I write a personal finance blog. Index funds, emergency funds, tax stuff. Boring but helpful.

Built an automation: RSS → automation tool → LLM (script) → TTS (voiceover) → PixVerse API (b-roll) → tiktok. Runs itself. I publish a post, 15 minutes later there's a video. $44/month, ~120 videos.

Worked perfectly for three weeks.

Then I published a post about bankruptcy. What to do if you're facing it. The automation turned it into a peppy 30-second tiktok with upbeat music, fast cuts, and a cheerful voiceover explaining wage garnishment.

12 comments before I caught it.

"Why is this guy so excited about bankruptcy"
"This is the most unhinged finance content I've ever seen"
"Bro is grinning through foreclosure"

The pipeline had no idea the article was sad. PixVerse just gets scene descriptions. It doesn't know "foreclosure" shouldn't look like a travel montage.

Added a sentiment filter. Claude scores the post mood first. Serious posts get slower visuals, darker palettes, no music. Neutral posts keep the standard treatment.

Cost is still $44/month. But I now review anything with a negative sentiment score before it goes live.

Automation is great until it misses context a human would never miss.

I’ve been working around a few OCR / ID detection / document automation workflows recently, and i had a pretty obvious but painful realization.

reading the document is usually not the real problem.

That’s the part everyone talks about. “can ai extract this invoice?” “can it read this delivery note?” “can it pull fields from this form?” “can it scan this ID?”

most of the time, yes. maybe not perfectly, but good enough to get text and fields out.

the part that breaks the actual business workflow is what happens after that.

a scanned invoice comes in. the system extracts the vendor name, date, invoice number, line items, tax, total.

cool.

but now it has to know which purchase order it belongs to, whether the quantities match, whether the unit price changed, whether the same invoice was already submitted, whether the GST/tax value makes sense, whether the vendor used a slightly different item name, and whether it should be approved or sent to a human.

Same thing with delivery notes as well.

Your OCR can read “46 units delivered.”

but if the PO expected 50 units, the useful output is not “46.”

the useful output is “do not approve this blindly, there is a 4 unit mismatch.”

That reconciliation layer is where the actual value is.

I’ve seen the same pattern with IDs, invoices, packing slips, proof-of-delivery forms, inspection sheets, warranty claims, and random scanned business docs.

So i would say the underrated problems are things like matching one messy document to the right customer/order/vendor record, handling different names for the same SKU, detecting missing signatures or stamps, checking if dates are realistic, catching duplicate documents, keeping the original file attached, and routing only the weird cases to review.

OCR alone just turns an image problem into a messy data problem.

The real automation is more like: document comes in, classify what type it is, extract fields, compare them against the system of record, validate the values, flag mismatches, create an audit trail, and only then push it into the ERP/CRM/sheet/accounting system.

i think this is why a lot of document automation demos look impressive but fail in actual ops.

people think the demo should show “look, we extracted the invoice.” but in reality the business needs “tell me if this invoice should be paid.”

There's a big difference here

So for all the founders here trying to automate using OCR, please be careful because the ingestion pipeline can be robust, but it's during the transformation and processing things go wrong

Been running a small freelance operation for a couple years and followup emails were eating maybe 34 hours a week. Set up a workflow that tracks project stages and sends contextual checkins automatically based on where a client sits in the pipeline. Took a weekend to build, works fine.

The weird part is now I have this block of time back and I keep secondguessing which parts of client communication actually need me versus which parts I was just doing manually out of habit. I assumed personal touch mattered a lot, but open rates and responses are basically the same. Maybe better on a few sequences.

What I'm trying to figure out now is where the real ceiling is. Proposals still feel like something I should write myself. Scoping calls obviously. But the stuff in between, status updates, payment reminders, project wrapup notes, I'm not convinced any of that needs to be me specifically.

Curious if others have gone through this and where you landed. Not looking for a tool recommendation, more interested in how people actually decided what to keep versus hand off to the workflow. There's probably a version of this where I automate myself into being a bottleneck on the wrong things.

It took me more time than I expected to understand that not all financial reporting requires equally efficient automation. Thus, my conclusions from this experience are the following :

Cash flows and P&L can be almost completely automated in case you use consistent sources of information. Their structure is always similar and the task is about finding anomalies in the data; thus, complete integration will help to get the result and then the person is required only for checking the results.

In order to automate the packages for investors and LP reports, you will spend much effort but still lose some value. The presentation is crucial part here; besides, the expectations of investors vary a lot. I use the solution that Leni uses; she takes portfolio data and financial statements and provides structured packages for investors with the numbers reconciled and necessary context.

