u/chipskaapacket

Got a hot outdoor wedding next month and I’m wearing a low back dress, so my normal strapless bra is basically out.

I’m worried a sticky bra is just going to start peeling once I’m sweating and dancing. I’ve seen puff mentioned because of the front clasp, but I’m still not sure if that actually helps or if all sticky bras give up in the heat.

Has anyone worn one for a full outdoor wedding? Did it stay on, or were you checking it all night?

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I'm visiting Heraklion soon and instead of collecting random recommendations, I'm curious how locals or frequent visitors would build an actual day itinerary.

If you were planning a day that shows off the city at its best, what would the route look like from morning to night?

I'm looking for a mix of things great local food, interesting museums, nice areas to walk around, sea views, shopping streets or malls, historical sites, cafés, viewpoints, nearby villages, hidden gems, and anything else that feels worth experiencing.

The idea isn't necessarily to see the most famous attractions, but to have a day that feels complete and memorable. What places would make the cut, what order would you do them in, and are there any spots that are often overlooked?

I'd love to hear how you'd personally design the perfect Heraklion day rather than just a list of recommendations.

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Wanted to compare the actual return-per-hour across different passive income approaches because the marketing for all of them tends to overpromise.

Ran four in parallel for six months. Survey sites, cashback portals, settlement filings, and a small dividend reinvestment strategy on an existing position. Tracking time spent and dollars returned on each.

Cashback came out with the highest absolute dollars but only because of normal spending volume. Settlements came out with the highest dollars per hour spent. Surveys were the worst on both metrics.

Curious how others have ranked these when they've actually tracked them.

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We moved a fairly large site about 6–7 weeks ago.

Everything on paper looked fine redirects mapped, canonicals correct, metadata carried over, and no obvious crawl errors during testing.

What’s confusing is the timing. Nothing dropped immediately after launch. But around week 3–4, traffic started slipping gradually, mostly on pages that used to be stable.

Feels less like a technical issue and more like Google slowly reinterpreting the structure over time.

Anyone else run into this kind of delayed effect?

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We spent a couple months improving Core Web Vitals on a fairly large ecommerce site.

Load speed improved a lot, CLS is basically clean now, and UX definitely feels better.

But rankings didn’t really change afterward.

Starting to think CWV alone doesn’t really move things anymore unless other signals shift too.

We've been on Loopio for two years and it's starting to feel like more work than it's worth.

Two things that pushed me to start looking:

● Responses still need a lot of rewriting. It keyword matches from the library and what

comes back is rarely good enough to use as is.

● The library is always a mess. Someone has to keep it updated and nobody does, so old answers keep going out in proposals.Started looking at alternatives and came across the whole autonomous agents angle. The idea

that AI can pull from your live company docs and draft responses without someone manually maintaining a Q&A bank first really caught my attention.

We have planned to switch to Inventive AI because we need agents that handle both the knowledge side and the drafting side on their own, not just a nicer wrapper around the same old library model.A demo is booked for next week but wanted real feedback first.

Would like to know about:

● How accurate are responses day to day, not just in the demo?

● Does it actually reduce how often SMEs get pulled in?

● How does it handle outdated or conflicting source docs?

Anyone else spent months trying to get an AI meeting notetaker approved only to have compliance kill it? Feels like every tool is either built for individual consumers with zero governance, or it's Gong priced for 500-person sales orgs.

We went through the evaluation at our fund (about 60 people, 12 active portfolio companies and the compliance filter eliminated most options before we even got to features. Sharingwhat survived and why since I couldn't find anything PE-specific when I was researching. What compliance cared about (in order): Does it train on customer/client dataAdmin-level access controls (not user-optional) Configurable data retention with auto-delete SOC 2 certification Ability to scope deal teams to only their own meetings

What got eliminated:Otter AI: minimal admin controls. Transcription is fine individually but compliance wouldn't approve it for firm-wide use. Data handling policy is vague on whether content improves their models. Fathom: Good product for personal use. No org-level admin controls, no compliance certifications beyond basic encryption. Can't scope access by deal team. Solo analyst tool,not a firm tool. A Chrome extension one of our associates was using that explicitly trains on data per its own ToS. That discovery is what triggered the whole evaluation. What passed: Fellow AI: SOC 2 Type II, HIPAA, GDPR. No training on data. Admin controls. Botless option for LP-facing calls. AI meeting summary quality was strong across IC meetings, board prep,and portfolio reviews.

