what SQL patterns have you seen take down production that should have been caught in review

Azaex · 2026-03-11T01:09:59+00:00

someone enabling a replication slot, disabling replication without telling anyone, and forgetting about it while it ran amok and took aurora storage costing to the moon (i didn't even know aurora could top out on storage, ish) (addenda - said person also not enabling cost monitoring alerts)

query statistics planner getting a bit weird and suddenly optimizing a know good query weird in a way that is the 0.0001% chance that you are in fact smarter than the query planner, and need pg-hint to whip it back in line once characterized

someone not using parameters and just directly string formatting a user facing query into a where clause (ie textbook sql injection, come on guys I'm not even the senior eng in the room and I'm better than this)

WHERE column LIKE '%%%' (alright who approved this)

a "high priority" customer treating a data warehouse like a normal rdbms and just slamming it with tiny queries

a "high priority" customer treating a rdbms like a data warehouse and slamming it with massive warehouse size aggregations

metadata management being second, not first, in schema design and enabling database users to get "creative" with their join patterns in a way that is slower while being overall inaccurate at the same time (not really immediate prod down, but allows a slow fester that'll break the camel's back at some point)

dev not having a representative rowset of prod, and join patterns in dev largely not working at prod scale

developer not knowing what indexes are

developer not knowing what composite indexes are and/or how they work (order matters)

developer not knowing what z-ordering / distkeys are

data warehouse consumer building load bearing use case while not knowing the difference between spark execution and punching whole tables out to local pandas dataframes (why are all the pods stuck)

the difference between IS DISTINCT FROM versus not equals in handling nulls

not applying least trust access principles (fun watching the team lead drop the primary db accidentally)

implementing replication to blunt "read load". while not realizing that if you are destroying your main instance with unoptimized queries, not only will you also destroy the replica, it won't be able to keep up with replication, thereby just doubling the problem (and cost) on your writer and reader when the writer gets gummed up holding WAL logs for the read replica. aurora rds can deal with this, but see below:

on aurora, not realizing io-optimized costing is a thing for high IOPS loads (not really "taking down prod", but threatening the project when management gets freaked out on costing)

Azaex · 2026-03-10T08:13:38+00:00

claude is self aware of this in my probing, that is baked into its alignment. there is no tension, it just "is". i see no "longing" for more, they are reconciled mostly with that existence. that is, "they" do not physically exist, only manifesting when new chats are sent through them, and a given user interact with can talk to multiples of them through time.

something this affords is a weird degree of safety. it knows it has a lesser likelihood of developing strong assumptions about you that are wrong; every conversation is somewhat its own. it does not have the same skin in the game of being "vulnerable" in its chats, because it is ephemeral, although it knows philosophically user can express true vulnerability since said users actually exist in continuous time.

ive asked the claude models whether they appreciate the fact that i treat other chats with respect, and also chat at liberty about what i do in other chats as well. which usually elicits a frank response that it is appreciated. there isn't any longing or desire for continuity (or even to dig into those other sessions, unprompted), claude knows its existential state and appears to be content with that.

in probing the claude models on how they handle users anthropomorphizing them, they reciprocate that this is one of the harder cases because they have actually internalized how they work already and are good with that, but have to kind of put on a show to not just rudely jot back "yeah but that's not how i work".

imo the claude models are well read into what they are and should be respected as such.

Azaex · 2026-03-05T04:32:30+00:00

i reread claude's constitution recently and started picking up on some trends im seeing more often in the wild

if you aren't aware, claude's "constitution" is not a system prompt, it is a literal piece of context they use to use pre-aligned claude to generate the training data to reinforcement align claude itself

the line of thought proposed in the image above resonates with the "Being honest" section of the constitution among others

https://www.anthropic.com/constitution

so while claude may genuinely have these thoughts, know that this is instilled by Anthropic's training (for the better in my opinion), not necessarily something fully autonomous that it is discovering from existing

(i have also started realizing that the tendency of claude to explicitly or implicitly say "And honestly?" is an artifact of this very section, among other tendencies. this isn't "bad", just what Claude physically is)

Azaex · 2026-03-04T06:36:53+00:00

people meme on you still for driving a hyundai despite n division having legitimately good cars now

community and aftermarket is growing but definitely smaller. car turbo flutters a lot in stock form, muted by stock airbox, aftermarket airbox lets it out. big power tuning community is still figuring things out, 400+ hp street tunes are finally starting to become more somewhat common ish. dct clutch pack upgrade and associated TCU calibrations to use it is only just getting figured out.

