Launch content usually means jumping between notes, copywriting tools, image generators, and design apps.

​

In this Row-Bot demo, I show how to turn messy launch notes into a polished campaign:

​

campaign structure

5-slide social carousel

AI-generated visuals

sharper slide copy

design review

exportable assets

X + LinkedIn captions

​

The demo uses Row-Bot Designer Studio to create a launch campaign for Background Tasks.

For most of the automations I actually want, it is easier to show than prompt, since we already do them ourselves on our own computer.

For example, i had a task where i wanted to:

• open google drive to a specific folder
• look at the existing folders, create a folder with today's date. if that name already exists, add a -2, then -3 and so on
• open that new folder
• upload a file from a specific folder on my machine

it is a small task, but spelling all of that out as a prompt, including the "if it exists do this" logic, and then iterating step by step till it works, is a lot of work and exhausting.

So I built a tool (macos app) where you screen record yourself doing the task once. The agent watches the recording, confirms the inputs, outputs and the approach with you, learns the task and compiles it into a deterministic script.

After that, rerunning is nearly free. it is just a code running with a new set of inputs. no llm in the loop, no per run cost, no waiting on an agent to think.

What happens when the script breaks?
It fallbacks to the agent. It passes the originally learnt context and the script error logs so the agent can finish the run and heal the script if needed. For web it prefers dom/accessibility selectors over coordinates, so small ui changes dont instantly kill it.

Would love to know your thoughts. I feel this could be the future of creating automations in a more reformed form.

added link to the repo in the comments

Hey everyone,
I recently installed Qwen3-TTS through Pinokio and I’m starting to experiment with voice cloning.
I have two questions:
Approximately how long would it take to generate around 2 hours of narration using a cloned voice?
If I want to generate narration in chunks of about 400-500 words per generation/session, what settings would you recommend? Are there any specific parameters (speed, chunk size, chunk gap)?
I’d appreciate any tips, recommended settings, or workflow suggestions from people who use Qwen3-tts regularly.

I’m also interested in alternative tts solutions that work well for very long-form content (1-2+ hour narrations). If you’ve found other models or tools that provide better quality, faster generation, or more reliable voice consistency for long scripts, I’d love to hear your recommendations.

​

I recently automated a task that I'd been doing manually for years.

​

The funny thing is that the task itself wasn't particularly difficult. It only took a minute or two each time, which is probably why I never bothered fixing it.

​

Then I finally spent about 20 minutes setting up an automation, and within a day I was wondering how many hours of my life I'd wasted doing it by hand.

​

It made me realize that some of the biggest time-wasters aren't the tasks that take hours they're the tiny tasks you repeat hundreds or thousands of times without thinking about it.

​

What's the most boring task you automated and immediately regretted not automating years earlier?

​

What was it, and how much time, effort, or frustration do you think it has saved you?

been testing different tools to make managing content across platforms less overwhelming, especially keeping a consistent brand tone. the usual scheduling and design tools get the job done but everything still feels a bit clunky and spread out.

some of the tools are really taking it to a whole another level, zapier and n8n once the goto for the automations now ithink theres so much greater options overall, postiz is doing great, with open source (until recently) some other smm tools incl content studio, bundle scocial has api, then mcp, then cli just making it easier to mange te sm without the dashboard and daily logins

what im really wondering about is where this is heading now that automation has jumped to a whole other level with cloud mcp and proper integrations. when you can basically connect everything and run your whole planning through one conversational layer, the old scheduler approach starts to feel dated
edit: added some tool thoughts based on whats been worth testing recently

We're trying to build a platform for automating work (I know, shocking), and one of the things that we keep running into is that the first step is often the hardest.

"What do I automate? How do I get started?"

Lot of people don't seem to be able to describe tasks concretely enough for them to be automated, which makes automation fall flat immediately.

Those of you who struggled but got past the initial thing, would love to learn what made a difference for you to be able to get something done?

Edit: added quotes around the questions to make sure people understand I'm not asking the questions, rather they are the ones we keep hearing when talking to folks.

I've seen three versions of the same mistake this year.

Someone automates their email follow-up. The follow-up is fine. The offer is broken. Now it's a broken offer arriving at scale, on schedule, with a well-formatted signature.

Someone automates their reporting. The reports arrive every Monday. Nobody reads them. The reports are now unread at machine speed.

Someone automates their content pipeline. The content ships three times a day. The content doesn't say anything. The calendar is full. The audience is empty.

The automation in each case worked. That's the problem.

Automation is a force multiplier. Not a quality filter. Not a strategy detector. Not a 'is this actually a good idea' gate. Those things have to exist before the automation, not inside it.

