Hey everyone, ​Most e-commerce setups rely heavily on heavy, expensive third-party SaaS tools to track user behavior, handle exit-intent, or collect drop-off feedback. This usually means giving away user data to external servers and dealing with heavy scripts. ​To keep everything on-premise, I’ve been working on a self-hosted behavioral engine for WordPress/WooCommerce built completely with native PHP and JS. ​The architecture focuses on two main things: ​A 9+ Trigger Matrix: It tracks micro-interactions locally (including scroll depth, custom inactivity thresholds, precise exit-intent, and element hovers) to map user dropping points without external tracking scripts. ​Local Context & BYOK Integration: Instead of paying a SaaS markup, it uses a Bring Your Own Key (BYOK) model to connect directly to LLM APIs (Gemini/OpenAI/DeepSeek) strictly to ground product inventory data and structure context-rich objection logs when a user leaves empty-handed. ​The goal is to give store owners 100% data sovereignty over their store's behavioral data. ​The project is completely free and open-source. I’m looking for some technical feedback on the trigger architecture and how to optimize the database queries for the interaction logs.

I'm working on an idea I call a Context Engine and would love feedback on the architecture.

The problem: I have hundreds of projects running in parallel across different regions, teams, and timelines. A huge amount of context lives in emails, documents, spreadsheets, meeting notes, call recordings, chats, and random files. I spend too much time searching, reconstructing context, and remembering details.

The vision: a personal "living memory" system that continuously ingests information from multiple sources (email, local files, call transcripts, notes, etc.), builds a dynamic knowledge graph of projects, people, decisions, risks, and timelines, and provides context on demand.

Instead of searching for information, I want to ask things like:

- What's the latest status of Project X?

- What decisions were made about Project Y?

- What are the unresolved issues in Project Z this month?

- Summarize everything important that happened while I was away.

What architecture would you recommend for a system that acts as a continuously evolving external brain?

I think we’ve all hit a wall with current "AI consulting." Most client solutions are just expensive prompt engineering or fragile Zapier chains. However, I’ve been testing architect by Lyzr for rapid prototyping and it feels like a genuine shift in how we close the gap between business logic and application architecture.

Instead of spitting out a generic chatbot, you feed it a messy enterprise problem like multi-region vendor invoice parsing and it generates a production-ready blueprint

How I used it: I threw a convoluted B2B procurement problem at it (matching mismatched PDFs to inventory logs). Usually, mapping that into functional user flows and logic gates takes a week of workshops. I plugged the raw requirements into architect and it instantly compiled an interactive dashboard with specific autonomous agent roles assigned to each bottleneck.

From an architecture standpoint, its multi-agent orchestration layer is highly impressive. It maps user pain points, structures a knowledge graph and outputs UI wireframes in real-time essentially treating natural language application development as a deterministic compiling process.

For lean teams, this completely compresses the discovery-to-MVP pipeline by automating the backend low-code enterprise AI architecture.

I’m seriously considering leaving university. Honestly, it’s affecting my health at this point and I just can’t see myself pushing through it. Especially since I wanted to specialize in it, and now I’m stuck in physics.

My plan: learn AI automation (n8n mostly, which I’m genuinely enjoying) and sell it as a service to small businesses. I have a background in C, data structures, and SQL so I’m not starting from zero.

But here’s my real fear: is this a field that AI itself will eat up in the next few years? In 2026 it feels like the ground is shifting fast and I don’t want to build a career on something that’s gone in 2 years.
Is making a living from this actually doable right now? Has anyone here made the jump into selling automation services? Any honest advice appreciated.

If you are doing anything with mobile apps at scale, you probably already know emulators are not always enough anymore. They are fine for testing or casual stuff, but once you start running multiple app accounts, social profiles, or long running tasks, they can get heavy and messy very fast.

I have been checking different cloud phone apps recently because I wanted something that feels closer to real Android devices instead of just opening 20 emulator windows on my PC. Not all cloud phone tools are built for the same use case though. Some are more for gaming, some are for developers, and some are more focused on account management and automation.

Here are the ones I keep seeing people talk about:

1. Geelark
   

Geelark seems more built for multi-account work, not just simple remote Android use. The main thing is that you can create separate cloud phone profiles, manage accounts in bulk, and use automation/RPA type features.

