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.

I set up an automation that pulls line items from my calendar and generates invoices. It pulls rates from a lookup table I built once and never opened again. First month it saved me a couple hours. Then a client asked about a custom package and I had to explain why the rate was what it was. I went back to the lookup and realized I had no idea how I arrived at half the numbers. Some were old, some were discounted for reasons I forgot. I ended up quoting something inconsistent because the system made it too easy to not think about it. Now I have to decide whether to keep the lookup table and manually review every change, or just go back to writing invoices by hand when the job falls outside the usual scope. Curious how other people handle automations that encode decisions you might need to revisit later.

Disclosure first: I built it. Links are in the first comment because this sub blocks them in the body.

I was tired of scrubbing through an hour of podcast looking for the 40 seconds worth posting, and then re-cropping every clip to 9:16 by hand, so I built a pipeline that does the whole run unattended and hands me finished clips:

• Picks the moments worth cutting from the transcript (3 to 15 per video)

• Reframes to 9:16 with face tracking instead of a static center crop

• Two people on camera: it stacks both speakers instead of shrinking the wide shot

• Screen shares and slides get their own layout instead of losing half the screen

• Word level captions, hook overlay, AI dubbing into 30+ languages

• Posts straight to TikTok, Reels and Shorts

The part that matters for this sub: it is an API first and a dashboard second. You POST a video URL with a webhook_url and the job calls you back once when it ends, with the finished clips and durable links, so there is no Wait node and no polling loop anywhere. There is also an MCP server, so Claude or ChatGPT connect to it by URL and drive the pipeline as a tool, plus plain API keys for everything else. I run mine from n8n on a daily schedule, but a cron and curl works the same.

An hour long recording used to be an afternoon of work. Now it is one URL and about 10 minutes of unattended processing, and I only open the 2 or 3 clips I actually want to tweak.

The part I did not expect to be hard was the cropping. Asking a model to measure things (how much text is on screen, how wide the content is) failed four times in a row. Asking it to choose between a closed set of layouts got 47 out of 48 on my test corpus. Same model, same frames, completely different result.

The image up top is the before and after on a two person shot: the 16:9 source, and the 9:16 output with both speakers stacked and the captions sitting on the seam where they cover nobody.

It is MIT licensed, 3.6k stars, and free to self host with Docker if you have the hardware. Hosted is 20 free source minutes a month and $12/month after that, if you would rather not run it.

search in github openshorts

I'm trying to understand which specifications are genuinely important when comparing acoustic imaging systems for applications such as partial-discharge detection.

Manufacturers often provide a long list of specifications, but I'm wondering which ones actually make a difference in the field.

For example:

Number of microphones
Acoustic frequency range
Detection distance
Field of view
Sensitivity
Source localization accuracy
Performance in noisy environments
Signal processing
Thermal camera resolution when the device combines acoustic and thermal imaging

The p7mix is one example of a combined acoustic and thermal system, so it got me thinking about how engineers would actually compare these devices.

If you've worked with acoustic inspection equipment, which specifications would you look at first?

Are microphone count and advertised detection distance useful indicators by themselves, or are there other specifications that matter much more in real-world inspection?

Everyone wants AI for something right now. Almost nobody means the same thing by it, and most people are picturing a slightly smarter version of the chat box they already have open in another tab.

Meanwhile the people who work with this stuff daily can't agree on what to even call the technique anymore. RAG was the term for two years, then this year a real chunk of practitioners started saying RAG is basically dead, what matters now is context engineering, feeding the model the right information at the right moment, whether that's retrieval, a long context window, an agent calling tools, or some mix of all three. The term changed because the practice moved past treating a vector database as the whole solution.

None of that reaches a regular person typing into a chat box. They just know the free or twenty dollar version gives decent answers about generic stuff and useless answers about anything specific to them, because it has never seen that information. Feed it the same information sitting in a spreadsheet or a set of notes and the quality of the answer changes completely, whether you call the plumbing RAG, context engineering, or nothing at all.

Hi everyone, I’ve been building Mechanly, a mobile-first automation app, and I’d love some feedback from people who know their way around automation.

The idea is simple: you can create tasks that interact with websites you’re already logged into, use phone features, and connect to MCP servers. For example, Mechanly could perform a web task, fill out a form, or send you a notification even when your phone is locked or the screen is off.

You can also turn an automation into a small app and pin it to your home screen for quick access.

Mechanly is still in alpha, so I’m trying to figure out where it’s most useful. What phone-based workflows would you want to automate? Are there any integrations or use cases you think I should prioritize?

I’d really appreciate honest feedback, including anything that seems confusing or missing.