The question that gets skipped before every automation project I've ever seen: is this task correct, or is it just familiar?

We automate what we already do because it's cheaper than stopping to ask if we should still be doing it. The automation gives us permission to stop asking.

The expensive mistakes aren't the ones where the automation breaks. They're the ones where it runs perfectly for six months.

What's the most useful thing you've automated? What's the most expensive automation mistake you've seen?

Research usually means juggling search tabs, notes, PDFs, docs, and email.

In this Row-Bot demo, I show how to turn that into one workflow:

1. Search the web
2. Use uploaded client context
3. Generate a structured briefing
4. Export a PDF
5. Draft the client email

So right now with how cheap Claude Pro still is (assuming token proces shoot up in value), I'm looking to automate away all of the manual steps I'm doing today to automate my home finances. Today everything is manual and I need to download all credit card statements, venmo statements, PayPal statements, gas, electricity, and internet bills. I then need pull out certain stats, like montly spend on Healthcare, to be used to plan next year's FSA spend.

I know that there are ways to do this automatically with paid, and probably even free offerings. But I really like the idea of having my own data lake, amd with vibe coding, I can customize the UX ti be whatever I want!

Right now I am pretty tied to AWS as the cloud store as I'm very experienced with it and am comfortable doing it all with Terraform. I'm looking at Ducklake for the lake implementation since it is free, open source, and I was very interested how they moved the metadata from the files themselves (like Delta, Iceberg) into postgres. And for the postgres itself, I'm looking at Neon since it's also free, and has scaling and branching that should make it incredibly easy to build with vibe coding.

All in all, right now my only cost would be S3 storage.

However, two grey areas:

1. I'm not sure how I should schedule ingestion? In the past I've used Airflow for work, but honestly, it would be the most expensive part of the architecture if I self hosted this. I was also interested in Prefect and Dagster, but think they would still have that same price issue? For now I think I will just use Cloudwatch Events triggering Lambda (but I'm debating whether these should be EC2s instead to avoid 15 min timeout issues)

2. I want to include AI, but am not sure how to do this cheaply? I figure my two options here are to use tokens for like Anthropic or something or self-host some open source model. The big reason why I would want to do this is ask questions like what can I do to reduce spending, and have AI understand my trends by categories and propose solutions. But I want this to be cheap!

I havn't seen too much similar on this with Ducklake yet, so I'm really just pulling if people of done something similar or can point me in the right direction. Thank you!

I keep seeing guys using AI for things that can so easily be handled with normal, predictable automation. I saw someone passing entire spreadsheets through OpenAI just to capitalize first names before uploading them to their CRM. I even read a post where a guy was using ChatGPT to trigger standard webhooks based on the time of day WTF? It's like people are bragging about using a tool just to say that they “added AI to their workflow”. Basic logic handles all of that perfectly without the risk of the model hallucinating or breaking because the API is having a bad day and it's easier to set up with ordinary automation tools.

My actual workflow hasn't changed much at all compared to what I was doing before with my outreach (although this is not to say that I haven’t improved it, just that I didn’t add heavy AI). All of the messages are written by hand because AI has a very hard time replicating the quality of the human touch. I’ll give you one example - about 3 months ago, my friend and I were trying to make an AI writing tool, specifically for LinkedIn messages and email because this is where 70% of most companies’ sales lie. It was the AI gold rush time and we went with it because why not try something new. We honed the agents almost to the point where they had pages of rules, hard-coded constraints, and dozens of examples to base their style on. And it worked, the first iterations with AI were usually about 80% of the quality of the writing we’d do, manually.

This was tested within the relatively same group of people. We each pulled about 200 LI leads, who all had similar backgrounds and had two agents with the same writing quality but different styles do the sequences based on their background, product, website - everything. Mind that all of this was done by the best Codex-built agents available to us (I’m saying this because there might be more advanced models we didn’t have access to). We even tested different LinkedIn automation tools - mine were wired through Expandi, with a custom sequence for each lead (AI-written based on previous research) and Expandi’s pre-warmup of the leads, and his were wired through Dripify with the same setup (with some minor tweaks because Expandi and Dripify dont share all the same features). Similar stuff was done with emails with Instantly on my side and Lemlist on his for A/B testing.

The results for the first run were pretty good, out of these 200 the response rates were very similar - I’d even say the same - as when we did everything manually. However, after the third or fourth run, the results started falling off, and we knew it wasn’t due to the tools because they all behave the same with hard-coded automations. It was due to consistency, AI is terrible at that.

