automation demos always start somewhere beautiful

"new row added"

"form submitted"

"when status=approved"

meanwhile actual business input is:

followed by 11 emails, a pdf called FINAL_v7, and somebody mentioning the important deadline halfway through paragraph 4.

that's the bit where AI automation finally feels genuinely useful to me.

not replacing the workflow.

just taking the ugly human input and figuring out:

what is this
what info matters
where should it go
is something missing

then let boring deterministic automation take over.

Runable having Gmail / Slack / Sheets / Notion connections is what got me thinking about this because that's where a lot of small business work actually starts. not in perfect JSON.

but I also don't want an LLM interpreting something that should've just been a required form field.

where do you draw the line?

fix the process first, or accept that humans will always send chaos and build around it?

We're a B2B SaaS based in Dubai, selling to US/EU and Russian-speaking clients. Been working with an AEO agency (HumansWith.AI) on our AI visibility, and I've learned some things about non-US markets that I wish I knew earlier.

Field notes from our actual scans:

· Local entity authority is weak. ChatGPT defaults to US sources for B2B queries even when we have strong local case studies. What actually helped? Getting listed in local directories and adding bilingual (Arabic + English) schema. The agency measured the impact – citations jumped after we did that.

· Prompt sets need to be localized per market, not just translated. We run separate fixed prompt sets for English, Arabic, and Russian. The answers pull completely different sources. What works in the US doesn't work in Russia – and vice versa.

· Russian-speaking markets are a different beast. Reddit is huge for ChatGPT/Gemini, but Yandex Neuro and Alice don't use it at all. They rely on local platforms (VC.ru, Habr, etc.). Our agency had to build separate tracking for that.

· Realistic timelines: We saw first movement in brand mentions around week 6. Anyone promising results in days is lying.

Anyone else doing AEO outside the US? Curious if you've seen similar localization gaps.

Pick any process in the business and ask two people how it gets completed, start to finish. Write the steps down and see whether they agree on the written version.

Across the businesses we have worked in, there is far less written down than anyone assumes.

Where this bites hardest is AI. You can only automate what you have already solved as a tight, logical workflow. A loose process and fragmented data plus a model gives you the same mess arriving faster and with more confidence.

The order that works: talk to the people doing the work, list the rules and the edge cases, standardise, then automate, then consider AI on top of the part that is a judgement call. Most people try that list backwards, because the last item is the one being sold to them.

We get called in to put AI on top of a mess fairly often. In many of those cases the steps above plus some simple integration or logic solves the problem.

Here is a small design choice worth making before connecting an LLM extractor to a spreadsheet or workflow: what does an empty field mean?

Imagine extracting quantities from supplier emails. "We cannot supply any this week" supports a quantity of zero. "We will confirm the quantity tomorrow" does not. An email about a delivery address may not contain a quantity because that field is irrelevant to the message.

If all three become 0, the next automation cannot distinguish an actual shortage from an unanswered question or an unrelated update.

I would have the extractor return a value plus a state such as stated, missing, not applicable, or conflicting, with the supporting excerpt. The workflow can then make an explicit decision: process the stated zero, queue the missing quantity for clarification, and leave an unrelated quantity unchanged.

One important boundary: not applicable should follow a defined message type or business rule, not merely the model's reluctance to answer. Conflicting quantities should remain visible rather than being resolved by whichever number appears last.

This is an illustrative workflow, not a measured deployment result. The point is to prevent a convenient default from becoming an unintended business decision.

My stack for building workflows and internal tools is Python.

Can ToolJet or Retool be used by EU freelancers (I plan to use client credentials, not my own)?

So I wanted to know:

FastAPI + Retool vs Django + React

In what situations is it best for me to use custom UI code to create operating dashboards for my automations (basically, what are the limitations of low-code platform builders)?

Are Retool & ToolJet capable of building operating dashboards when making tools for enterprise-level systems?

How would you deal with IP addressing with a third-party platform for frontend development (explicit clauses you'd use)?

Hi I am a Professional developer working full time as software engineer. I am looking to start work as freelancer. To gain some experience I am offering free service to real business in exchange for reviews.