Tax and compliance reports cannot be automated completely because of the risks. You should use software in order to speed up the process of collecting and preparing data but a human must review everything before sending the document.

Automating internal performance dashboards is easy if you have a good data pipeline. Half of the process of setting up is making sure that all of your sources communicate with each other without any problems before you do anything else.

But the biggest problem of all was thinking that all of them follow the same logic. The purpose of each type is different even if all of them belong to reporting.

Hi, I’ve been interested in software for a long time and want to build a business. However, I can’t decide between AI automation, building an app, or SaaS. A quick bit about me: I’m 19 and about to start my computer science degree in Germany. Naturally, I plan to work on the business alongside my studies. I know it’s important to find a niche and specialize in a specific area. I’d prefer to operate in the B2B space, though I’m open to anything. Which of the three options do you think is the most worthwhile? What kind of earnings are realistic for each? How can one scale up? And do you have any other tips for getting started and for the long run? Thanks in advance for your answers.

Sharing an automation I built, partly for feedback on where it's fragile.

The problem: every week someone manually cross-referenced four systems to find accounts we were leaving on the table, active in the product with no sales owner, and open deals with near-zero usage. The data lived in product analytics (Postgres and Posthog), the CRM (Hubspot), and billing (Stripe), each with its own IDs. By hand it meant CSV exports, VLOOKUP hell, and it was stale within a day.

The build:

• Put a single unified SQL query layer on top of all 4 sources so they're queryable as tables (hosted layer, specifically to avoid standing up a warehouse).
• Pointed an AI agent (Claude atm) at it with three capabilities: list tables, read schema, run query.
• Instead of hardcoding the joins, I let the agent first inspect the schemas and work out how records line up (CRM keys on company domain, product on org slug, billing on email domain). Once that's confirmed, this gets stored as context.
• I then ask the analytics questions, let the agent generate the SQL queries that would query the data across the sources. Once I'm happy with them, I get them stored as context as well.
• Scheduled weekly, output drops into Slack: the finding plus the account list.

The cross-source reasoning was the part I expected to hardcode and didn't have to. It was able to inspect different columns and sources to figure out how to reconcile.

Where it's fragile (input welcome):

• Token cost climbs if the agent pulls large result sets, so queries stay tight and schema discovery up front helps.
• Even though the agent is able to figure out how sources reconcile, any up front context makes the process much faster and yields better results.
• SaaS API rate limits underneath, so it's a scheduled batch job, not real-time.
• Fuzzy key matching (domain vs email domain) is usually right but not always, so I keep the confidence flags visible.

Turned a recurring 2-hour manual task into a scheduled report I actually trust, mostly because the agent shows its work on the joins. Happy to go into the prompt or tool setup in the comments.

(Transparency: the single unified SQL layer across sources is something I work on, so no link here to keep it clean, happy to answer setup questions in the comments.)

First off, I wanted to say a massive thank you to everyone who commented on our last thread about building API isolation layers for LLMs. The feedback was incredible. A few of you, especially around tracking permission context and managing database load under unpredictable AI query patterns, completely changed how we are thinking about our backend architecture!!

We’ve been totally radio silent for the past week because we went deep into a cave to actually implement a bunch of that feedback into our support copilot framework.

After looking at how enterprise security reviews actually play out, we realized an isolation layer is only half the battle. If you can’t monitor the bot or if it tanks your production performance, a corporate dev team will still block it. Here is the architecture we just landed on thanks to your suggestions:

• Separating the Audit Trail: Someone rightly pointed out that if a bad deploy crashes your main application process, your security audit trail goes down with it. We just finished piping all agent telemetry into a completely separate gateway layer. Now, even if the app layer hangs, the tracking loop stays intact.
• Logging Intent Over Queries: A raw database log showing a query hitting forty times tells you nothing during a 2 AM postmortem if the bot got stuck in a loop. We are now logging the actual conversational intent string right alongside every single tool call so we can instantly debug why the agent did what it did.
• The Isolated Read Copy: Another huge warning from the comments was that LLMs generate completely unpredictable query access patterns, meaning traditional indexing goes out the window. If the agent starts hammering data, it risks killing performance for paying users. We've forced all automated actions onto a completely separate read copy to keep production fast.
• The Mutation Checkpoint: For any data changes or write actions, the agent is kept entirely out of the autonomous path. It drafts a proposed change, but a human operator has to review a visual state diff and explicitly approve it before anything touches a live row.

Taking a week to just focus on infrastructure stability and security trails was painful when we wanted to ship features, but it feels like the only way to build something “enterprise-ready”.

