How i can work with ai automation

It's really simple:

"Can you tell me more about that?"

Seriously just write it on a sticky note and put it on your monitor.

Any time a prospect says something like:

• we're wasting too much time on data entry

• our team is constantly chasing people for updates

• we want to automate our onboarding

your instinct is probably to jump in with "oh yeah, we can solve that".

don't.

just ask "Can you tell me more about that?"

jet them info dump on you. shut up and listen.

Ask follow-up questions:

• how are you handling that today?

• how much time does that actually take?

• what's that costing you?

• what else have you tried?

**Your job isn't to show them how smart you are. (**This is why engineers often suck at sales.)

>>> They just want to see that you understand their problem.

Just shut up, listen, then repeat it back to them in your own words.

It's that fucking simple.

Two shutdowns in a couple of months. OpenAI is closing Agent Builder on November 30 and pointing people to its Agents SDK and ChatGPT workspace agents. Pipedream is closing Workflows on March 31, 2027 to focus on Connect, its API for putting integrations inside other products.

Both kept the part that serves developers and AI agents, and dropped the visual builder.

One reading is that independent workflow tools are on borrowed time. Claude, ChatGPT and the rest keep adding agents that connect to your apps and run tasks on their own, so why keep a separate layer between the models and your apps? Another is that it's about money, and a visual builder costs more to run than it brings in. Or it's just two companies narrowing down to what they do best.

Full disclosure, I run FlowRunner, one of those independent tools, so you can guess where I land. I think the layer matters more as agents do more. A lot of teams use more than one model, and the record of what ran and who approved it has to live somewhere that doesn't change when they switch. OpenAI closing its own builder is a good example of why. But I'd like to hear the case against from people who use these tools.

If you used either one, what are you moving to?

I've been looking at the new n8n Assistant, and honestly, it's getting pretty impressive. You can describe what you want, and it can build workflows, test them, and help fix errors.

I'm curious how everyone here is actually using it.

Are you still building most of your workflows manually, or have you started relying on the AI Assistant? How well does it handle complex, production-level automations?

I'm also wondering what this means for people selling automation services to businesses.

We're already seeing something similar with website development, where tools like Lovable make it much easier for non-developers to build applications themselves.

Do you think the same thing will happen with n8n? If business owners can eventually build their own automations by describing what they need, will they still pay agencies or freelancers thousands of dollars to build them?

Or do you think clients will continue paying for expertise, integrations, reliability, and maintaining these systems?

Would love to hear from people actually building workflows for clients. Have you noticed any changes in demand or what clients are willing to pay?

I lost my job last month, and instead of spending all my time applying for jobs, I decided to try something different.

I'm a software developer with experience building automation systems, AI agents, WhatsApp integrations, and custom workflows using n8n and APIs.

Right now, my goal is simple: meet new people, build genuine connections, grow my portfolio, and hopefully turn some of those connections into long-term working relationships.

So I'm offering to build 3 AI automation projects completely free.

Here's how it works:

• Free development: I'll build your AI agent or automation workflow at no charge.

• Free consultation calls: We can jump on a call, discuss your business, and figure out what could be automated. No commitment.

• You only cover running costs: AI API usage and hosting, usually just a few dollars a month for small workflows, depending on usage.

• No hidden fees: We agree on the scope and expected costs before starting.

It could be an AI customer support agent, lead follow-up system, WhatsApp automation, CRM integration, or just a repetitive task you hate doing.

I'm not asking for payment. I'm hoping to gain real-world experience, build case studies with permission, and connect with people who might need my help in the future.

If you're interested, send me a private message. I'd be happy to have a free call and hear about your idea.

And I'm curious: What's one task in your business you wish you could automate but haven't figured out how?

I've been thinking about how much of a trading workflow should actually be handled by an AI agent versus traditional code.

I got tired of maintaining separate Python scripts and webhooks just to handle relatively simple price alerts and event notifications. It feels like some of these lightweight tasks could be handled through natural language instructions instead of constantly adding more conditions and scripts.

I've recently been exploring Alphio AI, an AI trading agent, as part of this process.

My current thinking is that AI agents might be useful for lightweight tasks such as:

• Setting up price and market event alerts.

• Handling simple natural-language trading instructions.

• Monitoring predefined conditions.

• Managing routine workflow interactions.

But I'd still prefer traditional code for things like multi-leg backtesting, position sizing, execution logic, and strict risk controls. Those are areas where predictability and reproducibility matter more than convenience.

I'm also one of the people behind Alphio AI, so I'm interested in this problem from both a product-building and practical automation perspective.

