ghc

> But what happens when sales numbers don't meet projections? The book is discounted. Then, at the publisher's discretion, the bookstore will receive a directive to rip the covers off the books, recycle the remainder of the book to be "pulped" or turned into other forms of paper, such as notebook paper and toilet paper. The bookstore is expected to mail the book covers to the publisher as evidence that the book has been destroyed.

https://www.offthebeatenshelf.com/blog/pulp-fiction-is-real

Yes, companies can get into trouble with Haskell compile times, memory use, etc., but enforcing disciplined coding rules and advances in tooling solved the majority of the problems by 2016.

We use LLMs to assist in writing Haskell and have for several years. We keep track of compile times to catch regressions and on our largest code base an agent-driven change takes an average of 96 seconds (as of this month) to compile. Agents looking to do quick compile checks during code writing can run with flags that make the compilation run in less than 5 seconds. In all cases, running out test suites takes a lot longer than compile times.

The only time this isn't true is when you compile from cold start, as you would when using multiple work trees...which is the expected behavior when vibe coding features. So yes, I believe the issues they're running into have a lot to do with vibe coding. Check out all their integrations...Haskell's probably a poor match for what they do, but vibe coding integrations is probably what they need to do to get new customers.

Given the number of small batch jams available at various farmers markets, my guess would be that for smaller farms, making jam is more practical than jelly.

I don't know about everyone else, but the code I reviewed as a Junior was high quality code I was expected to learn from. That's entirely different from doing code review on whatever CC outputs.

Turning and turning in the widening gyre

The falcon cannot hear the falconer;

Things fall apart; the centre cannot hold;

Mere anarchy is loosed upon the world,

The blood-dimmed tide is loosed, and everywhere

The ceremony of innocence is drowned

Except network databases have little in common with graph databases...they're much more closely related to hierarchical databases.

I would say that Graph databases are now a strict superset of relational databases, not the other way around. In a graph database a node's named edge can naturally point to a node of any type or having any property schema. Doing this in a relational model requires one of several approaches that could only be classified as fighting against the model (or torturing it, as my PI liked to say).

Well, I guess that at least confirms Oracle on Itanium (!?) still supported RAW 5 years ago.

I'm guessing everyone's on ASM by now though, if they're still upgrading. I ran into a company not long ago with a huge oracle cluster that still employed physical database admins and logical database admins as separate roles...I would bet they're still paying millions for an out of date version of Oracle and using RAW.

Build vs. buy is an eternal question in enterprises. I remember many in-house data teams trying to build tools for "digital transformation" and cloud migration about 10 years ago. The challenge was, building those tools was more expensive than those enterprises could budget for (IT as cost center), so a startup like Snowflake would easily outcompete in-house solutions with their custom, cloud-based tech stack that was necessarily complex because it needed to serve the needs of thousands of customers.

If he's right, the build vs. buy equation has shifted more towards build, at least as far as enterprise software is concerned. IT is still a cost center, but in theory an internal team can now handle more requests for custom tools without looking to outside vendors. Essentially the cost of building in-house might be collapsing and therefore enterprise software startups will be serving fewer customers (who would all pay you more because if solving the problem was cheap they'd do it).

If you had to build a stack for dozens of customers paying huge amounts of money, how would that stack differ from the stack you'd build to serve thousands of customers? Certainly it wouldn't need to be as scalable! And that's probably what he's getting at. I think what you'd do instead, to capture those higher price point customers, is solve their problems more specifically, in a higher value manner.

Many companies already do this, investing far more in field engineers than they do in their tech stack, since customization is essential.

What a world we live in, that suspecting an LLM guided by a specific prompt would be my first instinct.

When I was fresh out of undergrad, joining a new lab, I followed a similar arc. I made mistakes, I took the wrong lessons from grad student code that came before mine, I used the wrong plotting libraries, I hijacked python's module import logic to embed a new language in its bytecode. These were all avoidable mistakes and I didn't learn anything except that I should have asked for help. Others in my lab, who were less self-reliant, asked for and got help avoiding the kinds of mistakes I confidently made.

With 15 more years of experience, I can see in hindsight that I should have asked for help more frequently because I spent more time learning what not to do than learning the right things.

If I had Claude Code, would I have made the same mistakes? Absolutely not! Would I have asked it to summarize research papers for me and to essentially think for me? Absolutely not!

My mother, an English professor, levies similar accusations about the students of today, and how they let models think for them. It's genuinely concerning, of course, but I can't help but think that this phenomenon occurs because learning institutions have not adjusted to the new technology.

If the goal is to produce scientists, PIs are going to need to stop complaining and figure out how to produce scientists who learn the skills that I did even when LLMs are available. Frankly I don't see how LLMs are different from asking other lab members for help, except that LLMs have infinite patience and don't have their own research that needs doing.