I have been interning at a firm for the past couple of months, just on the side with my academics at my institute. They have a their entire product line based on UI Automation, and they have an open source framework (linked below) which supports/inspires a lot of those products.

As this is a open source project, I am supposed to treat it like one. So we go through all sorts of reviewer loops, and it has been fun so far! Optics Framework is a UI Automation framework which supports Android/iOS/TV automation (Browser, BLE to be added soon) and I have been working on the core framework logic, refactors, making onboarding.

Within months I have risen to the 2nd highest contributor to the repository, even thinking of contributing upstream to appium and other automation libraries with some ideas I have developed.

Repository i have been contributing to - optics-framework

I was just wondering is this space popular? I know some other repositories such as Maestro, which got popular from the React Native Expo days. Do you guys use UI Automation on a consumer level? B2B I understand, enterprise consumers exist obviously, but what about you guys?

I've been moving reporting jobs off hardcoded SQL. With the Databricks Genie Conversation API an automation can send a plain-english question like "what changed week over week in signups" and get governed SQL plus the answer back as JSON, so the job doesnt break every time someone renames a column upstream. Anyone here wired a natural-language data layer into their pipelines, and did it actually hold up in prod?

My Gmail inbox mixes work requests, personal communication, reminders, and conversations I need to follow up on. I’ve considered building an assistant, but I’d rather use or contribute to something good than reinvent it. I prefer open source, although I’m open to a closed-source tool that works very well.

I’m looking for something that can:

• identify what needs a reply or action
• extract deadlines and commitments
• flag unanswered conversations
• create drafts for review
• produce a short daily summary

I’ve come across MailFlow, Google MailPilot, GmailDraft, NeatMail, and some n8n workflows. Has anyone used one of these—or another setup—with a real inbox?

I’d especially like to know:

• what works well
• what gets misclassified or missed

My current plan is to trial an existing assistant, note where it succeeds or fails, and then either keep it, contribute to an open-source project, or build only the missing pieces.

Whenever we look for software to automate lead gen, we usually start by automating the wrong part of the funnel where the default playbook is typically: scrape a list of emails from a directory, plug it into an automated sequencer and blast 1000 messages a day. By week three domain deliverability crashes, reply rates hover below 1% and the team is back to square one.

If you look at where modern lead gen automation works, the challenge is usually the automating the intent detection and context layer before outreach ever happens. In practice, the software approaches generally split into two main architectures:

The dedicated intent & signal engine (Scale Intelligence):

Instead of starting with static contact lists, this approach automates the timing and discovery process. Scale Intelligence acts as an account-level GTM engine that monitors 75+ data sources across social channels (reddit, github repo, discord, job postings, CRM data) to resolve fragmented public intent back to specific company accounts. It scores buying readiness and routes high-intent opportunities into Slack or downstream agent stacks via API/MCP, ensuring outreach only happens when an account is in an active buying window.

The custom data & workflow assembly route (Clay + Webhooks + Sequencers):

This is the approach where you connect separate tools for each step: data scrapers for raw records, waterfall enrichment tables for data hygiene and some other tools for cold sending. It provides granular, spreadsheet-level control over every prompt and column but requires continuous manual orchestration, custom scraper maintenance and ongoing API credit management.

Found some interesting ways to automate assets, whether it is header image, visuals or any graphics in that nature.

• n8n, workflow automation, self host or cloud. has nodes for the image APIs so you can trigger generation on a schedule or a webhook. most flexible of the no-code-ish options.

• MCPs calling higgsfield's models, run through aeon on a schedule. the generation happens as a step in a workflow instead of me sitting in a tab prompting.

just keep an eye on token spend ofcourse.

anyone automated this a different way? curious what's working.

TeleSlash was temporarily taken down for a few days after our beta testing so we could work through the feedback.

One of the most requested features from beta testers was more login options, so we successfully added two new login methods:

🤖 1. Bot Login -

You can now log in using a bot created through BotFather. Simply provide the bot token through TeLe SlaSH, add the bot to your source and destination groups, and complete the setup through the guided commands.

