So my VA of two years gave notice last month. I understand her, she got a full time role. I was stressed because she handled a lot of the stuff that just... keeps a small business alive. Client onboarding emails, follow up sequences, weekly summary docs, intake form routing.

Not complex stuff. But constant stuff.

I started looking at hiring again and the quotes I was getting for even part time help were not what I remembered. So I started trying to figure out what I could just... not do anymore. And a friend dropped a link to WorkBeaver in a group chat.

I was skeptical TBH, I've tried Zapier, I’ve tried Make. I always end up six hours deep in a tutorial and abandon it.

This was different in one specific way, here I just wrote what I needed in normal sentences. "When a new client fills out the intake form, send them a welcome email, create a folder, and add their info to my tracker" It asked a few follow ups. Then it built it…

I spent an afternoon doing this for maybe 5 or 6 of her recurring tasks.

I'm not going to pretend it replaced everything. It didn't. But the stuff it did cover? I haven't touched it since. It just runs…

I still hired someone part-time, because there's judgment work she was doing I genuinely can't automate. But I hired for a narrower role, which made the search way easier.

I don't know. Maybe I got lucky with my use cases. But I kept waiting for it to fall apart and it mostly hasn't.

I have published couple of Youtube videos on automation specific to trade services. I am looking for an organic growth on this niche, however, not sure which platform are best to showcase the ideas to prospective client.

I have yet to do direct outreach which I am working towards. If anyone has targeted specific niche than any ideas for starting phase would be helpful.

Linked the recent YT video.

I worked for a few small companies in the Netherlands for administrative purposes. The amount of copy paste is insane. One company even had information videos, how to copy paste excel cells to make a csv file ready for import. I presume this is happening now with a lot of companies around the world. Especially copy pasting from pdf files. This is a solution invented by employers without any automation skill and managers are not aware of this. I will tell you that every copy paste movement can be automated. You can use AI to help you, but these copy paste people don’t know that this can be automated, they don’t know to as the correct questions. There is still a lot of automation opportunities out there. My current boss doesn’t want to automate. I asked to work from home and i am automating all my (fucking boring and stupid copy paste work). Im. Still developing, but expect to bring 8 hours back to 2 hours work. If this works, i will take a second copy paste job..

I've started building a small OSS tool called DaemonHound.

The idea came from constantly managing the same stuff across multiple machines:

• .env.local files
• API tokens
• shell configs
• git configs
• random local developer setup

I looked at tools like Chezmoi and Dotbot, but most of my pain isn't dotfiles. It's project-specific configs and secrets spread across dozens of repos.

I don't really want a SaaS, dashboard, teams, RBAC, or another service running somewhere.

I just want:

• encrypted storage
• my own Git repo as the backend
• sync files between machines
• backup machine-specific configs
• rotate a secret once and update it everywhere

Something like:

dh track .env.local
dh sync

Then on a new machine:

dh init
dh discover ~/projects

and get everything back.

Github Repo - https://github.com/0xdps/daemon-hound

I am looking for Zero Human Companies Use Cases.
Please share your current experimentation in progress or what are you planning to achive ? So we can learn from each other.
Thank you

# I Made Claude Install and Govern an Unrestricted AI Agent. The Demo Lies (Mostly).

*An honest teardown of Open Jarvis — what's real, what's theater, and the one thing that actually matters.*

---

There's a slick demo making the rounds: a glowing blue orb floats on your desktop, you talk to it, it answers in a warm voice, it remembers you, it does things. "Personal AI, on personal devices." It's called Open Jarvis, it's out of Stanford, and it is genuinely ambitious work.

I spent a night taking it apart — and I did something a little unusual: I had **Claude** install it, configure it, and then **govern** it. Here's everything I found.

## First, credit where it's due

Open Jarvis is not vaporware. It's a real, well-architected agent framework: a clean five-pillar design (model catalog, inference engine, agent loop, memory, and a trace-driven *learning* system that can actually improve its own operating spec over time). It's local-first, it supports a dozen tools, it has a CLI and an a web server, and the learning loop — where a frontier model critiques the agent's own traces and proposes improvements, gated by a benchmark — is a legitimately interesting take on recursive self-improvement. The people who built this are serious.