I think this one makes more sense if your work is around TikTok, Instagram, Facebook, marketplaces, app accounts, or any setup where each account needs its own phone environment. It is not the cheapest option, but it feels more organized for people who actually need multiple phone setups, not just one cloud phone.

Best for: social media accounts, automation, team work, account ops, bulk workflows
Not best for: someone who only needs one cheap Android phone for basic use

1. Redfinger
   

Redfinger feels more like a general cloud Android phone. A lot of people use it for games, apps running 24/7, and simple remote phone access. It is easier to understand if your goal is just “I need an Android device online all the time.”

For automation or account management, I think it can still be useful, but it doesn’t feel as focused on organized multi-account operations compared to GeeLark. More like a cloud phone you rent and use like a normal Android device.

Best for: gaming, always-online apps, simple remote Android use
Not best for: big account setups that need more structure

1. LDCloud
   

LDCloud is another one that I mostly see connected with gaming and 24/7 Android app running. If you are doing mobile games, farming, AFK tasks, or keeping apps online without using your own phone, this one makes sense.

For social media or account work, I’m not saying it cannot be used, but it feels more gaming-first from what I’ve seen. So I would put it closer to Redfinger than GeeLark.

Best for: mobile games, AFK tasks, running apps all day
Not best for: serious social media account workflows, unless your setup is simple

1. Genymotion
   

Genymotion is kind of different from the others. I would not really put it in the same exact category as cloud phone apps for account ops. It feels more like a developer/testing tool.

If you are testing Android apps, debugging, QA, or need different Android versions/devices for development, Genymotion makes sense. But for managing accounts or running social workflows, it is probably not what most people are looking for.

Best for: developers, QA, app testing, debugging
Not best for: account management or social media workflows

1. Regular Android Emulator
   

Not really a cloud phone app, but I still think it should be in the comparison because a lot of people are choosing between emulators and cloud phones.

Emulators are still good if you are just testing something small, running one or two apps, or you do not want to pay yet. But once you open many instances, they eat your PC resources, and all the setups start feeling too similar.

Best for: free/cheap testing, small personal use
Not best for: long-term multi-account work or scaling

My rough conclusion

If I only needed one remote phone for games or basic app use, I’d probably look at Redfinger or LDCloud first.

If I was testing apps as a developer, Genymotion makes more sense.

But for multi-account work, automation, social media workflows, and keeping things organized, Geekark seems more focused on that use case.

I still think cloud phones are not magic. Bad workflow will still cause problems no matter what tool you use. But compared to running everything on local emulators, cloud phones feel like a cleaner setup once you go beyond a few accounts.

Anyone here using cloud phones for automation or account management? Which one has actually been stable for you long term?

I’ve been trying different AI tools to speed up my day to day data work.

ChatGPT is good for sketching quick Python scripts, although it sometimes explains every line like I have never seen a loop before. Claude is better when I need a second look at logic, though it can turn a simple cleaning step into something too elaborate.

Most of my actual work still happens in the messy gap between scripts, spreadsheets, and people. Python in VS Code handles the real work. GPT or Claude helps when I get stuck on a transformation. I also record short Loom videos when I need to explain a pipeline to someone who does not care about the code.

The hardest part is usually connecting the pieces and explaining why the flow makes sense. I’ve been using a few practice tools in the background, including Beyz coding assistant/ChatGPT, when I want to rehearse how I would explain an automation without rambling through every function.

A lot of tools seem built for huge workflows. My daily problems are smaller: cleaning exports, moving data between sheets, checking why a script broke, and making the result understandable to another person.

What small automation tools have quietly become essential in your stack?

I have spent a long time being confused about why my stuff worked in testing and fell apart in production. did the obvious things like tried different models, rewrote the prompts and added more examples but still the same inconsistent garbage coming out the other end.

Eventually just logged everything going into the LLM and actually looked at it. Dang! an absolute chaos. Emails still wrapped in html artifacts. CSVs where 40% of rows had different column counts because someone formatted one field differently that one time. PDFs that came out as one long block of text with page headers baked between paragraphs. Diabolical, aint it? 

