I’m building voiceraft.org , an AI voice-agent company focused on automating inbound calls for businesses.

Product and building are going well, but distribution is currently our biggest bottleneck.

I’m looking for someone who can help us bring in clients. We’re open to structuring this as a commission-based partnership, or discussing another structure that makes sense.

We handle the product, setup, deployment, and support — you’d primarily help us with distribution.

Everyone starts with something simple and reasonable and then a year later they have automations that make no sense. My first one was just turning on the porch light at sunset. Now I have an automation that sends me a phone notification when the coffee maker has been left on for more than 30 minutes, but only if nobody is home, and only on weekdays.

What was the first automation you set up when you started? What is the most absurd or over-engineered one you run today?

So this started as me trying to stop opening fifteen tabs every time somebody asked which tool to use for something.

I built the obvious thing. A scraper pulling tool pages and category roundups, dumping into Airtable, a scoring column on top, weekly refresh. On the plumbing side it worked fine.

Then I actually read what it had collected. Roughly the same twelve tools in roughly the same order across every source, because most of those roundups are affiliate pages and a fair number are copying each other. I had automated the collection of marketing copy and given it a tidy score column, which is worse than useless, because now it looks researched.

So I killed the scraper. What I am doing instead is TrustRank (https://trustrank.so/), where people vote on whether they would recommend a tool they have used, and a vote from somebody who wrote a review of it counts three times as much as one from somebody who did not. Demonstrated use is the only thing that weights a vote.

It is much slower and there is no pipeline to show anybody, but the bottleneck was never the collecting.

For a long time I built everything as one large automation. One workflow would grab the data, clean it, send it to a few places, post a notification, and update a spreadsheet, all in one long chain. It felt efficient to have it all in one place. The problem showed up when something broke. One step in the middle would fail, and the whole thing stopped. Worse, I could not always tell which step caused it. I would spend more time figuring out where it died than actually fixing it.

The moment that changed my mind was when a small formatting step failed and took down the notification part with it. So I never got told that anything was wrong. I only noticed hours later when the data wasn't where it should be. After that I split it into smaller pieces. Now each part does one job and hands off to the next. When something fails, I know exactly which piece it was and the rest keep running.

Do you build one big automation or several small ones? If you switched from one to the other, what made you do it?

im a bigginer, learning n8n! from nate hark and in one lecture he is using ai agent in which he is connecting a model (anthropic/claude-sonnet-4.5) of OpenRouter but when im trying to do the same it is showing error, is that like i need to but some credits from OpenRouter to use that model??

im on free plan rn! dont have any money to buy these stuff juss tell me im cooked or what?

I’m trying to design an automated sales funnel for a consumer internet/telecom service, without relying heavily on sales reps manually handling every lead.
The acquisition channels would be Facebook, Instagram and TikTok.
The basic idea is:
Social media content/ads → prospect sends a message → automated qualification → recommend the appropriate plan → collect customer information → schedule/coordinate installation → follow up automatically.
I’m interested in automating as much of the process as realistically possible.
For example:
How would you capture leads from FB/IG/TikTok?
Would you send everyone to WhatsApp, Messenger, or keep the conversation inside the platform?
How would you qualify someone automatically? (location, coverage, budget, household needs, etc.)
How would you structure the chatbot so it doesn’t feel like a terrible scripted bot?
How would you handle checking whether service is available at the customer’s address?
How would you automate plan recommendations?
At what point would you hand the conversation over to a human salesperson?
How would you automate follow-ups for people who don’t buy immediately?
What would you use for the backend/CRM and integrations? n8n, Make, Zapier, custom API, etc.?
What metrics would you track to know where leads are dropping off?
I’m especially interested in hearing from people who have actually automated high-volume B2C sales through social media, rather than just basic “send a welcome message” automations.
If you were building this from scratch today, what would your architecture look like?

https://betterauds.com/tech/present-of-ai/

Looking forward to explore workflows which are really a blockers.

Hey everyone! I'm Zach. I am on a quest to build the best automation tool in the world, and I have been for nearly 4 years. This is my manifesto, but full disclosure: it's also promoting my product.

