538 karma · joined August 1, 2012
I'm currently living as a Software Engineer and amateur Norwegian in Oslo.
A mixture of defending against a disastrous mid-implementation compaction (where suddenly things would veer off the rails) and also allowing the fresh execution to double-check the assumptions and notice any subtle mistakes before context was poisoned.
I’ve found that for large enough changes I still prefer having a parent theorizing about the root cause of issues based on evidence and then dispatching targeted child sessions to fixed based on theories and concrete telemetry examples.
There’s something clean about having sandboxed context and a session you can quiz about architecture while one is heads-down working against a spec.
Try using an LLM to rewrite an LLM output without the slop (vs asking for no slop to begin with) or sandboxed subagents that critique a parent's draft.
There is absolutely a step-function improvement in quality but: 1) not everyone wants to explode their cost by adding extra calls 2) this can't just be "trained in" to a system as obviously they have attempted this but the technique still provides an uplift.
I don't hate the idea
You can endure as long as you want to, but we're all going to erode eventually.
I’ve done plenty of performance architecting in my day-job and rule #1 is generally “you can’t fix what you can’t see/measure”. I have a suspicion that many folks aren’t investing in letting AI actually introspect iterative execution via the appropriate harness, and are then acting surprised that it is no oracle.
High performance algorithms are quite well documented so it isn't unreasonable to expect an LLM to apply them appropriately when given the ability to "see" where they need to be applied.
(Or they’re working in an organization with lower budgets and not cranking the frontier models of today)
I fully agree about the cost/sustainability parts, but to suggest you can’t build a high quality coding/verifying/iterating loop for _most_ problems is disingenuous.
They received a crash course in the power of Haskell ADT and `deriving (Show, Read)`.
Just make sure you alternate which side of the road you are running on somewhat evenly so you are getting the benefit on both sides.
Presumably you have to have a lot of experience skiing for this to work, but there are probably isomorphic activities (weaving around a crowd? Playing dodgeball?) that can hack your brain in a similar way.
I understand pension contributions, but what are the other "hidden" costs that could equal the net salary?
A free market is not a means to an end. Part of the reason that the USA was (until recently) doing so well was that the winner-takes-all mentality of the free-market benefits Silicon Valley, but that doesn't mean that other nation states have to submit to that philosophy.
With the current surge, everyone is (expected to act as) a senior/mentor to a swarm of workers that lack interpersonal/business context.
It’s not a huge shift if you’re already deeply invested in business lore, but it’s unfortunately a brutal speedrun of skills that were previous slowly accumulated for new/junior hires.
You'll have to reassess what a "software engineer" salary looks like, but this is unironically part of the pathway towards living in a more-equal society where perhaps we shouldn't be earning 3x as much as everyone else just because we can invert a binary tree.
It's a huge undertaking, but you _can_ vote where your tax money gets sent. You can ensure it bootstraps a more equal system instead of propping-up an unequal one.
I did this myself, and I feel good about having done it.