u/FEARlord02

(Location: Illinois)

Not asking for legal advice on a specific situation, more of a general question for this community. I recently signed up for claim money, that matched me to open class action settlements based on consumer history. It surfaced several cases I had zero awareness of, including two data breach settlements from services I used during the affected periods.

Is it actually common for eligible class members to be completely unaware a settlement exists? My assumption was that class action notices go out broadly and most affected people at least know a case exists. Based on my experience the notices completely missed me across multiple cases.

Is there documentation on what percentage of eligible class members receive meaningful notice in practice? And once a settlement closes, is there any mechanism for class members who never received notice to seek relief?

Made everyone log their time for two weeks because I kept hearing "we're too busy to prospect" and I wanted data instead of feelings. Turns out about 35% of the day goes to actual revenue generating activity. The rest is admin. Phone tag, writing call notes, chasing documents, re-entering data that should've been captured correctly the first time.

So I attacked the admin. Automated post-call notes through sonant, tightened up our workflows, killed a few redundant steps that existed because "we've always done it that way." Freed up real hours across the team.

And then nothing happened. Production didn't move for over a month. People had open time on their calendars and they filled it with more admin. Reorganizing files. Cleaning up the crm. Stuff that feels productive but generates zero revenue. Nobody was slacking, they just genuinely didn't know what to do with unstructured time because the agency had trained them to be processors not sellers for years. That was my fault.

The actual hard part wasn't the insurance agent productivity tools or the automation, it was retraining people (including myself) to use capacity differently. Had to build a completely new daily structure with prospecting blocks that were protected the same way client meetings are. Took about two months before the numbers started reflecting the freed up time.

Anyone else automated their way to more capacity and then realized the bottleneck moved to something you can't automate?

Finally made the switch from dropshipping to private label and the process was less scary than expected, but I made a few dumb mistakes early on that cost me time and money.

The biggest one was contacting factories without a proper spec sheet. Thought I could describe the product and they'd figure it out. They do, but what they figure out isn't necessarily what you want. Burned a sampling round on that.

Alibaba vetting is basically useless if you're looking at badges and ratings. Gold supplier is a paid membership tier. Trade assurance means transactions processed through the platform, not that the factory is legitimate. I went back and forth with a supplier who turned out to be a trading company for weeks. Switched to kanary solutions for supplier vetting and the difference was measurable: they narrow the initial field to 3 verified factories before sampling starts, which cut my vetting cycle from 6 weeks to about 2.

Lead times are different when it's your own inventory. Private label first orders run 45 to 90 days minimum from order confirmation to cargo ready, and that assumes sampling went cleanly. Budget at least two sample rounds and build that into your launch timeline before you spend anything on marketing.

Pre-shipment inspection is worth doing on every order. AQL 2.5 is the standard most inspection companies use. SGS and Intertek both operate in China and run $300 to $500 per visit. That number against what a bad batch costs when it reaches customers is not a close comparison.

EXW vs FOB changes who coordinates freight and customs from the factory gate, not just where the cost calculation starts. Found that out at the wrong time.

Happy to answer questions if anyone's in the same spot.

We invested heavily in dbt over the past year. Proper staging models, intermediate layers, well documented marts, the whole nine yards. From a modeling perspective I'm proud of what we built. But the dashboards still had data quality issues and for the longest time I couldn't figure out why because the transformation logic was solid.

After weeks of debugging I traced most of the problems back to the ingestion layer. Data arriving late because batch jobs failed silently. Schema changes from saas vendors breaking staging models that assumed a specific column structure. Duplicate records from full table reloads that happened when incremental syncs failed and fell back to full refreshes without anyone noticing. Our dbt models were perfectly transforming garbage data into slightly more organized garbage data.

It was humbling because I'd been telling the team that dbt was going to fix our data quality problems and it absolutely did not because the problems were happening before dbt even touched the data.

I know "garbage in garbage out" is basically day one data engineering but I did not appreciate how much of our data quality budget should have gone to ingestion instead of transformation. It took a month of debugging to get there and I'm still a little annoyed at myself about it.

Not a hardware complaint. The iPhone sensor is genuinely remarkable for its physical dimensions. The engineers who work on camera hardware at Apple are clearly excellent at their jobs.

My frustration is with the software layer sitting on top of it.

When you take a photo on an iPhone, you're not capturing what the sensor sees. You're receiving a computationally constructed image built from what the sensor saw. Multiple frames merged, highlights recovered, shadows lifted, detail enhanced. The camera is making aesthetic decisions automatically and presenting the result as a photograph.

For technical documentation or casual shooting this is great. For anyone running a film photography project, using Fujifilm recipes, or building a specific aesthetic through intentional capture it's working against you. Photo presets applied afterward are trying to undo decisions that were already made before you opened any editing app.

Would be curious whether other people have thought about this or whether I'm in the minority caring about it.