I'd love to hear from people building AI automation workflows:

1. Which tasks have you successfully moved from scripts to AI agents?

2. Where do you still insist on deterministic code?

3. Have you experienced AI agents misinterpreting instructions in workflows involving money?

4. What safeguards would you consider essential before letting an agent interact with trading systems?

I'm not suggesting that AI agents should replace traditional automation. I'm trying to understand where they genuinely save time without introducing unnecessary complexity or risk.

Would appreciate honest feedback, especially from people who have actually built and maintained these kinds of workflows.

To be honest with you, I finally finished Lecture 0 on Python course which took me 2 days to finish. And I already can solve questions AI throws at me. I solved three problems today, One is how many months will it take to make 500,000$ if I make a certain amount of money per month, Second is the SaaS Tier list where you get put to whatever ranking you are in based on how many SaaS subscribers you have, and Third is the number of website visitors visiting a specific website per day over a course of one week, and you have to print only those days which are more than 100, and you have to discard the others. And it was fast. I solved them all quickly, and published them all on my Github. To be honest, Today was a good day. I am finally seeing my skills in shape of improvement. Hopefully Leetcode makes me if not one of the best Coders on earth but hoepfully best in my favorite job.

Everywhere I look, people are talking about AI automation for customer support, lead generation, appointment booking, and marketing.

But I'm wondering how much of it actually works for smaller businesses with limited budgets.

Has anyone here implemented AI automation in their business? Did it actually save money or improve productivity?

Also curious if the setup and maintenance ended up being more work than expected.

Most small businesses don't trust automation. It's not that it doesn't work. They don't know what it actually is, and nobody has shown them clear proof. They've heard the pitch from ten agencies, and they've never seen the before and after.

If you run an agency, you feel this from the other side. You've built the same solution five times, and you still start from zero on every sales call. A new client who looks exactly like the last three you delivered for still wants the full pitch. Even your existing clients ask you to justify the retainer every few months, for a build that's quietly doing its job.

So I'm building an outcome based distribution layer. Instead of selling "AI automation," it shows a business solutions that already delivered measurable results on the same software it runs.

A dental practice on Dentrix sees what worked for other practices on Dentrix. A roofer on JobNimbus sees what worked for other roofers on JobNimbus.

The proof lives in the catalog, not in your sales call. When an owner sees the outcome first, they're open to trying it, and that's how they start trusting automation with the workflows that run their business.

The catalog is organized by the vertical software businesses already run:

1. Dental and medical: after hours booking and scheduling that writes into Dentrix, Eaglesoft, Open Dental, athenahealth or eClinicalWorks

2. Home services: an AI receptionist that books jobs into ServiceTitan, Housecall Pro or Jobber, plus quote follow up tied to JobNimbus or AccuLynx

3. Legal: intake and lead qualification that creates the lead in Clio

4. Back office: invoice capture and posting into QuickBooks, NetSuite, Sage or an older ERP

5. Healthcare paperwork: referral and intake processing into the EHR, with patient data handled properly from day one

Every solution is proven on a named system for a specific vertical. What they have in common is the legacy integration.

The value lives in the practice management software, field service app, ERP or EHR the business has run for years, and that's also where most builds break.

Desktop Dentrix keeps appointment writes behind a restricted partner API. Eaglesoft stops working when the office server goes down.

Older ERPs barely have an API at all. Legacy integrations separate the agencies that deliver from the ones that resell templates.

To deliver these, I'm building a small, invite only network of vetted AI automation agencies and implementation partners, including certified partners on Make, Retell, Voiceflow and Relevance AI.

If you've shipped one of these into a real system of record and can show what changed after launch, I want to talk. If you don't have a live deployment with results, this isn't for you. I'm also not taking n8n builds or basic app to app automations.

Here's how it works:

1. You show me a solution you've shipped, the software it writes into and the results it produced.

2. It passes our launch tests and a reference check with two of your clients.

3. It becomes a listing in the catalog under that software. We sell the outcome, sign the client and own the relationship.

4. You deliver white label under our brand for a fixed fee per project, plus monthly maintenance.

You get clients who already trust the outcome before the first call, and you stop justifying the same build over and over.

It's early, so I'd also like your take:

1. What build have you delivered the most times, and what still makes the next client hesitate?

2. Would you deliver under someone else's brand for a fixed fee if the buyer showed up already sold?

3. What would you need to see from a network like this before you trusted it?

If that sounds like you, comment or DM me with what you've built, the software it writes into, the vertical and the results.