Business/workflow automation can be painful. If you choose a no-code tool, you're either compromising on ability or walking up a steep learning curve. If you choose custom code, you now own integrations, trigger logic, and infrastructure -- i.e. a ton of tech debt -- for even simple automations.

(P.S. that was a good ol' fashioned human-written em dash -- you can pry them from my cold dead hands)

A year ago, I made a similar post in this subreddit asking you all to try WorkflowDog. WorkflowDog was/is a no-code automation tool I built to give people a way to build more powerful automations more fluidly.

The good

I accomplished my goal. It turned out awesome. I commonly reached for WorkflowDog to solve my own problems and those of my entrepreneur friends. It was genuinely one of the best tools I've ever used. I built a small but passionate user-base.

The bad & the ugly

The more I used it, the more I bumped up against the problem that all visual programming tools eventually run into. Because that's essentially what these tools (WFD, Zapier, n8n, Make, etc.) are: visual programming languages.

GUIs are not fit to handle what's accomplished naturally in code: mapping objects & arrays, looping, composing, and more.

All your favorite programming languages handle these 100x more gracefully than a visual tool ever will.

Furthermore, entire ecosystems exist to solve all the most common code-related problems. Version control/rollbacks? Git. Pre-made libraries published by others? Package registries like NPM.

Enter: AI

The year is 2026 and AI is fantastic at writing code. I'm a software developer, I know it still writes a lot of horseshit. But it's good enough to empower non-coders to build functional things. Which is amazing.

Now that anyone can code, the case for no-code tools shrinks significantly.

But wait! I just said like 20 lines ago:

If you choose custom code, you now own integrations, trigger logic, and infrastructure -- i.e. a ton of tech debt -- for even simple automations.

Well, that's what I spent the last year solving.

And here's the pitch

What if you could just code automations? What if you could just....

• ...use Typescript, a language Claude Code / Codex / Cursor is already excellent at writing?
• ...not worry about credentials and infrastructure?

What if all you had to do was write (or have your agent write) simple, expressive code like this:

import { automation } from "automate.ax"
import { slack } from "automate.ax/slack"

export default automation("React to Slack messages", () => {
  const message = slack.onMessagePosted()

  slack.addReaction({
    conversationId: message.conversationId,
    emoji: "eyes",
    timestamp: message.timestamp,
  })
})

That's a reality now. It's called AutomateAX. And there's even cooler capabilities that are too much to get into here.

The end

I don't think I'm allowed to post links here. If you're interested in trying, leave a comment and we can chat.

I'm also running a 50-person feedback cohort in a private slack channel. These people get free 3-month coupon codes, but you need to have some relatively high-volume automations running and be willing to hop in some regular workshops. If that sounds interesting to you, let me know!

Thank you so much for reading my novel here.

- Zach Sents

When teams talk about automating lead gen, 90% of the time they just mean automating cold email sending so they set up 10 domains, buy a static database of 5000 contacts, connect a sequencer and hit send. Within a month, bounce rates spike, domain health drops and reply rates sit below 1% cause the outreach has zero timing context.

Real lead gen automation works much better when you automate timing detection and intent discovery before any message is sent and the software landscape for this generally splits into two approaches:

1, Autonomous intent & signal discovery (Scale Intelligence): this approach automates the research and discovery layer where Scale Intelligence monitors 75+ data sources across technical and social channel to resolve fragmented public intent back to verified company domains. It scores buying readiness and routes warm opportunities straight to Slack or automated agent workflows so outreach only happens when an account is actively trying to solve a relevant problem.

1. Multi-tool manual pipelines (scrapers + spreadsheets + sequencers): the approach of chaining multiple point tools together: one tool for web scraping, one for waterfall email enrichment, and another for delivery. It gives you deep custom control over every spreadsheet column, but requires constant manual oversight, API credit balancing, and weekly maintenance.

If you are automating lead gen this year: stop spending budget on static CSV exports that decay in weeks and automate the listening layer to detect trigger events (like competitor complaints or hiring spikes) while keeping high-intent outreach context-rich and targeted rather than blasting high-volume cold templates.

I have uploaded my 2nd video, which incorporates several changes. The previous video had issues, such as repeating clips, which have been addressed by including multiple distinct clips with one frame rates as different clips were in different frame rates so I have been getting issues related video got frozen at a specific time. Subsequent videos will consistently feature a variety of clips.

