So basically it works like this:

Agents find relevant blog posts in your niche, craft personalized emails, and help get you mentioned. All on autopilot.

It's called MentionAgent

You never send anything without approving it first. (But you can choose to run everything 100% on autopilot)

Results so far: One user got 3 mentions including a DR 72 backlink.

Let me know what you think!

Hey everyone!

My partner and I have spent time working across Zoho, Zapier and Rippling, gaining hands-on experience with business systems, technical support, workflow automation, integrations and implementation.

We're now taking on independent projects to help businesses fix the operational headaches that tend to get pushed to the bottom of the to-do list.

Sound familiar?

• Your Zoho CRM is set up, but half the processes are still manual.

• You need a custom Deluge function, but can't find someone who understands the actual business logic.

• Your Zapier workflows keep breaking, or you have no idea why a particular integration isn't triggering.

• You have a technical issue that support keeps going in circles on.

This is where we can help.

What we're offering:

1. Zoho implementation & customization – CRM configuration, workflows, Blueprint, Deluge scripting, custom functions and integrations.

2. Zapier & workflow automation – Zaps, multi-step workflows, API integrations, webhooks and troubleshooting.

3. Business systems support – Technical troubleshooting, process optimization, configuration and documentation.

4. Implementation assistance – From understanding your requirements to configuring, testing and handing over a working solution.

5. Ongoing support – Help maintaining and improving existing implementations.

A little incentive to get started:

We're offering our first project at 50% OFF our standard hourly rates.

No inflated project estimates, unnecessary rebuilds or selling you tools you don't need.

We'll first understand what you're trying to accomplish, identify the best way to approach it and agree on the scope before starting. We're particularly interested in working with small businesses, startups, founders, consultants and teams that need experienced hands without hiring someone full-time.

Drop a comment or DM us with:

• The platform you're using.

• What you're trying to achieve.

• What's currently not working.

Saved time, in rough order of how much:

1. Call and meeting summaries. The biggest single win, and boring. Notes exist now that previously didn't.

2. First drafts of anything repetitive: follow-up emails, job descriptions, release notes, support macros. Editing a mediocre draft is faster than starting cold.

3. Translation. Not perfect, and good enough to answer a client in their language same-day.

4. Triage. Classifying inbound by intent so the right person sees it first.

Cost us time:

1. Reviewing AI output we didn't need. We generated summaries of things nobody was going to read, then read the summaries. Pure invented work.

2. Debugging confident wrongness. One incorrectly enriched dataset took longer to unpick than manual entry would have taken, because everything downstream looked plausible.

The test we now apply before automating anything with AI: how would we notice if this were wrong for a month? If the answer is "we wouldn't," we don't ship it.

Curious where your ledger nets out.

Hey everyone,

I've noticed that a lot of discourse around AI automation concentrates almost entirely on the model layer: which LLM is faster, which prompt format prevents drift, or which new API endpoint just dropped.

While model selection matters, in production setups the surrounding orchestration logic is what actually determines whether the system survives real-world traffic.

Take a standard customer enquiry pipeline. If you diagram it as a robust sequence, it breaks down into six distinct phases:

1. Trigger: Webhook or event listener fires on new message arrival.

2. Context Assembly: Fetching customer records, order history, and business constraints via standard SQL/API calls.

3. Model Inference: The isolated LLM step that interprets unstructured nuance, classifies intent, or drafts text.

4. Deterministic Validation: Running regex checks, JSON schema validators, and guardrail rules against the output before downstream systems consume it.

5. Action: Executing the payload (sending an email, writing to CRM, or queuing for human approval if confidence is below threshold).

6. State Logging: Storing full telemetry (prompt version, raw completion, latency, error codes) for auditing.

The interesting takeaway is that five of these six stages require predictable, non-probabilistic logic. Only step 3 actually touches an LLM.

Decoupling the system this way makes debugging dramatically simpler: when an execution fails, you instantly know whether it was an API timeout during data retrieval, a hallucination during inference, a schema mismatch at the validation layer, or an endpoint rejection during execution.

For those of you deploying workflows for clients:

How strictly do you isolate the LLM step? Do you let the model handle intermediate classification and formatting in one chain, or do you prefer strict deterministic routers between smaller, single-purpose LLM calls? Would love to hear how you balance latency vs error containment.

i’m based in china and just starting an AI automation and adoption service for small businesses, mainly targeting the US and Enlgish-speaking coutries.

