u/AccountEngineer

After going through the whole process of setting up our store on swaggy shop i have some thoughts for anyone considering it. first: curating your product list matters more than you think, too many options and people get overwhelmed and order nothing second: gift codes are the move if you want to control spend, way cleaner than refund people after third: you will still get questions from coworkers no matter how self explanatory the store is, just accept this now, people are kinda stupid and it's just part of dealing with these things fourth: the no inventory thing is actually as good as it sounds, not just good marketing, not having boxes of hoodies in odd sizes taking up closet space was a big relief fifth: don't expect a massive catalog, it's curated which is mostly a good thing but if you need super specific items you might not find them.

Edit: No idea why was it removed, here’s me trying again

For people running Jason AI without a sales team behind them, how much oversight does the follow-up sequence actually need day to day. Is it genuinely set and forget once it's running or do you find yourself checking in constantly to make sure nothing weird is going out to prospects.

So I was searching for ai web scraping tool and finding the ad was so crazy now. Same title, totally different company. Im new to seo.

With how things are going lately, I feel like more people are starting to question relying on just one income source.

For me, it was when I started seeing how unpredictable things can be, and how many people are now exploring alternative income streams; online business, side hustles, even network-based models.

So I’m curious about your turning point…

In this economy, what made you realize you wanted to start your own business?

How are you handling consistency when managing multiple SEO projects at scale?

I keep running into issues where processes drift over time despite having systems in place. It becomes harder to track and standardize efficiently.

Any suggestions or tools that have genuinely helped streamline this without adding extra complexity?

I swear most "guilt free" candy tastes like someone described candy to a robot and the robot tried its best. I've been on 1200 since January and the sweet tooth is the one thing that almost broke me multiple times. So here's what I actually keep buying week after week because they genuinely taste good and not just "good for diet food." Frozen red grapes. The cotton candy ones if you can find them are next level. Two squares of lindt 90% dark chocolate with a pinch of sea salt on top. About 80 cals and it feels fancy. Shameless gummies for the nights when my brain won't shut up about sugar. A single frozen outshine fruit bar. The lime one is 60 calories. Sugar free jello cup with like one tablespoon of reddi whip. Maybe 20 cals total. Frozen blueberries eaten straight out of the bag like little ice pellets. The secret for me is not trying to make healthy food taste like junk food. I'd rather just find stuff that's naturally low cal and happens to taste good on its own.

There's an obvious version of this that's clearly bad: mass blasting with "Hey [FIRST NAME], love your content" templates that every creator has seen 400 times lol. But there's a subtler version too where even "personalized" automated sequences still feel hollow because the personalization tokens don't actually capture what makes a specific creator worth reaching out to

The question of where the automation vs human judgment line should sit is genuinely hard. Upfluence automates initial influencer outreach sequences for teams that need volume without losing targeting quality, with manual touchpoints reserved for higher priority creators but even that division feels somewhat arbitrary. A micro influencer with 12k followers who's deeply relevant to your niche probably deserves more care than the automation playbook suggests.

For people who've scaled outreach programs: where do you draw the line? Like is there a tier or engagement threshold where you make outreach fully human again or does the math just not support it at scale?

We’ve been managing AI coding tool adoption at a 300-dev org for a little over a year now. I wanted to share something that changed how I think about these tools, because the conversation always focuses on which model is smartest and I think that misses the point for teams.

We ran Copilot for about 10 months and the devs liked it. Acceptance rate hovered around 28%. The problem wasn't the model, it was that the suggestions didn't match our codebase. Valid C# that compiled fine but ignored our architecture, our internal libraries, our naming patterns. Devs spent as much time fixing suggestions as they would have spent writing the code themselves so we decided to look for some alternatives and switched to tabnine about 4 months ago, mostly because of their context engine. The idea is it indexes your repos and documentation and builds a persistent understanding of how your org writes code, not just the language in general. Their base model is arguably weaker than what Copilot runs but our acceptance rate went up to around 41% because the suggestions actually fit our codebase. A less capable model that understands your codebase outperforms a more capable model that doesn't. At least for enterprise work where the hard part isn't writing valid code, it's writing code that fits your existing patterns. 

The other thing we noticed was that per-request token usage dropped significantly because the model doesn't need as much raw context sent with every call. It already has the organizational understanding. That changed our cost trajectory in a way that made finance happy.

Where it's weaker is the chat isn't as good as Copilot Chat. For explaining code or generating something from scratch, Copilot is still better. The initial setup takes a week or two before the context is fully built. And it's a different value prop entirely. It's not trying to be the flashiest AI, it's trying to be the most relevant one for your specific codebase.

My recommendation is if you're a small team or solo developer, the AI model matters more because you don't have complex organizational context. Use Cursor or Copilot. If you're an enterprise with hundreds of developers, established patterns, and an existing codebase, the context layer is what matters. And right now Tabnine's context engine is the most mature implementation of that concept.

B2B app, 14 day trial. Trial to paid was 8%. Started recording sessions with uxcam filtering for users who churned before day 3 vs converted.

Pattern was embarrassingly clear. Churning users almost all hit the same wall: create project, try to invite team member, get confused by permissions modal (5 role types, no explanation), never open app again. Users who converted either figured it out or skipped the invite step entirely.

We simplified permissions to 3 roles with one line descriptions. Conversion went from 8% to 14% first month. The drop off dashboard had shown invite flow as "25% abandonment" which didn't seem urgent. Watching the actual confusion told a completely different story.

Three of our mid-market clients (300–800 employees each) have asked us in the last month to help evaluate and deploy AI coding platforms. The pattern is striking enough that I'm wondering if other MSPs are seeing the same thing.

Client A is in healthcare. They need HIPAA-compliant AI coding tools, want on-prem deployment, and have 120 developers.

Client B is a defense contractor that needs air-gapped deployment and wants the tool to actually understand their codebase before making suggestions.

Client C is in financial services with around 200 developers. They're currently spending $15k/month on Copilot inference and leadership wants that cut in half.

What's interesting is none of these conversations are saying, should we use AI coding tools. They've already decided yes. The questions are about how to deploy securely, how to manage costs, and how to actually govern usage across teams.

Is there enough consistent demand here to build a formal practice around this? And for those already doing it, what tools are enterprises actually choosing once compliance requirements enter the picture?