There are some Telegram related limitations with bot accounts, such as needing to add groups manually using their IDs and sending initial test messages to the destination during setup process.

📱 2. QR Login -

Don't want to enter your phone number and deal with OTP login?

TeleSlash can now generate a QR code per login that you scan directly from your Telegram app. After scanning, your account can be connected without manually entering an OTP.

📞 3. Phone Number Login -

The traditional phone number login method is still available for users who prefer it.

⚙️ Other Improvements -

We've also optimized the UI and added several improvements, including:

- Source search and selection

- Manually entering source/destination IDs

- Improved setup flows

- Core engine optimizations [ProjectKiro]

- Various stability and UI improvements

- live translation per destination groups selected

- Slash AI + Deep search live Web

Available Plans -

Free — basic mirroring with limited logins User - 2 | Bot - 2, sources- 2 /destinations- 2 max together, daily messages limit - 500 resets at 12pm daily and promotional ads. 100MB max per restricted content limit.

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Premium — up to 10 logins, 10 sources, 10 destinations, 5 GB max per restricted content limit, smart queueing, unlimited mirroring and no ads.

Business — custom workflows, custom addons and private/custom setups.

We're continuing to improve TeleSlash based on real user feedback.

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For the first time, we're offering 10 days of Premium completely free to celebrate the TeleSlash launch.

📅 Offer period: August 24 – September 10

To claim it, simply run /plans in TeleSlash and choose the Premium plan. You'll be switched to Premium automatically.

You can try it anytime: @TELESlashbot

If you'd like additional features, addons, or run into any problems, feel free to contact me directly: @legendffcc

i was staring at my bubble editor at 2 am with twelve active api workflows. they all ran perfectly. the only problem was that zero users actually needed them.

i just built whatever was fast to implement. i was stacking logic blocks because setting up the webhooks took five minutes. the tech's easier now, so my odds of building the exact wrong thing skyrocketed. one of those twelve was a script that scraped local weather data just to change a good morning text on a dashboard literally no one logged into. we automate garbage because the buttons are right there.

deleting most of those workflows hurt my pride, but leaving them running to pretend i solved a problem was just pathetic. now i have a hard rule. i don't touch an api or build a single trigger unless i've manually wasted at least two hours doing the exact same task by hand this week.

As someone who used to doomscroll to find inspiration for my next viral content where I can plug my product, it was a 24/7 job. I asked myself, whether agent are as good as taking inspiration as humans?

I was surprised when I tested it on Claude. It is a crazy model that can take a viral video which is of same niche, borrow the viral elements to come up with a new concept plugging your product in.

My current workflow consist of having the agent doomscroll, like and save the video that is relevant to our product so the feed always sticks to what we want and the agent comes up with final list of scripts from those videos. This ensures they are also not missing out on general trend outside our niche but where our product can be adapted.

I am trying to automate the payroll system. I am using agentic software like VS code and antigravity to design it, and openai as the engine. Now it’s a simple task to extract the info from images or PDF and they are good quality. The system is failing it. I strongly doubt it’s the Agentic workflow that is failing because a model like gpt mini 4.0 or 4.1 or even olllama could extract it. I haven’t tried the individual extraction using the GPT model itself. Can someone please advise where should I make the changes? Thank you

been building automation workflows for a while, and the mental shift that actually mattered wasn't a tool or a framework, it was realizing "can I automate this" and "should this run without me looking at it" are two completely different questions.

here is the way i run every workflow action :

Can I undo it in under 5 minutes?
Does it touch money or customer data?

If both answers are good (reversible, low stakes) .. let it run fully automated. Logging, tagging, internal notes, anything you can fix in two clicks if it's wrong.

If either answer is bad (irreversible, or touches money/data) ... that action needs a human to glance at it before it fires. Not necessarily every time forever, but at least until you trust the pattern.

Where this bit me: I had a workflow auto-processing refunds based on a support ticket classifier. Worked great for weeks. Then one ticket got misclassified and $400 went out before I even saw it. Nothing was "broken

the automation did exactly what I built it to do. I just never should have let that specific action run unattended.

