This is why there are a ton of corps running the open source models in house... Known costs, known performance, upgrade as you see fit. The consumer backlash against 4o was noted by a few orgs, and they saw the writing on the wall... they didnt want to develop against a platform built on quicksand (see openweb, apps on Facebook and a host of other examples).

There are people out there making smart AI business decisions, to have control over performance and costs.

From "scrum masters" to "planing poker" it's all very silly.

2003: Nvidia accused of cheating in 3DMark 03 https://www.gamespot.com/articles/nvidia-accused-of-cheating...

It's almost like the benchmarks were designed with zero understanding of the history of benchmark manipulation.

I like what LLM's are doing and providing. But the industry as a whole seems to live in a vacuum that ignores so much of the hard lessons that have been learned over the last 50 years of computing. It is doing itself a disservice.

SQS is dead simple, and if your in AWS (forever) it is "in the stack" with some easy to use features that may make sense to you (delay queue is a great one).

Kafka is... a lot. If you need what it provides, then it's great. You just have to be able to support it, and thats non trivial.

I can point to more than a hand full of Kafka project that exist because it was clear that someone wanted it on their resume. I dont think any one is doing that with SQS, it is just (a fairly good utility). However if you want to leave (or branch out from) AWS and you're reliant on it, good luck.

ZScalar No PE

Palo Alto Networks Inc (PANW) 86 PE

Fortinet : (FTNT) 31.63 PE

That last one, didn't get hit at all by the Mythos announcement, because at some level it has at least some grounding in fiscal reality.

Your argument for host os, virtual os, container is the very point im making. Rather than solve for security and installablity, we built more tooling, more layers of abstraction. Each have overhead, security surface and complexity.

Rather than solve Rusts performance (at build time), switch to a language that is faster but has more overhead, more security surface, more complexity.

You have broken down the stack of turtles that we have built to avoid solving the problem, at the base level...

SABRE, what the article is discussing, is the polar opposite of this, it gives us a hint that more layers of abstraction arent always the path to solutions.

How many banks and ERP's, how many accounting systems are still running COBOL scripts? (A lot).

Think about modern web infrastructure and how we deploy...

cpu -> hypervisor -> vm -> container -> run time -> library code -> your code

Do we really need to stack all these turtles (abstractions) just to get instructions to a CPU?

Every one of those layers has offshoots to other abstractions, tools and functionality that only adds to the complexity and convolution. Languages like Rust and Go compiling down to an executable are a step, revisiting how we deploy (the container layer) is probably on the table next... The use case for "serverless" is there (and edge compute), but the costs are still backwards because the software hasn't caught up yet.

Payment processing, is better than it was in 2000, but still not good.

Micropayments: this is obnoxiously expensive to do.

Discovery, and discoverability: again here we have better but not good solutions (and many of the ones that were once good are enshitified).

Pricing: this is a problem everywhere, and frankly we need the law to change in a way that is pro consumer. Publishing prices, disclosure of fees, in both services and for payment processing (that 3 percent back from visa looks a lot less attractive when it's part of a 5 percent mark up).

Customer service: well there are already companies promoting models where they cut you off and send you into a black hole (google is a prime example). Good customer service will become a differentiator, and maybe a "paid for" service as well.

> We are creating not only a new kind of Git client,

Nope, not going to be the tool of the future.

The fundamental problem is it is still based on git.

Till this addresses submodules and makes them a first class citizen it's just tooling on top of a VCS that still ONLY supports single project thinking.

Im saying that in the before time, complexity emerged over time (staff changes, feature creep). AI coding (and its volume) is just speed running this issue.

Time, feature changes, bugs, emergent needs of the system all drive these sorts of changes.

No amount of "clean code" is going to eliminate these problems in the long term.

All AI is doing is speed running your code base into a legacy system (like the one you describe).

It's leaning in a good direction, but the author clearly lacks the language and understanding to articulate the actual problem, or a solution. They simply dont know what they dont know.

> Human effort is still a moat.

Also slightly off the mark. If I sat one down with all the equipment and supplies to make a pair of pants, the majority of you (by a massive margin) are going to produce a terrible pair of pants.

