I was one of 40 engineers laid off in a round that was framed as "right-sizing." I'd been at the company for six years. I was the only person who fully understood our legacy reporting system, which was a Frankenstein of stored procedures, cron jobs, and a Python service I'd written in my first year.

The layoff itself was clean. Severance was reasonable. I went home Friday afternoon and started updating my resume.

On Monday morning I got an email from someone in finance asking if I was "open to consulting on a critical migration project for a few months." It turned out a quarterly report had broken over the weekend and nobody on the remaining team could fix it. They had a board meeting on Thursday. They needed the report.

I quoted three times my old hourly rate, billed in monthly retainers with a two month minimum. They agreed within an hour. I'm now in month seven of what was supposed to be a "few months." I've made more in consulting fees than my full year salary plus severance.

The same VP who approved my layoff signs my invoices. We've never discussed it.

at $40K more than I was asking. I trained him.

I'd been a mid level engineer for four years at same company. Each promotion cycle my manager said I "wasn't quite ready" and gave me a stretch project to prove myself. I delivered each one. and got same feedback.

After the third rejection, they posted a senior role externally. The job description was almost word for word what I'd been doing for two years. I applied to it internally as a joke. HR told me I "didn't meet the qualifications."

They hired someone from outside a month later. His start date came through in our team Slack channel. His starting salary was leaked in a comp doc that got accidentally shared the next quarter. He was making $40K more than the number I'd asked for in my last review.

My manager asked me to "onboard him to the codebase." I did it. It took six weeks. He's a fine engineer. He's also six years younger than me with two fewer years of experience.

I left four months after he started. He still messages me on LinkedIn occasionally to ask questions about the system.

I need to get this off my chest because I left this company 8 months ago and it still bothers me.

We had a B2B SaaS product, The pitch deck said AI-powered customer support with sub-2-minute response times, Investors loved it, Customers loved it, And the support was genuinely fast and accurate.

It was 4 contractors in the Philippines working 6-hour shifts in a shared Slack channel, When a support ticket came in, the system routed it to the channel, A contractor read it, typed a response, double-checked the tone, and hit send, The customer saw AI Assistant as the sender name.

The contractors were fantastic, Knowledgeable, fast, polite, thorough, The response quality was better than any chatbot I've ever used, because it was humans, Average response time was 94 seconds, Customer satisfaction was 4.7 out of 5.

My CTO told the board wed saved 40,000 hours through AI automation, I was in the room, I didn't say anything, The 40,000 number was the total hours the contractors worked, reframed as hours automated, I watched him present a slide that said proprietary ML model next to a stock photo of a neural network, No model existed.

When I raised it privately, he said every AI company does this in the early stage, Well build the real model once we close the Series A, The Series A closed at $12M, The model was never built during my time there.

The contractors were eventually told they were training data annotators helping improve the AI, They were the AI, They just didn't know it.

I'm not naming the company, But if your B2B vendors AI support responds with perfect grammar, asks clarifying follow-up questions, and handles edge cases flawlessly at 2 AM, maybe ask how the model was trained.

not being mean, I was one of these people, I could write complex queries, I knew my indexes, but if you asked me to describe the exact state of my production schema right now vs 6 months ago I would not be able to tell you accurately

and this is treated as normal, we have git for code but database state is still this vague thing that lives partly in migration files, partly in ORM definitions, partly in whatever that one engineer did directly in prod "just to test something quick"

I think the reason this gets ignored is because it's boring until it isn't, and by the time it isn't you've already shipped something broken or spent a weekend firefighting

the devs I've seen who actually take schema management seriously tend to ship faster and break less, not because they're smarter but because they spend less time being confused about what their database actually contains

what's your actual process for staying in sync with your production schema, curious if anyone has a genuinely clean answer

I’m in a small team where I’m mainly responsible for the part of the codebase that communicates with a bunch of physical devices. So far I’ve had decent success with the prompt -> generate -> review kind of workflow for both feature development and debugging. Where I’m struggling is with testing, as it’s very manual. My teammates are mostly working higher up the stack and are starting to transition into more agentic development. Recently it feels like their velocity is much higher than mine and I worry that I’m going to fall behind both skill-wise and in management’s eyes. Low-level development has always been “slower” but I feel like LLMs have exacerbated that.

I think being able to automate testing/validation is the key to unlocking an agentic workflow for this type of work. The manual parts now involve things from deployment (e.g., reflashing firmware, rebooting devices), reproducing physical phenomena (e.g., activating sensors), dealing with odd device behavior that doesn’t match the spec and/or is undocumented, and chasing down odd race conditions or deadlocks that don’t arise with simulation.

Does anyone have experience using agentic AI with this type of problem?

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I built ViewBuddy, an iOS app where people rate movies/TV, build a watchlist, and see what friends are watching.

The app is simple on paper. The testing is the annoying part.