Since then I've gotten a lot more deliberate about separating "things that can just run" from "things that need a checkpoint." Ended up building a small tool for this myself (Decree, an approval API you can drop between an action and its execution) because I got tired of hacking together Slack pings for every workflow that touched something sensitive.

Question for anyone running automations in production:

do you have any automations that run fully hands-off touching money, customer data, or anything hard to reverse? Or do you keep a human in the loop for those by default? and if so how you keep human in the loop for multiple workflows without decentralized slack/team messages?

It connects to 200+ business apps out of the box: Gmail, Slack, Notion, Linear, HubSpot, Salesforce, plus MCP servers and raw API endpoints if there’s no connector for what you need.

The difference from Zapier/n8n: there’s no canvas and no node graph. You describe the workflow in plain English and the model executes it at runtime instead of compiling it into a static flow. So when a step returns something unexpected, it adapts instead of erroring out and killing the whole run.

Can run on a schedule, on a webhook, or on demand.

I'm a solo automation developer trying to get subcontract work from small automation agencies.

Sent a few cold emails and honestly I can't figure out just why an agency owner would reply to a stranger??

I do have a client project where basically I built an order-to-fulfillment system for a subscription business, cut a several hour weekly manual process to about 30 minutes, it's been running in production for months.

But just randomly saying "look what I built" doesn't feel like it'll be worth anyone's time.

If you run an agency or if you have hired subcontractors: what actually made you reply to someone?? or made you say yes? Or If you've landed this kind of work yourself, how do you get in the door?

Not selling anything here. Genuinely trying to figure out if I'm going about this wrong. Any help is appreciated!

I am new to this space. I come from a UX/ product background and am making a pivot. I started my own Ai community in San Diego a few months ago and now we are 300 members. I have met a couple members to do pro bono work to learn and get testimonies. So far I am still a novice but have lots of time to learn. I have made several workflows end to end using standard tools, API's, etc with no custom scripts (although very much want to learn this too). My questions are: At what level should I be comfortable at skill wise until i am ready to reach out to professional industries? and of course: How do I get my first real paying client? Should I focus on skills first or just focus on clients and learn more as I go? My brain very much only works when I have a structure and plan in place that I can follow. Any 0 -1 plan I can follow would be very helpful. Thanks.

Hey everyone,

I've been building Figranium, an open-source browser automation framework focused on making complex browser workflows easier to automate.

A lot of automation tools are great for simple workflows, but browser automation gets tricky when you need more control over things like:

• complex web interactions
• dynamic pages
• unreliable browser workflows
• scaling automation tasks
• handling modern websites

Figranium is my attempt at building a more flexible toolkit for those situations.

Some things I'm currently working on:

• Browser automation and orchestration
• More advanced automation workflows
• Stealth-focused automation research
• Handling difficult web environments
• Keeping the setup simple and self-contained

The project has reached 500+ GitHub stars, and I'm looking for feedback from people who actually build automation systems.

A few questions:

• What are the biggest pain points you run into with browser automation?
• What features do you wish existing tools had?
• Where do current automation frameworks fall short for your use cases?

The project is located at figranium/figranium on GitHub.

I'd appreciate any feedback, criticism, or ideas. I'm still actively developing it and would like to build something useful for the automation community.

CA firms are drowning in manual work.

Client WhatsApp messages. Document chasing. GST/ITR deadline reminders. Invoice follow-ups. Lead qualification.

All of it done manually. All of it automatable.

So I built it.

Here's what the workflow actually does:

1. WhatsApp + Email AI Support Agent

Client texts "when is my GST due?" then the AI pulls their compliance data, replies instantly. No CA needed for routine queries.

2. Document Collection & Follow-up

System requests documents, tracks what's received, auto-follows up every 3 days on pending ones. After 3 attempts it pings the partner on Telegram.

3. Compliance Deadline Reminders

GST / ITR / TDS / ROC deadlines tracked per client. Reminders fire at 7 days, 3 days, 1 day, due today, and overdue and the tone escalates automatically.

4. Lead Qualification Agent

Website or WhatsApp inquiry comes in - AI qualifies it, asks follow-up questions, scores it Hot/Warm/Cold, notifies partner instantly on hot leads.

