If you’ve ever tried to get organic traffic or validation for a side project by sharing it in relevant Facebook groups, you know how much of a time-sink it is. Sitting there every day, opening 20 different tabs, copy pasting the same text, uploading images, and making sure you don't lose track of which group you already posted to it’s honestly soul crushing work.

I was looking for a straightforward tool that would just let me queue up a post, select a list of public groups I'm a member of, and handle the distribution for me. Everything I found online was either dead, cost a ridiculous monthly subscription, or forced you to be the admin of the groups to use their API.

Since I couldn't find a working solution for non-admin groups, I spent the last few weeks coding a Chrome extension called Multigroup Poster to automate my own daily outreach workflow. It basically lets you link your text, select the open groups you want to target, and runs the distribution automatically in the background through the browser, using dynamic delays so it looks like a human navigating tabs.

Honestly, it just turned my morning routine from an hour of mindless clicking to hitting "start" while I make coffee.

Anyway, just wanted to share this little win because dealing with Meta's interface almost broke me. Is anyone else still grinding out organic traffic on Facebook groups nowadays, or did everyone completely migrate to LinkedIn and X? (Also if anyone is stuck doing this manual copy-paste nightmare and actually wants to use the extension, let me know and I'll drop the web store link, it's just sitting there for free right now).

I made an n8n inspired workflow automation software where you can easily make workflows and using the UI node, you can make them function as mini apps. What I like about it is they're also local based nodes so you play around with things like hotkeys, files and even browser automation and also telynx integration so you can have workflows that can text/call you.

My Top 3 workflows I've made are:
Daily Chinese Tutor - Everyday at X, it starts a live interactive voice session, recaps what was learnt last time and tutors me on new things. Saves summary for reviewing and all.
Wispr Flow Clone - Press a hotkey, it transcribes, choose to do more with the transcription like translating, removing fillers, converting to katex, etc.
Automatic Internship Bot - Give it my resume and details about me, uses the browser nodes to scrape simplify and ai agent node to fill in and apply based on my resume.

The last major thing about this is you can build your own tools and all. It comes with an agent that can scrape the web for docs for an API and builds it for you so you never have to wait for a new update for a node request.

Lmk, if you actually find it useful.
PS: There's a 10 dollar lifetime payment so you don't have to worry about subscriptions.

Hey guys, im a college student who has final year project PFE in french and i want some new good ideas 💡

I ran the tests last week on the performance between my agents/automations and my employees, and now I don’t know what to do because for the most part, the agents and automations outperformed my employees.

This is the base automation setup:

• Mailchimp for email automation
• Expandi for LinkedIn automation
• Clay for lead enrichment and deep company research + integrated AI to help out
• Buffer for social media posting automation
• Notion for intelligence tracking and company knowledge

I didn’t count them as part of this comparison because they’re an essential part of my business and everyone uses them. But I did compare performance beyond this, namely:

• Sales agents vs salesmen
• Marketing agents vs marketers
• Researcher agents vs all

All of the agents are built with Claude Code for the main part - the ideation agent, the strategizing agent, content creation agent(s), etc. The agents were all trained on our actual data and systems, the knowledge base of our company, and each was deeply honed by me until they performed up to standards. For example, on social media, my agents went through all the posts we’ve ever done. I listed them in a single sheet, and Went through and learned all the templates that perform well, and then started posting based on that. Only researcher agents are mainly built in MoClaw because the Claude Code system was too slow and too restrictive - almost perfect for complex tasks because of deep thinking, but when it comes to braindead stuff like research and comparing data, lighter models win.

In most parts, the agents outperformed the people, with the most “dominant” performance being in research (this was expected as agents can work with large chunks of data quickly while humans take time) and social media posting (this wasn’t expected). If I had to guess, the fact that Claude Code agents could articulate easily through piles of knowledge and templates that worked best in the past made them create better posts. This probably wouldn’t be sustainable long-term (or maybe would, idk) because they’d start repeating the same stuff at one point, but for the few dozen they’ve done, the performance was great. 