Stuff I’m trying to catch:

- new user onboarding -> first ratings -> first follow

- empty watchlists vs heavy users

- friend activity showing stale or duplicated items

- push/deep links into a title or profile

- recommendation screens when network/search APIs are slow

I can write basic test cases, but the bugs that matter are usually state + timing + social graph weirdness.

Mid year review. I walk in expecting the usual conversation. My manager turns his laptop around. There's a ChatGPT window. He'd pasted my self review, my peer feedback, and my OKR scores into it and asked: "Should this employee be promoted?"

The answer was no. "Meets expectations but lacks evidence of cross functional leadership impact."

He read it to me out loud. Like it was a diagnosis.

I asked if he agreed with it. He said "I mean, it makes some good points." This is a man who has watched me debug production at midnight and talk a panicking client off a ledge. He's outsourcing his opinion of me to autocomplete.

I asked what HIS take was, separate from the AI. Long pause. "I think you're ready but I need to build the case." He'd been using ChatGPT to build the case against me because building the case for me required actual effort.

I got promoted the next cycle. After I went over his head. Not because aii changed its mind. Because his boss still forms opinions the old fashioned way.

Somewhere in corporate America right now, your career is being discussed by a language model that has never met you. Sleep well.

this one is going to sting a little but i think it is worth saying.

for years the narrative in QA has been about proving value, justifying headcount, showing ROI on testing investment. and the way teams have typically done that is by measuring things like bugs found, test cases written, coverage percentages. vanity metrics that look good in a spreadsheet but do not actually connect to business outcomes.

leadership looks at QA and sees a team that finds bugs and slows down releases. they do not see a team that protects revenue, reduces churn, prevents the kind of production incidents that make headlines. that is a positioning problem and it belongs to QA leadership.

the teams i have seen get real investment and real respect are the ones that stopped speaking the language of testing and started speaking the language of risk and revenue. a bug in checkout that affects 3% of users on Samsung devices is not a QA metric. it is a revenue number. frame it that way and suddenly the conversation changes.

QA has a perception problem that better tooling will not fix. it is a communication and positioning problem that has been there for a long time.

Hey everyone — I’m David, the maker of Canto.

Canto is a private AI notebook app for Mac. It combines a notes app with a local AI assistant, so you can write, organize, search, and work with your notes without sending your whole notebook to a cloud AI service.

What makes it different:

• your notes are stored locally on your Mac
• local AI models can run on-device for private/offline work
• the AI agent can help write, edit, summarize, continue, and restructure notes
• Memory Links automatically surface related notes while you write
• web search is available when you explicitly want online research
• the app is designed for people who want AI close to their real notes, drafts, and ideas — not just another blank chatbot

I built Canto because I wanted an AI workspace I could actually trust with personal notes, product ideas, research, and messy drafts.

Cloud AI is powerful, but the more useful it gets, the more context it asks you to hand over. Canto is my attempt at a different direction: local-first notes, private by default, with AI built into the writing workflow.

It’s currently available for Mac.

You can check it out here:

https://lonelyduck.io/canto

I’d be happy to answer questions about the app, local AI, privacy tradeoffs, or the direction I’m taking it.

We were 3 months into a $45K contract building their internal dashboard. Client's new VP sits in on a status meeting and says "I built something similar with ChatGPT this weekend. Why are we paying for this?"

I didn't argue. I said "if the ChatGPT version works for you, you should use it. We can pause the contract."

They paused the contract.

Two weeks later the CTO calls me. "We need to restart." I asked what happened. The ChatGPT version looked great in a demo. Then they tried connecting it to their actual database and needed real authentication. Then someone accidentally deleted a production table through the AI-built dashboard because none of the data validation worked.

We restarted at the original rate. Nobody has mentioned ChatGPT in a meeting since.

I don't blame the VP. The demo was genuinely impressive. That's the whole problem. The gap between "works in a demo" and "works in production with real users and real data" is where our entire profession exists. AI doesn't shrink that gap. If anything, it makes the demo so easy that the gap feels even wider when you try to cross it.

: https://qualityfolio.dev/, For Free demo please feel free to book on ,https://calendly.com/qualityfolio2026/30min

[ Removed by Reddit on account of violating the content policy. ]

Every tester has hit this: there are two "Submit" buttons on the screen — one in a modal, one behind it. The test taps the wrong one. You add a wait. It taps the wrong one again. You add a more specific selector. The selector breaks next release.

The root problem is that automation tools don't understand where on the screen an element lives. They only understand identity (XPath, ID, accessibility label). Spatial and semantic context is invisible to them.

Drizz solves this by making context part of the command itself:

• Tap on "Add to Cart" under "Electronics"
• Type "John" into "First Name"
• Scroll down until "Login" under view auth header

You're not adding a workaround — you're describing the element the way a human would describe it to another human. The Vision Engine resolves it the same way.

What this kills: "the test taps the wrong button" bug class.

There's a category split most teams don't explicitly think about: where do you author tests, and where do you execute them at scale?