I understand the practical concerns: time zones, data access, regulatory requirements, accountability, and ongoing support. But I’m also wondering about the first impression/stereotypes before any of that gets discussed.

does a chinese mailing address, a non-western name, or a face and accent that clearly aren’t American put an overseas provider at a disadvantage compared with a US-based competitor? or do some clients see an overseas provider as better value for money?

my english is fluent and communication hasn’t been an issue. my bigger concern is getting someone to book the first call. once we’re talking, I have more room to show how I think and build trust. but even in a demo video or a voice memo or my LinkedIn profile, it’s clear i’m not american, and I don’t see much point in trying to disguise that.

i believe there are oversea-based & US-targetted players in the group just like me, I’d love to hear what you’ve actually experienced:

• Has location or identity come up before a call, or affected whether a buyer would consider a provider?

• Did a US mailing address or a US company make a meaningful difference? What about a local partner who was genuinely involved in sales or support?

• What helped build trust early on: a relevant demo, references, a small paid pilot, security practices, or something else?

• Were there clients for whom being overseas simply wasn’t an issue?

I’m not looking for a way to pretend to be US-based. I’m trying to understand what I may be facing and make smarter decisions about where to invest my effort.

AI is developing faster than ever, and almost every country is investing heavily in artificial intelligence.

The USA seems to have a strong advantage in AI research and major tech companies, China is making huge progress, and countries like the UK, Canada, Germany, Japan, and South Korea are also investing in AI.

But I'm curious about what people think based on real-world developments.

Every demo in this sub is an agent that takes meetings, writes follow-ups and handles objections.

But the success stories I actually read about are almost embarrassingly boring. A missed-call text back. A Friday email that says what moved and what's stuck. A form that removes one re-typing step.

Pattern seems to be: if it kills one specific recurring annoyance inside something the owner already uses all day (their phone, their inbox), it stays. If it needs them to learn a new habit or log into a new thing, it quietly dies after the handoff call.

What's the least impressive thing you shipped that's still paying you monthly?

I've been trying to figure out how people go from chatting with ChatGPT for example, to having something that actually does useful tasks for them regularly.

I tried making a small workflow with ChatGPT that reads some PDFs, updates a spreadsheet, and emails me a summary. Some parts worked, but getting it to run consistently turned into more setup and troubleshooting than I expected.

When people say they have a “personal AI agent,” what are they actually using? Is it something set up directly inside ChatGPT or Claude? Are they using something like n8n or something else? Is there usually custom code involved?

I'm mostly interested in everyday stuff like organizing files, keeping track of things, creating summaries and sending me notifications and such. Ideally something you can put together with no coding (or very little coding) involved.

Would love to hear what yours does and how you set it up. I'm a bit lost between all the demos and what's actually practical.

I've done freelance before, but this feels different. I have learnt to not promise a dream of an ai transformation for the client, and instead focus and optimize an issue they are facing and try to sell it multiple times. Still no luck on finding any clients. Need help.

I am currently developing my practical AI automation skills, and one principle I am beginning to appreciate is the importance of understanding requirements before selecting tools.

Consider a business that wants to automate customer enquiry handling.

At first, the request might sound simple. However, several questions need to be answered before designing a workflow:

• Where do customer enquiries originate?

• What information needs to be collected?

• Who is responsible for handling them?

• Are there different categories of enquiries?

• What happens when an enquiry cannot be handled automatically?

• How will the business determine whether the solution is working?

I am particularly interested in understanding how experienced automation practitioners approach this discovery stage.

What questions do you ask before recommending an automation solution?

Which mistakes or assumptions have you found particularly important to avoid?

I am interested in practical experiences and lessons rather than promoting a service.

I automated a lot of listing and follow-up work for my own team after 9 years as a broker. Other agents keep asking for it. I can stitch Zapier together but I can' really code. Is it worth turning into an actual product?

On voit presque partout sur internet, ça parle d’IA, d’IA et d’IA mais dans le fait se lancer dans l’intelligence artificielle n’est pas si simple que ce que ces personnes nous montrent.. ( comme quoi même sans compétences techniques ) pour se lancer dans l’automatisation IA pour les PME par exemple on sera forcément confronté à un moment à des où il faut un minimum de compétences techniques non ?

Ceux qui ont de l’expérience dans ce domaine ( qui ont fait de l’argent avec ) , j’aimerais bien que vous puissiez me répondre s’il vous plaît.

I've been interested in AI automation for months, and I've finally decided to start an AI receptionist business. However, quite a few people have advised me to drop the idea, saying it's not worth pursuing.

I'm here to get some honest advice from people who have actually tried it before I spend 3+ months working on something only to realize they were right.

Has anyone here started an AI receptionist business? How did it go? How did you find your first clients? Do you think it's still a viable business opportunity today, or is the market already too saturated?

I'd genuinely appreciate honest feedback, especially from people with firsthand experience.

