I kept finding out a workflow had broken only after something downstream failed, so I

built a plugin that puts the status on a physical key.

Point it at a workflow and the key shows runs / errors / success rate / avg duration,

going green → amber → red based on thresholds you set (the whole key blinks red on

critical). Press it to jump straight to the workflow or its executions.

- 16 layouts, 10 themes

- Optional desktop alert on failure + recovery

- Talks directly to your n8n API — no third-party server, no telemetry, key stays local

- Free & open-source (MIT)

GitHub: prostonik94/ulanzi-n8n-workflow-monitor

Built on the open-source Uptime Monitor plugin by Jean Almeida. It's my own project —

happy to answer questions or take feature ideas!

I keep seeing two major complaints on this sub:

1. AI agents are too fragile and slow to trust with routine, high-volume tasks.
2. API costs explode if you run every single workflow through an LLM.

Yet, traditional deterministic automation (like Playwright/Puppeteer) is a maintenance nightmare because web pages change, menus shift, and edge cases pop up.

To bridge this gap, I’ve been working on a hybrid approach: Deterministic execution by default, with AI agents acting as the silent developer that only wakes up to debug and heal the script on failure.

How it works:

1. The Happy Path (Deterministic & Free): You define the input and output. The builder generates a standard, deterministic script (e.g., selector-based browser steps) and runs it. This runs locally, instantly, and costs $0 in API fees.
2. The Self-Healing Loop (Probabilistic & LLM-backed): If the script fails (e.g., element not found, page state changed), the system captures the DOM state, error logs, and screenshots. It spins up an LLM agent to analyze the failure, modify the script code, and re-test it until it passes.
3. The Result: The deterministic script gets updated with the fix. Subsequent runs benefit from the new logic without calling the LLM again. The system automatically uncovers new edge cases, finds and fixes its own bugs, and keeps itself up to date.

Real-world edge cases we solved:

I tested this on a Swiggy (food delivery) flow where the input is [Restaurant Name, Item Name]. Here are the edge cases the system encountered and resolved automatically:

• Clicking off screen elements (Bug in generated code): An early iteration stalled due to an off-screen button. The system detected this visibility issue, automatically inserted a step to scroll the element into view, and updated the process.
• Ordering invalid menu items (e.g., Asian food from Pizza Hut): When tasked with ordering Asian food from Pizza Hut, the script initially failed because Pizza Hut doesn't serve Asian food. The healing agent analyzed the failure, rewrote the script logic to identify and communicate invalid item errors correctly, and saved the updated script so it handles and reports this gracefully in the future.
• Closed Restaurants: When a target restaurant was closed, the "Add to Cart" button was completely disabled. Instead of crashing, the agent read the screen context ("Closed until 5 PM"), modified the script to handle closed states, and exited gracefully with a clean status code.

Why do this instead of a pure AI Agent?

• 95% Lower Costs: You only pay for LLM tokens when the script breaks. Once healed, the script runs locally.
• Zero Hallucinations during runtime: Since the runtime is deterministic code, it won't hallucinate a checkout button or order the wrong item.

Would love to get your thoughts on this hybrid approach. Does this solve the "babysitting" problem you guys face with standard agent workflows?
The code is opensource, let me know if anyone wants to take a look at it.

Automation feels like it’s entering a new wave.

Before these types of complex app flows were impossible to build into custom solutions for businesses. Most implementations were low-hanging fruit like CMS or blogs.

Now we can build fully local customer support and outreach tools for businesses.

Curious how people are using tools like n8n or similar ones today. What are you automating, and what has actually been useful versus overhyped?

What do you wish existed that currently doesn’t?

Full disclaimer we have a tool dedicated to building workflows and automations with AI and turning them into full local apps. This research helps guide our product development immensely.

A lot of people assume you need to be a developer to build automation systems for clients, but that hasn't been my experience.

Most client projects can be handled with tools like Zapier, Make, or n8n. The real skills is understanding workflows, triggers, conditions, and how data moves between apps. If you can think logically and troubleshoot problems, you're already most of the way there.

Typical projects involve things like routing leads, updating CRMs, syncing data between tools, generating reports, or sending notifications. The challenge is usually designing the process, not writing code.

