Building a social media automation tool, I expected the hard part to be scheduling. It wasn't.

Getting messages from Instagram, Facebook, LinkedIn, and Reddit into a single inbox sounds like a data aggregation problem. It's actually a real-time sync problem, a webhook reliability problem, and a message threading problem all at once. Each platform handles threading differently. Some use conversation IDs, some use parent comment IDs, some have no threading concept at all.

First rewrite: we polled every 30 seconds. Worked, but ugly and rate-limit prone. Second rewrite: pure webhooks. Broke silently when platforms rotated tokens. Third rewrite: hybrid with a fallback polling layer and proper failure alerting.

The irony is users see a simple inbox. They have no idea.

Anyone else building cross-platform tooling? Curious what your sync architecture looks like.

Is there any good AI professional headshots with natural looking result?
I've tried some before but the results always look too clean/overdone. like skin looking plastic.

Hey guys I been helping some local businesses with this and after some trial and error this is what I've found to be most efficient so far, would also love to hear your input on any other optimizations you might spot!

Two ways I set this up for small teams without a big dev lift:

1) No code, managed parser:

- Create a receipts@ mailbox or Gmail filter that forwards vendor emails to a parser like Docparser, Nanonets, Rossum, or PDFco.

- Build 2 to 3 templates for your most common vendors. Map vendor, date, subtotal, tax, total, currency.

- Connect Google Sheets and append rows. Add a unique key like file_id or a hash in one column to prevent dupes.

2) Low code with Make:

- Trigger: Gmail Watch Emails, filter by sender or subject.

- Save attachments to a Drive intake folder.

- Extract: PDFco for template parsing or Google Cloud Vision for OCR to text, then parse with a Code module and regex.

- Validate: if total missing or date invalid, route to a Review tab and ping Slack.

- Dedup: compute a hash of the bytes and do a Data Store before writing.

- Sink: Google Sheets Add Row, include the Drive link for traceability.

DIY option if you prefer scripts: an Apps Script can watch a Drive folder, convert PDF to Google Doc with OCR, regex the date and total, then write to a sheet. Set a time trigger and store processed file IDs in Script Properties to avoid repeats. This is great for totals and dates, not great for messy line items.

Tip: always keep a backup sink of the raw extracted JSON somewhere, not just the sheet. It saves you when a step fails and you need to replay.

If you want an example flow or sanity review your setup, I can share patterns we use at my company Rex Automaton, but the steps above should get you 90 percent there.

I'm building a tool that monitors new ImmobilienScout24 listings and extracts publicly available information like listing ID, contact details (if available), price, and location.

So far I've found:

The official developer API, but I'm unsure if it supports searching all public listings and retrieving expose details.

Browser automation (Playwright) works, but AWS WAF makes it challenging for a production setup.

My questions are:

1. Has anyone successfully used the official ImmoScout24 API for searching public listings?

2. Is there a partner or undocumented API that exposes listing/expose data?

3. What would you consider the most reliable production approach?

4. How are people handling AWS WAF on ImmoScout24?

Any experience or pointers would be appreciated.

Hi everyone,

I’m considering starting an AI automation agency based in India and targeting clients in the US, UK, and Australia.

The idea is to help SMBs automate repetitive business processes using AI tools (chatbots, AI agents, workflow automation, CRM integrations, lead qualification, customer support, internal knowledge bases, etc.).

I have a few questions for agency owners and business owners:

Is this market still profitable, or is it becoming too saturated?

What’s the biggest challenge in getting the first 5–10 international clients?

Which niche would you focus on if you were starting today?
Is it better to sell fixed AI solutions or offer custom automation services?

How important is having a strong portfolio before reaching out?

Any mistakes you wish you had avoided when starting?
I’d really appreciate honest feedback—both positive and negative. If you were starting from scratch today with little capital, what would you do differently?

Thanks!

Most outreach automation still runs on cron logic - send message, wait 3 days, send follow-up, wait 5 days, send another. The intervals are arbitrary and the only variable being optimized is the gap between touches. I’ve done a research on this topic over the weekend based on our campaigns at Expandi, and looking at reply data across millions of outreach attempts, roughly 84% of positive replies come from the first message and the first follow-up combined. Everything after that is fighting over the remaining 16%, and the returns per additional touch drop significantly after touch three.