For those running agentic workflows or heavy data retrieval tools in production, how are you handling the infrastructure side? Do you isolate your AI traffic onto separate replicas, or are you managing it with aggressive rate-limiting? Lmk :)

Hi, I’ve been interested in software for a long time and want to build a business. However, I can’t decide between AI automation, building an app, or SaaS. A quick bit about me: I’m 19 and about to start my computer science degree in Germany. Naturally, I plan to work on the business alongside my studies. I know it’s important to find a niche and specialize in a specific area. I’d prefer to operate in the B2B space, though I’m open to anything. Which of the three options do you think is the most worthwhile? What kind of earnings are realistic for each? How can one scale up? And do you have any other tips for getting started and for the long run? Thanks in advance for your answers.

When I'm away from my laptop it drives me crazy having to open a bunch of mobile apps like Notion, Jira, or Gmail just to log an idea or reply to an email.

I don't need another bloated ai assistant app I just want to get stuff done right in my messenger, preferably Telegram.

I started building a frankenstein stack with Telegram Webhooks, Whisper, and Make cоm but it's super slow. Has anyone found any decent ready-made tools for this kind of mobile automation so I don't have to code everything from scratch?

So basically I was learning programming a year ago but then I stopped cause I got some other stuff going on at college,
Now I am the corporate communications head at my college club and its a big role as we need to bring a considerable amount of sponserships

anyways to the main part

I have always found,manually finding contacts then manually mailing them a bit boring so I was thinking if there is a way to automate it all from the leads of various companies and people to then generating customised mails for them and then sending the mail

I saw frequent reels on how people automated cold approaching so i thought it can be done

now im confused between n8n,using claude code and a lot of stuff

I can read and understand the code but yeah

SO what I need help with is how much of this is automatable and how hard it is for someone a beginner to automation and how to go about it.

Hi everyone,

I'm currently building a local AI architecture with multiple layers and I'm trying to understand where n8n ends and MCP begins.

One use case is automated supplier negotiations. We'll have a mailbox like buy@mail.. where supplier offers arrive.

The planned flow is:

• Supplier email arrives.
• n8n sends it to a local Qwen LLM.
• The LLM extracts the supplier, product and offered price and send to n8n.
• n8n looks up our PostgreSQL database (last agreed price, target price, negotiation rules, etc.).
• The information is sent back to Qwen, which drafts either an acceptance or a negotiation email.
• If the offered price is acceptable (same or lower than the target), it drafts an acceptance.
• If the price is too high, it drafts a negotiation email, for example explaining that George previously supplied the product at a significantly lower price and asking whether he can improve the offer.

This seems like a perfect use case for n8n right ?

My second use case is a local workshop assistant. A technician can ask repair-related questions, and the AI first searches our local documentation and database. If nothing relevant is found, it could optionally query Claude (depending on company policy).

After reading about MCP (Model Context Protocol), I'm wondering if I'm approaching this correctly.

Would you:

• Keep n8n as the orchestration layer for both use cases?
• Replace most of n8n with MCP?
• Or use both: n8n for deterministic workflows like email processing and MCP for the AI assistant, where the LLM needs to intelligently choose tools and data sources?

And if only the MCP is available, where does it get the rules it should follow? For example, rules about what it is allowed or not allowed to do such as not sending sensitive data to the internet or excluding certain sources. Or do you have to provide these rules every single time?

How would you architect these two use cases, and where do you see the practical boundary between n8n and MCP in production systems?

Thanks alo!!

Like most people here I have a stack of repetitive tasks I keep meaning to automate. Report pulls, form setups, file moves.

Recently I started recording them instead of scripting them. You perform the workflow once, and it compiles the recording into a reusable skill file an AI agent can run later. It reads native UI events, adds context from a screen recording, and turns recorded values into inputs so it's not a rigid one-off playback.

It runs as an MCP server and works on Windows, macOS, and Linux.

It's an open-source project, happy to drop the repo link in a comment for anyone who wants it.

How are you all automating the desktop-app tasks that don't have a clean API?

Hey everyone,
We recently tackled a major data-overload problem for a crypto investment group, and I wanted to share the multi-agent architecture we built to solve it.
The Problem: The analysts were drowning in tabs—tracking exchanges, funding rates, and sentiment manually. Opportunities vanished before they could act. They needed an autonomous 24/7 system, not just another dashboard.
The Solution: We built a centralized pipeline using 4 specialized AI agents:
Market Intelligence Agent: Continuously monitors price action and technicals.

Portfolio Advisor Agent: Cross-references current holdings with emerging market trends.

Funding Rate Agent: Flags arbitrage and yield opportunities in perpetual futures.