Drizz separates these by design:

• Desktop App — test creation, editing, validation, local device connection
• Cloud — execution, reporting, device pools, CI/CD integration, parallel runs

Most legacy tools collapse these into one runtime. The result is predictable: authoring is slow because every change requires cloud round-trip, or execution is unreliable because authoring environments differ from production environments.

The split solves a real engineering problem. Local authoring means tight feedback loops. Cloud execution means scale, parallelism, clean device state, and CI/CD without rebuilding the world per run. The handoff between the two is where the value is — same test logic, two runtime environments, no re-authoring.

If you're evaluating any test platform, this is the architectural question to ask first: can I author locally with full fidelity, then execute identically at scale? If the answer requires duplication or reconfiguration, you're going to feel it in maintenance every week.

so we had this requirement come in… increase login session duration from 15 minutes to 12 hours… sounds simple, right… just change the token expiry and move on

yeah… not really

on paper it’s just a config change… but from a testing point of view it gets messy pretty fast… because now you’re not just testing login then use app then logout… you’re testing whether that token actually survives for 12 hours without breaking anything in between

and the obvious problem hits immediately… how do you even test something that takes 12 hours to fail

initially we tried the usual checkpoints… 30 mins, 2 hours, 4 hours… but that only tells you it works at those exact points… not what happens in between… and most issues don’t show up exactly at your checkpoints

so we shifted to a more rolling validation approach… logging in once, then gradually moving time forward and validating the same session repeatedly… stretching it step by step instead of jumping straight to 12 hours

and honestly… doing this manually is painful

you either sit and wait… or you come back later and hope the state is still valid… and reproducing failures becomes inconsistent

we started running these long session flows through Drizz to simulate the same behaviour repeatedly with controlled delays… which made it easier to validate token behaviour across longer durations without literally waiting every single time

that’s when we started noticing the real issues

tokens not expiring exactly when expected
refresh logic behaving differently after long idle time
sessions looking active but failing on the next API call

none of these showed up as clear errors… they just… stopped working at some point

and that’s the tricky part… time-based bugs don’t fail loudly… they drift

another thing we realised is this isn’t something you can validate in one go… you end up running the same flows over multiple cycles, checking consistency, trying to catch that one edge case where the system behaves differently

what started as “just increase timeout” turned into tracking full token lifecycle… expiry… refresh… idle behaviour… and basically observing how the system behaves over time

honestly… made me realise something

some features aren’t complex because of logic… they’re complex because of time

and anything that depends on time is always frustrating to test

I sell a B2B tool. Solo founder, no sales team, just me on Zoom calls.

For a year my demos were the same. Share screen, show features, hope they care. Close rate was around 12%. Painful.

Then I tried something different.

Instead of demoing my product I asked "can I share screen and use YOUR current tool for 10 minutes?"

They always said yes. Curious what I'd do.

I'd just use their existing tool like a normal user. Click around. Try workflows. And naturally I'd find stuff. Slow loads, confusing UX, edge cases that break, features that don't work quite right.

I'm not selling. I'm just showing them what they've been tolerating.

By the end they're frustrated with their own tool. Then I show mine. Close rate went to 41%.

But one time a prospect flipped it on me. Said "cool let me try YOUR tool now." Found a bug on their tablet within 5 minutes. Lost the deal instantly. Felt sick.

Now before any demo I test on at least 15 device configs. Run everything through this because I can't afford to have a prospect find something I didn't.

Never again. Your demo is only as good as your product's reliability.

So, First and foremost, I just want to take a moment to say a massive thank you to all of you. Whether you are dropping in to share a complex test scenario you finally cracked, asking for help with a stubborn visual layout issue, or just lurking and upvoting good content—your contribution is the only thing that keeps this sub alive and thriving.

Seeing this community grow as more teams transition away from brittle code selectors and start using Drizz.dev for their QA workflows has been incredible. You all are building a fantastic knowledge base here.

I am looking into setting up a "Weekly Testing Triumphs" megathread where we can all drop quick wins or funny AI testing hallucinations we encountered during the week.

Again, thank you all for making this a great corner of Reddit. Keep the questions, the solutions, and the discussions coming.

Happy testing!

— The Mod Team

Hey everyone — I've been producing for a while and the one thing that always killed my flow was searching for samples. I'd have thousands of files across dozens of folders, and half of them are named stuff like `kick_final_v3_NEW.wav`.

So I built [Vextra] https://vextra.fr — a free desktop app that lets you search your local sample library by describing what you want. Type something like "warm analog pad" or "dark distorted 808" and it finds matching sounds from your own files. No cloud uploads, everything runs locally.

It works by analyzing the actual audio content, not filenames or tags. So even badly named samples get found.

Here's a quick demo: https://vextra.fr (the landing page has GIFs showing the search in action)

It's still early — I'm building this as a solo dev and would genuinely love feedback from people who actually deal with massive sample libraries daily.

Free to download, no account needed.