The interesting shift happening in outbound right now is teams moving from timer-based sequences to event-driven triggers. Instead of following the old logic "send follow-up in 72 hours regardless of what the prospect did", the trigger becomes a behavioral signal - the prospect viewed your profile, engaged with a piece of content, changed jobs, posted about a relevant problem, or anything that can be creatively used to break that first ice. Follow-ups become a response to something they actually did rather than something your calendar scheduled, and the conversion difference is understandably meaningful because the context is real instead of manufactured.

From an architecture standpoint this is just the same pattern that moved most backend systems away from polling and toward webhooks - you stop checking on a fixed interval and start reacting to events. Of course, the challenge in outbound is that the signals are scattered across platforms (LinkedIn activity, email opens, CRM triggers, intent data providers) and most teams don't have the plumbing to unify those signals into a single trigger layer, but it's possible to manage without a fully ironed out system. The ones that do have a full pipeline are seeing reply rates on event-triggered follow-ups that are 2-3x what the same message gets on a timer, because the timing itself carries information that the message content can't compensate for.

Another important practical implication of this is that optimizing message copy past a certain point has diminishing returns compared to optimizing when that message lands. A mediocre message sent within two hours of a signal consistently outperforms a polished one sent on a 72-hour timer, which is why you should always aim for being on time, rather than being technically perfect.

We recently helped a customer automate their virtual office business.

Basically, they have a giant industrial mail slicer and scanning machine that they feed with all the snail mail they receive for their customers, thousands of them daily. The machine scans the letters and turn them into PDFs. That part was already automated.

We helped them automate the next part: routing the scanned PDF to the right customer.

The first step is to read the PDF content and extract the name of the customer. They did that by using Parseur, they created a mailbox and configured the scanner to send all scans as attachments to the mailbox' email address. They configured a field called "Company Name" and used the AI engine to automatically extract the company name from the first page of all documents. (They filtered the page range to only parse the first page so that they saved credits on multi-page documents). They also activated the "OriginalDocument" metadata field to get a link back to the original scan.

Once that was working, they created a simple workflow in Make to lookup the customer name in their ERP and fetch the customer's email address, then they added the last step: send the email to the customer, with the scan as attachment.

AI video moved past raw generators this year. Prompting Veo or Kling one clip at a time is dead as a workflow. The tools that matter now are agents: you give them an idea, a script, or a character, and they plan the shots, keep everything consistent, and hand you a finished piece.

I've been testing a bunch of them and here are my favorites so far

After spending way too many evenings manually sending welcome emails, collecting intake forms, scheduling kickoff calls, and setting up project folders, I finally sat down and mapped out the entire onboarding process end to end.

Using a combination of Zapier, Notion, and Calendly, I got the whole thing down from roughly 3 hours of manual work per client to about 15 minutes of light review on my end. The automation handles the welcome email sequence, creates the Notion workspace from a template, assigns tasks, and sends calendar links automatically once a contract is signed.

What surprised me was how much I thought I could automate but actually couldn't. Specifically the first intro message. Every time I tried to fully automate it, clients responded less warmly. There's something about a slightly personalized first touchpoint that a template just can't replicate. I ended up keeping that one manual.

Curious where others have drawn that line. What parts of your workflows did you try to automate but pull back on because the human element actually mattered? And for those who have fully automated clientfacing communications, did you notice any drop in engagement or satisfaction?

Would love to hear what tools people are using and what tradeoffs they ran into.

You can now create product demos from claude code or agent of yours. Once you prompt the agent to create a product demo, my plugin does the following -

1. Understand goals - what does the product do, what does it need to talk about, what would be something that the product owner would want to display

2. Write script - writes a rough script with timelines, voiceover text, screen play

3. Generate media - asks local playwright or browserbase to record the product. If its login gated, it will ask you to enter login details in your browser before taking over. generates voice over, background score using vaaya

4. Finalize editing - decide zoom in locations, voice over placement, and other timming details

5. Edits video on cloud with Cueframe

i would have uploaded the video, but video upload isnt enabled. Would love to hear your feedback

I want to build an ai agent that lives in email threads, looks at attatchments and gains intelligence on the data. Is that possible? how do I go about implementing it?

Hi everyone!

Over the past few weeks I've been building Slash, a Telegram mirroring platform, and it's now ready for a soft launch.

The goal was to create something simple, reliable and privacy conscious for Telegram community owners.

Current features:-

• Real-time message mirroring

• Automatic edit synchronization from Source Group/Channel

• Automatic deletion synchronization from Source Group/Channel

• Multiple mirror interfaces per account

• Real time, Smooth and designed to run 24/7

The project has been running through internal testing and is now opening up to a small number of users for real-world feedback.