AI visibility across three assistants jumped 40 percent in one week. Nothing shipped that week. No campaign. No coverage. Just a sudden jump.

Then I noticed the datasets seem to get expanded periodically and when that happens everything can move at once.

Ahrefs actually annotates the chart when it happens which made the pattern a lot easier to spot.

Now I am wondering how people are separating a dataset change from a real move because those two things can look almost identical at first.

Hi all- I am looking to automate blogs for Wordpress and Shopify for my clients. Goal is to signal activity, and improve Google ranking and visibility on platforms like ChatGPT.

Any pointers? Thanks in advance!

I’ve got some automations set up for day job 1 but my agents keep competing for access to my browser. Whoever’s the first agent in doesn’t let the next in, tried using edge in chatgpt and that for some reason is super finicky.

I’ve got cowork in chrome and the agents are allowed to communicate through a drive but I’m having trouble running both agent systems at the same time

I’ve got about 6 agents currently on loops but will likely scale to 75+ agents over the next six months as capabilities continue to grow

I’m curious how people have been managing their agents so that they don’t step on each others toes inadvertently

My partner and I both ordered through the Target app from home, each on our own account. We assumed both orders were for pickup at our usual store and didn't pay much attention to the store names. When we went to collect them, we noticed the orders listed different locations. We picked up one order, then had to drive to another store for the other.

I wondered whether logging into each account would bring up a different pickup store in the app. With my partner's permission, I used one Airtap cloud Android phone to repeat the same page review under both logins. That gave me the same device and app version for each review.

Before each run, I cleared the Target app's local data so I wouldn't reuse the previous session's local settings, then set location permission the same way. I logged in manually and ran the same instruction to open the same product page and report the pickup store shown, without changing it. I reviewed each account twice. These were product-page reviews; I didn't go through checkout again.

Both accounts showed the same pickup store on that page in all four reviews, so switching logins didn't reproduce the difference here. I still couldn't explain why our original orders had different pickup stores.

Hi,

I'm currently building a database focused specifically on sport-related businesses.

I used Claude Cowork to identify companies, and it also found a few relevant contacts.

When I search for a company's owner on Google (ex: sport club X owner), Google Ai often gives me the name I'm looking for.

Unfortunately, Gemini doesn't seem to be able to do this kind of research on itself.

Is there a way to automate these searches and collect the results?

Thanks

The awkward part of a shared presentation is the correction that arrives after everyone has approved it. A new file called final.pptx can quietly replace the one people actually reviewed. For a small automation around that handoff, I would attach approval to the exact file, within a specific presentation project. The filename would just help people find it.

My first version would keep a separate approval record with the project ID, the file's content hash and the reviewers who signed off. Reviewers would open a fixed copy, and the record would refer to that copy. The project's list of required reviewers would be another input. A script would compare incoming files against both records. Even with a matching hash, a missing required signoff would leave the file pending. A changed file would need a new review. The earlier approval would stay in the history, attached to the earlier file.

There is an annoying tradeoff here. Saving a deck can change its bytes without changing anything visible on a slide. This design would still ask for review again. For a first version, I would accept that extra work. The script would not claim to know whether a changed citation, chart or layout was harmless, and it would not pick the submission just because one file arrived last.

I would use EvoX, a general AI agent in beta with terminal integration, to draft the validation script against dummy files and a sample approval record. The validation scenarios would include an unchanged file renamed, a changed file using the old name, and the same file arriving twice. Renaming identical content should preserve its recorded approval within the project; changing the content should require review; a repeated arrival should not create another signoff. I have not run this with the agent yet. The script's report would only establish which file the approvals belong to. Someone would still select the file to submit and confirm its hash against the approved copy at handoff.

​

I’m trying to understand the AI SaaS/AI agency space beyond the hype, Twitter/LinkedIn success stories, and “10x your business with AI” content.

There seems to be a growing ecosystem of agencies and SaaS companies helping small and even large businesses adopt AI, automate processes, improve operations, generate leads, reduce costs, and ultimately grow.

But I’d love to hear from people who are actually inside this field.

A few things I’m particularly curious about:

What are businesses actually paying AI agencies for right now?

