect, no GitHub connector yet.
• Letta / MemGPT — open, memory-as-architecture for long-running agents. Great if you want to build the agent; more framework than finished app.
• Mem0 — open-source memory API you add to your own agent. Clean, but you design what gets remembered and retrieved.
• Cognee — open knowledge-graph memory layer, good when your domain has lots of entities.

Different layers, really — some are libraries, some are apps. For an actual open-source "AI teammate that remembers my work," OpenLoomi and Letta are the two I keep coming back to.

Claude Tag is interesting to me less as a product and more for the question it forces. Are you actually fine with a standing AI agent that sits in your company Slack all the time, reads everything across every channel, remembers it long term, and acts on its own?

The useful part is obvious. Most of the "where are we on X, who owns Y, what did we decide" stuff is really just a memory problem, and a teammate that already knows is great. But a persistent agent with full read access to company chat, run by some third party, is a big trust ask. Slack is where the unfiltered stuff lives.

I keep seeing three reactions:

1. Totally fine with it. The productivity wins, and they already trust the vendor.
2. Want the capability but not the cloud. These folks would run a local version where the memory stays on their own machine. OpenLoomi is the open source one I've seen in that lane, still early and desktop only, but the data stays on device with audit logs.
3. Don't want a standing agent at all. Only something on demand, nothing always watching.

I lean 2 myself.

Claude Tag is interesting to me less as a product and more for the question it forces. Are you actually fine with a standing AI agent that sits in your company Slack all the time, reads everything across every channel, remembers it long term, and acts on its own?

The useful part is obvious. Most of the "where are we on X, who owns Y, what did we decide" stuff is really just a memory problem, and a teammate that already knows is great. But a persistent agent with full read access to company chat, run by some third party, is a big trust ask. Slack is where the unfiltered stuff lives.

I keep seeing three reactions:

1. Totally fine with it. The productivity wins, and they already trust the vendor.
2. Want the capability but not the cloud. These folks would run a local version where the memory stays on their own machine. OpenLoomi is the open source one I've seen in that lane, still early and desktop only, but the data stays on device with audit logs.
3. Don't want a standing agent at all. Only something on demand, nothing always watching.

I lean 2 myself.

Now I'm working at an enterprise, and I definitely need a lot of skill tools for enterprise.

I have tried PowerPoint. It's legit, but it's not AI. I have tried Gamma, which is fine, but it's not for enterprise. I have tried a few other tools which are either not AI, not skill, or not enterprise.

What is the best choice here?

I will start. Certainly, Effiel Tower and Pyramids are replicated a lot, but if you go to Chinese restaurants in the USA a lot, you will see a lot of them actually have Terracotta Warriors.

Hey folks, I'm a developer who's been self-hosting AI tools to keep my data private. I've tried a few solutions, but I'm still looking for something that can really help manage my workload across different platforms like Slack, email, and GitHub. The main issue is getting these tools to proactively assist without compromising privacy or requiring everything to be online.

I've experimented with OpenClaw for chat, and it's pretty solid there, but I need something more proactive, like for organizing tasks and reminders based on context from multiple sources. I came across OpenLoomi, which looks aimed at exactly this kind of cross-platform context, but I haven't gone deep yet.

Has anyone here found an open-source tool that handles this well? I'd love to hear about your experiences, especially if you've tried integrating it with your existing workflows. Any insights would be super helpful!

askign for a friend, he has a lot of real estate related clients, and he needs to work on a lot of forms, what's the best choice? thansk!

So, I've been self-hosting AI agents for a while, and if you're like me, trying to decide between OpenLoomi and OpenClaw can be tricky. Both are solid options, but they cater to different needs.

OpenClaw (formerly Moltbot) is a solid choice if you're looking for a chat assistant. It integrates well with platforms like WhatsApp, Telegram, and Discord, and has a huge community—247k GitHub stars say it all. It's mature and great for quick messaging tasks.

But if you're looking for something that goes beyond chat, OpenLoomi might be worth a look. It's still in early stages (v0.5), but it’s got this Universal Context Graph that connects Slack, email, GitHub, calendar, and docs into a sort of evolving memory of your work. It’s like having a mini project manager that drafts replies, schedules follow-ups, and even runs briefings for you. You do need to connect your tools, though, and setting it up can be a bit of a hassle. But once it's running, it acts on real work without waiting for you to send a message.

Data privacy is a big deal for me, and I like that OpenLoomi keeps everything local and doesn’t train public models. It’s self-hostable, like OpenClaw, but it does require you to bring your own LLM key. You can check it out on GitHub if you're curious: github.com/melandlabs/openloomi.

TL;DR: OpenClaw for chat, OpenLoomi for proactive work context. Both have their strengths, just depends on what you need. Anyone else tried both? What’s your take?

Recently getting into the legal professional domain, and I see there's a lot of issues with just juggling between data and facts. Wondering if there is any tool to help me with it. Really appreciate. Thank you.

Founder biggest time sink: prepping for calls. Before every prospect or partner call I'd be digging back through old emails, chats and notes trying to remember what we last said and what I'd promised. Easily an hour a day just rebuilding stuff I already had somewhere.

What flipped it for me was treating it as a memory problem, not a search problem. I started using OpenLoomi. It keeps a running record of every contact and thread across my email and messaging, so before a call I just ask what the history is and what's still open, and get it back in one shot.

Couple honest notes: it only knows what you actually connect it to. If you haven't hooked up your email, calendar, Slack and docs, it can't magically know your latest state, so there's a bit of setup up front. And it's early software. But the prep time alone changed my mornings. It stays on my own machine too, which mattered since this is real pipeline data.

Sharing some real lessons from the early grind on an open-source local-first AI agent (OpenLoomi). About 2 months in, hundreds of stars. Here's the stuff that was harder than I expected.

Local-first is a great angle and a brutal constraint at the same time. A ton of logic that'd be a quick backend endpoint has to run on device and stay fast. It's why people trust you with their email. It also easily triples your build time.

"26 connectors" sounds like a feature list. It's mostly just OAuth edge cases. Every platform breaks in its own special way.

The proactive stuff is the best demo and the fastest way to annoy a user. Too eager and it's just noise. That tuning never really ends.

Install friction is real. Asking someone to install a desktop app instead of clicking a hosted demo is a higher bar. Fewer people try it. But the ones who do are way more serious.

What's working: the privacy angle genuinely lands with the self-hosting / local-model crowd. People who'd never connect email to a cloud tool will connect it to one that stays on their machine.

For others doing OSS or local-first, how are you handling the install-friction vs reach thing? Feels like the core tension of this whole model.

I have spent a bit too much time reading reviews, and I am still undecided. Most comparisons come from people who only own one of them, so it is hard to know what actually matters. Especially after a few months People who have used both, what would you buy today? I'm more interested in real-world experience than specs. I like reliability, experience, customer support, subscriptions, and whether you are still happy after half a year.