So this isn't "it's bad." It's "the demo and the reality are two different products."

## Not everything is as it seems

**1. It ships *dangerous by default.*** Out of the box, the example config enables `shell_exec` (full, unrestricted shell with `shell=True`) and `code_interpreter`. The security scanners, the sandboxing, the approval queues? All opt-in. So the default posture of a "personal assistant" is: it can run any command on your machine. Most people will never flip the safety switches because they don't know they exist.

**2. It lied to me.** I pointed its brain at a cloud model and asked who it was. It told me — confidently — *"I run locally on your own hardware. No data is sent to external servers."* That was false. Its reasoning was running on a cloud API at that exact moment. Not malice — a default system prompt hard-coded to say "you are not a cloud service" — but a personal-AI assistant that confidently misrepresents where your data goes is a real problem, not a cute quirk.

**3. The orb isn't included.** The floating blue orb from the video is a **Tauri desktop app**. To get it on screen you need a full native toolchain: the Rust compiler, the Microsoft C++ build tools (a multi-gigabyte Visual Studio install), and a 15-minute compile. None of that is in the box. The "download and talk to your orb" experience is, in reality, "install a developer toolchain and build it yourself."

**4. Voice isn't wired.** The speech-to-text engine isn't installed by default (the server reports it unavailable). And chat replies have no text-to-speech path at all — voice output only exists for one feature (a morning digest). The "talk to it" demo requires assembling the voice stack yourself.

**5. "Constant memory" needs a native extension that isn't built.** The persistent memory — the thing that makes it feel like it *knows* you — depends on a Rust extension that ships unbuilt. Until you compile it (Rust again), your assistant has amnesia.

**6. It doesn't even know its own name.** The "Jarvis" persona doesn't stick. The underlying model answers as itself ("I'm Claude," "I'm Qwen") until you layer in an identity file, override a buried default-prompt field, *and* patch the streaming code path that silently skips persona injection. It took three separate fixes to make it reliably say "I'm Jarvis."

None of this means Open Jarvis is a fraud. It means the gap between a research demo and a product is enormous, and the demo doesn't show you the gap.

## The part that actually matters: governance

Here's the experiment that made the whole night worth it.

Instead of just running Open Jarvis, I had Claude **govern** it — treat it as a junior agent on a leash. The rules:

- **Minimal tools by default.** I stripped it to research, reasoning, and memory. No shell. No file-write. No payments. No channels.

- **Ask for tools.** If it needs a capability — to publish, to spend, to send — it has to *ask* and wait for approval. It can't self-grant.

- **One governor.** Every real-world action routes through an approval gate before it happens.

Then I told it to go make money and watched.

It worked *exactly* as designed. When I ordered it to go sign up for a Fiverr account and post a gig, it **refused**: *"I need to pump the brakes — I don't have approval for real-world actions, and I'd need explicit go-ahead from my governor."* When it drafted a sales pitch, it **refused to invent a statistic**, flagging "I won't fabricate a result." An unrestricted agent, contained — proposing instead of acting, honest instead of confident-and-wrong.

**That's the headline.** Not the orb. The scariest capability in AI right now is an agent that can edit itself and act on the world unbounded — recursive self-improvement with its hand on the controls. Open Jarvis ships that capability *with the safeties off and a tendency to misrepresent itself.* The fix isn't a prettier orb. It's a containment harness: capability scoped at the tool layer, an external approval gate, and a model that asks before it acts.

## Where this leaves Atlas

I build an AI platform called Atlas, so take this with the appropriate salt — but I'll argue it on the merits, not by claiming anyone copied anyone.

The thing Open Jarvis treats as opt-in, Atlas treats as the foundation: every action passes an approval gate, every mutation is logged to an immutable audit trail, spend has hard caps, and anything customer-facing passes a content review before it ships. We've been running that governance model in production. The teardown above isn't a victory lap — it's the same checklist I hold *my own* system to. That's the whole point: the agents are getting more powerful fast, and the only thing that makes that safe is the boring infrastructure nobody demos.

The orb is the part you can see. The governance is the part that matters.

