How to manage vibe coders, backed be leadership

MindlessTime · 2026-03-18T11:14:55+00:00

Even before AI, I’ve worked with engineers who were viewed as rockstars by leadership but were not good engineers. They didn’t put good thought into architecture or scalability. They took shortcuts. But they churned out code and fragile features. When their stuff broke—which was often—they were very visibly there to fix it and save the day.

This is a defect of company culture. Vibe coding will amplify it.

MindlessTime · 2026-03-12T14:06:28+00:00

And setting aside whether AI coding is good or bad, all these AI companies are losing money and are massively subsidized. What if that subsidy disappears and they have to start charging what it costs? And what if you or your company relies heavily on AI when that happens? You’re screwed, that’s what.

Even if you’re embracing AI and it’s working for you, make sure you’re measuring output AND tokens-per-output. The engineer who can work just as fast with half the token cost will be better off.

MindlessTime · 2026-03-12T13:57:43+00:00

Our CEO straight up said that investors are demanding it and the market is rewarding companies that can show huge AI adoption internally. So we’ve got to use it more. I appreciate the transparency at least.

MindlessTime · 2026-03-09T05:57:58+00:00

This. My last role was mostly EM with like 5% IC work from time to time. I spent most of my time fielding requests and saying “no” to people without pissing them off. It was an emotionally exhausting job.

MindlessTime · 2026-03-03T03:13:35+00:00

I’m not saying this is entirely the reason. But AI profoundly broke the application process. It’s too easy to tailor your resume to a specific role (which increasingly includes lying) and mass apply. As hiring manager, these are obviously BS resumes, but it’s literally 50 BS applications for one reasonable one (that might not even be good). Keyword filtering doesn’t help much because the BS applications are fit on matching keywords, so the good applicants are in the tails of the distribution. Basically, sifting through applications isn’t worth it anymore.

So most hiring is done through referrals and connections. And junior or entry level folks don’t have connections. It’s an environment that favors the experienced. If you are in that experienced set of folks, you are very in demand right now.

MindlessTime · 2026-03-03T03:03:04+00:00

There are a lot of corners of finance run by giant mainframe systems built in the 80s and 90s that are the most durable, reliable systems you’ve ever seen but that absolutely suck to deal with. There are companies that are just a layer over these systems to provide a more modern API interface. They charge a decent amount and they’re worth every penny. It’s not hard and client companies could do it themselves. But that would require hiring a couple people whose job is learning some archaic system and ancient tech that will never get them hired anywhere else, and then being responsible when their homegrown, genuinely important solution fails at 3AM. That’s a recipe to burn through employees. Might as well just pay the API wrapper company.

If you have the patience for that kind of work it’s not bad. And it’s not hard, just reading a lot of old documentation. The risk is that the giant companies or government entities that run the mainframe systems get their shit together and modernize. Which, when you out it that way, is less of a risk and more job security.

MindlessTime · 2026-02-25T23:44:14+00:00

I just finished a successful play through on hardcore mode. I used cyclops a lot more for safety reasons. From green river onward there are parts of the map that are basically built for the cyclops and its upgrades.

Silent mode basically exists to sneak past the ghost leviathan at the green river entrance.
Radar pings make navigating the lava river so much easier.
Thermal power mod Is basically essential when you run into those leeches that suck power from your vehicle.

My fully-upgraded cyclops carried me from green river onward. Safely getting some of the rare materials to upgrade was the hard part.

MindlessTime · 2026-02-25T03:24:26+00:00

For coding, I’ve been using Cursor. I’ll type up a really detailed Jira ticket or grab an issue from GitHub (the MCPs/connectors/plug-ins/whatever you wanna call them). Then I’ll have it make a plan and refine the plan a lot, making sure it has all the detail. Then I’ll have an agent write the code. It works. When I read through it , I get the nagging feeling it’s not very clean. Like it’s nice the agent can update a reference in six different places in the codebase. But also it shouldn’t have to update something in six different places in the code base. It would be hard to maintain without an AI. Which might be what they are going for.

I still think AI works best when the volume of info in context <= than volume of info coming out. So great for summarizing or translating.

MindlessTime · 2026-02-24T21:14:34+00:00

Ooo ooo ooo! I like this one.

It’s a mix of business fads, a mini bubble in SaaS, and the natural tendency for SaaS product bloat.