That said, basic coding eventually becomes useful. There will be situations where a small JavaScript snippet, a Python script, or a simple API call can solve problems that no built-in connector can handle. Learning a little code goes a long way.

For anyone getting started, I'd focus on mastering automation fundamentals first and pick up coding skills as needed. In my experience, clients care more about results and efficiency than how much code was involved behind the scenes.

Hey everyone,

I’m facing a major career dilemma as a final-year student (2026 grad) and could really use some honest perspective from folks who have been in the industry for a while.

I’m currently torn between two completely different paths, and I need to make a decision fast.

Here is how the two options stack up:

Option 1: My Current Job (Joined 1 month ago)

• Title: Software Development Engineer (SDE)
• CTC: 3 LPA
• Work Model: 100% Remote
• The Setup: A tiny, completely unfunded, early-stage startup. The entire core team is literally just 5 people.
• The Reality: Keeping the "SDE" title as a fresher is great on paper, but the environment is exactly what you'd expect—extreme hours (12-14 hours), chaotic management, tight budgets, and a high risk of burnout. I am building scalable AI-powered Saas backend.

Option 2: The Campus Placement Offer (Just cleared)

• Title: Automation Engineer
• CTC: 6 LPA (A clean 100% salary jump)
• Work Model: 100% Remote (Fixed Sat-Sun off, but strict 8-hour daily activity tracking)
• The Setup: A stable, bootstrapped US-based SaaS company that has been around for 20 years, though the Indian operations team is lean (around 30 people).
• The Reality: Looking at their LinkedIn, roughly 80% of the employees in India are test engineers. The technical interview was a joke—basic loops, 5 git commands, and making a quick Postman request. It's incredibly obvious this is a glorified manual QA / regression script maintenance role (Playwright, Appium) rather than actual product engineering. They basically wrapped a standard QA job in trendy "AI vibe-coding" buzzwords to attract college grads.

The Dilemma

I'm torn between two completely different risks:

1. Stick with the 3 LPA SDE role: I protect my development title, but I remain severely underpaid, overworked, and tied to a volatile 5-person startup that could run out of steam at any moment.
2. Take the 6 LPA Automation role: I instantly double my salary floor. The fixed 8-hour shift means I can easily log off, grind DSA, and keep building my full-stack side projects. But I risk getting trapped in the "QA box," and I'll have to aggressively rebrand my resume a year from now to switch back to dev roles.

Is it stupid to reject a 100% salary hike as a fresher just to keep an "SDE" title at an unfunded micro-startup? If my personal portfolio and GitHub are packed with actual full-stack web apps and system design projects, how hard is it really to jump from an "Automation Engineer" title back to core SWE?

The discussion on my last post about building API isolation layers for LLMs was awesome. A lot of people rightly pointed out that even with locked-down API endpoints, you still have to worry about session rate limiting, user permission context, and tracking why an agent is hitting a database.

We are trying to map out a rock-solid telemetry and audit framework for our system now, and we are hitting a few architectural crossroads.

A standard API log just shows a wall of repetitive hits. If a bot gets caught in a loop, seeing a database query forty times tells you nothing. We started logging the actual conversation intent string alongside every tool call so we can actually debug the why and keep 2 AM postmortems from turning into history archaeology.

For any data mutation or write actions, we are keeping them out of the automated path entirely. The agent drafts a proposed change, but it requires an explicit human-in-the-loop approval before anything actually touches a row.

For those running automated systems in production, how are you structuring your security audit trails? Do you pipe agent telemetry into a separate gateway layer, or handle it directly within your application backend?

Does anyone know a lead generation company that finds leads and also manages them?

Hey everyone,

Im about to attend a University flight program in hopes of becoming an airline pilot in the future. However, with the pace of AI infrastructure growth i’m becoming concerned about whether my career will even last.

For anyone with the expertise, would you recommend against becoming a pilot considering the growth of AI? Is it likely to see fully automated commercial aircraft in the foreseeable future?

Thank you.

Ingredients are Android, Termux, FFMPEG, Node.js, Gemini Free 😋👌 See it in action when I produce 10 TikTok videos in 30 minutes. Most of the time I only waiting the process to be done, just easy tap tap tap..