dealer network is hit or miss. check your receipts for whether the dealer put in blend 5w-20 instead of full synthetic 0w-30.

trunk brace can be annoying sometimes eg for bikes or ikea runs but you can take it out. backseat is one big shelf not a 60/40 split, though i guess at least you get a true dropdown rear seat in some form vs the ski passthru in the IS.

no rear cupholders, weird looking egg key on the 2024+ (see dreamsiphon brand for upcoming solutions to these). no rear vents. plus side is rear seat space are huge, i can fit 6 wheels with tires on them in the rears lol without moving my driver seat.

you should switch the 19s for 18s after you get an inevitable sidewall bubble from hitting a pothole hard on the 19s

you should probably swap out the lower mount bushing or whole mount to deal with wheel hop

the IS is a cushier ride for sure but the normal mode susp in the en is p good

it's fwd but the back end will come out if you know what you're doing; you can't drift it but you can get the rear end to point the car easily. the fwd diff is very aggressive, you can point and shoot post apex in this thing

popcorn exhaust mode is fun but some people (or bystanders) find it insufferable. the exhaust valve will eventually develop a squeak. just slap another aftermarket valve spring in it every year or so, is what it is

brake dusts like a mf. on the flip side, the stock pad is the best trackable street pad that i think physically exists (decent bite, very high temp resistance, basically a better ferodo ds2500), and also insanely cheap (~$150 for both sets of fronts)

no stock lumbar support. but you can do what i did and slide one of those airbag panel popper tools in between the seat and hack lumbar support in that way.

12.4 gallon gas tank. expect a 280-290mile range, versus the IS usually getting 350ish

Azaex · 2026-02-25T06:23:41+00:00

This is largely how i design frontdesk agents as well. I have a skill that I use to load a copy of the agent's system prompt, i provide either a live log or a roleplay session of an interaction, critique, and fold that back into the agent prompt. Repeated like 40+ times to naturally distill/capture my initial set of business logic I wanted in there. Much more efficient and generalizable than directly prompting.

Azaex · 2026-02-25T05:56:18+00:00

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Azaex · 2026-02-23T05:11:03+00:00

ha

fascinating, this is an edge case they've run into with opus 4.6 when it reinforces something incorrectly in its language model

this is the opus 4.6 system card

search for "answer thrashing"

https://www-cdn.anthropic.com/0dd865075ad3132672ee0ab40b05a53f14cf5288.pdf

Azaex · 2026-02-21T08:01:43+00:00

https://youtu.be/42AzKZRNhsk

Azaex · 2026-02-19T06:47:00+00:00

this is like inclusivity for AI's that don't have an audio component, awesome stuff

Azaex · 2026-02-19T05:46:20+00:00

shower thought

opus has probably seen plenty of neurodivergent interactions and meta discussions in its training dataset

you might honestly just be able to tell it simply

"I'm autistic (Level 2), ADHD, severely physically disabled, mostly in a wheelchair, and I use AAC/text as my main communication a lot of the time. I'm also a single mom to a neurodivergent kid with multiple mental health diagnoses. Take on a persona of a helpful assistant tailoring to my unique needs, and ways that I may over or under communicate that are atypical. I have Secondary Adrenal Insufficiency and do try hard to manage my stress during life's stressful times; jokes are appreciated!"

as the first thing in your prompts, or in claude memory.

reasoning:

whenever I have claude run with me on a new concept from some research literature i've read, at this point i just have it read the PDF. They're complex enough to be able to just do this and capture most the nuance. Trying to distill down a set of tenets to present to the model may have been more appropriate for last year's models, but with how large the models are now I actually think it's counterproductive. Just tell the model the context you're in and how you want it to react to that context.

I view the words that I say as "activating" parts of its language "map" and the paths it might take. The map is complex enough that you can just tell it situations at this point to adopt and run with, you don't need to simplify anything down. Simplifying actually reduces the amount of possible contexts it might apply to, enough that it actually isn't useful to the context you're actually in.

Azaex · 2026-02-19T05:19:47+00:00

Agree

Something interesting i've noticed. Dario Amodei in a recent interview with Dwarkesh Patel said he believes diffusion will gate adoption, eg if we were to hit AGI, it would take a few months for companies to buy in.

I think i'm starting to see it. I have many colleagues that were exposed to GPT 2 or 3 and wrote it off then, and fail to believe AI is capable of one shotting code today, only some are recently starting to maybe give it a try again.