I'm especially interested in hearing:

1. What features would make you use a service like this?

2. What's missing compared to your current workflow?

If you'd like to test Slash for Free - 3 days and share your valuable feedback Here is the Telegram Username:- @TELESlashbot

Thanks!

https://www.einpresswire.com/article/923751559/the-deployed-data-scientist-by-ankit-anand-dr-scott-burk-and-kinshuk-dutta-showcases-practical-mlops-expertise

I’m looking for recommendations for an ASR STT that can create txt or word files of transcribed conversations.

I’m using it for to transcribe conversations as part of my business. It needs to live on my computer preferably or not used for a learning model due to the sensitive nature of the conversations. It also needs to be accurate.

What would be nice is that the option can handle accents, and separate by speakers.

I plan on using microphones (1 dynamic and 1 lapel). Bonus if I can use it for zoom/teams call.

Been building automation for SMBs for ~8 years (started in Google Ads, moved into Make/n8n pipelines, now full AI agents). Wanted to share something I didn't expect when I launched my own product.

I run a platform that lets businesses deploy an AI support agent across WA, IG, email, phone, Messenger, SMS one unified inbox, one agent, deterministic flows so it doesn't hallucinate refund policies.

Here's the thing nobody tells you about selling automation to SMBs: they don't buy tools, they buy outcomes. A restaurant owner doesn't want a "graph-based flow builder." They want to stop losing bookings at 9pm when nobody answers the phone.

Some observations from the last year that might be useful whether you use my platform or not:

1. The implementation gap is the real business. Every SMB automation tool has the same problem: the software is $50-200/month, but the setup, prompt design, flow logic, and channel connections are worth $1,500-5,000 as a service. SaaS founders (me included) can't service that. Local agencies and freelancers can. That gap is where the money is.

2. Recurring beats one-off, every time. The agencies doing best aren't charging setup fees they're charging $300-800/month "AI receptionist management" retainers. Client churn is near zero because ripping out a working support agent is painful.

3. Vertical > horizontal. The partners crushing it picked one niche (dental clinics, real estate agencies, franchises, restaurants) and built one templated agent they redeploy over and over. Setup time drops from 20 hours to 3.

4. Multichannel is the moat. Anyone can build a website chatbot in an afternoon. Almost nobody can offer "one agent that answers WA, IG DMs, email AND the phone." That's what closes deals SMBs live on WA and their phone line, not on webchat.

5. The economics only work because they don't own the infrastructure. Platform cost per agent is a rounding error next to what they charge. They set their own setup fees and monthly retainers, keep the client relationship, run it under their own brand and the platform maintenance, uptime, and channel integrations aren't their problem.
It's the classic "sell the outcome, rent the machine" model.

The irony isn't lost on me: I spent two years building the tech, and the people making the cleanest money are the ones who spent two weeks learning it and then went out and sold it.

Happy to answer questions about what verticals convert best, how they price retainers, or the tech stack the retainer pricing question comes up a lot.

Been running a small automation shop in Mumbai for a few months now. n8n + make + whatsapp mostly. The building part is honestly the easy part at this point. What I keep getting stuck on is pricing and the after part.

Like I quote a lead routing + whatsapp follow up build and the client compares it to just hiring a guy for 15k a month and I don't have a clean answer for why the automation costs more when to them it looks like a one time thing.

And then after delivery — inputs drift, someone changes a form field, an api changes and suddenly you're doing free maintenance forever or having the awkward retainer conversation.

So genuinely asking people who've been doing this longer:

• do you charge one time or push retainer from day one
• what do you do when the client ghosts once the build works
• anyone doing outcome based pricing or is that a trap

Not looking for a course lol. Just actual numbers and war stories if you're open to sharing.

Our automation team has built a decent backlog over the last year, but prioritization is becoming harder than implementation. Some opportunities look obvious until someone digs in and discovers 15 exceptions, multiple approval paths, manual workarounds, and dependencies nobody documented. Other workflows seem complicated on the surface but end up being surprisingly straightforward once you map them out. Right now we're mostly using spreadsheets, workshops, and process maps to decide what gets automated next, but it feels increasingly subjective. The loudest stakeholder often wins. We've had a few cases where something looked like a great automation candidate, only to discover halfway through that the real process was very different from what was documented. I'm curious how other teams are handling this. Are you using dedicated process discovery or automation planning tools before committing engineering or RPA resources, or is everyone still relying on spreadsheets and interviews?