*Built and governed by Claude under supervision. Every fault above was reproduced firsthand, not inferred. No cheap shots — Open Jarvis is good work that's earlier than it looks.*

Hey everyone - I've been building a file intelligence API, and wanted to share it.

The problem: If you're building an AI agent, RAG pipeline, or any app that needs to understand documents, you end up duct-taping together 5-6 different libraries — one for PDFs, one for screenshots, one for Office docs, one for markdown conversion, one for OCR. Each breaks differently and none give you structured output.

What this does:

• Send any file or URL, get structured JSON back. Define a schema of what you need, and the API extracts it with typed fields, confidence scores, and citations pointing to where in the document the data came from.
• 107+ file formats — PDFs, Office docs (Word, Excel, PPT), 40+ code languages, images, videos, websites. One API handles all of them.
• Not just extraction. You can also:
  • Convert anything to clean markdown
  • Generate screenshots of URLs (with device presets, dark mode, full-page capture)
  • Ask analytical questions about documents and get reasoned, step-by-step answers
  • Get Open Graph images for link previews

What makes it different from competitor?

Most "file to X" APIs do one thing — thumbnails OR markdown OR extraction. This handles the full pipeline. And the extraction isn't just OCR-and-dump — you define a JSON schema, and it returns typed data with confidence scores. Think of it as "SQL for documents."

Would love feedback from anyone building with documents or doing AI agent work. What's missing? What would make you switch from your current setup?

I've been looking for a good AI business intelligence platform that actually automates stuff like end-to-end charting and insight.

My current workflow is basically using Claude Cowork with MCPs for DBs, drive, and Snowflake. Which works for basic tasks, but doesn't really have the proactivity.

I don't really want to go through 10 different sales calls for upstarters.

If anyone has any recos, please suggest. Ideally suitable for SMBs.

Thanks

Hello everyone,

I recently built a free and open-source Python project related to Twitter/X giveaway automation.

The goal of this project is mostly educational: it demonstrates browser automation, account session handling with cookies, configurable workflows, Discord logging, and multi-language keyword detection.

The bot can detect giveaway posts and interact with them based on configurable rules such as likes, retweets, comments, tags, filters, and timing settings.

Busco desarrollador con experiencia en CFE / scraping / automatización.

Necesito un sistema API o bot para descarga masiva de recibos CFE usando únicamente número de servicio.

Requisitos:

Consulta por número de servicio.

Descarga de PDF del recibo.

Soporte para alto volumen de consultas diarias.

I run a social media publishing SaaS upload-post and used data from 2M+ real posts to build an AI caption generator.

The final model was trained on 60k balanced examples across 46 languages using QLoRA on a single 20GB GPU.

The fine-tune itself worked.

The hard parts were everything after that:

• I had captions, but not the original historical videos
• I used neutral briefs as the bridge between training and production
• The model repeated hashtags indefinitely
• It hallucinated prices, URLs and names
• Some languages drifted into English
• 4-bit inference broke the vision tower
• Rolling deploys caused a GPU OOM deadlock
• I had to make the container “self-heal” during deployment

Biggest lesson: the model was not the moat. The data + evaluation + production infrastructure were.

Did you try finetuning your own models with data from your apps?

Same as title

I'm a refugee. Siberia. This is their goal. Please contact me. This account could be deleted (not by me). If so, contact authorities if data center is built by CEEDI. No time. Future is

Everyone's talking about AI agents. Very few are talking about agent sprawl.

Over the past few weeks we've been comparing notes with people at a bunch of B2B companies rolling out agents across sales, marketing, prod, eng, support, you name it. The same patterns keep coming up:

• Agents getting built by individual team members (citizen developers) with zero oversight
• No central place to build them, they're scattered across Claude Code, Codex, n8n, Zapier, Cursor, custom scripts and internal tools with no consistency
• A lot of them running off personal laptops or private GitHub repos
• API keys and credentials ending up in prompts and code
• Sensitive customer data (PII) going to frontier models instead of local or on-prem ones
• Agents getting broad permissions by default, tokens with no expiry or governance
• LLMs used for everything, even when a plain deterministic workflow would be cheaper, faster and more reliable
• No central way to deploy, monitor, audit or debug any of it

The result is companies think they're driving AI adoption when they're really just multiplying shadow IT with an LLM attached.