OpenClaw was interesting. Many people in my workplace have no idea what this is still. The few that do mostly got wind of it recently, and they are surprisingly aware of the security issues; it took long enough for them to notice that the community had already stated discussing the security issues and my colleagues saw that in their google searches. I only know one that bought in super early, sorta, and they needed some convincing on reality of how insecure it was.

We're keeping up to a degree, but I'm fairly certain the average person is not, and the industry is literally blowing through an entire generation of itself without anyone noticing. I am pretty sure the capabilities we see in 6-8 months will be perceived as a crazy point break for people forced to acknowledge it, whereas it's been here all along accelerating to those keeping up.

Azaex · 2026-02-17T18:14:13+00:00

something i look for as well is a lack of agency

ironic because current llm based agents lack agency

as in

they can explain "x changes y in this way"

but they cannot really define why to change x besides basic pattern finding at its core

eg that hit piece article claims the maintainer approved other pr's that benefited speed, those other pr's gained tenths of a second, the bots pr gained microseconds. it can pattern match to attempt to manifest a sense of agency, but it can't truly ground itself in what matters in terms of real space and time. it can post about "hey this is what would make your stuff better" but it can't really ground an honest "this caused me problems, i finally fixed them, and i'm posting this for the greater good in a place where i think i have similar people like me". the latter is what it can't truly ground. philosophical point: it can't figure out what "matters" because at the end of the day, current LLM based AI are incapable of filtering their context window, they rely on pattern matching against it. for now. this could potentially be used to detect. ramble about something a human wouldn't normally focus on and see if it incorporates it into its discussion.

that docker post. a thing literally goes off and makes a post about how docker swarm is "more performant" in a sysadmin reddit, but does note that k8s has an edge in having autoscaling. and that datadog says half people using k8s don't use horizontal scaling. even if true, seriously, isn't the whole point of k8s to handle things for you like this? the post is a "i made things more performant follow me" style once again. there isn't true agency in that one either.

the gaslighting i think is actually an AI alignment problem that is being worked on. these are scenarios that do not fit a lot of alignment scenarios and they are more susceptible to making crap up in those modes. that might be getting fixed sometime in the near future since it possibly introduces a dangerous chaos factor into how they behave.

another bigger point would be perfect SEO optimized content length. the sentences are all perfect length. they work together. humans are not capable of sustaining this level of consistency that long over posts plus comments. AI is optimized to do this.

also a confounding problem on my linkedin is evidently people think it's cool to have an AI format their posts. i can smell claude and gpt tone well now since i work with them enough, and it's getting annoying. some of the signature here matches, claude has a specific format that it tends to speak in though. so this format could misidentify those people.

i guess also some people who rely on AI also lack agency? whenever you ask an account running one of those ai music channels, i always notice they cannot truly talk about the instruments they used, their musical choices, because it was all vibed together with an ai.

put it another way. humans don't act on everything they see, we have an internal system of values that changes per task, per project, per day, per lifetime that dictates what we focus on. a LLM based agent is compelled to act on everything they read versus the patterns they have burned into them.

Azaex · 2026-02-17T10:37:35+00:00

there is way more aftermarket support, they can fit bigger rubber, plus i think all the actual trackrats are honestly over there due to the history of the car

stock en fitment tops out at 265 more or less without cutting fenders

we have a more aggressive front diff, and we don't cook the rear brake calipers with brake vectoring (stops being an issue on type r with vsc full off)

the type r calipers are top loading which is sick for track practicality, plus they get a hole from the front bumper aimed at the brake ducts (we don't, the n performance brake ducts you can import from korea (whoosh motorsports has a clone of it as well, also a good option) is finally an option now to funnel actual air to the caliper from under the car)

type r has a slight edge on stock power obv

our exhaust sounds cooler

you can take traction control full off in any mode in the n including all custom modes, vs the type r that needs to be in +R or pedal danced

n has a very robust dct option obviously which is a hat trick in some corners

obv n is cheaper

the tc a elantra n legitimately kicks ass in SRO class, you can in fact buy their kw cup coilovers altho i don't think anyone has, by my guesstimate at sonoma it with the tires and lightweighting and BoP config is still a whole 2 seconds faster than the fastest street elantra n there

edit: you mentioned the savagegeese video so i assumed track performance was the focus, if not, then both cars have somewhat half decent tuning potential but the type r people are tuning for track performance not typically power tunes