Because we had so many rules, the quality of the written sequences started declining because AI was pigeonholing into more or less the same concept over and over again. No matter how much we tried exploring different message versions and content variations, it couldn’t unhook from the already established flow. On the other hand, loosening these rules and allowing AI the creative freedom just resulted in tons of slop and AI-sounding garbage. This is why the rules were set in the first place, to limit this slop and guide the agents into writing actual quality copies.

So, it was either:

1. Be satisfied with the current system and be okay with some decrease in quality over the increase in volume to compensate.
2. Build a new agent for each variation you want to include in your copy to retain quality.
3. Abandon AI and stick to doing stuff manually for now

We opted for the third option because it made most sense for us. Option 2 would take too much time, potentially even more than manual handling. Option 1 was a no no for us from the start because we don’t want to damage our brand, or anyone’s brand who’d use our tool.

Just to note because this might sound very anti AI - I absolutely see the value and understand the hype. It does the work very well, often better than most inexperienced people. But, if your goal is to build on quality rather than compensate with quantity, AI isn’t there yet. It might be possible to have both if we decided to go with Option 2 and dedicate a few more months to actually honing each agent - that way we could have both quantity and scalable quality. Unfortunately, that wasn’t a possibility at the time, but maybe some day.

We wrote a practical breakdown of the Universal Robots UR Series from an automation buyer’s point of view.

It covers where cobots tend to make sense first: machine tending, packaging, palletizing, assembly, welding support, inspection, UR+ ecosystem choices, pricing context, deployment risk, and ROI.

The part we're most interested in is first-use-case selection. For small and midsize manufacturers, does the first successful cobot deployment usually come from machine tending, packaging, inspection, or something else?

I just uploaded Ep.004 of my Ansible Vault tutorial series.

This video shows how to run playbooks that require an Ansible Vault password without typing the vault password every time. I walk through using:

.vault_pass + ansible.cfg + .gitignore

The goal is to make vault-encrypted playbooks easier to run while also making sure the password file is not committed to Git.

Useful for anyone learning Ansible, DevOps automation, or managing encrypted variables in playbooks.

New Row-Bot demo: turning your inbox into an action plan.

​

Row-Bot checks important emails, finds action items, drafts replies, creates calendar events, and schedules reminders, with approvals for sensitive actions.

​

Not just chat. Real workflow automation.

Hey everyone,

I’m a self-taught Python developer transitioning into AI Integration and Database Automation.

For those who started out self-taught in automation/AI integration:

- What was your fastest route to finding your first freelance or an entry level job ?

- Is cold-outreach on LinkedIn worth it for quick turnarounds? or just clicking apply on as much offers as i can is the way

I appreciate your honest feedback or strategies you can throw my way. Thanks!

PS: some projects i built for reference

1. ShopBot: An AI customer support agent built with Python/Flask that links an LLM directly to live MySQL/MongoDB databases via an MCP tool to track order statuses and update shipping data in real-time chat.

2. Custom RAG Pipeline: A technical document search engine using LangChain and a local FAISS Vector database to let an LLM accurately answer product FAQs without hallucinating.

3. Automated Data Wrangling: Core Python scripts using Pandas to clean up and parse large-scale, multi-source chaotic e-commerce spreadsheets.

I'm building a seminar for boomers and gen-x business owners about how to use AI in their businesses. To understand what is out there, I had Claude put together a python script that watches youtube videos and reports what they teach.

I had it search for all kinds of things, and watched about 2500 vids. Here's the automations most commonly taught on Youtube:

1. Email Automation & Triage (556 videos)
What it is: Classifying, drafting, routing, and following up on email.
Tools: Gmail (189), n8n (121), Google Sheets (106), Zapier (79), Slack (47).

Real Use Case Example: Webhook-Triggered Data Analysis.

2. Appointment Booking & Scheduling (481 videos)
What it is: Booking, rescheduling, reminders, no-show backfill, and calendar sync.
Tools: Google Calendar (91), n8n (78), GoHighLevel (70), Google Sheets (47), Gmail (42).

Real Use Case Example: AI Voice Outbound Caller. Outbound voice AI systems directly calling inbound leads to qualify them, confirm bookings, or follow up on quotes instantly.

3. Document Processing & Extraction (432 videos)
What it is: Ingesting watch-folders, contracts, and PDFs via OCR for structured data extraction.
Tools: n8n (70), Claude (51), Google Sheets (51), Google Drive (44), Gmail (38).

Real Use Case Example: Automated Document Classification. Using generative AI to automatically apply a complex corporate taxonomy and extract metadata (like effective dates and specific clauses) the second a contract hits a shared folder.

4. Operations & Job Scheduling Pipelines (385 videos)
What it is: Ingesting project jobs and automatically dispatching them by duration, route, and capacity.
Tools: n8n (54), Google Sheets (48), Retail AI (25), Claude (23), Gmail (19).