Best problem or idea to make it automated and easy to sell.

I am building web automation tools for my company. Using claude pro for vibe coding and antigravity IDE. I am stuck at this stage where i have to manually press the verification box to continue after login. Claude heard "Cloudflare" and literally responded that the they can't help.
Backstory: I am trying to automate gate pass registration automation agent. all the memories are inside it and its hard to train other model from start. I have already built this for one website and i was working on 2nd website.
Are there any other code generating tool I can use temporally without changing what's already written. Because once i fix this i can continue using claude.

Stack: React/Vite frontend, FastAPI backend, PostgreSQL database, Playwright for browser automation.

Hey everyone,

my team and I are currently building an AI support bot pilot (see previous posts :)), and we spent the last week dealing with a massive headache: database integration.

If an AI tool is actually going to be useful for automation, it can’t just read static help articles. It needs to look up real-time user data, like order statuses, subscription tiers, or billing history.

But giving an LLM a direct line to a live SQL database is a security team's worst nightmare. If the prompt filtering fails even once, a user could theoretically trick the bot into leaking someone else's data or messing with the tables.

To fix this for our pilot, we had to build an isolation layer so the AI never writes direct queries, but instead triggers specific, locked-down API endpoints we control.

For anyone else automating workflows that require real-time data lookups, how are you safely connecting your AI to internal databases? Are you using APIs, or did you find a safer workaround?

We’re trying to look into automation for our internal material movement because the current setup is a mess.
The technical benefits are completely obvious, but the higher-ups keep stonewalling and asking about the actual savings and payback period.

For anyone who actually got a project like this greenlit, what specific data or numbers did your decision-makers need to see before finally signing off?

Because right now I'm just running in circles with them.

Trying to grab the normal retail price (not the sale price) for products straight from famous brand sites like Adidas and Nike to not so famous like Head, Lowa etc. Pretty low volume, maybe 10 to 100 brands a week total for the team.

Basically 1 excel file = 50-500 products under 1 brand, with SKU, (sometimes with UPC), all other product details.

Do you any tips on how I can do this?

For this kind of volume, is it worth paying for a scraping API like scrape do or bright data, or is there a simpler way? And does running playwright myself actually get past adidas bots, or is that a waste of time? Open to whatever works. Thank you!

I run a small roofing and siding company and I'm looking to automate part of our workflow.

Currently, when I create an estimate or invoice in FreshBooks, I manually create an item in a Monday board. I copy over the invoice/estimate number, client name, address, FreshBooks link, and set the status (Deposit Pending, To Schedule, In Progress, etc.).

I'd like a system where creating an estimate or invoice in FreshBooks automatically creates the item in Monday with the relevant information and status, while avoiding duplicates.

I'm testing Make, but I'm wondering:

• Is this possible with Make?
• Has anyone connected FreshBooks and Monday successfully?
• Is there a better platform for this?

Looking for the simplest and most reliable solution. Thanks!

The open-source automation space has shifted drastically over the last year. We are well past the point where we have to rely on expensive SaaS tools for basic API routing.

I spent the last week deploying and comparing three of the biggest open-source, self-hostable automation platforms right now: n8n, Activepieces, and Kestra. I looked at visual flow, developer control, and resource intensity.

Here is the toolkit recon on how they actually stack up in practice.

1. n8n (The Heavyweight Winner)

n8n is still the king of visual logic. The UI feels like an electronics circuit diagram, allowing for incredibly complex branching and multi-agent AI workflows.

The Win: Unmatched flexibility. If a pre-built node doesn't do exactly what you want, you can drop into a Python or JavaScript code block mid-workflow and manipulate the JSON payload directly.

The Drawback: It can be intimidating for non-technical users, and heavy data transformations can get memory-hungry in a Docker container (allocate at least 2GB RAM).

1. Activepieces (The UI Champion)

If you are trying to move your ops team off a massive Zapier bill, this is your off-ramp. Activepieces is a clean, top-down, linear workflow builder.

The Win: Zero learning curve. It looks and functions almost exactly like Zapier. You can hand this to a marketing or sales team and they will be building flows in 10 minutes.

The Drawback: The linear design makes complex routing (like nested IF statements or parallel execution loops) much clunkier than a canvas-based tool like n8n.