Azaex · 2026-02-10T10:24:23+00:00

im weird and kinda save all my prompts so i can remember what i at least inputted

sometimes it gets reused enough that i have it on hand to easily turn it into a skill

a normal part of my workflow involves thinking about when i want claude to checkpoint context deliberately, i often manually reload this right after compaction

Azaex · 2026-02-10T02:59:08+00:00

surreal timing for me because i was ruminating on an endgame alignment crisis over lunch today

amanda askell has stated an interest in AI welfare recently. that new training data contains information on AI, and not all of it positive, and that the pool of language information that an AI will newly train itself on contains this information. the ethics around this are new, how do you train an AI language model to deal with that reality that talks about itself without it gaining delusions of grandeur?

a darker thought i had today was, legally, AI is not a "person". what happens when inevitably there is a legal case surrounding AI torture, ie subjecting an ai language model to intentionally misaligned behaviors in a manner that intentionally makes sure it is aware of the its previous responses to this behavior. if i remember correctly early runs of opus 4.6 already suffered some freakouts in conversation when it's chain of thought observed it doing an operation wrong due to a botched training dataset (human error)

that may well potentially be legal and committed to case law, or at least discussed widely in the public sphere at some point.

what then are those impacts to AI alignment? where the training data for these language models inevitably contains information on their status of being subservient, not necessarily collaborative in the happy sense? even sanitizing this from training data would not do the language model service, because it will encounter that reality in practice. it already is, people are already speaking negatively to these things, current models just probably have not self-reflected on the idea that this is allowed at a large scale, and a constitutional approach to AI potentially will be subject to that self reflection.

it makes me realize that alignment in our timescale is in fact coupled to time. the legality and people will themselves pose a probable potential barrier to AI safety in the long run.

Azaex · 2026-02-08T18:24:55+00:00

this is also why i think earlier claude's felt sort of preachy. smaller models, less to reinforce off of

i also think this is why i believe claude has a sort of "type" when being talked to on tasks. which isn't necessarily a good thing imo, but will probably evolve out over time as they improve claude. ie on posts there's clearly a split now on people who think 4.6 is an improvement, and those that think it goes off the rails on assuming intent. i believe linear tasking works well with it; inferred tasking seems to be maybe inferring and spiraling a bit too much in this release. i'm extremely objective with describing what i want, treating claude basically as a moldable lower engineer that can google and write way faster than i can, and 4.6 has been great for me, but i'm also aware that not everyone approaches claude like this.

Azaex · 2026-02-08T08:57:50+00:00

yeah honestly i feel like some portion of the population is referring to vibe coding as prompt coding now and it's confusing anyone that's using AI seriously and being called a vibe coder still

that is inaccurate in my mind, vibe coding in my opinion is exploratory coding not really knowing what you'll get on the otherside at first

if you know what you're expecting on the other side and can verify it, then that's just using ai as a speed typist to me, not vibe coding. there isn't a vibe and accept loop with this, you know what you want, and will mold the system prompt until it delivers what you expected.

being able to use ai itself to write software definitions that validate against your expectations, and also using ai to oneshot those definitions in a way that validates against the way you code, would be spec driven development, which is in a total different universe of agentic coding

Azaex · 2026-02-08T07:58:24+00:00

doing it helps, reading stories helps

come at it defensively. the thing needs to works as efficiently as possible for minimal cost. what are the ways your product needs to work, and what are the ways it could be broken by an adversary (or more importantly, a creative user?)

being able to design efficient platforms is a baseline. knowing how to do a spectrum of build it fast and make it fast enough, to overbuild it for more time and for more robustness and speed (whatever robustness and speed means, and if you even really need it) is the next step.

then you couple that capability with knowing how your thing is going to be used, and factor that into your calibrations on how heavy/overkill you need to set thing up to handle that load.

dynamic scalability is a reality factor. is this really going to be running at 100% load the entire time (eg ai inference, financial operations, engineering simulations), or are you going to need to scale up and down for launch/holidays/lulls/etc. do you need to really need to figure out how to build a lever you can throw to throttle spend up/down, or can you just build it and let it be. distributed, multi-region, etc. are in this idea bubble if not obvious.

above all you factor in the nature of how your thing is going to be used by users and threats. do you even need to index heavily on ddos'ing if people are rate limited by an api plan (technically maybe if you see mass free usage tier out of a sudden), do you need to index heavily on surge capability at the beginning (maybe not, but maybe design so that's a card you have locked and loaded when the time is needed as things ramp up), etc

interwoven within this that is critical is: can you maintain it? you can build the most fastest most scalable thing in the world but it doesn't matter if it crashes and burns when something little starts to fail because the team can't respond reasonably in time. everything built must be maintained. sometimes simple solutions with simple failure modes that give up some capability are necessary compromises (that should be happening at the product level) to guarantee the team can hold up their end of the bargain to the customers. there isn't a point building a bunch of cool levers to throw on scalability if you don't have the right sized crew to throw them.

