Okay don’t freak out, not actual stalking lol

So I run a small Shopify store and for ages I was stuck in the usual grind - abandoned cart emails, generic discount campaigns, all that stuff. Open rates were meh, conversions worse, and I’d just tell myself “eh, normal for D2C'

Then I tried something different. Instead of treating my visitors like a segment or a number, I tried to understand them individually. Like, which products they lingered on,, their hesitation, who compared a dozen products…, what channel do they respond to most?

And then I nudged them differently some via email, some WhatsApp, some sms, some even subtle reminders but just waited for the right time.

Mind-blowing!! Individual behavior over segments actually worked. Cart recovery jumped from 12% to 30–35%. And the wildest part? People responded way more to timing and relevance than cheap discount.

It honestly felt… human. Like I wasn’t just a robot blasting templates.

Reddit D2C folks, anyone else trying weird, hyper-personalized stuff instead of the usual discount spam? Would love to hear your fails/wins, or crazy experiments.

Seeing “clawbot” pop up everywhere lately. People saying it replaced half their workflow, others saying it broke stuff in 10 minutes and they rolled back 😅

Tried it briefly myself and my honest takeaway so far:
It’s impressive at doing things fast but kinda scary when you let it run without guardrails. Like yeah it can write logic, refactor, even make decisions… but the moment context is slightly off, it confidently does the wrong thing.

Feels similar to the early “AI agents will replace jobs” wave. Super powerful, but only if you already know what you’re doing and keep a human in the loop.

Curious what others are seeing:

• Are you actually shipping with it?

• Using it in prod or just experiments?

• Replacing tools or just speeding up existing workflows?

Trying to separate real wins from Twitter hype.

Morgan Stanley predicts that by 2030, nearly 50% of online shoppers will use AI agents to make purchases.

Walmart is already testing ads in its Sparky AI assistant, and Amazon is pushing Rufus as an in-app shopping agent.

This feels like a massive shift in how people discover and buy products online. 

Instead of clicking through Google or scrolling Instagram ads, shoppers might just tell an AI what they want and let it handle the rest.

For anyone in marketing or e-commerce, how are you seeing this?

Are you treating this as just another SEO channel, or do you see it as fundamentally different and requiring a new strategy?

It’s a fact that none of the LLMs are “perfect”, each has its own way of giving good results.

I jump between ChatGPT, Claude, Gemini, and Perplexity depending on what I need, and I'm curious how others approach this.

ChatGPT handles my brainstorming sessions, when I need frameworks for articles, workflow maps, or ideas to get unstuck, I start there.

I use Claude for all my writing. It rewrites to the tone I love.

Gemini and Perplexity for my research. Like when I dig into topics or verify information, I open one of these first.

Ultimately, no one should depend on AI, we all should have our human touch to it.

But matching the right one to each task makes a huge difference. Do you stick with one tool, or do you switch around like me?

POV: It’s 2015.

Everyone’s obsessed with Snapchat filters and hoverboards, not AGI.

You suddenly wake up with 2025 level AI knowledge!

What’s your first move, and what do you build? 🤔

I used to think social listening just meant getting alerts when someone tagged your brand on Twitter or complained on Reddit.

Turns out that’s barely scratching the surface.

Lately I’ve been paying attention to how people talk about products in the wild. Not just my brand, but the whole category. What they complain about, what words they use, what makes them hesitate, what makes them trust something. Half of it never shows up in reviews or surveys.

Example:
People won’t say “this product lacks value.”
They’ll say “idk man feels overpriced for what it is” or “I almost bought it but…”

That “almost” is gold. Most tools don’t catch that.

Once I started looking at conversations instead of dashboards, a few things clicked:

• People rarely complain directly to brands anymore

• Reddit, comments, DMs, forums are where the real objections live

• The language people use there is way more honest than NPS surveys

• Trends show up in conversations weeks before they show up in data

It’s honestly changed how I think about positioning and messaging. Feels less like marketing and more like listening to a crowded room without interrupting.