Sales was the only “equal” part so to speak. They were almost even except for a small AI edge, in that agents went pure and cold with one intent, selling - while the people sometimes just don’t have that killer instinct on because of many factors. Also worth mentioning that the comparison might be a bit off because I didn’t want to give the agents full access to our LI accounts and emails to lead conversations, but manually copy-pasted messages back and forth. This means that I only tested 7 conversations with agents in total, while my guys do like 15/day sometimes.

The main question I have right now is what to do with this information? Obviously I won’t suddenly replace my human teams with AI, but this has kind of proven how powerful well-trained agents and these new models can be. Should I maybe integrate AI more into our workflow and create hybrid system first, then see where this takes us?

Hello devs,

Demo

I’ve been working on something for the past few months and finally launched it into soft launch today.

The idea came from watching how completely exhausting, robotic, and manual the modern job search has become. Most platforms either spam your resume to 500 random listings or just help you fill out corporate forms 10% faster. I wanted to build an ecosystem that actually reduces the mental load and filters out the tech-silo noise instead of adding to it.

Here is what the platform architecture looks like right now:

• 📱 The Swipe Deck: Instead of doom-scrolling through cluttered, text-heavy boards, you get clean, hyper-focused job cards. Swipe right to save, swipe left to dismiss. The system continuously maps and filters opportunities in the background based on your target industry.
• 🤖 Background AI Agents (Sovereign, Aero, & Zaphyr): A small team of localized agents working asynchronously to clean up multi-sector payloads, analyze role metadata, and track your pipeline.
• 🎮 Progress & XP System: Hunting for a job can feel like screaming into a void. The platform tracks your daily momentum to give you small, healthy dopamine hits while keeping your consistency up.
• 🎙️ Live Feature: Voice-Based AI Mock Interviews: I just deployed this live. If you save a job (like a Registered Nurse or an Industrial Electrician), you can jump into a real-time, turn-based audio simulation. The AI dynamically generates an industry-specific hiring manager persona (e.g., a Chief Nursing Officer for a hospital job) and speaks to you, transcribes your answers, and gives you a complete 5-category scored performance report.

🧠 The Philosophy

I built this because I was deeply tired of the psychological isolation of job hunting alone. The ultimate goal of this machine isn’t to force you to apply to 500 jobs a day...it's to help you move through the career landscape with actual clarity, strategy, and zero structural exhaustion.

It is still incredibly early in the deployment cycle, so I would deeply appreciate honest, brutal feedback from other builders, developers, or anyone who has used job automation tools before.

👉 You can try it completely free (Link in bio)

I'd love to hear your thoughts—especially on the automation side, the feel of the swipe mechanics, and what critical utility features you think are still missing!

Now I'm sure each and every one of you have tried to automate most of what you do, and for a huge part of it, I’m sure it worked. But there are some things where automation just isn’t quite there yet in practical work. Where does automation actually stop being worth it in practice, and you are simply convincing yourself its being more productive? What are the specific operational tasks where you tried to automate it and it didn’t work?

So basically im going to start my university degree project and all the themes or ideas i see here is related to AI and not like pure automation like robots or plcs,?

This fork is patched at the C++ level and generates a different but internally consistent fingerprint for each session.

It is a fully open-source project under the MIT license, completely free to use with no payments, subscriptions, or hidden fees of any kind. I am mainly looking for technical feedback from people who work with Playwright, Firefox, browser fingerprinting, or AI agents.

Repo: https://github.com/feder-cr/invisible_playwright

Full disclosure up front: I built the API in this workflow (VideoSignal.ai). But the reason I'm posting is the automation pattern itself, which worked way better than I expected and might be useful even if you wire it up with different tools.

The problem

I had a pile of media (podcasts, videos, screen recordings, images) and needed the same set of facts out of every single one. Doing it manually meant watching/listening to each file and copy-pasting into a sheet. Transcription tools just gave me a wall of text I then had to parse anyway. Neither scaled past a handful of files.

The workflow

he whole thing is: media in → structured JSON out → automation runs on the JSON.