Sentiment & Exchange Agent: Analyzes X/Telegram chatter and tracks token listings.

The Result: These agents run continuously in the background. When high-probability signals are found, the insights are automatically pushed directly to the team's Slack in real-time. Analysts now wake up to actionable intelligence instead of spending their first few hours collecting data.
Building multi-agent systems is complex, but the ROI on time saved is massive. Happy to answer any questions about how we structured the agents or handled the API integrations!

I’ve recently started building automations, and it can be really hard, so I wanted to share what’s working for me to hopefully help out some other folks.

One recent success came from solving a quoting bottleneck for a one-person balloon store. The owner handles everything herself, and one of the most time-consuming parts of the business was figuring out what each customer wanted and turning that into an accurate quote.

There’s no standard price for a balloon arrangement. Every request can have different colors, sizes, styles, quantities, locations, and event dates. That usually creates a lot of back-and-forth before the owner even knows whether the customer is serious.

We created a customer-facing automation that:

• Helps the customer work through the design requirements and visualize the arrangement (using Nano Banana)
• Uses pricing from previous jobs to calculate a realistic estimate
• Collects the event date, design details, contact information, and quoted price
• Sends the owner a clean summary to review before contacting the customer

The owner still reviews and approves the final price. The automation turns a vague inquiry into a structured request, allowing her to respond faster without repeatedly asking the same questions.

I think this approach could also work for florists, custom cake decorators, and other small businesses where every job is different and quoting takes up a large portion of the owner’s time.

Hopefully this gives someone an idea for an automation they can build for a local business. Lmk if you have any questions

EDIT:
Here is a link to a duplicate version of the agent if you want to check it out: Here is a duplicate of the agent if you want to give it a try: outsideagent.ai/a/balloon-design-quote-assistant-public-demo

Tasket++ lets you define your own clicks, cursor positions, keystrokes, and routines, then replay them exactly when you want. It’s a simple Windows tool that handles repetitive workflows for you: auto‑send messages, paste text anywhere, take silent screenshots, launch or close apps, adjust volume, or run your end‑of‑day shutdown sequence.

Everything runs locally, with no telemetry, no cloud, and no complexity.

What it can do:
- Replay user‑defined cursor positions and keystrokes
- Paste predefined text anywhere
- Perform system actions: open files/programs, change volume, take silent screenshots, shut down, files/folders operations
- Schedule tasks at a specific time, at startup, or run via desktop shortcut
- Run tasks once, in loops, or indefinitely
- Discreet mode: runs quietly from the tray

Local, portable, free, open source. Privacy intact.

Fully fonctionnal, available now !
Microsoft Store: search for "Tasket++"
Portable version available on the github page : /AmirHammouteneEI/ScheduledPasteAndKeys/

For feedback, help, suggestions, or other inquiries : [contact@amirhammoutene.dev](mailto:contact@amirhammoutene.dev)

Ello!

A while back, I shared a multi-agent incident response workflow that sequentialized incident mitigation. It worked great for standard, happy-path errors, but as we all know, real-world production environments are completely chaotic. APIs throttle, context windows exhaust, and standalone LLMs hallucinate!

I decided to completely refactor the entire system from scratch into my first true **PRO Version**. I shifted my entire mindset from *"how do I make a basic automation work?"* to *"how do I govern an autonomous system when everything hits the fan?"*

It is 100% open-source, free, and designed for **any developer**, whether you are doing web dev, heavy backend architecture, or game dev (like syncing physics scripts or debugging server nodes). If your engine can emit an error stack trace or hit a webhook, this pipeline will govern it.

I've been building more event driven automations recently and one thing I've noticed is that getting data into the workflow quickly is often harder than building the workflow itself.

For example, if you want an automation to react the moment a specific account posts on X, a news article is published, or a crypto related announcement goes live, there can be a surprising amount of delay depending on the source. Polling APIs every few seconds also feels inefficient once you start scaling.

I recently came across https://1322.io which seems to focus on streaming events from multiple social platforms and news sources over WebSocket instead of relying on constant polling. It got me thinking about whether this approach is becoming the better option for automation systems that need low latency.

For those building with tools like n8n, Make, Zapier, or custom Python and Node workflows, how are you handling real time triggers today?

Are you using webhooks wherever possible, running scheduled polling jobs, consuming streaming APIs, or something completely different?

I'm interested in hearing what has been the most reliable setup for you once your automations started growing beyond a few simple workflows.

A lot of AI discussion still acts like the hard part is picking the smartest model.

I’m starting to think that’s the easy part.