Starting from day 3, we will endeavor to introduce a unique hook in each of our videos and yes all this processing is done under 512 mb so that render will not create any issues.

https://betterauds.com/lifestyle/mydrinx-shop-10000-liquor-bottles/

I have successfully published my inaugural video, which I personally created using a YouTube automation tool. The exceptional video and audio quality should effectively attract an audience.

Let's see guys

Built another one this weekend. The goal: find people publicly complaining about manual work, show up in the comments with something useful, and start a conversation. Automated signal collection, human posting. It never acts on its own.

Engagement Scout. A curated signal feed for real back-office pain. Runs twice a day on schedule. Every candidate gets scored, drafted, and queued. The operator decides what gets posted.

How it works:

Four sources feed the pipeline. A keyword filter runs first. Pure Python, zero LLM spend. Cuts vendors, job posts, promos, and stale content before any AI touches the batch.

What survives hits 3 scoring gates. Is this a real, fixable problem? Does the poster have the authority to make decisions? Does the stack actually solve what they're describing? All three have to pass.

The only LLM in the loop handles scoring and drafting. It judges each candidate on the 3 gates and writes a reply with hard mechanical guards. No service pitches. No overclaims. Language checks baked in.

Cards land in Notion — full list, drafts, run log. Telegram fires an alert. Approve or skip from there. Every decision gets logged so the system learns what signals are worth chasing and what replies actually land.

Stack: APIs (Reddit, X, LinkedIn, Facebook) · Python · Hermes · Telegram · Notion.

We run ~200 daily n8n workflows in production. Last month a single bad IF condition caused an infinite loop: 47,000 runs in 6 hours before budget alert fired.

Direct costs: ~$2.5k (API calls, scraping, execution time). Indirect: 3 days DB cleanup, client nearly churned ($45k/yr).

Guardrails we added after (homegrown): 1. Hard limit per workflow - max 100 runs/hour, auto kill switch 2. Budget guard - Slack alert at 50% estimated daily spend
3. Static analysis pre-deploy - catches loops without exit conditions, unbounded retries, unfixed model versions 4. Canary runs - first execution in dry-run mode with real data, no DB writes

Static analysis caught 12 critical issues last month that would've been expensive.

Question: What automated checks do YOU run BEFORE deploying automation to prod? - Custom scripts (static analysis, cost estimation)? - Existing tools (specific ones)? - Purely reactive monitoring (alerts after the fact)? - Something else?

Not looking for war stories — looking for concrete technical patterns/tools you use as safety nets.

kept hitting the same problem: ask an agent for "an image" (a chart, a card, a banner) and it reaches for an image-gen model. That burns real tokens and credits, takes a while, and the output is a guess: close to your brand, never exact. Wrong shade of blue, logo redrawn from memory, layout different every run. You end up regenerating three times and still touching it up by hand.

So I built Render MCP: an HTML-to-image and template-to-image API, shipped as an MCP server. Give it a template name and data, or raw HTML, get back a hosted PNG. Your brand kit (exact colors, exact logo, exact font) is baked in, so the output is deterministic: same input, same image, every time, no regeneration lottery.

Where this actually gets used:

- Automated reporting. An agent turns last week's numbers into a metric-card or bar-chart and drops it straight into Slack, instead of a wall of text nobody reads.

- Social content, without Canva. Blog post becomes a quote-card or carousel-slide, a tweet becomes a shareable tweet-card, a stat becomes a story-card. One call per post instead of a design pass.

- OG images that don't look broken. Every page's title and subtitle render into a real og-image at build time, so link previews in Slack and X actually match the page.

- Ad creative at scale. Script through headline and offer variants with feed-ad, display-banner, sale-promo, and urgency-promo, and test a dozen versions without opening a design tool.

- Product surfaces. Changelogs (announcement-card), testimonials (testimonial-card), pricing pushes (product-card), job posts (hiring-card), event invites (event-card), all templated and on-brand.

- Dev content. code-card for tweeting a snippet with syntax highlighting, blog-header for post banners, youtube-thumbnail for video creators.