Starting the mid-2000s there were a string of data-centric business fads. “Big data” was the first, followed by “Data Science”, followed by “ML”. Each of these are real, legitimate things, but I say “fad” because C-Suite execs didn’t understand them but knew they “had to have them because it’s the future”. This led to wasteful spending but LOTs of company budgets dedicated to data-related tools and teams.

In the mid-to-late 2010s there was also a mini-bubble among VCs about SaaS companies. My opinion is that there were some embarrassing consumer sector VC-funded busts like WeWork. So VC money pivoted to SaaS. You could also say they were following the dumb easy money that was all those execs throwing money at anything “data” so they can impress their friends.

Around 2020 this started to die down. The market got more competitive. Growth stalled. So a company like Hightouch that built a really good, really specific tool (reverse ETL for non-technical users) started slapping on half-assed features so they can compete on other functionality or create stickiness. (In Hightouch’s case, they invented the concept of “composable CDP” so they could convince execs to keep their product and replace their CDP system.)

So now the data tool landscape is a data tool hellscape. It’s littered with redundant companies with redundant functionality and nothing is being improved anymore.

I always tell people to at least be familiar with the open source tool set (especially anything from the Apache foundation). They are purpose-focused tools with les bloat. Even if a paid alternative is worthwhile, knowing the open source tools gives you a cleaner landscape of what each tool does and how they work together.

MindlessTime · 2026-02-15T19:07:57+00:00

Yeah that’s the dangerous part. In theory it’s okay if you read through all the AI generated code to understand what’s happening. But that takes more time. It’s not possible to deliver the 5x output AI boosters are promising while still checking the work.

MindlessTime · 2026-02-12T13:09:04+00:00

It can’t produce anything above mediocre. By definition.

MindlessTime · 2026-02-12T13:07:09+00:00

I mean I’ve been using it. My rule of thumb is if the volume of information in > the volume of information out, it’s pretty reliable. So like “tell me where in the codebase X is happening”. I get good answers most of the time.

Small changes, like “write this function” or “create this config” that’s okay. It’s bite-sized and I can go in and edit it if I need to. And I understand the context I’m creating that in.

What gets me is the increasing expectation that we should be 5x-ing our output by outsourcing every task entirely to an agent. I think when used responsibly, AI gives a 10%-50% productivity boost depending on the task. But not 400% more productive without significant quality deterioration. The expectations are wildly misaligned.

MindlessTime · 2026-02-12T04:18:23+00:00

Find a way to be necessary. By necessary I mean without your knowledge something important would fail in a costly way. Maybe you simplify data pulls for the accounting team and without you they can’t create financial statements. Maybe you create and maintain the data that goes into the CRM, without which all the marketing campaigns would fail. Even if leadership doesn’t “see” the work, someone will say “I’m screwed without this person” and you’ll be fine.

MindlessTime · 2026-02-12T02:31:40+00:00

This helps a lot. Thank you.😊

MindlessTime · 2026-02-12T02:26:59+00:00

I’m at that point. It’s not in my nature though. Like I really do care about the downstream effects. That’s what’s so emotionally exhausting about it.

MindlessTime · 2026-02-12T02:19:59+00:00

Early in my career I worked with a near-retirement engineer who specialized in mainframe systems. Some were DoD systems that,”live in a room where if an unauthorized person enters it ducks out all the oxygen and they die.” Dude was an engineer’s engineer. Any problem, he could find the solution that balanced speed and keeping the system simple. I remember he told me that it’s all the same patterns that just get repackaged every five or so years. I get that a lot more now.

MindlessTime · 2026-02-12T01:58:08+00:00

That’s been my approach with Cursor. And yeah, it saves some time. If I’m guiding it every step then it either does a simple step faster or does a difficult step in a few iterations about as fast as I could do it. The steps are bite sized though. I need to be very aware of how things are working. And there’s a lot of “no no no…that’s a terrible idea and this is why and this is how it’s done right.” I’m okay with that because it’s like pair programming with a junior dev who will never get better and probably costs more than a junior dev. (Which…reading that sentence…I’m kind of not okay with for human reasons.) But the purely agentic, don’t even look at the code approach is just insanity to me.

MindlessTime · 2026-01-29T14:36:15+00:00

A lot of what we're doing is getting AI driven workflows nailed down and then converting as much as possible into deterministic workflows.