Thats not due to lack of effort, rather lack of skill.

> judgement is as important as ever,

Not important, critical. And it is a product of skill and experience.

Usability (a word often unused), cost, utility, are all the things that people want in a product. Reliability is a requirement: to quote the social network "we dont crash". And if you want to keep pace, maintainability.

> issue devs would run into before AI - the codebase becomes an incoherent mess

The big ball of mud (https://www.laputan.org/mud/ ) is 27 years old, and still applies. But all code bases have a tendency to acquire cruft (from edge cases) that don't have good in line explanations, that lack durable artifacts. Find me an old code base and I bet you that we can find a comment referencing a bug number in a system that no longer exists.

We might as an industry need to be honest that we need to be better librarians and archivists as well.

That having been said, the article should get credit, it is at least trying to start to have the conversations that we should be having and are not.

I think a "clean" and "contemporary" version of this would look amazing as well:

Along the lines of: https://www.modustrialmaker.com/blog/2018/8/14/making-an-imp...

Maybe with: (for weight) https://en.wikipedia.org/wiki/Foam_concrete (there are plenty of DIY versions of this: https://www.youtube.com/watch?v=z4_GxPHwqkA

I live in the SF Bay Area, and have Sonic internet. 10gbe for 60 bucks a month.

I ended up building my own router/firewall because it was cheaper than anything off the shelf that could deal with that sort of speed.

It is entirely possible to have these sorts of services in the states, when competition is allowed.

90 percent of the things users want either A) dont exist or B) are impossible to find, install and run without being deeply technical.

These things dont need to scale, they dont need to be well designed. They are for the most part targeted, single user, single purpose, artifacts. They are migration scripts between services, they are quick and dirty tools that make bad UI and workflows less manual and more managable.

These are the use cases I am seeing from people OUTSIDE the tech sphere adopt AI coding for. It is what "non techies" are using things like open claw for. I have people who in the past would have been told "No, I will not fix your computer" talk to me excitedly about running cron jobs.

Not everything needs to be snap on quality, the bulk of end users are going to be happy with harbor freight quality because it is better than NO tools at all.

Sir Roger Penrose, on quantum consciousness (and there is some regret on his part here) -- OR -- Jacob Barandes for a much more current thinking on this sort of intersectional exploratory thinking.

All of this is spot on!

> FTA: One that’s superior to the admin panel of WordPress or Drupal?

When you get to that multi person team, has any one asked them if they LIKE the CMS they are using? Because I assure you that they appreciate the functionality but it isnt a tool that any one is happy about using.

Usable and Usability are NOT the same thing, it is a lesson that was lost in the dot com bubble burst, that we might need to get back to.

Much as Diogenes mocked Platos definition of a man with a plucked chicken, LLM's revealed what "real" ai would require: contigous learning. That isnt to diminish the power of LLM's (the are useful) but that limitation is a fairly hard one to over come if true AGI is your goal.

If you haven't been paying attention anthropic burned a lot of their developer good will in the last 2 weeks, with some combination of bugs and rate limits.

But the writing is on the wall about how bad things are behind the scenes. The circa 2002 sentiment filter regex in their own tool should have been a major clue about where things stand.

The question every one should be asking at this point is this: is there an economic model that makes AI viable. The "bitter lesson" here is in AI's history: expert systems were amazing, but they could not be maintained at cost.

The next race is the scaling problem, and google with their memory savings paper has given a strong signal what the next 2 years of research are going to be focused on: scaling.

I have several friends who work in education.

At one point there were computer labs in school, there was education around computing. The pervasiveness of computing killed these programs, along with various kinds of skill based classes, like wood/auto/home economics (cooking and or sewing).

All of them tend to agree that the losses of these programs is, in hindsight, problematic. Many of them think that a return to computer education (and conveying deeper insight) would be a net positive.

> EdTech

To a person, every one I know thinks their EdTech platforms suck. One of them is in a support role as part of their job and often tells me stories of how lamentable the software and faculties interactions with it is/are.

"Progress is at fault" is a tale as old as time: https://xkcd.com/1227/