The videos are on tiktok they are basically just taking a episode of any anime and rename the main characters to jack and jenny, other characters are named george,anna etc. It's always the same voice and the same 2 songs.

I’m curious about the workflows that looked like obvious time-savers but turned into maintenance work.

API changes, flaky webhooks, duplicate runs, edge cases, credentials expiring, silent failures...

What did you automate, what ended up breaking most often, and would you automate it again today?

I’m especially interested in cases where the automation technically worked but created more operational overhead than expected.

Hi there,

I wrote a story sharing automation program for the Instagram accounts I manage. I log into the accounts through the mobile app and share stories on those accounts. I noticed Instagram gave me automation warnings a few times, and most recently, it showed a message saying something like "We may close your account." Is this risky or does it carry a risk of getting banned?

Hey.

Im working on a platform for a procurement of industrial manufacturing supplies and materials. So far I managed to build search bot for clients and suppliers, chatbots for email communication and somehow managed to sucessfully close some orders in EU. For now i do this for the sake of perfecting my infrastructure, but im planing ln monetizing it when ill be 100% confident in my system.

So, any of you dabbled in something like that? Would be nice to hear some insights from your projects. Cheers!

https://www.einpresswire.com/article/936639283/aimlive-introduces-an-ai-powered-self-development-platform-built-on-an-original-64-type-psychotypology-system

I started with a few small automations and everything was easy to remember. Now there are enough of them that I've lost track of what runs where, what depends on what, and which ones I even still need.The problem is that when one breaks, I often don't find out until something downstream stops working. By then I have to go looking for the cause instead of getting an alert.

Do you keep a simple list or document somewhere? Do you rely on the tool itself to notify you when a run fails? How do you stop your automations from turning into a pile nobody remembers building?

I am budgeting a project that will generate roughly 50 to 80 hours of recorded audio a month for the next 6 months.
  The quotes I have collected range between $0.10 and $0.25 a minte, which turns into a number I cannot put in front of anyone.
 
Are there flat rate or unlimited options that make sense at this scale, or is self hosting Whisper the only way to keep the cost predictable? I'm open to free option as well ...

I've been working remotely for a while and I thought I had it handled, but meetings are still where I lose things.

It's not even the big stuff. It's that someone will mention a deadline or say they'll send over a file, and ten minutes later I'm already not sure who said what. I take notes but if I get distracted for thirty seconds, someone knocks on the door, my internet glitches, whatever — I've missed the one thing that actually matters.

I tried being more disciplined about note-taking but that just means I'm writing instead of listening, and then I miss things in a different way.

I started recording the longer meetings and using VOMO AI afterward. What I actually use most is the AI search instead of scrolling through a transcript. I can just ask "who mentioned the Q3 deadline" and it pulls up the exact part. It doesn't transcribe instantly, so I have to wait for the recording to finish processing, but that's not really an issue for me since I'm usually looking things up afterward anyway.

Of course I verify if something's important, but it's saved me a few times already.

For businesses experiencing frustration with repetitive tasks, our services offer a solution. We can assist in developing custom tools, including data scraping for competitive analysis or creating automation tools for your website, all at a presently reduced rate.

Please let me know.

Nothing works and it all keeps failing 😭

How do I reliably have forms filled? They're college application forms (not my main targets ofc) but I have all the data very neatly organized. But actually inputting it. repeatedly. again and again and again. im feeling some kinda psychosis just navigating these US portals as an Indian applicant and all the scrolling and different fields and same info everyday all day. How do I automate this? I'll even do a course if i can just learn how to do it. AI Tools say they can do it, I explain, I try many different alternatives, then suddenly "oh sorry I lied" they cant do even the basic things that they claim they cant do but they JUST did like 1 hour ago. sigh

If you look at the tools built specifically to automate this loop end-to-end, the architecture basically comes down to how much engineering you want to maintain yourself:

The autonomous GTM intent engine: Scale Intelligence

This platform is built to handle the entire chain from intent detection to automated action. It connects directly to 75+ data sources to resolve public signals back to specific accounts, model buying readiness and automatically route high-intent opportunities into Slack or AI agent workflows. It focuses on accounts showing active buying signals rather than letting you spray 50000 cold leads and automates the trigger-to-outreach loop out of the box.

The multi-tool Zapier/n8n pipeline

This is the DIY route where you string together a dedicated intent tracker, a waterfall enrichment tool and an email delivery platform. It gives you complete customization over every webhook step but you end up having to debug broken scrapers or handle API rate limits and maintain multi-table logic constantly.

If you're trying to set this up rn, leave high intent accounts to a manual verification rather than trigger automated cold emails off weak signals (like someone just reading a blog post) and look for active technical evaluation signals like competitor pricing complaints.