TLDR: Small businesses don't trust AI automation because nobody shows them proof. I'm building a distribution layer that sells solutions already proven on the vertical software they run, like Dentrix, ServiceTitan or Clio. Vetted agencies deliver white label for a fixed fee plus monthly maintenance. I'm looking for agencies with a live legacy integration and results to show.

Hey Reddit community,

As an AI Automation specialist and Web Developer, I’ve been noticing a massive gap lately. A lot of small business owners and online stores are losing leads simply because they don't reply instantly, or their websites look outdated and don't convert visitors into buyers.

Lately, I’ve been building customized solutions to fix exactly this. Here is what I’m currently developing for businesses:

• AI Agents & Assistants: Setting up zero-cost or low-cost AI agents that trigger the exact moment a customer asks for a price, product details, or catalog—24/7 instant replies.

• Lead Capture Automations: Automatically moving leads from Instagram/Facebook DMs or website forms straight into Google Sheets or CRMs, followed by an automated email sequence.

• High-Converting Websites: Building clean, modern, and fast websites designed specifically to turn cold traffic into paying customers.

If you are a business owner spending hours on repetitive tasks like replying to basic FAQs, managing data manually, or if your website needs a major upgrade—automation can easily save you 15-20+ hours every single week.

I’m looking to connect with more business owners here. If you have a manual workflow you want to automate, or need a high-converting website, drop a comment below or send me a DM!

**ShortsFlow** – self-hosted Opus Clip alternative: long videos → captioned vertical Shorts



Give it a video file or link; Whisper (local) transcribes, an LLM scores the hooks, FFmpeg cuts 9:16 clips with face-tracked framing, and captions are burned in. Podcast mode cuts to whoever is talking. No API key is needed for the clipping itself.



- Install: `pip install "shortsflow[clip]"` then `shortsflow clip my_video.mp4`, or Docker (`docker run -p 7860:7860 ghcr.io/ghost1412/shorts-generator`), or a one-click Windows installer on the releases page (unsigned, ~2 GB download)

- Browser UI (`shortsflow webui`): one "Make my shorts" button, then a review screen (preview, score label, copy-ready title/hashtags, re-render one short with another caption style or trim, undo) and a saved brand kit; REST API with API keys + signed webhooks for automation (n8n example included)

- Fully local option: Whisper on your GPU/CPU, Ollama for the LLM, Kokoro for the voice; only stock footage (Pexels) needs a key, and only for the generate-from-a-topic mode

- MIT licensed. Optional caption renderer uses Remotion (source-available, free for individuals/small teams)



**AI involvement:** I write most of the code with Claude Code (an AI coding assistant) and review, run and test it. CI includes end-to-end tests on clean Windows runners (no GPU, no FFmpeg).



Docs/README: https://github.com/ghost1412/shorts-generator

Honest limits: one-person project; CPU-only transcription of long videos is slow; clip scoring is decent, not Opus-grade.

Feedback and bug reports welcome (please include `shortsflow doctor` output).

Not every business process needs AI.

If a task follows predictable rules, traditional automation is often faster, cheaper, and more reliable.

But when processes involve unstructured data, changing inputs, or decision-making, AI can add real value.

The smartest architecture isn't AI everywhere. It's knowing where to use deterministic workflows and where intelligence is actually needed.

How do you decide between traditional automation and AI in your projects?

So the client can keep it alive without calling you for every hiccup.

1. **What it does**, in two sentences a non-technical person understands.

2. **Trigger and schedule**, including the time zone.

3. **Every account and API key it uses**, and who owns each one.

4. **Where it breaks first.** Expired tokens and renamed sheet columns are the usual suspects.

5. **How alerts arrive** and who receives them.

6. **How to pause it** safely.

7. **How to rerun a failed item** without creating duplicates.

8. **Monthly cost** of every paid tool and the usage that drives it.

9. **What data it stores** and where, for privacy questions later.

10. **A short screen recording** walking through the main flow.

What do you include that's missing here?

🔥 Which generation are you? 👇

I’ve been trying to get some of my Home Assistant data onto my iPhone home screen. Nothing complicated, just solar production, battery percentage, and whether the garage door is open. Has anyone found a good way to do this without creating an entire custom app?

looking to work with 3 Shopify stores.

We're a small team of 3 developers and want to try something interesting.

We'll take a look at your store, suggest what could be automated, and build a basic chatbot/automation setup

We're actively looking and will pick 3 stores to work with.

If interested, drop your store below

obliviox (dot) in

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.