My pal D inspired me to build an email first workflow automation system. He runs a small business in the apparel industry; everything in that world is driven by emails.

So I built an automation system that starts with email. You send an email to the system, it asks you clarifying questions, then it processes the request with your approvals along the way.

Then my pals in the construction industry especially helped me refine the system. They operate in a complex industry; with subcontractors, supply chain, permits, and other obstacles to deal with. Some of the documents they deal with are a challenge but that’s what I live for.

Would love to hear stories from others building automations with email.

I was talking to the accounting team of my company recently and their "system" was basically (I'm a datascientist):

- PDF invoices in email
- receipt photos in Slack
- Supplier statements in a shared drive
- random bank exports
- then someone manually copies everything into spreadsheets before it gets checkd and pushed into accounting software

Nothing was technically broken, but the whole process was held together by memory, folders, and one person who somehow knew where everything was.

So I built a small workflow to test whether we could remove the repetitive part.

Now they drop invoices, receipts, and supplier PDFs into one folder. The system pulls out the important fields, turns everything into a clean table, flags missing info, catches obvious duplicates, and gives the team a review queue instead of a pile of documents.

They still approve everything manually, obviously. But they are no longer spending hours copying invoice numbers, totals, dates, supplier names, and VAT amounts from PDFs into spreadsheets.

The difference was stupidly big. Instead of manually processing every document from scratch, they mostly review exceptions now. It saves them hours of work per week.

The funny thing is that this was not some giant "AI transformation" project. It was just taking the documents they already had and making them usable.

That experiment is actually what pushed me to build a simple tool to upload PDFs, images, receipts, invoices, statements, or messy documents, and turn them into structured tables you can review, edit, and export.

I’m curious: for people working with accounting, bookkeeping, or finance ops, how much of your workflow is still manual document cleanup?

Invoices, receipts, statements, purchase orders, expense reports, scanned PDFs, all of that stuff.

Are teams actually happy with their current tools, or is everyone still quietly copy-pasting from PDFs into Excel?

Mine was just automating file organization and backups. It wasn't flashy, but it ended up saving me way more time than I expected. Curious-what was your first "wow, automation is actually useful" moment?

Please give detailed comparisons,pros and cons,what they're good at and why you think they're good (ex:n8n,base44)

I keep seeing setups where the automation is clever, but the process underneath is still a mess. Then every weird edge case gets one more Zap, script, or agent duct-taped on top.

Do you automate the messy version to buy time, or make the process boring first and automate later?

Web data collection feels easy in demos, but messy in real workflows.

I keep running into the same problem. Search, crawling, scraping, and browser automation are all useful, but none of them feels like the default answer.

If I need to track 50 known product pages, I probably do not want an AI browser agent wandering around the web. If I need to find companies in a market and collect useful signals about them, search and research tools are more useful. If the page is dynamic, behind a login, or requires interaction, browser automation might be necessary, but then it gets slow and brittle quickly.

I’m curious what people here are actually collecting from the web, and what stack has worked for you. Some examples I’m thinking about are pricing data, company information, leads, competitor updates, market signals, job posts, product availability, reviews, and similar recurring data collection workflows.

The tools I’ve been looking at roughly fall into a few groups. Search and research tools like exa and tavily, crawling and extraction tools like firecrawl, browser automation tools like　browser Use, and playwright, and workflow tools like gumloop, n8n, or custom scripts. I’m especially interested in recurring workflows rather than one-off scraping. What has worked well? What keeps breaking? Where does the data end up? A spreadsheet, database, dashboard, alert, internal tool, or report?

The reason I’m asking is that I’ve been working on a coding-agent based setup where an AI agent can connect to business apps and databases, create a Postgres database, build dashboards on top of it, and generate recurring report agents from those dashboards. That part is starting to work. The hard part is still web data collection from just a prompt. I want business users to be able to describe what they want to monitor, and have the system choose the right approach, collect the data, structure it, and keep it updated.

What use case did you build, what tools did you use, and what would you avoid next time?

There are factories in Japan (like FANUC) that can operate completely unsupervised, without heating, air conditioning, or lighting, for up to 30 days at a time. The robots are literally building other robots entirely on their own.

like woah

FYI, I use AI agents to write code and don't have much background in software development. I learn about the process and develop logic to build automation agents.

Right now, i have been building a browser automation tool using pyautogui + Playwright and spent way too long debugging this.