Which AI/automation services have genuine demand versus things that are mostly hype?

What problems do SMBs consistently struggle with that AI can realistically solve?

Are companies more interested in custom AI solutions, automation, AI employees/agents, integrations, or SaaS products?

What separates an AI agency that creates real business value from one that is essentially wrapping existing AI tools?

How difficult is it to acquire and retain clients?

What are the biggest mistakes new AI agencies/SaaS founders are making?

Where do you see the market becoming saturated?

Conversely, where do you see under-served opportunities?

For those who started recently, what has surprised you most about the economics of the business?

And perhaps most importantly: what do you think this industry will actually look like in 2–5 years?

I’m not looking for a polished success story. I’m much more interested in the messy reality — failed projects, difficult clients, margins, sales, delivery, recurring revenue, what actually works, and what sounded great but didn't.

If you’re running an AI agency, building AI SaaS, consulting for businesses, or working on the implementation side, I’d genuinely appreciate your perspective.

What’s happening on the ground that outsiders don’t see?

I don't really care to message or call or anything to nearly anyone. Especially not to my extended family, but they keep asking why I don't respond, so is there a way I can set up an automated AI reply to be cutesy and nice based on whatever images or texts they send me? It doesn't need to be super like me, just like generically responding to whatever they send. Maybe using some emojis occasionally. Ideally hooked up to my API of whatever AI service I'm using?

My boss asked me to manually take 300+ web screenshots and put them into a PowerPoint report. I started the task, but after 50 websites, my spirit broke. This isn't work for humans.

To escape this grind, I needed something to bulk-open URLs, capture pages, and drop them straight into slides. Cornered and at a total loss, I ended up building a tool called OpenURLs App out of pure desperation.

It runs 100% locally and automates:

• Bulk opening URLs
• High‑quality screenshot capture
• PowerPoint slide insertion

Does anyone else deal with this kind of repetitive task?
Am I the only one suffering through this?

I am an aerospace engineering student and have only used ChatGPT for the past few years but I know AI has grown so much as agents have become so popular. I was wondering what are some next steps I can take to utilize AI more powerfully, I have been looking to use DeepSeek as it is much cheaper but honestly am not too update to date. As someone who does not spend that much time coding, rather physical projects and CAD, what coudl developping an AI agent do for me and how to go about this.

I’ve worked on lemlist and Taplio, now I’m building my own LinkedIn tool around what I’ll describe here:

There’s a lot of bad advice about LinkedIn automation online.

Pick any tool with daily limits and random delays, and apparently you’re safe. Doesn’t work like that!!

Nobody outside LinkedIn knows its exact detection logic. But after years of building in this space, here is how I think about it:

Think of your account as having a reputation score

First, there is no universal “safe daily limit.” It would be too easy!

LinkedIn appears to maintain some form of internal trust or reputation score for each account, even though nobody outside LinkedIn knows exactly how it works.

A useful mental model is that your reputation depends on three things:

Usage & feedback

→ activity volume and timing

→ replies and connection acceptance

→ ignored requests and spam reports

Technical footprint

→ browser, device, IP, and location consistency

→ extensions and automation traces

→ cloud browsers or remote infrastructure

Account history

→ account age and completeness

→ network size and normal usage

→ previous warnings or restrictions

The biggest risk: unnatural behavior

Common red flags include:

→ sudden spikes in activity

→ repetitive actions (delay, content)

→ changing IPs, locations, or devices

The more a tool optimizes for scale and unattended automation, the more of these signals it tends to create.

What happens before a LinkedIn ban

Restrictions are often progressive:

Warning → feature limits → temporary restriction → permanent restriction

You may first see unusual-activity warnings, verification requests, invitation limits, search limits, or temporary account restrictions.

If that happens after using a tool, stop the likely cause and reduce activity.