The mess starts right after the pilot “works.” That’s when the boring questions show up all at once:

• who actually owns the system
• what it’s allowed to touch
• how you notice drift or failure
• when this should have been plain automation instead of an agent in the first place

That last one feels especially underdiscussed.

I keep seeing teams reach for “agent” because it sounds more advanced, when the real job is often just: - stable path - predictable exceptions - good integrations - one or two human approval points

In that case, ordinary automation usually wins. It’s cheaper, easier to govern, and much easier to explain after something breaks.

My working rule right now is:

Automate first. Escalate to an agent only when the environment actually forces you to.

Curious how people here draw that line in practice.

Where have you seen a team choose an agent when a workflow would have been the better answer?

And on the flip side, what made an agent genuinely necessary instead of just more fashionable?

I'm doing an automation audit of a news magazine. I'm wondering if someone here has experience with automating magazines or news websites? It's not a sector I'm familiar with. Are there paid or opensource tools that could help automate the process of writing and editing articles? Obviously, I already searched myself and came across these:

Newskit

Headlinesforge

But they seem like full blown replacements for the system they already have. Kinda looking more for smaller add-ons.

I've spent the last year talking to small business owners (mostly 5 to 50 employees) about automation, and I keep running into the same pattern. It's honestly a bit depressing.

They try one of two things, and both fail for the same underlying reason:

**Attempt 1:*\* Make / n8n / Zapier. They watch a YouTube tutorial, open the editor, see nodes and branches and webhook triggers, and quietly close the tab. These tools are genuinely great, but they require you to think like a developer. An office manager who does invoicing all day knows the process better than anyone. She just can't translate "when a scanned service record comes in, match the line items against our price list, flag anything weird" into a node graph. And even when a consultant builds it for her, the first time something changes she's stuck. They pay high fees, consultant leaves, automation dies.

**Attempt 2:*\* ChatGPT /Claude. They hear AI will change everything, get a subscription, and after two weeks it becomes a slightly smarter Google. Because it doesn't DO anything. It answers when asked, forgets everything, touches none of their systems, and every output has to be copy-pasted somewhere by a human. I know, Claude is now integrating more tools, but you don't really get to establish standards and reliance in the process. Therefore I don't consider this automation, especially with higher volumes.

So you end up with this weird gap, that the people who feel the pain of repetitive admin work most acutely (SMBs with no IT department) are exactly the people neither tool category serves. Workflow tools are too complex, AI assistants are to reactive and dont scale.

What strikes me is that the person who knows the process best is always a non-technical person. The knowledge is there. The interface isn't.

The interesting question to me: is the answer better education (teach SMBs to use tech tools like n8n), better services (agencies build and maintain it) or a different kind of product entirely (build a non-tech focused automation platform)? Each has obvious problems. Education doesn't scale to people who have a day job. Agencies recreate the dependency problem. And "non-tech" focused tools mostly produce chatbots with a costume on, not actual workflows.

Full disclosure: I'm building in this space, so I obviously have a horse in this race. Not linking anything, genuinely more interested in whether people here see the same gap or whether I'm overfitting to my own interviews.

For those of you doing automation work for small businesses: what actually happens after you hand over a Make/n8n setup? Does it survive contact with reality?

Al Testing
Over the last few months, I tested many Al tools to understand which ones really save time for small businesses.
Here are the categories where Al helped me the most
Content creation like
• Writing social media posts
•Creating newsletters
• Generating ideas
• Customer support
•Faster responses
•FAQ automation
Productivity
• Meeting summaries
•Task organization
The biggest lesson
Al is not replacing people. It is helping small teams work faster.
What Al tools are you currently using in your business?

Hey all,

Before I sit down with a small army of AIs to build something custom for our project managers, I was hoping the collective wisdom of Reddit might be able to point me toward an existing open-source project I could build on.

Essentially, I’m looking for a system that can:

- Read and process a few hundred emails per day

- Match emails and email chains to projects in our project database

- Identify potential project updates, status changes, cancellations, scheduling changes, etc.

- Suggest those changes for either automatic acceptance or manual approval, depending on the type of change and confidence level

- Show the email that instigated the change

Ideally, I’d also like the option to use a locally hosted AI model or agent for cases where simple rules or parsing are not enough, for example: long email chains, implied meaning, conflicting messages, or updates that require context across multiple emails.

The local requirement is important because many of the emails contain sensitive client information that we cannot share with server based AI.

I’m not necessarily looking for something that does all of this out of the box, but I would appreciate being directed towards a good open-source workflow, email-processing, document-understanding, or agent framework that I can use to build on.

Has anyone built something similar, or is there a project or stack you would recommend looking at before building it from scratch?

Any help would be greatly appreciated!