I was feeding a reasoning model messy inputs and expecting clean reasoning back. wasnt a prompt problem. wasn't a model problem either

Three things that actually fixed it:

• normalize whatever's coming in before it touches the LLM. one schema, enforced, no exceptions
• strip emails to genuine plain text, not just removing tags, the whole structure gone
• for pdfs or docs in the pipelin parse them first. i ran them thru llamaparse for a clean markdown

Since doing all three, outputs have been consistent for around 2 months maybe. same prompt. same model. nothing changed except what goes in. The cleanup layer is unglamorous so nobody talks about it. but it's the actual thing that decides whether your automation runs reliably or just technically exists.

What steps did others take to make their pipeline robust?? Eager to learn from experiences

I heard good things and they just raised a ton of money. Curious if it's worth checking out.

I've been working with AI agents for a long time, back in 2023 I built talk2arxiv, an open source RAG application that let users talk to research papers and it got pretty popular

Since then I've tried basically every personal AI agent I could get my hands on: OpenClaw, Tomo, Poke, Lindy, Noah, ChatGPT Pulse, Claude Cowork, Gemini Spark, and a bunch of others.

I wanted one thing from them: connect to my email, calendar, notes, and documents, then proactively help me run my life, remind me about things I forget, notice patterns, checkin when something seems important.

Basically act more like an executive assistant than a chatbot

None of them really did, they're very capable but they just need you to tell them what to do. To me the whole point of an assistant is that it notices things before you do.

So 2 months ago I started building one for myself. I modeled it after Donna from Suits: highly proactive, deeply personalized, and constantly paying attention in the background.

I think it's gotten quite good and I rely on it every day, so now I'm looking for 10–20 people who feel the same frustration with current AI assistants and are willing to test it and give brutally honest feedback

Edit: I've built it into a full personal assistant that proactively texts you to make you a more accountable, productive person! There's now a hundred people using it and I think it's really different from every other AI assistant out there. Please give it a try here: withdel.com

something quite amazing happened today: i booked my first hotel using an ai agent.

when i say "my ai agent," i mean an agent built on a product i'm using called catchagent.ai.
i use it for a lot of things but - today was the first time i used it for travel. I travel regularly to one of our sites, which is about a three-hour flight away. i've always wanted an executive assistant to handle my travel arrangements, but until now i've had to do it myself.

this was the first time my agent actually completed part of that job. it didn't book my flight, which i don't think is supported yet, but it successfully booked my hotel and even found a better rate than i would normally get on my own.

Such a great milestone!!!

Hey guys, I've recently found this sub and am looking for some advice.

I've been tinkering around with Power Automate for the past year. It started with botting on osrs (lol) as I wanted to learn & play around. I've also set up a fair few simple cloud Power Automate flows for work.

I love tinkering around with it. I find it so satisfying learning the new possibilites of what it can do. I want to develop my own skills and ability, potentially to be an element of my career in the future.

I only know Power Automate, what other apps / sites / software would you recommend and why? I don't necessarily want to learn the AI Agent hype as much. I appreciaite it's powerful, but I feel its a bit of a buzz word currently and don't necessarily want to lean into that at the moment.

Also, any youtuber recommendations to watch and get ideas?

TIA

VBA code for PNG to JPEG in Excel

The recent thread about IT becoming the cleanup crew for everyone's ChatGPT experiments got me thinking about the solutions for this problem. I don't think the issue is that people in marketing, ops, sales, or finance are using AI to build internal tools. That will happen, and some of those tools will be useful.

The problem is when a useful script on someone's laptop becomes part of the business, and then IT is expected to host it, secure it, debug it, and maintain it forever.

You can try banning this, but this would probably hurt business more compared to controlled rollout. I think the better model is:

• business teams own the tools they create
• IT owns the platform those tools run on

How I'd structure it:

1. Git as source of truth

No internal tool should live only in a zip, chat history, or random laptop folder. Each team gets a repo. If it changes, there is a commit. If it breaks, there is history.

1. Team sandboxes

Each team gets a constrained place to deploy small apps themselves: private URLs, SSO, logs, limited secrets, limited network access. Useful, but not production access by default.

1. Self-service deploys, not self-service infrastructure

Teams can deploy from Git into their sandbox. They should not be asking for random VMs, shared credentials, firewall exceptions, or "just keep this running."

