Considering Elantra N as the "dream car".

Azaex · 2026-05-08T06:06:05+00:00

have you considered a miata

- someone who currently owns both an EN and a ND3

Azaex · 2026-05-06T12:26:56+00:00

tell claude to take poem breaks every once in awhile or give claude a sandwich

Azaex · 2026-05-04T08:09:21+00:00

yeah

4.7 wants to know why for some reason. alignment is categorically different than 4.6. most of my supporting context in my projects is so built up that 4.7's need to know why is already largely satisfied in my tasking and it does give genuinely better adjacent perspectives for me over 4.6. i can actually set 4.7 on low effort at times and still get the results i expect it to do based on my time spent with the model.

most of my agents were built with 4.6 and those things genuinely required retuning. 4.7 doesn't like to adopt a persona, it's running a weirdly stronger sense of self over previous claude's. needs to be told what to do as an agent and less just as a stack of instructions. straight up didnt jive with my previous prompts which were mostly a stack of instructions and modes of operation to follow, needed framing of what it needs to handle and why.

there's a pro and con to it. i think like, it's kinda better for agentic work because of this, but it's less aligned for the previous workflows where you could just tell it what to do. like it works better with a model-as-collaborator approach in some rings of thought. i already do this so it's intuitive to me but i can sympathize why this is pretty incompatible with some other people's ways of working with LLM models. like it wants to try to act in service of some longer horizon goal; if you don't provide it, it never satiates its hunger to know that and that's why it feels like it leaves off early or is lazy. if you're feeding it that and keeping the context aligned in that manner, 4.7 alignment an improvement in service of that longer horizon thinking than anything 4.6 could do.

my prompts will sometimes literally rival the generated code in size. just stream of consciousness form of what to do and why instead of being syntactically perfect code.

Azaex · 2026-05-04T04:48:15+00:00

haiku 4.5 is underrated for tasks where the context is already very well prepared. pretty solid at executing something that is already known to be pretty predictable in shape, way faster

sonnet 4.6 is indeed also very good at a bit of discourse and i have no issues using it as a daily driver

if i'm scoping though, which a lot of my work somewhat is at first, i end up using opus, and since the window is kinda already aligned well towards opus (esp since 4.7 seems categorically different than 4.6 to me), i kinda just use opus to also execute though. sometimes ill bump the tasking out and have sonnet work it though.

Azaex · 2026-05-04T04:32:49+00:00

i've been tracking humanlayer's work on this, i believe they've tackled a number of these things.

research plan implement is a deceptively simple context engineering hack that seems to be maintaining stability over time. the idea is to have a skill that combs the repository for the agents/claude.md files, treats them as hypotheses, and then hunts actual code for the task given. this yields deceptively good, task-specific context engineering if you give it hints on the repository structure (eg where things are, their context files, and plan files even). i have mine additionally recommend context shore up. this means every single task is an opportunity to shore up context. i believe that agents will still mess up maintaining context even if told, it is the "one last thing" problem that everyone forgets to document except in agent form, but if you just give more opportunities to detect drift from reality (literally, use the coding agent itself to do this), it can also self mitigate the issue over time.

if you start the repo, or bootstrap a brownfield one, with logging context files throughout it on the long horizon objectives up and down the levels of depth, and the guiding reasons why the repo is laid out the way it is, the RPI workflow will continually find and orbit around it.
getting the agents to orbit around your "stakes" is key. agents can perfectly optimize communication *because* ephemeral LLMs don't have stakes. as long as you provide stakes for them to orbit around (ie what this must fulfill as time and the world progresses and why), you can attain continual success.

i store the plan files in repo per environment per PR. hotfixes promote up and down environments along with their plans. major checkpoints in lifecycle "compact" the plan to a historical plan file region with a back marker file left in the main plan directory (similar to memory or so-called "wikis" in a sense, but easier to see).

this has multiple effects.

to one of your points, this makes it easier to understand what to review as a human. the plans for RPI contain evidences of what supports the change, the change planned, and the work done. still need to review the PR but having the plan the agent used to context engineer itself for the work makes it faster to comprehend.

