I think your assumption about inlining is essentially correct. As far as I know postgres was the last major rdbms to have an optimiser fence around CTEs.
I concur, “the Germans” have created an algorithm that completely “see through” subqueries/CTEs when planning a query. The way the query is written has no bearing on the execution.
I've been working on a side project for ~10 years (very intermittently) that involves a tricky combination of mathematics, classical AI algorithms, and programming language design, and I've gone though this very slow but rewarding journey to work out how all of the pieces should fit together properly.
In the last year or so I've been able to prototype it and accelerate the development quite significantly using Claude and pals, and now it is very close to a finished product. One one hand there's no doubt in my mind that the LLM tools can make this sort of thing faster and let you churn through ideas until you find the right ones, but on the other hand, if I hadn't had that slow burn of mostly just thinking about it conceptually for 10 years, I would have ended up vibe coding a much worse product.
10 years of thinking before shipping is actually the move. The AI just becomes a power tool — useless if you don't know what you're building, unstoppable if you do
Openclaw has 20k commits, almost 700k lines of code, and it is only four months old. I feel confident that that sort of code base would have a no coherent architecture at all, and also that no human has a good mental model of how the various subsystems interact.
I’m sure we’ll all learn a lot from these early days of agentic coding.
> I’m sure we’ll all learn a lot from these early days of agentic coding.
So far what I am learning (from watching all of this) is that our constant claims that quality and security matter seem to not be true on average. Depressingly.
I think what we're seeing is a phase transition. In the early days of any paradigm shift, velocity trumps stability because the market rewards first movers.
But as agents move from prototypes to production, the calculus changes. Production systems need:
- Memory continuity across sessions
- Predictable behavior across updates
- Security boundaries that don't leak
The tools that prioritize these will win the enterprise market. The ones that don't will stay in the prototype/hobbyist space.
We're still in the "move fast" phase, but the "break things" part is starting to hurt real users. The pendulum will swing back.
This makes sense. Development velocity is bought by having a short product life with few users. As you gain users that depend on your product, velocity must drop by definition.
The reason for this is that product development involves making decisions which can later be classified as good or bad decisions.
The good decisions must remain stable, while the bad decisions must remain open to change and therefore remain unstable.
The AI doesn't know anything about the user experience, which means it will inevitably change the good decisions as well.
> So far what I am learning (from watching all of this) is that our constant claims that quality and security matter seem to not be true on average.
Only for the non-pro users. After all, those users were happy to use excel to write the programs.
What we're seeing now is that more and more developers find they are happy with even less determinism than the Excel process.
Maybe they're right; maybe software doesn't need any coherence, stability, security or even correctness. Maybe the class of software they produce doesn't need those things.
I still use excel to write programs. I use officescript and power query. I shy away from via but have also used it.. I’m not sure what your point is. The people stopping citizens’ development could ease off the job security lines and the deferral to lockdown
20 for me, and let's not exaggerate. We've given lip service to it this entire time. Hell look at any of the corps we're talking about (including where I work) and they're demanding "velocity without lowering the quality bar", but it's a lie: they don't care about the quality bar in the slightest.
One of my main lessons after a decent long while in security, is that most orgs care about security, *as long as it doesn't get in the way of other priorities* like shipping new features. So when we get something like Agentic LLM tooling where everything moves super fast, security is inevitably going to suffer.
I’m learning that projects, developed with the help of agents, even when developers claim that they review and steer everything, ultimately are not fully understood or owned by the developers, and very soon turns into a thousand reinvented wheels strapped together by tape.
> very soon turns into a thousand reinvented wheels strapped together by tape.
Also most of the long running enterprise projects I’ve seen - there was one that had been around for like 10 years and like about 75% of the devs I hadn’t even heard of and none of the original ones were in the project at all.
The thing had no less than three auditing mechanisms, three ways of interacting with the database, mixed naming conventions, like two validation mechanisms none of which were what Spring recommended and also configurations versioned for app servers that weren’t even in use.
This was all before AI, it’s not like you need it for projects to turn into slop and AI slop isn’t that much different from human slop (none of them gave a shit about ADRs or proper docs on why things are done a certain way, though Wiki had some fossilized meeting notes with nothing actually useful) except that AI can produce this stuff more quickly.
When encountered, I just relied on writing tests and reworking the older slop with something newer (with better AI models and tooling) and the overall quality improved.
I think the total volume idea is more flawed than you realise. Pretty much everyone would be able to achieve greater volume, on any exercise, just by decreasing the weight, so your high rep caveat is covering up for quite a lot. This is true mathematically for an Epley style model for example.
> Pretty much everyone would be able to achieve greater volume, on any exercise
I’m not sure this is true and it might be the opposite. Lactic acid will build up with light weight while trying to hit a volume number that will make it hard for people to finish.
Anecdotally, my gym had a "challenge" some times back where the goal was to achieve the max total volume in one set without pause.
I tried various combos of weight* reps, and in the end the optimum was somewhere in the middle because no matter how light the weight there was a limit for me at about ~150 reps.
In my case, the curve would be: total volume increases quickly initially at you go from max weight/1 rep to something like 20/30 reps, then something of a plateau as things equalise, then it goes down again as you reach the max reps threshold.
To add to this, I find talking to it about code quality or architecture issues can work quite well. Just treating it like another developer. Saying, “I’m not happy with the way the project is going because of X, and Y” and then making a plan for how to get things back on track. Maybe putting a complete rewrite on the table, or maybe just having it record the agreed code style principles in CLAUDE.md, etc
I think I have almost the opposite intuition. The fact that attention models are capable of making sophisticated logical constructions within a recursive grammar, even for a simple DSL like SQL, is kind of surprising. I think it’s likely that this property does depend on training on a very large and more general corpus, and hence demands the full parameter space that we need for conversational writing.
Concrete Mathematics is probably the best single book that you could read to prepare you for some the problems beyond the first 50. It’s extremely fun, and also mathematically serious. A large portion of PE problems are exactly in the cross sections of number theory, combinatorics, and computation that is covered in this book.
I mean, you can just do that in the browser too. "Enter ID" allows you to enter the MusicBrainz UUID (or just full URL). You can even do in the command itself.
It's just a good set of models to use to think about all sorts of different mathematical systems, kind of like a unified vocabulary. Beyond undergraduate level, category theory these days plays a huge role within many vast fields - e.g., algebraic geometry, algebraic topology, or representation theory.
I think your reply overstates the importance of category theory in mathematics and doesn't give any hint on what it is about.
IMO a better reply would be: category theory appeared to unify the concepts around using discrete objects to prove the properties of continous objects in topology, like fundamental groups, homology groups and homothopy groups. It is only practically useful for very advanced proofs like 2nd Weil Conjecture. Any usage of it in programming is only an analogy and is not mathematically rigorous (see https://math.andrej.com/2016/08/06/hask-is-not-a-category/)
I do this at the moment in my hand rolled personal assistant experiment built out of Claude code agents and hooks. I describe my workouts to Claude (among other things) and they are logged to a csv table. Then it reads the recent workouts and makes recommendations on exercises when I plan my next session etc. It also helps me manage projects, todos, and time blocked schedules using a similar system. I think the calorie counter that the OP describes would be very easy to add to this sort of set up.
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