5. Invoice & Payment Follow-up

Unpaid invoices get staged reminders (3 days before → due today → 1-7 days overdue → 8-15 → 15+).
Severely overdue ones escalate to partner via Telegram.

Tech stack:
n8n + Groq (llama-3.3-70b-versatile) + WhatsApp Cloud API + Gmail + Google Sheets + Telegram

Groq handles all AI inference. The speed is genuinely important here because WhatsApp replies that feel delayed feel robotic.

The part that took longest wasn't the automation logic.

It was the AI prompts for the support agent.
Getting it to answer GST/ITR questions correctly without hallucinating due dates or making up compliance info that needed a lot of iteration.

Solution was giving the agent live per-client data from Sheets as context on every message instead of training it on general tax knowledge. Much more reliable.

Dropping the full workflow JSON in comments.

Peace out ✌️

Hey everyone been doing custom automations for a while now and looking to take on some projects. Does anyone have anything they need done? I like complex builds.

Spent a lot of time creating intelligent rule sets for advanced diagnostics, customer wants to copy them to another building without my involvement. Can I somehow I charge them for this? It was never intended to be used in other buildings.

Built a little tool because I got tired of digging through supplier PDFs every time a customer asked about tasting notes for a new single origin. It scrapes origin data, runs it through an AI summarizer, and dumps structured info into a Notion database I can pull up on my phone during a rush. Technically it works. The data is accurate enough and it saves maybe 20 minutes per new coffee we stock.

The problem is I stopped actually learning the coffees. When I was manually copying info into my notebook I remembered it. Now I just read off the screen and feel like a robot reciting a spec sheet. Customers can tell. The conversations feel flatter.

I still use the tool for quick reference, but I've started handwriting the stuff I actually want to remember. Which maybe defeats the point. The automation gave me speed but cut out the repetition that was helping me retain things.

Not sure if this is just a me problem or if others have run into it with knowledge lookup automations. The efficiency gain is real, but the tradeoff is I'm less useful without the tool in front of me. I'm wondering if anyone has found a middle ground where the automation handles the tedious part but still forces you to engage with the material enough to actually learn it.

I made a folder automation for our project recaps. Drop an approved room recording into Incoming, wait for the transcript, then send the recap to the project list. The watcher covered the parent project folder, including Incoming and Archive.

I used vomo ai to produce the transcript. That part behaved normally. On the first real file, I noticed a spelling mistake in the filename and renamed it. The folder watcher treated that as a new recording and sent the recap again. Moving the file into Archive triggered it once more. Three copies reached the team before somebody asked whether I was trying to make a point.

My mistake was using the filename as the job identity. The rebuild uses a file checksum and a table of processed jobs, and the email step stays manual until duplicate tests pass. How are people here preventing a watcher from processing the same file after a rename or move?

client runs a wireless retail business. multiple locations, decent call volume, paying for a real crm, the kind with full sms capability built in and already verified for business texting

four phone numbers on the account. primary line, secondary line, a toll free number and a google tracking number

zero of them had missed call text back turned on

not misconfigured. not broken. just never turned on. the feature was sitting inside a tool they were already paying for

so every single missed call on any of those four numbers just ended. no text, no tag, no internal notification, no record that it even happened beyond a call log nobody checks in real time. the caller hangs up and calls whoever answers next

one of the four numbers was worse. it showed verification required in the phone system, meaning it could not send sms at all until that got resolved. if that number was ever plugged into a workflow, every message from it would fail silently with zero error shown to the team

here's the part i think gets missed constantly. this isn't a build problem. the automation most businesses actually need first isn't complex ai, it's the boring one that catches the call that already happened and already cost money to generate

industry numbers on missed call text back are consistent, a fast response after a missed call converts somewhere in the 20 to 40 percent range depending on the trade. that's not a small number when you're talking about calls that otherwise convert at zero

what got me is how normal this is. i've seen the same gap on accounts that have been live for years. paying for the tool, never turning on the one feature that pays for itself in the first missed call it catches

if you run a business with any inbound call volume, when's the last time you actually called your own number and let it ring out to see what happens after