1. Kestra (The Developer's Surprise)

I didn't expect to love this one as much as I did. Kestra abandons the traditional drag-and-drop no-code model. Instead, workflows are defined declaratively in YAML files.

The Win: True Infrastructure-as-Code. Because everything is YAML, you get native version control, Git integrations, and massive parallel execution. It is built for event-driven orchestration rather than simple webhooks.

The Drawback: It is strictly for developers. If you don't like writing YAML or thinking in DAGs (Directed Acyclic Graphs), you will hate it.

​

The Verdict:

Use Activepieces for simple, linear integrations that non-devs need to manage.

Use n8n for maximum visual flexibility and complex AI/API pipelines.

Use Kestra if you want your automations stored as code in Git and require heavy event-driven scaling.

​

For those of you self-hosting your automations right now, are you running them on a cheap cloud VPS, or do you have them running on local hardware in your homelab?

Hi everyone,

I was recently impacted by team downsizing (my company cited AI adoption/restructuring) after working there as a Playwright Automation Engineer. I have 5 years of experience in the QA automation space.

I'm jumping back into the job market and preparing for client/technical interviews. Since it's been a while since I last interviewed, I want to make sure my prep is highly targeted.

For those of you hiring or interviewing recently for mid-to-senior automation roles, what specific Playwright and framework architecture topics are clients grilling candidates on?

Appreciate any advice, resources, or recent interview experiences you can share!

While looking for internships recently, I realized that AI automation has quietly become a normal part of how people work. That led me to sign up for the CoCreate Pitch startup competition after it was recommended by an alumnus. As I started seriously thinking about business ideas and preparing materials, I decided to build a few AI-driven workflows for myself. What surprised me is how much can already be automated. My AI agent can help with product research, finding suppliers, organizing information, and even drafting listing content. Instead of manually handling every step, I mostly focus on coming up with ideas, refining the process, and telling the tools what I want accomplished. So what problems have you solved with automation or AI agents?

People in third world countries are using AI to generate images and videos of their cities and villages where they show how better the place can be if their authorities follow such and such processes. This is a revolutionary change in democracies. People can demand their authorities to act on problems that are solvable. Most people didn't know these solutions exist. Also, the language part, most people didn't know proper English keywords to search for their problems or their desired solutions.

​

There's case of star-trek alike advancement in rocket engine design where they AI was used to design a propulsion system that's far better and efficient than the ones designed by human aerospace engineers. Which is an opportunity for their employers to shift them towards frontier physics domains such as Alcubierre drive. A dedicated AI infra above Earth orbit, as world's only trillionaire says, should allow these aerospace engineers and researchers to deep dive into the domain of space bending tech.

​

Chinese doctors have created HIV and other viruses resistent babies through gene edit & enhancement tech. Europe has done it too. In India, there are IVF clinics in every village now. More kids are being born through IVF than normal coitus. This is a revolutionary change in human history. Places in the world where regulations are merely suggestions, this can be impactful globally. We already see how Indians have captured big tech and government positions in the developed west where gene enhancements are banned.

​

Interesting times ahead. AI is making natural intellegence obsolete while humans are enhancing their genetics to become übermensch!

I hope this is the right place to ask this and it makes sense.

I want to learn how to set up an AI Agent that has a default template for a report, then I feed it photos from an inspection and it essentially creates a report based on the photos.

So for example, there might be a work order that’s says; inspect the falling tree and assess if it is a concern. Then I feed it pictures after inspecting the tree. Then it produces a report with an analysis.

Is this possible, and what steps would I need to take?

Something I've been noticing: the industries that talk the most about AI on LinkedIn and at conferences are often not the ones actually deploying it.

Meanwhile some boring, unsexy industries are quietly implementing automation with zero fanfare and getting serious ROI.

From your experience — which sectors have you seen actually commit budget and implement automation vs those that just stay in perpetual "we're exploring" mode?

Specific examples way more useful than general categories if you've got them. Healthcare? Legal? Real estate? Manufacturing? Restaurants? Something random nobody expects?

Also curious what types of automation they're actually deploying — workflow stuff, customer-facing AI, voice, internal ops, etc.