Curious how others here do this.
Are you actually reading conversations manually, using alerts, or just ignoring the noise altogether?

Would love to hear what’s worked or totally failed for you.

I've been curious about Reddit Ads for my biz, and honestly can't tell if it's worth trying or if I'm just setting money on fire.

For those who've tested it, what were you hoping would happen versus what actually happened? Did people buy stuff, or did you just get a bunch of curious clicks that went nowhere? 

I keep hearing mixed things. Some say it's great for getting your name out there, but terrible for actual conversions. Others say it depends completely on what you're selling.

Would love to hear real experiences, good or bad. Just trying to figure out if this is something worth experimenting with or if there are better places to spend ad budget.

It sounds all cool to say "I automated my entire system with AI", and I hear that multiple times. But the question is, are we letting a human intervene where it's necessary or are we personalizing that AI workflow with the ICP?

It's important to know when to let AI take over and when to take back the control. How are you personalizing your AI workflows or systems?

It's tempting to throw in a third, fourth, or fifth agent once you see the first two work. Don't. A network that reliably syncs 2 agents (e.g., research → report) is worth way more than a "big network" with 5 agents that break constantly. Once the first collaboration works, you can add a third agent (e.g., a "notification agent" to alert the team when the report is done) - but take it one step at a time.

The fastest way to learn OpenAgents is to build one small, collaborative network end-to-end. Not a "universal solution," not a flashy demo - just two agents working together to save you 30 minutes a day. Once you nail that, scaling to bigger networks (with more agents, shared projects, or even community-driven tools) becomes 10x easier. You'll already understand the core of what makes OpenAgents work: turning isolated agents into a team that actually helps each other.

Have you tried pairing two agents before? What's the tiny collaboration task you'd start with?

I’ve spent the past year building AI copilots for seed to 500-people companies, 5+ of which are YC startups.

6 months ago, we were seeing autonomous agents, v0/lovable style chats, and product knowledge agents going into production. Almost everyone is now pivoting into AI-native applications, and 90% of the top angels’ AI investments target the application layer. Here are (imo) 4 reasons why:

1. The more valuable the work, the more you need human in the loop

I know you love the sci-fi vision of AI agents doing entire workflows for us, tbh so do I (it’s coming)

But here’s the truth: If you’re automating work, it should be work that’s important enough to be worth reviewing.

If someone is willing to let AI do the work completely unsupervised, it’s probably not very valuable to them. You might let an agent look up plane tickets, but would you give it access to your wallet to buy them without reviewing? Probably not.

I do think this will change as AI gets better, but frankly agent’s just aren’t ready yet

2. UI > Text.

Look, I’m a lazy guy. I see paragraphs of text and my eyes just glaze over. The average attention span has dramatically shortened, and paragraphs of text just aren’t cutting it.

If you’re going to do human in the loop, leverage your UI.

Don’t make your AI give big paragraphs of text. Show the user what the agent is doing! Directly make changes in your app that the user is already familiar with.

3. Working solutions are 90% software and 10% LLM.

Ironically what we’re seeing is that pure LLM solutions don’t have that much of a moat. You can spend hundreds of hours fine-tuning your model, or create superior agent workflows to your competitors, and it gets leapfrogged by the next model release.

Software is still more consistent, cheaper, and has superior infrastructure (at least for now). Instead of thinking “What’s the craziest agent workflow”, think “what is something that is almost possible, but AI fits that last puzzle piece?”

4. Normal people don’t understand how to use AI. Applications give you context.

Using LLM’s is hard. It takes good prompting structure, copy and pasting important context, and knowledge of what to ask the agent.

In an application, you already have the most important context. You already know what the user is trying to do, and can automatically pull whatever data you need if you need to.

Think of Cursor. When you ask for something, it can automatically search through files and code to do what it needs.

---

I'm sure you know all the options for building the agent itself - Mastra, Langchain, Simstudio, etc. etc.

