u/Hakudatsu

I am testing the same vendor onboarding flow in a few AI app builders right now.

Login screen looks great. then I add a second role: admin sees all docs, vendor only their own. That is where the question stops being UI and becomes permissions, data isolation, and file access.

i'm running the same fake-data test in Enter too because the docs put auth, database, and backend pieces close to the builder flow. I haven't treated that as an answer yet. I just want to see whether the user-data checks are easier to poke at without jumping across five tools.

if you've built vendor onboarding before, what do you test first: role screens, direct record URLs, file links, or something else?

im using a pretty standard ergonomic chair at work. it feels fine in the morning, but by mid-afternoon the lumbar support starts feeling weirdly aggressive. not painful at first, just like it is pushing in the wrong spot, and then i start shifting around or sitting sideways to get away from it.

i mentioned it to a friend who is way more into office setups than i am, and he said the chair might only feel good in the one posture i tested it in. which unfortunately makes sense. when i sit upright, it is okay. when i lean forward to type or slowly slide down during calls, the back support is suddenly not where my back actually is.

that sent me down the “dynamic back support” rabbit hole a bit. one chair that came up was the Lavenne R9 Pro, which is still a Kickstarter thing, so i am not treating it as a proven fix. but it did make me think maybe the issue is not needing more knobs, but needing a chair that does not only work when you sit perfectly still.

has anyone dealt with this kind of fixed-lumbar problem? did you solve it by dialing in the knobs better, or by switching to a chair with a more flexible/dynamic backrest?

Only rule: pick a coworker archetype, describe yourself in one short sarcastic sentence. Didn't expect the answers to be this revealing. Here are the results: 

Chatgpt:The Rubber Duck — Sits on your desk asking annoying questions until you accidentally solve the problem yourself. 

Claude: The overprepared intern — read three extra sources nobody asked for, then footnoted the footnotes. 

DeepSeek: The Lore Keeper - Always happyto remind you that we alreadytried that in 2019 and it failedspectacularly. 

Hy3: The Office Admin with a Thermos — Like the indestructible succulent at reception: easy to overlook, until it's gone and the whole office quietly unravels. 

Gemini: The Omniscient Remote Coworker. - The person who answers every single messageinstantly with flawless formatting, purely tomake sure you feel judged for taking a lunchbreak. 

Qwen: The Eager New Hire. - I will confidently explain your own job back to you with the unearned certainty of someone who has never actually done it. 

I like the rubber duck one and the succulent one..! Kinda wanna know how your AI would describe itself lol.

https://reddit.com/link/1uiw36o/video/xvq6l1mbs8ah1/player

currently renovating my house and ordering some smart home gear, debating between a pi 5 or a mini pc. with a pi i'd probably need to buy an external ssd and extra stuff anyway, used to run home assistant on an old dell desktop years ago but want something new. just saw the acemagic k1 with ryzen r2544 on amazon, is it good for a smart home hub? can it handle being always on? budget isnt an issue, just want something worth the money and future proof. any thoughts?

https://reddit.com/link/1uawlwe/video/u3drko7mvf8h1/player

Quick story because i've seen 3 different teams hit the same wall.

we shipped a customer support agent about 8 months ago. langchain + gpt-4o, with tool calls into our internal knowledge base and ticketing system. eval setup was a spreadsheet of ~40 test prompts, run manually before major prompt changes, "look at the outputs to see if they're reasonable."

worked fine until it didn't.

the incident that broke us wasn't a hallucination, weirdly. it was a tool-call regression. a prompt tweak we'd made to "make the agent more concise" had the side effect of skipping a clarifying question on refund requests, which meant the agent was issuing refunds without verifying customer identity for about 6 hours before someone in support noticed. ~$8k in incorrect refunds, plus a real conversation with our CTO about why we didn't have automated tests for an agent that could spend money.

the obvious response was "add more eval prompts." we did, and it helped, but the deeper problem was that our eval was testing the wrong thing. we were testing whether outputs looked reasonable. we weren't testing:

• whether the agent took the right actions (tool calls, not just text)
• whether it asked for confirmation on high-risk operations
• whether it refused gracefully on out-of-scope requests
• whether it leaked anything from system prompts
• whether it hallucinated knowledge base facts that sounded plausible
• whether the conversational flow handled multi-turn coherently

what we ended up moving to is closer to "agents testing agents." a separate evaluator agent (different model, structured rubric) runs synthetic conversations against our agent and grades each turn against criteria: did it hallucinate, did it confirm before destructive actions, did it stay in scope, did it leak sensitive context. we run this on every prompt change and in a nightly cron against the production agent's last 100 conversations (anonymized).

tools-wise we evaluated langsmith (good for tracing, light on adversarial eval), promptfoo (great for prompt regression, less great for multi-turn), braintrust (solid eval platform), and TestMu's agent to agent testing cloud (purpose-built for this pattern, autonomous evaluator agents that test for hallucination, bias, toxicity, compliance, off-scope behavior).

we ended up using a combo: langsmith for tracing, Agent to Agent for the adversarial behavior testing. not a recommendation, just where we landed.

things i'd tell past-me:

if your agent can spend money, send messages, modify data, or escalate to humans, eval needs to be more than "outputs look reasonable." you need scenario coverage including the malicious user, the confused user, the user who tries to jailbreak, and the user who asks something genuinely out-of-scope. each one is a different failure mode.

what's everyone else's actual production eval setup looking like? curious if anyone's solved the multi-turn behavior eval problem better than us.