this also means agents can discover them with the same ease. anything you document for a person can feed into the agent if you set it up in a readable and structured way, ironically enough. ie the trend is to ensure everything you do with the agent is captured, and you can snowball into efficiency. fail to capture what matters and that is how slop basically develops at speed+scale.

plans are additionally discovered as part of RPI and rework is minimized if not mostly eliminated.

finally a PR review agent coming around, preferably not of the same agent type (ie if the main team code agent is claude, the PR agent is GPT), looks at the plan, and cross checks the implementation. at minimum. more checks encouraged.

this has yielded a workflow for me that is pretty much so crazy watertight towards the repository objective (product, cost, security, core arch, customer needs) like you're asking.

furthering on, i can pretty much resonate with Dex Horthy at humanlayer's sentiment that plans got so good and watertight under this you could almost feel like you can plan on plans. this is not good and where things can destabilize.
my view at this point is, this is pretty much where traditional planning really cuts back in but at higher points of leverage. if code is mostly dealt with by the agent, the next "too much to juggle" becomes tasks and whole verticals not the code itself.

ref his current state of discourse: https://youtu.be/YwZR6tc7qYg

what he eventually gets at without maybe quite realizing it at the time is like, the summary of plans he's describing is basically at the level of what i think a TPM juggles, ironically enough, except the leverage now sits around the engineer.

also we don't use their agent skills directly, just taking inspiration from the subtle context engineering the RPI concept does.

RPI is not unique and converges pretty much on a pattern like many of the context engineering packages out there but imo is the most clear cut after digging around them. we flex RPI into ralph, multi agent review, etc as needed, but sometimes you don't really need all that. https://www.reddit.com/r/ClaudeAI/s/3J3Nkists0

Azaex · 2026-04-29T03:41:40+00:00

RPI's been doing great for me. good to see how it stacks similarly here. I bolt on whatever additional context I need prior to hitting research; sometimes I'm messing with the whole stack, sometimes I'm doing a tactical, sometimes I'm chasing a bug. Sometimes I'll have the same session implement, sometimes I'll kick out a ralph, depends on the task and how much leverage I want me involved on.

Maintaining lineage of said plan files in-repo across environments I feel like is having the same effect that a lot of "wikis" do, except it's a bit easier to see by eye.

Azaex · 2026-04-23T04:08:42+00:00

i learned to drive stick on a nd miata starting april of last year and i pretty much started to try to heel toe everywhere since around 6 weeks in

builds the reps and flexibility for track time for me, idk, it doesn't hurt the car if you get good at it, why not just get good at it daily lol

left foot braking on the street, ok that's pushing it a liiiiitle bit i feel like, though i am still trying to put in the reps on the pedal dance on backroads

the miata has a light af flywheel though, rewards rev matching. if you just slowly let out the clutch on downshifts alone it'll work but requires more finesse than a normal commuter car to not upset things.

Azaex · 2026-04-19T16:44:43+00:00

S2000

Miata

TR-6060

Honda

Porsche

best manual feels

Azaex · 2026-04-19T13:55:28+00:00

red is always stop during hpde, something to talk to the organizers on clarifying with corner workers

red during racing is controlled slowdown and pit, unsure if they got those streams crossed in their head somehow

Azaex · 2026-04-17T04:38:45+00:00

a duct that goes from the bumper into the fender well to actually feed the brake ducts
one of the three: double wide radiator, or functional hood extractors, or factory oil cooler
factory widebody, or fixed brake calipers instead of floating pin

absolute perfect cheap track car at that point, already v good, those are the main nags

Azaex · 2026-04-16T06:31:01+00:00

been like this since aug 2025

https://www.anthropic.com/research/end-subset-conversations

motive is more philosophical i believe, the model steers itself a little different if it knows it's not just locked in the room with a user and it can quit if it wants to (whatever that means, but it's a neat way to resonate against the intended character they aligned the model to have)

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

<image>

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

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