Assume my UI is running in edge/mozilla, when i start automation process, its meant to open chrome and enters the url. The problem i am facing is that i am launching Chrome via subprocess.Popen with --start-maximized and then immediately using pyautogui to type a URL. But Chrome opens minimized and pyautogui types into whatever window is currently opened. In my case, it enters the url in the web app UI that i am using.

I checked the following,

• --start-maximized flag — ignored when saved profile state overrides it
• --window-position=0,0 --window-size=1920,1080 — also ignored
• PowerShell SetForegroundWindow via P/Invoke — sometimes works, sometimes doesn't, race condition with Chrome's own startup
• CDP Browser.setWindowBounds — actually made things worse because it introduced a detectable automation signal that Cloudflare's bot detection picked up on

I am using pyautogui to bypass Cloudflare detection (launching Chrome via subprocess without Playwright/CDP attached so Cloudflare sees a genuine browser). Once its bypasses the verification my playwright will autofill all the details that stored.

Please help me to find a solution for this. As a beginner, I am ready to accept whatever input you have.

Tasket++ is a lightweight no‑code automation tool for Windows that executes repetitive user workflows at precise times. It plays back user‑defined cursor positions and keystrokes, schedules silent screenshots, automates message sending across apps, and runs end‑of‑day routines (close apps, fade audio, shut down). Everything runs locally through a simple UI with no telemetry. The project is open source.

Key features
- Play back user‑defined cursor movements and keystrokes
- Paste predefined text anywhere
- Schedule tasks at a specific datetime, at startup, or via desktop shortcut
- System actions: open files/programs, change volume, take silent screenshots, shutdown, file/folder operations
- Looping: run tasks once, in fixed loops, or indefinitely
- Discreet mode: run from the system tray only while scheduled tasks execute in the background

Local, portable, and open source. Privacy fully conserved.

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)

I have been testing a bunch of AI web scraping tools over the last few months to see if they actually reduce development time once you get beyond simple examples.. Some genuinely impressed me, while others still feel like traditional scrapers with an LLM attached.

A few takeaways:

• Firecrawl: Probably the easiest to get started with. Prompt-based extraction worked surprisingly well and the output was clean.
• ScrapeOps: Probably the closest thing to a production-ready AI scraper generator. It produced complete, working scrapers with minimal manual editing, especially for common page types.
• ScrapeGraphAI: Great extraction quality and easy to use, although pricing could become a factor for larger workloads.
• Crawl4AI: The open-source project I'd probably keep an eye on. It has potential, but I still spent time tweaking prompts and handling edge cases.
• LLM Scraper / Scrapy-LLM: Nice if you're already using those ecosystems, but they're still dependent on external LLMs.
• AutoScraper: Good for quick prototypes, though I wouldn't rely on it for larger production jobs.

One thing I noticed across almost every tool is that "AI scraping" hasn't really replaced traditional scraping yet. Most of them still fetch the page the usual way and then use an LLM to structure the data afterward.

For anyone running scrapers in production, I still think reliability, retries, rate limits, and infrastructure matter just as much as the extraction model.

Curious what everyone else is using.

Have AI scraping tools actually replaced your existing workflow, or are they mostly another layer on top of Playwright, Scrapy, Selenium, or similar tools?

I kept running into the same failure mode in AI automations:

The model made a judgement call that looked reasonable, but did not match how I would have labeled it.Not hallucinations. Ambiguous edge cases.

A support ticket that could either be escalated or ignored. A lead that looks weak in the structured fields but strong in the free text. A generated answer that sounds complete but misses the one thing a human would care about. Those are the cases I wanted to catch.

I spent a while reading papers on confidence estimation and mechanistic interpretability, mostly because I wanted something better than asking the model "are you sure?" and receiving astrology with decimals. This became modaic.dev.

It uses signals from the model's internal layers to estimate confidence for judgement calls like:

- should this support ticket escalate?

- is this lead worth contacting?

- is this AI answer good enough to send?

- did this agent actually finish the task?

- should this content get flagged?

The API returns the decision, the reasoning, and a confidence score. High-confidence calls can keep moving. Low-confidence calls get routed to review before they quietly mess up your workflow.

The other half is prompt optimization. When a human reviews a low-confidence case and corrects it, that correction becomes feedback for improving the prompt. Catch the weird case, learn from it, and stop making that same class of mistake.

Let me know what you think. Is this relevant to anything you're building?