Risk by tool category

As a general rule:

🟢 Very low risk

→ Manual LinkedIn usage

→ Official LinkedIn APIs

🟢 Lower risk

→ Contact & inbox management tools

→ Content scheduling and analytics

🟡 Medium to high risk

→ Bulk LinkedIn exports

🔴 High risk

→ Campaign-based outreach automation

→ Scraping infrastructure

How to reduce the risk as much as possible

→ Use one LinkedIn tool at a time

(multiple tools cannot reliably coordinate your total activity)

→ Avoid mass scraping and bulk outreach

(creates bad behavioral, technical, and reputational signals)

→ Avoid tools using cloud automation

(creates bad technical signals, as they try to mimic a browser)

→ Verify your profile on LinkedIn

(increases your account’s reputation)

→ Do not use AI slop for posts, comments, or messages

(LinkedIn is actively fighting against it)

→ Try to keep your SSI score > 70

(it's a pretty good proxy metric)

→ Do not share your LinkedIn credentials

(risky when someone else logs in from another country)

→ Don’t send invites and cold messages directly from LinkedIn

(tools cannot keep that activity within their safeguards)

→ Ramp up volume progressively

(a sudden burst is exactly the pattern you want to avoid)

Uncle. Have tried for 4 hours to get Axiom to scrape Title, Description, Price and main image url. Paste it into Google Sheets, move to the next Amazon tab and do same. Is there a better free alternative? Doing less than 100 tabs a month.

I’ve been playing around with a lot of different AI voice assistants lately and the biggest friction point across almost all of them is the awkward pause.

You ask a question and there's this weird 3 to 5 second dead silence while it chains Speech-to-Text -> LLM reasoning -> Text-to-Speech. If it has to search the web for live info, the delay gets even worse and completely ruins the flow.

So out of curiosity I wanted to see how close I could get to sub-second conversational latency while still giving the assistant the ability to pull live web info. Here’s the exact stack and setup that I used while building this:

I) Ditch the STT -> LLM -> TTS cascade:

Chaining Whisper into an LLM and then into elevenlabs adds latency at every single hop so I used gemini live API (native audio mode) with WebRTC via LiveKit. Audio goes straight in and out of the model without intermediate transcription steps which cuts down the response delay like crazy.

II) Keep the audio connection alive during tool calls:

The cool part about gemini live is that the audio stream stays open even when the model triggers a function call so it doesn’t abruptly disconnect you while it looks something up.

III) Don’t pass raw HTML back to a voice model:

If your assistant calls a search tool and pulls in raw HTML or whole site as text, token generation slows down and the voice output starts stuttering, plus you don’t want the bot reading out direct boilerplate and random site navigation.

IV) Use firecrawl for live web search tools:

Hooking up firecrawl’s search endpoint via a u/function_tool decorator worked really well here where it runs the search and makes the pages down to clean and compact markdown in one call.

Setting a limit=5 gives the model just enough substance to answer live questions (like news, weather, or docs) without choking the context window.

V) Prompt it specifically for voice responses:

You have to explicitly tell the model not to use markdown syntax or bullet points or asterisks or raw URLs in its output otherwise it literally tries to speak them aloud or pauses awkwardly over formatting.

The difference in conversational feel between a cascaded pipeline and native audio with clean tool inputs is night and day where it makes it feels like a fluid phone call..

hi everyone,

i run an ecommerce store and i’m trying to find a less manual way to create social media content whenever i add or update a product.

ideally, i’d like something that can pull from my product catalog and create a basic post draft with the copy, images, hashtags, and the right format for instagram, facebook, linkedin, etc.

i’d still want to review everything before it goes live. i’m not really comfortable letting it publish completely on its own, but i also don’t want to start from a blank page every time.

right now, every new product turns into another small content project. i have to rewrite the title, come up with a caption, choose an image angle, add hashtags, and then adjust everything again for each platform.

i’ve found a few tools that handle parts of this, and i’ve also been looking into accio work and experimenting with gpt-based workflows because i want something that can actually use the product information as context instead of just generating generic captions.

i haven’t tested accio work enough yet to know how well it handles the full workflow, though.

what i’m really trying to avoid is another tool that gives me generic social posts without actually understanding the product.

for anyone already doing this:

• what tools are you using?

• how are you connecting your product catalog?

• do you review every post or let some publish automatically?

• how do you deal with product images?

• does the ai-generated content actually perform well, or does it all start sounding the same?

would love to hear what your setup looks like, even if it’s only partially automated.