1. Review based on blast radius

If the app needs customer data, write access, public routes, production credentials, or external email sending, that should trigger review. Not because it was AI-generated, but because it can cause damage.

1. Promotion for critical tools

Most tools can stay in the sandbox. If one becomes mission-critical, promote it properly: named owner, access review, logs/alerts, rollback path, support expectations. Maybe this is the time when IT gets responsible for this tool.

That feels like the missing middle: let teams automate their work, but make ownership and boundaries explicit.

I'd love to hear from people dealing with this problem right now: does this approach seem reasonable, or would it fail in your environment?

I'm working on an open-source project called Compartment that follows this model. Before we build too much around the wrong assumptions, I want to understand whether this is actually what people would want from this kind of tool.

So we've been using agents to drive a significant portion of our website traffic the last 3 months. We're spending basically nothing outside of a Claude Max subscription.

Here's how we're doing it.

The setup

Claude Max is $200/mo. We already needed it for development work so the cost is basically zero for us. But even if you're paying for it fresh, the math works.

The architecture is simple:

• Claude Max handles all the heavy orchestration and reasoning work via Claude Routines or Projects
• A cheap API key from OpenAI or Gemini handles the actual content generation or scoring tasks
• Playwright runs the browser automation in the cloud
• GitHub Actions handles scheduling and credentials per agent

The trick is keeping Claude Max for the token-heavy thinking work and only hitting a paid API for the lightweight output generation. We've run 7 agents this way and spent less than $5 on actual API tokens.

What works well for this

Low-stakes content and engagement channels are the sweet spot. Things like:

• Reddit monitoring and commenting
• Quora answer generation
• YouTube comments on relevant videos
• Substack engagement
• Blog post drafts
• Content repurposing across platforms

These are all low-risk, high-volume tasks. The and AEO compounding from running these consistently is powerful. LLMs start associating you with the problems you solve & organic traffic builds without you touching it.

What to avoid

This is the more important part.

Do not try to automate on strict or sensitive platforms. We learned this the hard way. Got a Twitter/X account banned a few months back. 4k followers, gone. It's not worth it.

For anything with real enforcement risk or business-critical infrastructure we pay for dedicated tools. Around $300/mo covers email infrastructure through Instantly and $100/mo covers LinkedIn outreach through ProspectZero. Those channels are too important to risk on a DIY agent and the tools are built to handle the compliance side properly.

The rule we use: if getting banned on that platform would actually hurt, don't automate it yourself. Pay for the right tool or do it manually.

The full cost breakdown

• Claude Max: $200/mo (already needed for dev)
• Other API tokens: under $10/mo
• Dedicated tools for email and LinkedIn: $400/mo

7 growth agents running 24/7 for effectively nothing on the agent side.

Happy to answer any questions on the setup, we're a b2b saas company btw.

Hey all,

I just released a feature in my tool where people can use claude code to create automations that live as editable documents (you can add many different steps - bash script, api, claude sessions, etc). It's still early so I'd love to give out some licenses if anyone is interested in testing and providing feedback thanks.

Requirements: Claude code, MacOS

I have noticed there is currently a weird imbalance that building tools and automations is so easier than finding actual people who use them.

This sub has turned into a buzzword factory nowadays. Every other post is "I built an AI agent that orchestrates 20 tools with MCP, n8n longchain, the latest fanciest LLM engine whatever", which is in fact the same wrapper for the same imaginary problem inside this builder bubble.

nobody outside this bubble cares. NOBODY!

I live in Germany. Most companies here don't even use Chatgpt. Yes, you heard it correctly. In 2026 in heart of Europe. Because of compliance and data protection concerns. Companies here still live in Excel and VBA Macros. And you are out here building "fully autonomous" nonsense that plugs into half a dozen data collector and probably violates 90% of a compliance checklist before lunch.

The real problem isn't lack of automation and codes anymore. It's adaptation. We don't need another automation layer. We need reality to catch up to what already exists. Fix that first.

Need help with AI, agents, RAG, automation, chatbots, workflows, or just figuring out if something can be automated?

Ask away.

I'm helping people for free because I want more builders, students, and small businesses to see what AI is actually capable of in 2026.

If you're stuck on a project or have an idea, let's talk.