u/Additional_Twist_595

Hey guys,

I really need some help with the stubborn keratosis pilaris strawberry legs chicken skin on the backs of my arms and thighs. I've had these tiny, rough bumps for as long as I can remember, and they honestly make me so self conscious, especially now that the weather is warming up and I want to wear short sleeves and shorts.They don't really hurt, but they're super red, rough to the touch, and just won't go away no matter how hard I scrub with a regular loofah (which I think might actually be making the redness worse, ugh).

I've read that physically ripping them off doesn't work because it's a keratin buildup issue, not just dirt. Has anyone actually managed to smooth these bumps out, what routines or specific products changed the game for you? I'm desperate for anything that actually works and doesn't just waste my money. Thank you!

I cannot believe i did this. we have been testing this new ticket to execution integration where internal service requests from across the company get auto parsed and dropped as sprint tasks in our dev board. supposed to close the gap between internal requests and actual engineering work, seamless handoff without leaving the tool.

was doing what i thought was a staging test run on monday inside our company environment. had a batch of a few hundred low priority internal tickets from different teams, mostly feature requests and minor bugs that had piled up across departments. wrote a quick script to simulate the conversion, map ticket fields to sprint cards, assign to dev queues based on tags. ran it on what i swore was the test instance. watched the logs everything green, sprints populated perfectly, felt good.

except i transposed two letters in the api endpoint. hit production. those tickets instantly transformed into high priority sprint items across every dev team in the company. internal IT complaints about login issues now critical bugs assigned to three different squads. random ui gripes escalated to P0 features with auto generated epics. sales and ops requests for dashboard tweaks suddenly blocking the entire next sprint cycle.

dev slack channels lit up within 15 minutes. engineers pinging why their boards exploded with garbage, product owners freaking because sprint planning is today and now they have 300 bogus items skewing velocity. support ops called it a complete derailment of quarterly priorities. spent all morning in emergency war room rolling back the api calls, manually nuking sprint cards before grooming started.

boss pulled me aside says explain this in the incident review tomorrow. damage mostly contained but some sprints already kicked off with wrong priorities and one internal ticket got executed as a hotfix that broke staging deploy. how do i even frame this without sounding like an idiot?

I cannot believe i did this. we have been testing this new ticket to execution integration where internal service requests from across the company get auto parsed and dropped as sprint tasks in our dev board. supposed to close the gap between internal requests and actual engineering work, seamless handoff without leaving the tool.

was doing what i thought was a staging test run on monday inside our company environment. had a batch of a few hundred low priority internal tickets from different teams, mostly feature requests and minor bugs that had piled up across departments. wrote a quick script to simulate the conversion, map ticket fields to sprint cards, assign to dev queues based on tags. ran it on what i swore was the test instance. watched the logs everything green, sprints populated perfectly, felt good.

except i transposed two letters in the api endpoint. hit production. those tickets instantly transformed into high priority sprint items across every dev team in the company. internal IT complaints about login issues now critical bugs assigned to three different squads. random ui gripes escalated to P0 features with auto generated epics. sales and ops requests for dashboard tweaks suddenly blocking the entire next sprint cycle.

dev slack channels lit up within 15 minutes. engineers pinging why their boards exploded with garbage, product owners freaking because sprint planning is today and now they have 300 bogus items skewing velocity. support ops called it a complete derailment of quarterly priorities. spent all morning in emergency war room rolling back the api calls, manually nuking sprint cards before grooming started.

boss pulled me aside says explain this in the incident review tomorrow. damage mostly contained but some sprints already kicked off with wrong priorities and one internal ticket got executed as a hotfix that broke staging deploy. how do i even frame this without sounding like an idiot?

Working on some agent driven automation for internal support tickets at a mid size place it handles routing, approvals, and a few actions automatically, which is great until someone asks what actually happened on a specific ticket.

like a request gets auto approved or rerouted and all we really see is the final state. no clear way to trace the full path of the ticket in a way that makes sense to anyone outside the system. you can dig through logs, but that’s not something a manager or ops lead is going to do just to understand one decision.

we tried adding more detail like activity logs, context pulled, and intermediate steps. but instead of clarity it just turns into noise. technically everything is there, but practically it’s hard to follow what happened and why the system moved the ticket the way it did. the bigger issue is ownership. once automation touches the ticket, it feels like it disappears into the system and comes back changed, without a clear narrative of what happened in between. when something goes wrong, you’re stuck reconstructing the story instead of just seeing it.

feels like the gap isn’t automation itself, it’s the lack of a clear, shared view of the ticket lifecycle after automation gets involved how are teams making automated ticket workflows transparent and easy to follow without turning it into a deep dive every time?

Spent months building out these autonomous agents to handle the endless grind of ticket resolution. you know the drill, password resets, access provisioning, simple workflows that eat 70 percent of our queue. figured why not let the bots do it, scale like a digital sweatshop without the coffee breaks or attitude.

set them loose on low risk stuff first. provisioning test accounts, basic resets, even some permission tweaks across ad and okta. looked beautiful in the logs, resolution times dropped 40 percent overnight, slacks quiet for once. management high fived me in the all hands, called it the future.

then reality hit. first agent brute forces a reset on the wrong tenant because some naming overlap nobody caught. finance director locked out mid quarter close, screaming about sabotage. rollback took two hours while i prayed. next one provisions access to prod db for a summer intern because the request form had a fuzzy match on department name. kid spent 20 minutes poking around before we noticed.

now every fixed ticket needs a human sniff test because the agents are like overeager puppies chewing the furniture. false positives everywhere, edge cases they choke on, and the audit trail is a nightmare to untangle when legal asks why finance had db god mode for 45 minutes.

team is back to babysitting bots instead of projects, i feel like the guy who replaced the horse with a car only to spend all day fixing flat tires. anyone running agents at scale without this mess??

Rolled out this shiny ai ticketing system six months back thinking it would auto categorize tickets, suggest fixes, maybe even resolve the easy stuff without me touching it. sounds great right. users love self service. tickets drop 30 percent. pure bliss.

reality hits like a truck. ai confidently dumps password reset tickets into hardware queue because someone typed laptop once. suggests rebooting the server for a vpn timeout because correlation equals causation apparently. users now submit five tickets for the same issue because the ai replies with some generic platitude and closes it as resolved before they notice nothing changed.

spent half of yesterday manually reassigning 40 tickets it butchered while it proudly reports 98 percent accuracy in the dashboard. yeah accurate if you ignore the 20 percent that land in the wrong department and the 15 percent it just ignores entirely. leadership sees the pretty metrics and asks why slas arent improving. i explain the ai is basically a toddler playing support engineer and they nod like thats normal.

self deprecating part: i keep tweaking the training data like its going to suddenly get smarter. spoiler it doesnt. now im the guy defending why we spent good money on glorified autocomplete.

what ai ticket disasters have you all survived, any setups that actually work without turning your queue into chaos or did you just rip it out and go back to manual?