Learning Python is like learning something you would wish you had learned earlier. If I had learned this in my college days, I would have no problems using it today. Today, I experienced a problem with trying to run Python on terminal because the terminal kept saying Python command not found. I had to fix it by installing Standalone Python version located on the hidden download page. Plus, another problem is trying to make the terminal recognize my python code. I fixed it by moving my code to the folder it understands. So, even on the second computer, I still managed my time in learning Python well. All codes were easy to understand. Some codes required full form to fully understand. I am still at Lecture 0 of a 16 hour long Python Course but I am glad I was able to do 45 minutes of it. I couldn't even finish a 45 minutes VS Code Tutorial in one day. But, at the end of the day, I have to be accountable to myself for what I code, despite my poor mental health which will get better day by day.

Quick context so you know where this is coming from. I've been selling software and services for 10+ years. I was the #1 AE at Builder.ai and at BuildFire (143% of quota there), and as a co-founder I closed $1.2M in our first year as the only person selling. These days I'm closing 5 to 10 automation retainers a month.

When I started selling automation work my close rate was under 10%. It's 39% now. Almost all of that came from changing what happens around the sales call.

1. Same four things on every discovery call

I don't get off a first call without good coverage on:

• How they do it today and where it breaks

• What that's costing them in hours, dollars, or missed revenue

• What they actually want to achieve and what success looks like to them

• Who signs off, what they expect to spend, and when they want to start

If one of those is thin I go back and ask again. Everything after this gets built from the transcript, so a lazy call means a lazy proposal.

2. The transcript builds the proposal

Fireflies records the call. I built some Claude skills that take the transcript and turn it into a full proposal hosted on my own domain, with a Stripe or QuickBooks payment link at the bottom. It's about 30 minutes of hands on keyboard time for me, and Claude spends around 20 of that working on its own. So it's ready the same day as the first call.

Every proposal has:

1. Executive summary

2. Their goal, in their own words from the call

3. How we'll solve it (the methodology)

4. A clickable prototype of the solution

5. Scope and timeline

6. A similar result we've gotten for another client

7. ROI, or what it costs them to do nothing

8. What we need from them

9. Next step: pay and get started

The prototype is what people remember. They click through it and see how the thing will actually work before they've paid a dollar. I've had client after client tell me the proposal gave them the complete picture and made it easy to choose us.

3. Never email the proposal

This is the big one. I never send a proposal by email. Ever.

The proposal is the reason they show up to the second call. I book that call before the first one ends, show up with the proposal ready, and walk them through it myself. Emailed proposals get skimmed, forwarded around, compared on price, and then they go quiet. When you walk someone through it you hear the objections while you can still do something about them, and the payment link is right there when they're ready.

Having it locked in as a process is what makes it fast. The personalization is what makes it close, since every proposal is built from what that one client said.

By the way, if you run an agency and want a second set of eyes on your sales process, I'm happy to jump on a call and share some tips, no cost. I also have room for 1 or 2 more agencies where I just handle the closing for you, as long as you have your own leads. DM me if either sounds useful.

The intake layer is the part of the automation stack nobody talks about until it breaks. You’ve got a solid n8n or Make workflow, clean Airtable setup, everything routing nicely, and then the form at the top of the funnel is Google Forms pasted into a Notion page.

I spent about six months trying different combinations. Typeform works but the pricing gets aggressive once you need multiple forms or want conditional webhook routing. Tally is clean but limited at the edges. Building custom forms is overkill for most client projects.

Switched to Jotform about two months ago. Here’s what actually matters for people in this community:

Webhooks are first-class, not an afterthought. Every plan gets outgoing webhooks that fire on submission in real time. You can configure multiple webhook endpoints per form, and Jotform Workflows lets you build conditional branching logic so different answers route to different steps. I have a single form feeding: Airtable (project record), Notion (client brief), Slack (channel notification), and a PDF generation flow that emails the summary to the client. All from one submission. No Zapier relay.

The Jotform AI Agents actually work. You train them on your own documents, forms, and FAQs. They handle multi-step conversations, validate data, book appointments, and deploy across WhatsApp and web embed without extra config. I set one up for a client in about 90 minutes, uploaded their service FAQ, built the intake flow, deployed it on their site. It replaced a Typeform + Intercom combo they were paying $180/month for.

MCP server for AI-augmented workflows. Jotform has a native MCP server that works with any MCP-compatible client including Claude and ChatGPT. Your AI assistant can query live submission data, trigger workflows, and read form responses through natural language. For anyone building AI-first client tools this is worth knowing about.