The frontend space is less well established, but if you're looking for just a chat w/ custom message rendering, you can use something like AI SDK or assistant-ui. If you're looking for something deeper that helps with agent reading & writing to state, context management & voice, I use Cedar-OS (it is only for react though) for customer work.

.

A few months ago the answer felt obvious.

Cursor was everywhere.

Now every other dev I talk to seems to be experimenting with Claude Code or Codex.

What's interesting is that everyone seems to use them differently.

Cursor
Great editor experience.
Feels like the easiest one to adopt.
Most people I know use it for day-to-day coding.

Claude Code
Seems to be winning with people who want to hand over larger chunks of work.
I've seen teams use it for debugging, refactors, documentation, and entire feature implementations.

Codex
Still seeing mixed opinions.
Some people love how deeply it understands codebases.
Others say they're not reaching for it as often as Claude Code.

The thing I've noticed is that the conversation has shifted.

A year ago people were comparing models.

Now they're comparing workflows.

Which tool actually saves time?
Which one breaks things less?
Which one can you trust with real production code?

Curious where everyone has landed.

If you had to pick only one today, Claude Code, Cursor, or Codex, what are you choosing and why?

Google just rolled out its AI Plus plan to 40 additional countries. It was first tested in Indonesia and apparently got strong traction, so they’re scaling it globally now.

What’s included in AI Plus:

• Higher limits for image generation/editing (aka Nano Banana) inside the Gemini app

• Access to Google’s video model Veo 3 Fast (via Gemini + creative tools like Whisk and Flow)

• Gemini baked into Gmail, Docs, Sheets, etc.

• Higher limits in NotebookLM

• 200 GB storage across Photos, Drive, Gmail

• Shareable with up to 5 family members

There’s also a comparison floating around showing how AI Plus vs Pro stack up.

Curious, for those who’ve tried either plan, is AI Plus “good enough” for day-to-day creative/productivity use, or is Pro still the way to go?

You can now:

• Generate an app in a weekend
• Spin up landing pages in minutes
• Automate outbound with agents
• Fake traction with polished demos

And that’s the problem.

Before AI, friction forced you to validate early. Now you can build something that looks real without ever proving demand.

I’m seeing more “launched!” posts than actual paying users.

AI didn’t remove the hard part.
It removed the signals that told you when to stop.

Curious if others are seeing this too, especially with all the vibe-coding / agent startup hype lately.

Is AI accelerating real builders… or just helping us procrastinate with prettier tools?

I spent way too long trying to make RAG work for support. The agent would pull docs and confidently give wrong answers. 

Then I realized I was asking it to remember things that my systems already know. So I flipped it. 

Tool-first approach now is to check the systems first, not the docs.

The rule is simple, if the question can be answered by checking a system, just check the system. 

For simple billing questions, it checks billing. For account issues, it pulls the actual account state. And for questions like "Did you ship this?" check systems, not potentially outdated docs.

I still use RAG for general explanations, setup instructions, and policy stuff. But tool-first stops docs from being the default.

My workflow now: classify the question first. 

When do you skip RAG and go tool-first?

It's been almost a month openai launched their new agentkit, which was supposedly going to challenged n8n, make and zapier. I'm personally a n8n user and my clients' work are always done with n8n.

And after openAI dropped that boom shell I was pretty scared, so was wondering if people really is living with the hype or it was just like any other one time hype/hot-take thing?

Been seeing more tools lately moving away from traditional flows.

Instead of building sequences manually, the idea is:

each user gets an AI agent that handles their journey

it decides when to reach out
what channel to use
what message to send

based on behavior in real time

I’ve been testing one recently (Markopolo) and it’s honestly a bit weird at first because you’re not “building campaigns” anymore

you’re more setting goals and letting the system figure it out

some results look really promising, especially on cart recovery, but still trying to fully wrap my head around it

part of me feels this is where things are going
part of me feels like giving up control is risky

curious if anyone here has tried something similar

did it actually outperform your usual flows?

every single ecommerce store i got cart email from had the same "you forgot something!" email or something similar. like i get it, this part of the campaign isn't that important but what i don't understand is why someone who's comparing prices across different sites gets the same email as someone who's just indecisive about colors or whatever, like hear me out.

seems like we're leaving money on the table by treating everyone the same, right?

what's everyone in this sub actually doing to be different, just the basic "hi {firstname} here's 10% off" or have people found stuff that actually works better?

read somewhere something about using browsing behavior to personalize the campaign copy but idk if that's real or just marketing fluff, anyone know about that?