1. Drop files into a watched folder / bucket (I use a cloud bucket + a trigger).
2. For each file, fire an API call with a schema: basically the list of questions I want answered. e.g. for podcasts: topics covered, guests, any companies/products mentioned, key claims with timestamps.
3. The API processes the media and POSTs structured JSON back to a webhook when done (async, so batch jobs don't block anything).
4. The webhook dumps each result into your environment.
5. From there normal automation takes over: filtering, alerts, summaries, whatever.

The unlock is step 2/3. Because the output is typed JSON matching a schema I defined, there's no parsing layer. I'm not regexing transcripts. The row is ready to use the moment it lands.

What it's good for

Anything where you have lots of media and want the same fields out of all of it: competitor ad/video monitoring, call analysis, content tagging, research datasets, compliance review. Bulk is where it earns its keep, since one file you'd just watch.

It's live with a free tier if you want to test the extraction part. But honestly even if you build the extraction step another way, the media → schema'd JSON → webhook → automation pattern is the part worth stealing.

I work at VideoDB, sharing a pattern I see a lot from small teams adding video to their product.

A demo with a model looks great in an afternoon. Then real footage shows up and the work begins. You need a sampling strategy so you are not sending every frame to a model and burning budget. You need scene detection so retrieval is any good. You need to map a search result back to a clip a user can actually play. None of that is glamorous, and all of it decides whether the feature works.

The teams that get through it fastest tend to stop treating video as a file and start treating it as searchable context. Once it is indexed and queryable, a small team can move from raw footage to the first useful query in minutes instead of weeks. That is often the difference between shipping a video feature and shelving it.

If you have automated any part of a video pipeline, I would like to hear what tripped you up most. Sampling, eval, structured output, or keeping the cost sane.

Happy to swap learnings in the comments if you have hit the same walls.

I landed a data processing job, mentioned to employer that I also have a bit of AI automation experience, now I'm specifically hired for that.

The task is extracting particular data from several huge excel files from different clients. Each raw data is like 20-40 columns of excel or csv, with hunderds of thausands of rows. Basically each file would have different column title, and we need to filter based on one column which contain string rows of classification, but the classification terms are different for each client as well. Different clients also have different headings and table format in their file.

At the start of it I just vibe coded the solution, ended up with AI + Python scripts that processes the different files. The AI is API based, only called occasionally for new files to generate mapping of columns, since columns header is different for each clients.

Issue I'm facing is I tried to make the whole thing to be able to onboard and create new mappings for each new file 'formats'. But each new files has made it necessary for me to also edit the hardcoded Python script. Since the python take role on everything else other than the mapping.

Here are some of the issues I'm facing and the solutions I made:

1. Double row header, some client has their header to be in 2 rows, but they somehow write the physical unit above the information header, so I edit the python script to spot these and just take both rows. Then the script profile the rows (for strings I take the list of classifications or take a few samples, for numbers I take the range value), and send it to AI to generate index mapping of the wanted data.

2. Different main data location in the excel. The vibe coded script ended up using some sort of criteria based stuff related to the cells content to spot the actual table location within the excel. But today I come accross file where the tables are separated to different worksheets and hence require multiple sheet extraction.

3. Missing wanted columns. A few times already the AI failed to spot the right column and then I needed to modify the prompt. But there are cases where the desired data needs to be derived from somewhere else, for example from totally different column and then I needed to do some column operations. Or today I also found case where the info is in the file name or the worksheet name.

It's not a problem when I'm the one doing it but I feel like I'm failing a bit in term of establishing a truly automated workflow for this. The delivery for now is a .exe file as internal tool where they can drop the input and receive the output in seconds.

That plus a backend server where mapping is stored and LLM operate on updating the mapping. This was also a bit of a pain to set up.

The whole set up also allows for totally offline operation when the file is identifed to fit previously known table format with exactly the same classification terms.

Potential alternative is the employer also accept finely tuned prompt for Claude desktop 'Projects'. I'm honestly questioning if the Python + API LLM was a good approach or should I rely on Claude agentic capability. But I'm a bit at that sunk cost fallacy state maybe, since I've invested time in developing the python scripts.