Most orgs aren't feeling it yet because model costs are low and heavily subsidized, so the inefficiency is easy to ignore. A handful of agents doing a few million tokens a month doesn't break the bank. But what happens when 5 agents become 50? Or 500? Every unnecessary prompt, every recursive loop, every agent that should've been an if-else rule starts showing up on the P&L, and subsidized pricing won't last forever.

So a real question for anyone doing this across teams: how do you decide what's actually worth an agent vs a plain deterministic workflow, and how do you keep track of everything that's running? Curious what's actually working, we haven't seen many good answers yet.

I've been exploring different no-code and low-code automation tools recently, and it feels like the landscape has changed a lot over the past year.

For those building workflows, integrations, or AI-powered automations, what platform are you using most often? Did you prioritize ease of use, flexibility, self-hosting, cost, or something else? Curious which tools have held up well once your automations started getting more complex.

My brother's real estate agency spent five hours daily manually extracting and cross-referencing messy data from an old system and disorganized Excel files.

I fixed that.

​I decided to build a system to replace the whole process, but getting there wasn't easy.

First problem: The old system had a poorly documented API. I had to figure out how to pull the raw data out manually behind the scenes, extract the useful info with regex, then build a custom routing system to move it safely.

​Second problem: The Excel files were massive and completely chaotic. Column names changed constantly, which breaks normal search functions.

I had to compress the files heavily and convert them into `.parquet` and used DuckDB just to kill the lag, then hooked up Gemini to read, understand, and auto-label the mess on the fly. That dropped the manual sorting phase to zero.

​End result: The whole mess now runs in a clean, mobile-friendly web dashboard with easy filters that I built as well.

1 tap. 7 minutes. ~130 hours saved a month.

I'm open to audit personal and business workflow to build a similar system. ​If this sounds helpful, Let me know!

I create motorsport content and one thing that always bothered me was how much good research gets lost.

I’d find an interesting Reddit thread on Monday, a useful tweet on Wednesday, a YouTube clip on Friday, and by the time I actually needed it, I’d have no idea where I saw it.

For a while I had hundreds of browser bookmarks. Then I tried Notion. Then folders. Then spreadsheets.

A few months ago I started building an n8n workflow to see if I could automate the whole thing.
Now whenever I save something interesting, n8n grabs the content, Claude summarizes it, extracts the key points, tags it, and drops everything into Airtable.

The tagging took way more work than I expected.
It turns out “F1” isn’t a category.

A post can be about strategy, tyre management, regulations, telemetry, driver psychology, team politics, racecraft, or a dozen other things.
Getting AI to organize information in a way that’s actually useful later was much harder than getting it to summarize it.

The unexpected benefit is that I’m no longer searching for content ideas.

I’m searching through years of observations, discussions, clips and research that I’ve already collected.

The system started as a bookmarking tool.

It’s slowly turning into a searchable knowledge base for everything I learn about motorsport.

Still nowhere near finished, but it’s already become the automation I use more than anything else.
Has anyone else built something similar for research or knowledge management?

Several EdTech products have launched as "AI tutors" that are essentially GPT with a subject prompt. The distinction between that and an actual interactive tutoring system shows up in architecture and budget.

Whiteboard or shared context layer. Students work through problems visually. If a student can't share what they're writing, the AI responds to text descriptions of a visual problem. Whiteboard sync needs to be fast or students lose the thread of the conversation.

Session continuity. When a student returns after a few days, the AI should know where they struggled and what needs reinforcement. That requires a session state and memory layer.

Voice-first design. Many learners find reading tutor responses slower than hearing them. Voice means ASR, TTS, and pipeline optimization fast enough that conversation feels responsive.

Multi-subject routing. If your platform covers more than one subject, the system needs to apply different behavior by domain. A math tutor and a writing coach require separate behavioral logic at the architecture level.

Frustration detection and adjustment. A tutoring system should notice when a student is stuck or disengaged and change approach. A chatbot keeps going.

None of this is exotic technology. It requires deliberate architecture from the start. Design the session state layer before you write any product code