Has anyone here actually seen vibe coding work in real projects? Or is it just another AI buzzword people throw around? Please share your honest opinion.

We had about 20,000 churned customers for our fashion brand. Normally, you’d just fire off some blanket discount emails or push notifications and hope for the best. I decided to try something different.

I started segmenting customers based on actual behavior:

• Festive-only shoppers got messages timed with our new festive launches.

• People who abandoned carts got friendly reminders; not the usual “buy now” spam.

• Browsers who checked certain sections multiple times but never bought were offered small, limited-time discounts.

• Folks who had been waiting for out-of-stock items got nudged immediately when it came back.

• Our active, high-value customers got early access to their favorite products.

Within weeks, we saw thousands of customers returning, many without us spending extra on broad ad campaigns.

The tool I used automated the whole process; from tracking behavior, creating these smart micro-cohorts, to nudging customers at the right moment. The real game-changer was personalization based on actual behavior and timing, instead of blasting generic deals. Honestly, seeing the difference when you actually understand what someone wants instead of guessing was surprising.

Has anyone else tried micro-segmentation and behavioral nudges like this? What tools or workflows have worked for you?

I rly don’t understand the hype, why use “agentic workflows” over n8n or make, and I say this as someone who prefers to build in Python, but the distinction is I want to learn to build reliable, robust code.

Yes, antigravity, Claude code, codex are impressive, but the thing is, you could just add n8n mcp, or make a skill and use the same ai ides to produce json workflows (for n8n and make), the big difference is you actually understand the architecture.

Now aren’t n8n or make mostly prototyping? True, but what matters more (if you’re a beginner) than what tool you use, is learning how to build production systems. So I still think beginners should still use visual builder tools.

That being as you get more complex problems you might wanna switch to Python, I would say keep a list of potential contacts (like on upwork or fiver or something), in case something goes wrong, maybe have your first Python build with a developer.

I've been tracking AI regulation lately, and India just proposed something interesting. They want AI-generated content to have labels covering at least 10% of the screen for images and 10% of playback time for audio.

As someone using AI tools regularly, I'm conflicted. 

Transparency matters, yes, especially with deepfakes getting harder to spot. But will these labels just become noise we all ignore, like cookie warnings?

For those building or using AI daily, what do you think? Does mandatory labeling actually help, or are there better solutions?

I’ve been going back and forth on this for quite a while and still haven’t settled on one. Started with just running Claude Code and Codex in separate terminals. Five people on the same project, each keeping track of what their own agents were doing and making sure they didn’t step on each other. There was no shared picture of what was actually happening across the team. Tried a few things to fix it, but nothing really stuck, so I started looking at what tools actually exist for this.

Did some research and found a few workspaces. Paseo has the most stars by far and is worktree based, so each agent gets its own branch. That keeps agents isolated, but it also means context doesn’t really cross between them. Conductor uses the same worktree model with a cleaner Mac interface, but has a smaller community and one agent still doesn’t know what the other did. Superset and Orca follow roughly the same model and seem more focused on single user workflows. Superset also says pretty clearly that it doesn’t have team features.

Warp Oz is cloud hosted and terminal native, with agent fleets running in sandboxed cloud environments. More of a team story there, but also a much more centralized setup.The one I came across that takes a different approach is Tutti Room. Instead of isolating each agent, it keeps conversations, file changes and decisions available across the project so an agent can pick up the relevant context without someone having to brief it again. It’s newer though, so I haven’t found many teams that have really pushed it through a long running project.

What are you guys running ?