Gong: Passed compliance but pricing and feature set is built for revenue teams, not deal teams. Would've been paying for call scoring and pipeline analytics we'd never use. Stayed on the shortlist for sales coverage specifically. The searchable AI meeting notes archive across months of portfolio company discussions has been the highest value feature. When a board member references something from a previous quarter or a deal team member transitions, the context is actually retrievable instead of gone. If your fund is evaluating, start with compliance requirements and work backwards. Feature comparisons are irrelevant if the tool can't clear your CCO.

i fell hard for the vibe coding dream last month. prompted up a gorgeous saas dashboard in like twenty minutes. felt like magic.

then i tried to hook up real users, and the whole thing imploded.

turns out, generating a pretty react frontend is incredibly easy. the actual nightmare starts when you try to deal with the backend plumbing, i'm talking about payment flows hanging, auth tokens expiring silently, and database schemas turning into an absolute mess.

The real issue is that most AI builders are designed to sell you on the flashiness of the initial demo. They just don't have a reliable post demo infrastructure setup. you get a nice UI, but the moment you need real users to log in and pay you, you're stuck duct-taping databases, Stripe setup, and auth APIs together while the AI agent hallucinates the integration scripts. its an endless loop of fixing one thing and breaking three others.

i wasted weeks trying to keep my DB schemas from getting cooked every time i let the LLM modify a basic frontend component.

i ended up wiping the slate and testing a few options that actually address this backend gap. currently experimenting with Enter because its cloud setup packages the database, functions, secrets, and payment setup in the same workflow. It’s not some magic silver bullet that replaces code review, but at least I'm not spending my nights writing prompt workarounds to fix broken database handoffs.

how do you guys handle database migrations when the AI goes rogue and alters the database schema without telling you?

You just have to join a server and that'll earn you 0.50 dollar , you can accept 1 i vite per day which will roughly be 350 rupees per week.

My dofotea ripe green mandarin puer tea is pure. It soothes my throat, helps my dry cough and aids in digestion. Very refreshing. Citrusy. Earthy sweetness.

I never liked green tea but I have acquired the taste finally and I have been drinking 3 cups daily. Is it too much? Or is it okay? Guide me.

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All right, I’ve read ten articles on this and I still don’t think I understand it.

I have a small project to scrape product prices from some sites. Nothing crazy, just e-commerce stuff. someone told me i need residential proxies but they're like 5x the price of datacenter and i don't understand why i would need them for something this basic Like, what actually happens if i just use datacenter? Will I be blocked right away or is it fine for most normal sites? And what the hell is an ISP proxy? is different again

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I’m just trying to not spend money on something I don’t need. any help appreciated

I keep seeing openclaw ai agent described as more than a chatbot or an automation tool, but most explanations stay at the category level rather than giving specific examples.

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What I'm actually asking: has anyone used it for something that Zapier, a standard chatbot, or a regular LLM API genuinely couldn't handle? Not looking for the pitch version, just what people have actually run into in practice that surprised them.

The answer depends entirely on how the agent makes money and most people do not ask that question until something goes wrong.

Fee structure is the single most important thing to establish before anything else. The features worth looking for and where to find them:

Go ship pro: Bundled sourcing and fulfillment under one vendor. Good if you prioritize vendor simplicity over granular cost visibility.

Kanary solutions: Management fee separated from factory cost, factory invoice and service fee as two distinct line items on every order. Removes the incentive for the agent to find expensive suppliers and lets you audit COGS accurately.

Ask any agent before signing: "Will you show me the factory invoice alongside your invoice?" That answer tells you everything about the incentive structure you are about to be inside.