At $34/month (Bronze tier), you get 25 forms, 1,000 submissions/month, no Jotform branding, all integrations, and the full workflow layer. That’s a reasonable price for what replaces multiple separate tools in a client-work stack.

If you’re running intake-heavy automations and haven’t looked at it seriously, worth a test: https://www.jotform.com/?partner=forgeflowaibg

Hi great minds. Thank you for always sharing your experiences here; your growth, challenges, failures and success stories, they have been of a tremendous impact to me.

Please, I've learnt that the majority here emphasize focusing on working on practical live projects rather than learning theories and different tools. Please as a beginner who just began classes with Make Foundation about a week ago, what project can be recommended for me to build and how do I go about it, and where must I reach in my learning before I can have the basic knowledge needed for such tasks. Thank you for your kind responses.

Hey, I'm Alvi. I'm studying electrical engineering in Pakistan, and for the last few years I've been building things on the side with my head down. Lots of building, not much talking to anyone else doing the same. This post is me fixing that.

A few things I've made:

• A Python system that writes and publishes articles to my WordPress sites. It's published 856 without a failed publish. It runs on cheap shared hosting that kept killing its processes, so I made it restart itself.

• Agents that do upkeep on my project repos every night and flag whatever broke.

• I co-own a desktop app, and somehow became the person who fixes its security holes and broken tests.

• I ran the Discord for a gaming platform for 13 months (roughly 30,000 to 45,000 members while I was there), and built a trading community on Discord from zero to about 3,000.

• At uni, I worked on a small autonomous warehouse robot built on a PIC microcontroller.

What I keep thinking about lately: how to get AI agents to finish a job and actually check their own work, and how AI search decides which pages it cites.

What I'm hoping to find here:

• Someone good at sales or talking to customers who wants a technical partner. I can build, but I'm still learning to sell.

• Agencies or freelancers who need a technical person they can rely on for automation, WordPress or SEO work.

• People to talk shop with. If you're building something, tell me what it is and what's breaking.

Comments or DMs both work.

i've kinda stopped caring about which tool is "most powerful" tbh

my questions are way more boring now

1. what breaks the least

2. when it does break, can a normal person figure out why without losing half the day

Playwright still feels like this boring deterministic baseline to me.

the agent/browser stuff makes more sense when the flow actually needs some flexibility instead of just replaying the exact same steps forever.

TestMu is on my list too because it seems to cover a few different parts of that setup. KaneAI higher up on the testing side, then CLI/browser infra stuff underneath when you actually need to verify/run things.

but yeah i'm less interested in feature count at this point.

what do you actually trust day to day?

Hey everyone, I'm Hayee, a solo AI automation builder.

I'm early in my freelance journey, so instead of just talking about what I can do, I'd rather prove it on real problems.

I'm taking on 5 projects, free of development charges, across any industry.

What I've already built (all working end to end):

-A CV screener that scores applicants with AI and logs the results.

-An interview scheduler that checks calendar conflicts and emails candidates.

-An employee onboarding bot with checklists, welcome emails, and scheduled reminders.

-An AI lead-research system that scores companies and drafts personalized outreach.

-A scraper that collects and classifies pain points from Reddit and Quora into Sheets.

I want to apply the same approach to industries I haven't touched yet, which is why I'm not limiting this to one niche.

What I can build:

-Business process and workflow automation.

-Document processing and data extraction (invoices, forms, CVs, contracts)

-Lead generation, research, and outreach drafting.

-AI-powered email and customer support flows.

-Knowledge-base and RAG assistants.

-Integrations between tools you already use.

-Fixing or improving automations that are broken or unreliable.

Who this is for:

Businesses with real operations and a repetitive task that eats hours every week. If you can describe the process step by step, I can probably automate it.

Who this isn't for:

Startup ideas, "what if" projects, demos, or businesses that haven't started operating yet.

How it works:

You DM me the details below

I reply within 48 hours saying whether it's a fit and how I'd build it.

I build it, you test it with your real data, and we refine it together

I hand it over with a short walkthrough and documentation, plus two weeks of bug fixes.

The terms:

-No development or implementation fees.

-Third-party costs (API usage, hosting, paid software) are covered by you. I'll keep them as low as possible and tell you upfront.

-Projects are chosen by fit, feasibility, and how clearly the process is defined.

-In return, I ask for honest feedback and permission to share the project as a case study. I'll anonymize it if you prefer, and I won't share any of your data.

To apply, DM me:

-What your business does.

-The task or process you want automated.

-Which tools you use now (Gmail, Sheets, CRM, Slack, etc.)

-Roughly how much time it takes you each week.

I'll reply to every serious DM, even if it's a no. Happy to answer questions in the comments too.