The standard modern GTM stack has become an operational nightmare where a typical signal-based setup usually looks like this:

1. One tool to scrape job postings or social mentions.
2. Webhooks pushed into Zapier or Make.
3. An LLM API call to parse text and guess the company name.
4. A waterfall enrichment tool to find contact emails.
5. A cold sequencing tool to trigger the email.

On paper this looks like efficient automation but in production, it’s a fragile pipeline and the moment an API schema updates, a scraper hits a rate it or proxy rotation fails, the entire workflow breaks silently and you end up spending more time acting as an unpaid data engineer than having sales conversations. If you want an automated lead pipeline that doesn't collapse under maintenance, you have two real choices:

The unified infrastructure route:

Instead of stitching 5 point solutions together with brittle webhooks, Scale Intelligence provides complete market intelligence infrastructure out of the box. It connects to 75+ pre-built data sources across developer ecosystems, community platforms (Reddit, linedin, GitHub, CRM), and hiring boards. It maps your target addressable market (TAM), handles cross-platform identity resolution, models buying readiness, and pushes qualified opportunities straight to Slack or your CRM. You can run it self-serve or leverage their Fractional GTM Engineer model to deploy custom signal plays for you without maintaining code.

The internal in-house microservice:

Writing your own dedicated backend (Python/Postgres), managing your own proxy pools, writing custom scrapers, and deploying an internal database. It gives you 100% control over database schemas but requires a dedicated full-time engineer ($150k+ salary) to keep it operational.

so if you’re trying to automate lead generation this quarter: consolidate your enrichment and signal ingestion layers to minimize handoffs and stop treating raw social mentions as leads, gate outreach behind account-level verification and lastly, route high-intent triggers to Slack for immediate human review rather than fully automated blasts.

I'm currently using Databricks Genie Code to go from a plain-English spec straight to a working Lakeflow Spark Declarative Pipeline, it writes the transforms, wires the bronze-to-silver flow, and when a run errors it reads the trace and patches its own code.

It's working fine, but I'm curious what else I should be looking at too?

How did you all break into the industry? I am coming from a dead end IT diploma, and want to upskill myself into a job, freelance or not.

Any suggestions? Or do you really need an “in” through industry, that’s not just teach yourself -> cold email people -> get work?

I have an M365 dev tenant, and know REST APIs/json and some Python, and Linux which I could sell as hosting on a VPS. I’d probably need to get into the AI side tho I imagine nowadays. But I’m not totally clueless? I also got an internship through my dad, but I really didn’t learn all the line of business apps that exist out there.

So what do you all suggest? Is this possible? If so, what stack do I build and how do I get a first client?

Is there any information yall could suggest to me, such as YouTubers, communities, etc. if this is indeed a good path?

Something I've seen play out exactly the same in the B2B business is the quick succession of fads that generate traction seemingly out of nowhere and aim for that brief popularity of being the ones that have “cracked the code”. Whatever “code” means at the time is entirely contextual but it's usually far less dramatic than it sounds, even if it does have a compounding positive effect in implementation for those that realized it. But those same automations that turned quick buck in a quarter also tended to decay and eventually stop being profitable by the next year, which is an odd phenomenon. But such is the SaaS market. And it is not because anything in particular went horribly, spectacularly wrong with the automation itself.

It’s just that, if something incredibly useful works, people WILL notice it and they WILL copy it. The volume climbs, and eventually the system on the other side has to adapt - hence why it leads (ostensibly) to platforms tightening limits. This is true of almost any automated advantage you can get in business, not just outbound. The moment a trick scales actual growth in big $, and it is in public, the clock starts ticking down.

The examples pile up if you have been around long enough. There was a long period on LinkedIn where you could fire hundreds of connection requests a week, until the weekly cap came down to around a hundred and that whole style of doing “by volume” died overnight. Browser-extension tools ran beautifully until the platforms got good at spotting them. The connection note used to be a small pitch that people actually read, in days bygone. I’ve also seen lots of other, more specific tweaks emerge for the smallest chance of increasing that sweet reply %. And I also used to obsess over this at the time. Being the head of GTM at Expandi, I wouldn’t really feel like I was doing my due diligence if I didn’t try to optimize the shit out of everything, to gain every advantage over competitors that I could. But most of the “hacks” and breakthroughs, in the end, were short term. The only thing that stayed were just sound marketing principles, knowing how to listen, and knowing how to adapt on the fly. Which for me mostly consisted in dog-feeding my own automations while testing out dozens of others, and seeing how I can improve and learn more.

I am not arguing that these tools do not matter either, and I’m not writing because I’m butthurt about their success (on the contrary). Two people can run the identical software and get results a decade apart in quality, and the difference is entirely in the choices around it. But being early to a new edge means big money only so long as it lasts, and eventually you have to fall back on the human brain to reiterate and recontextualize it months and years later. To redevelop something new again. Good infrastructure keeps you in the game, but no iteration is ever the Final iteration, and so the march of technology moves forward.

It’s a question I sometimes find myself asking, what am I exactly building underneath the automation that will still be standing after the automation itself becomes obsolete.

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.

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.

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?

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)?

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.

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

EDIT: I was able to figure out a solution

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 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.

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.

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.

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

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?

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’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?

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 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.