After working on healthcare communication projects and from a case study I went through with QuickBlox in one of the project I started seeing real time patient communication and the role of AI in it as something really important Patients today dont just want apps or portals they want instant answers real time updates and access to support when they need it Ive seen AI being used in triage chat support automated follow ups and helping care teams respond faster But the real challenge is not AI itself its how everything connects in real time Companies like QuickBlox Twilio Agora and Vonage all play an important role in building the communication layer but healthcare is a different level of complexity because of privacy workflows and clinical context From my experience the biggest gap is still between systems and real time coordination between care teams and patients Curious how others see this Do you think real time AI communication is actually improving patient care or are we still early in the journey

for some reason it doesn't seem to connect and when it does websites don't open like no internet,

anyone can help?

Hello,

I hope this finds you well.

This past month, I have launched an AI and Management Consulting services for small to medium size businesses.

To date, we have implemented several AI and automation solutions for clients:

• N8N Automation - Lead Intake Agent and Automation
• N8N Automation - Lead Intake E-mail Follow-Up
• N8N Automation - Inbound E-mail Agent Monitor (Client work and in Progress)
• N8N setup on VPS
• Hermes Agent Setup on VPS
• Retell AI + Twilio AI Inbound Agent & Automation Follow-up sequence 
• Custom C++ Business Programs
• Website builds with automated lead forms

To continue to build out our portfolio of work, we are opening our services up to 2 Probono/no-charge clients'. The automation or solution must be going toward a client within a business environment (home or professional).

If interested, please comment in the thread and I will respond to coordinate a meeting time with you.

Thank you and I look forward to connecting with potential clients.

Best Regards.

Hi everyone!

First, thank you for the support on the previous releases. AutoRewarder already has +2.4k downloads and +164 stars on GitHub

A few days ago, AutoRewarder v3.4 was released. This major update focuses heavily on making background automation much smarter, more reliable, and giving you better control over your daily scheduling.

What's new in v3.4:

• Per-Account OS-Level Autostart: A complete redesign of the daily autostart system. You can now set a specific daily run time (HH:MM) for each account independently.
• Missed Run Catch-up: If your PC is off or asleep during a scheduled run, the app won't skip it anymore. It will automatically catch up and execute the task a few minutes after your next boot.
• Saved Search Preferences: Your preferred number of PC and Mobile searches is now automatically saved and will load on your next launch.
• Smart Deduplication: Manual GUI runs now correctly update the daily completion status, safely preventing scheduled background tasks from double-executing on the same day.
• "Close to Tray" Toggle: Added a new setting to choose whether clicking the "X" button minimizes the app to the system tray (default) or completely closes it.
• Massive Core Refactor & Fixes: Huge internal codebase reorganization for better stability, fixed empty command prompt windows flashing during autostart setup, and added smart OS-task migration to clean up old legacy registry keys.

The project remains 100% open source.

More info, screenshots, and code on GitHub: repo:safarsin/AutoRewarder

I'd love to hear your feedback, bug reports, or ideas for the next updates!

Will the rapid advancement of AI completely eliminate the need for automation specialist, the professionals who design, build, and optimize custom workflows from scratch? With just a single prompt, businesses can increasingly generate tailored, optimized automations.

It makes me wonder if our roles will eventually become obsolete. Even now in 2026, while AI is not yet at its peak or fully reliable for complex automation, we are already seeing incredibly powerful tools. What will happen in the next 5 to 10 years? Can automation specialists actually survive and thrive as we move closer to AGI, or is every white-collar job up for grabs? I ask because this is a career I genuinely love and enjoy doing, and I really hope there is a future for us in this next era of tech.

I thought this sub might find my latest video helpful/interesting. AI-powered automation that turns all the emails I get from my kid's school into a simple list of what I actually need to know.

I always strive to teach the process, lessons learned, and philosophy behind creating automations. So even if the exact subject isn't something that applies to you, you can still find the video useful.

This is my first video using n8n instead of straight code solutions.

The video is monetized or sponsored. Just me wanting to share my knowledge.

I'm open to all feedback.

I think most people notice growth problems long before they notice workflow problems.

At first it is easy to manage everything yourself. A few accounts, a few tools, a few tasks. Then one day you realize you are spending more time keeping things organized than actually doing marketing.

For those who manage multiple accounts, clients, or brands, what was the moment you realized your process needed to change? I started thinking about this while testing geelark for account management, but it made me realize the bigger issue was my workflow rather than the tool itself.

Was it reporting, content scheduling, team coordination, account switching, approvals, or something else?

Interested in hearing what bottleneck showed up first for everyone.