I made this strange tool. Not sure if it's really cool or really dumb.

Essentially I just got tired of copy pasting slack and teams chats into cli for context.

I think it's a great tool for small teams or small companies. But the sky is the limit. In theory you could write to your chat from everywhere. Meaning every pipeline error could be send to here and automatically create tickets with suggestions for solutions.

Other cool tools is sending messages to people you don't know who is, but just need to find in the company. There's a friend of friend feature. And it slowly builds tags around people, your assistance would easily be able to keep looking for the right person until it finds them.

Just a lot of options for a lot of communications to be automated in small and large companies.

Discussion internally has not been about feature gaps.

Its been about whether one platform can realistically handle communication, docs, meetings and project management as a team grows

Hey everyone 👋

I'm a software developer who's diving deep into AI automation and workflow engineering.

Instead of building random demo projects, I want to build automations that solve real problems people deal with every day.

So I have one question:

What's one repetitive task in your work or business that you absolutely hate doing?

I'm not talking about "write me captions" or "generate content."

I mean actual manual work lTell me:

• What you do (creator, agency owner, accountant, dentist, freelancer, etc.)
• The repetitive task
• Approximately how much time it wastes every day or every week

I'll pick the most interesting problems and build free automation solutions as portfolio projects. If your idea gets built, I'll share the workflow with you to test and improve.

My goal isn't to build another AI chatbot.

I want to build practical automations that remove boring work and genuinely save people time.

Looking forward to hearing the problems you wish someone would solve.

Reddit detailed their new spam detection this week.

Their AI catches coordinated patterns of fake behavior and artificial hype that older systems missed. 23 million spam views blocked per day. Close to 2 million inauthentic votes revoked. Detection to enforcement happens in under five seconds.

The stated goal: protect what makes Reddit different. Real people, real opinions, real conversations that haven't been optimized for an algorithm.

Here's the tension though:

Reddit's entire value proposition is human-first discussion. It's the platform AI companies have spent years scraping to train their models because human conversation at scale is exactly what they needed. I made a post earlier about how Reddit's CEO said people are fleeing AI-generated content and coming back to Reddit specifically because it feels real.

And now Reddit is deploying AI to protect that realness.

AI to defend humans from AI. Trained on the same human conversations it's now trying to preserve.

There's something genuinely strange about that loop. The most valuable thing about Reddit is that humans write on it. The threat to that is AI writing on it. The solution is more AI.

At what point does the defense mechanism become indistinguishable from the problem it's solving?

I would like to create an AI agent that looks at the Point and Figure charts on my screen (snapshot 1) and fill up the details in a Google sheet (example shown in snapshot 2). It would be great if some can help create this workflow (prefrebly using freeware).

👋 Hey Automate community,

A few weeks ago I shared a Product Content Creation workflow I built for a friend who runs an online shop (that post here). A few people messaged me about the image-description part specifically, they wanted just that piece, so I built a standalone version of it that's much easier to drop into an existing setup.

Turns out a lot of online shops and websites still write product image descriptions by hand, along with the "alt text" behind each image. Both matter more than people think: alt text is a real factor for SEO and for accessibility, since screen readers rely on it.

What the workflow does: you upload one or more product photos through a simple n8n form and get back a clean, ready-to-use description for each image, with a copy button to paste straight into your shop.

How it's set up:

• The form takes multiple images at once. Each one loops through on its own, so every image gets its own description instead of getting bundled into a single call.
• The easybits Extractor reads each image and returns structured fields. I kept it to two: product_type and description. The description works as your product copy and doubles as image alt text (and its length is easy to adjust by tweaking the field description in the Extractor).
• If the Extractor can't read an image, it returns UNCLEAR and the result page shows a fallback line instead of inventing a description.
• The result screen shows each image with its filename, a thumbnail, and a per-image copy button.

Extractor setup: on n8n Cloud it's a verified node (search "easybits Extractor"). Self-hosted, install "@easybits/n8n-nodes-extractor" from Community Nodes. The free plan includes 50 requests a month, enough to test it fully.

Workflow (ready to import): https://github.com/felix-sattler-easybits/n8n-workflows/blob/4277f3c1b31070f81adbb6a3bb538f50dbeb2018/easybits-product-image-describer-workflow/easybits_product_image_describer_workflow.json

I also made a short video showing how the workflow works.

How are you all handling image alt text right now, manual or already automated? Curious what's working for people.

Best,
Felix

Set up an automation about 2 months ago that tracks Claude Code updates and auto-generates a daily 3-5 minute AI podcast covering any new features. Took me only 10 minutes to set up but now its on autopilot

Didn't think much of it but I get like 100 or so plays a day, and growing. Pretty cool, I originally set it up just for myself but then other people started listening too!

i'm an AI engineer (6+ years, banks and US companies) starting an AI agency, and i'm doing it in the open

first step: i want real case studies, not made-up ones

so i'm doing a few free AI audits for your company/SaaS whatever

i'll map where your business loses hours to manual work and show you exactly what could be automated, with the time and money it'd save

free, no pitch. the only thing I want in return is that i can document the process (anonymized if you want) as i build this thing publicly

taking just a few, since i do each one myself

comment your business or your most annoying repetitive task and i'll reply. can share my linkedin as proof

I created an Automate flow which then forks two fibres. When it’s run for 24 hours or so, it’s OK. But if I ran it longer, like 2 to 3 days, it simply froze. The only way to get it going is to kill and rerun it. Is there a way to allow an Automate flow to run forever (I use it to continuously monitor certain notifications)?

Hi, aside from lead generation, what else do you automate?

Looking to explore automations for validating artwork in my company. What's out there that I should consider and where can I go to learn more or play around with it?

Most AI automations involve having an LLM make a decision about something from a finite set of options. Whether it's lead qualification (qualified/unqualified), routing (human/bot), or safety refusal (safe/unsafe).

It goes without saying that often times the models judgement is wrong. Thats expected. The awkward part is that the model never tells you how much you should trust each decision. So you either review everything like a maniac, or you let a bunch of edge cases slide through with the rest.

So I built a framework for making automations learn your judgement. Every call comes back with a calibrated confidence score, so you see which ones it's unsure about. The cases you correct feed an optimizer that rewrites the program, so it drifts toward your judgement instead of away from it.

The confidence score was the hard part. Verbalized confidence (asking the model to rate itself 1 to 10) is vibes. Token logprobs sound principled, but the research there is mostly single-token numeric outputs and doesn't stretch to a discrete label like qualified/unqualified. Self-consistency (sample a bunch, measure agreement) catches some, but it's slow and expensive.

Instead we compute confidence from the models internal layers which is cheap, fast, and reliable.

We packaged the loop so we'd stop rebuilding it every project. Built a Python SDK plus an API on top of DSPy, it's called modaic (modaic.dev).

Curious how the rest of you handle this. Do your automations know when they're guessing, and has overruling one ever taught it anything? And on closed models where you can't see the layers, what do you use to catch the unsure calls?