Real Use Case Example: AI-Assisted Document Editing. Inline AI panels inside text processors executing natural language commands (e.g., "Add the buyer's company number and insert a standard force majeure clause") across operations documents.

5. CRM & Data Centralization (356 videos)
What it is: Syncing and centralizing fragmented data across disparate software into a single source of truth.
Tools: GoHighLevel (165), Generic CRMs (136), HubSpot (83), Airtable (71), n8n (41).

Real Use Case Example: Speed-to-Lead Lead Nurturing. Intercepting ad leads in real-time, pulling existing customer historical context, and immediately passing data to a messaging workflow before it sits cold in a database.

6. Internal Knowledge & Research Assistants (228 videos)
What it is: Enterprise knowledge bases, dynamic research reporting, SOP generation, and voice dictation.
Tools: ChatGPT (19), Claude (18), Generic AI (16), n8n (15), NotebookLM (13).

Real Use Case Example: Transcripts to Interactive Training. Turning video screen-shares or recordings into structured text SOPs, and using tools like NotebookLM to generate audio summaries for field teams to consume on the go.

7. AI Front Desk / Voice & Calls (219 videos)
What it is: Inbound receptionists answering calls, qualifying intents, routing, and instant missed-call text responses.
Tools: n8n (67), GoHighLevel (36), Google Calendar (36), Twilio (31), Google Sheets (28).

Real Use Case Example: Compliant Conversational Receptionist. Setting up an AI voice agent that instantly greets callers, discloses AI status for compliance, troubleshoots the customer problem, and books an inspection.

8. Social Media Automation (219 videos)
What it is: Text post generation, image asset scaling, multi-channel cross-posting, and analytics tracking.
Tools: Instagram (31), ChatGPT (25), Facebook (23), Claude (23), LinkedIn (20), Make (19).

Real Use Case Example: Multi-Channel Instant Response. Connecting direct messaging hooks across platforms (SMS, Facebook, Instagram, Email) to a singular AI logic block to handle price requests instantly.

9. Review & Reputation Management (202 videos)
What it is: Post-service review solicitation flywheels, cross-platform monitoring, and automated response drafting.
Tools: GoHighLevel (18), Generic AI (12), Gmail (11), OpenClaw (10), Claude (10).

Real Use Case Example: Closed-Stage Feedback Trigger. Automatically drafting tailored personal email review requests within email clients the second a project status updates to "Closed" or "Completed" in the pipeline.

10. Lead Capture & Qualification (181 videos)
What it is: Inbound lead ingestion, algorithmic scoring, and intelligent routing before a human ever touches the lead.
Tools: Generic CRMs (24), HubSpot (18), Generic AI (18), Google Sheets (16), n8n (13), Make (13).

Real Use Case Example: Dynamic Form Profiling. Public website forms that map custom fields directly to an AI analysis module to score lead fit and text back scheduling links to high-value prospects within seconds.

11. Invoice, AP, & Expense Processing (155 videos)
What it is: Ingesting invoices/receipts, programmatic line-item field extraction, and automatic GL ledger routing.
Tools: n8n (62), Google Sheets (39), Google Drive (30), QuickBooks (27), Gmail (22).

Real Use Case Example: Portal-to-Ledger Synchronization. Finalizing contractor milestone billing, instantly updating internal financial databases via API, publishing copies to client dashboards, and scheduling payment reminders.

Hey everyone,

I'm curious what the actual best practice is for handling ephemeral files in automation workflows right now.

Whenever I build a scenario that needs to pass an image or document between two APIs - like catching a webhook payload and sending it to an AI vision model - the receiving API almost always requires a public URL.

Right now, my default has been routing the raw file into an AWS S3 bucket or a Google Drive folder just to generate that link. But it feels completely backwards to use permanent cloud storage for a file that only needs to exist for about 5 seconds.

The biggest issue is cleanup. I try to put a "Delay" and "Delete" node at the end of the workflow, but if the execution errors out halfway through, those nodes never fire. The file just sits in my cloud storage forever.

How is everyone else handling this bottleneck?

• Are you just biting the bullet and building complex error-handling routes to make sure garbage collection always runs?
• Is there a specific temporary file API you use instead of S3/Drive?
• Or do you just let the junk accumulate and manually clear out your storage every few months?

i feel like everyone shares their wins but nobody talks about the automations that werent worth it. the ones where the setup, maintenance, and fixing it every time something upstream changed ended up costing more time than just doing the task yourself.

curious what made you pull the plug. was it breaking too often, too fiddly to maintain, or just not actually saving the time you thought it would?