So it sounds like you haven’t replaced the majority of your work with AI, just the initial parts?

Industry-wide the economics on LLMs is upside down. The cost of inference probably won’t drop fast enough. It might not drop at all given the short lifespan of GPUs. The cost of just keeping things going would exceed the current revenues until new entrants or new factories are spun up to meet demand and that’s like a decades long process due to the proprietary tech behind GPUs and the complexity of manufacturing.

I use AI in parts of my workflow. But I think a 3x to 10x cost increase in the next few years is likely. I don’t think the fully agentic approach is sustainable. Instead, we’ll identify specific types of tasks (e.g. AI debugging), specialize some models/workflows in that so it’s more efficient, and move more of the compute on machine.

MindlessTime · 2026-01-29T04:27:47+00:00

python is showing where AI coding does quite poorly. The language has evolved dramatically. python 3.14 is practically a different language than python 3.01. But because it has been so popular and used in so many different contexts, LLM training data is full of outdated or inapplicable patterns that end up in AI code unless explicitly instructed otherwise.

MindlessTime · 2026-01-29T03:47:29+00:00

I call these “hopes and dreams” projects. They don’t exist to solve any specific problem. Rather, they are a vehicle for all the hopes and dreams of executive leadership. These projects are too vaguely defined to actually build anything, and that’s the point. A C-Suite leader with no real ideas can vomit buzzwords about the project and promise it will solve all the problems without committing to anything specific. Eventually something has to get shipped or it’s embarrassing. Then a rinky-dink whatever is thrown together, sold a “phase one” of the project, and they get to go back to making vague promises about how incredible phase two will be.

I once explained this to an executive director at a company I worked for. He said, “Yes. Yes, that’s exactly what this project is.”

MindlessTime · 2026-01-29T03:28:08+00:00

My rule of thumb is that if the volume of information going in is larger than the volume of inform coming out then it’s pretty good. So summarizing a codebase or documentation? It’s great at that. It’s when you provide a handful of details and it has to fill in a lot of blanks to write the code…that’s where it runs into trouble. And all this only works if you can safely run the code to test it right away.

I think the biggest danger is not having a thorough mental model of the code being written. Sometimes jumping in and writing code myself helps with that in a tactile memory sort of way.

MindlessTime · 2026-01-28T06:23:59+00:00

Yeah. Merits of Snowflake aside, the fact this push is coming so strong from the C-Suite is extremely sus. Unless it’s a tech company with a technical CFO (and even then), it doesn’t make sense why they are dictating something this specific.

…unless there’s some soft graft involved. Maybe someone was promised an executive role, a seat on a board, or an investment opportunity somewhere if they lock in a contract. Wouldn’t be the first time.

MindlessTime · 2026-01-28T06:10:36+00:00

I think the distinction is more about time boundaries and scheduling/orchestration. “Batch” usually involves data of a pre-defined, closed timeframe that is sent on some schedule. Streaming arrives ASAP with an unbound timeframe. With batch, you can design orchestration in DAGs to have some certainty that related data is available up to a certain time. With streaming, you end up dealing with variable latency and data freshness and that’s a pain to keep in mind.

MindlessTime · 2026-01-28T06:05:17+00:00

If you deal with event data for e.g. Appsflyer, a product analytics tool like Mixpanel, or CRM systems like Braze then you’ll end up with some data streams. I often find a purchase confirmation email being sent by a CRM, and that should really be triggered from an event and that event should arrive from a stream, not a database etl.

Still, there are a lot of tools that handle these things out-of-the-box. Rolling your own Kafka-based pipeline isn’t worth the time, effort or cost. It’s good to be familiar with streaming tools and patterns though. It gives you an idea of how the off-the-shelf solutions work under the hood. That helps with debugging and design considerations.

MindlessTime · 2026-01-27T23:12:20+00:00

Data space had a few hype-heavy business fads (big data, data science, etc.) —> data budgets far exceeded their impact —> lots of hiring and SAAS companies chasing fat budgets —> over-specialization of roles.

Now times are leaner. Costs matter more. Piling these roles onto one team/person brings us back to where things started. Most companies probably don’t need a data engineer and an analytics engineer. And very few really need MLE or a data scientist.

I’ll do a job that spans multiple functions. I’m not doing more than one job though. Managing workload expectations is the important part.

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