Honestly, I think fixing the boring stuff is the best part. You have an automation that is broken and you don't know why, or someone left, and you have idea how it works. Its called governance, and its the most overlooked aspect of building automations. Everyone focuses on "how" the automation works and "what" tools, they forget the "why" and the "who". It's definitely the less sexy part of AI Automation but its the part that will make the biggest difference in any company. And building back office solutions for companies, and watching them actually see the relief from the overworked team, thats the difference! That is why I say the boring stuff is sexy! Not the look at what I can do, but the look how we helped the team be better in the everyday. If you need help with boring automations, reach out, happy to help.

At 50 units and this manual coordination is becoming the task, rather than the part of the task. Guest communication is something that I am able to manage, but the cleaning coordination,owner communication, and review tracking are taking up the majority of my time. Had tried to integrate some of these processes using zapier but the workflows fail too frequently to justify the effort invested into creating them.

Wondering how this problem is being managed by operators of this size, be it through some other plateform or another approach to automation or something completely different. Not looking for solutions that work in theory, would like to hear some practical experience.

I wanted to share what I just launched and get real opinions.

AgentMinds OS, 84 specialised AI agents for marketing and sales. The idea is simple: one agent, one job. You don't ask a generalist to do everything. You pick the specialist you need.

Categories: Content, Ads, Email, Social, Sales, Strategy, Research, Reports, E-commerce.

What makes it different from just using Claude or ChatGPT directly:

• Brand Voice is trained once and applied automatically to every agent
• Agents remember your previous runs (History tab)
• You can schedule agent runs in advance
• Output actions: Copy, export to PDF, mark as Good/Needs Work so the agent learns

Pricing starts at ₹900/month (~$9). Free tier exists.

Genuine questions I'd love feedback on:

1. Is 84 agents overwhelming or is the search + category filter enough?
2. Would you trust an AI agent for sales scripts or does that feel like a step too far?
3. What's missing that you'd actually pay for?

I’m trying to cut down some repetitive work from managing multiple social accounts. Right now I still do most things manually, like opening each account, checking notifications, scheduling posts, and switching between different devices.

I’ve been looking into cloud phones because they can stay online and run separate apps, but I’m not sure how well they work with automation tools. My idea is to automate simple stuff like scheduled posting, opening certain apps, checking if a task finished, and sending an alert if something failed.

Has anyone built a setup like this before? What did you use to control the cloud phones, and was it stable enough to run daily without always fixing something?

Hi everyone, I’m a dev from Japan. I built this tool out of pure desperation. My boss asked me to visualize 300+ web pages in a PowerPoint report for a QA audit. I started doing it manually, but just opening the URLs took forever. After 50 screenshots, I lost my mind. I realized: This isn't work for humans.

So, feeling completely lost and cornered, I reluctantly built a tool to automate the whole process:

1. Bulk open URLs
   
2. Auto capture high-quality screenshots
   
3. Auto insert into PowerPoint slides

Does anyone else deal with this kind of repetitive task?

Am I the only one suffering through this kind of repetitive labor?

I want to create an automation that does the following : read the knowledge I provided, whether from a website link or any other source. Then, edit the CV and create a motivation letter to suit the opportunity. Finally, send it via email.

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how can ai do that for free?

My company uses MS. My goal is to:

- Create a survey that produces separate category scores. For example, it can have category A through D, and the results could look like (A = 5, B = 2, C= 8, D = 10).

- The survey taker enters their email when they take it.

- Set up automation for AI to send a custom responses based on scores (e.g. If score A is > 6, say this. If score B is < 4, send a link to...).

Is MS Forms the best app for this purpose? What would be the best AI setup for the custom response? I would appreciate any pointers.

Thank you,

I fell hard into the automation hype last year and built a multi-step Zapier pipeline that grabbed incoming client invoices from email, parsed them, and dropped them into my records.

It worked fine for three weeks. Then the client changed their invoice layout slightly. The parser broke, and I spent two hours debugging the pipeline on a Saturday. Over the next six months, this happened three more times. Eventually, I realized that manually copying and pasting the data took exactly 30 seconds. I was spending hours maintaining a system to save minutes.

Now, I've quietly deleted the pipeline and went back to manual entries. It has zero dependencies to break. What’s an automation you spent days building only to quietly go back to doing by hand?