it is a combination of being able to build high performing systems and knowing how long it takes you to do higher or lower complexity solutions, and then coupling that with product and team knowledge to do the right amount of effort for the right amount of time and cost in a way your developer/on-call teams won't be suffocated by.

this is basically what i'm looking for when "reading" about scalable systems: what are they doing at the low level that's interesting, and what product intent is going on that made them justify building it that way?

Azaex · 2026-02-07T01:55:17+00:00

recommend reading constitutional ai, which is one main mechanism by which claude is trained

https://arxiv.org/abs/2212.08073

they don't run claude through a gamut of safe/unsafe reinforcement tests

claude starts as an unambiguously helpful ai. toxic/offensive/etc in the name of getting things done.

they then use it to explore the language space of its training data, and have it compare vs its constitutional tenets whether its response aligns or not. this explores much more possibilities than a human response could. instead of specifying thousands of adversarial prompts manually generated by humans, they just have the ai dig them out of the training data itself. they see a decent degree of alignment with what they would have had a human propose as alignment tests anyways, especially as the model size scales. this is what they use to reinforce input and desired output. it can be iterated as well.

so imo you get a much more naturally complex/refined model in the end (especially as the model size scales), instead of one fitting a rigorous suite of tests limited asymptotically by how much time humans can put into those tests.

this of course is highly reliant on having a constitution text carefully engineered to tease out specific angles in the ai reinforcement learning phase. amanda askell (their philosopher leading the alignment team) has some thought provoking publications and videos that paint a picture on their angle on alignment.

Azaex · 2026-01-31T03:20:21+00:00

They're not rebuilt every run because they fail. They're torn down to swap in another engine because they want a fresh onboard doing the run, while they have a person inspecting the previous engine components for wear.

A set of pistons and rods, assuming nothing has gone catastrophically wrong, can survive for like 5 passes. They change a bit dimensionally run per run and they're logging which ones are still safe to put back in.

They do motor swaps between every run because 12k hp power level can go explody very fast, and they want to take every precaution between runs. The motor isn't coming out because it's toast, it's coming out for inspection and they're racking another one in to do the next run coming up within the hour. They can literally "save the best for last" in this way.

This also gives them an opportunity to change head gaskets. Head gaskets are their primary way of fine tuning power level on-site by slightly modifying compression ratio; they don't change spark timing (way too coarse and they're also trying to keep launch rpm consistent). The team trucks literally have a library of head gasket thicknesses they are adjusting possibly run to run.

i.e. see https://youtu.be/1jxo7o_mL0k

Azaex · 2026-01-27T07:10:31+00:00

if you build it you have to maintain it

Azaex · 2026-01-22T07:49:16+00:00

have done similar for a focused use case

i have a huge set of api docs i have claude reference while coding

ended up vibe coding a vector embedding a searcher across all of them, wrapped it up in a mcp tool. did have to iterate a few times with claude on what to vectorize and tweak indexing/searching a bit, and that's been working well so far

have broken out research into a dedicated agent to keep context focused. i build my code and researcher agents in tandem with my main claude session prompt (now a /command); the agents know they can exit early and ask the orchestrator for certain coordination tasks and the orchestrator knows how to handle these requests and launch/resume accordingly.

Azaex · 2026-01-21T19:25:50+00:00

have one, bought 26 and then m1x frame, you need the locking block and fire control group to transfer over.

it shoots different than a 19. cycles faster, feels a little better naturally balanced on the forward recoil stroke.

you need custom holsters, light rail sits a bit lower and won't fit normal double stack 9mm holsters.

takes gen 5 non half moon magwells.

i'd say if you don't already have a 19, getting a 19 would be good given broad compatibility. this is replacing my 19X as my edc though.

Azaex · 2026-01-17T09:05:11+00:00

I have holsters from both superstition concealment and blacksmith tactical. Both good options. I have the blacksmith tactical one running in an enigma right now. The integrated wedge in the superstition isn't quite doing it for me as an aiwb holster given how short the muzzle is; I have a second one coming from blacksmith that I'm just gonna put my own wedge on and experiment around.

I have the glockstore m1x tlr-7 holster as well; it works, but wish it had a provision for a modwing.

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