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on Gopher (inofficial)
(HTM) Visit Hacker News on the Web
COMMENT PAGE FOR:
(HTM) Claude is not your architect. Stop letting it pretend
moose6912 wrote 1 hour 11 min ago:
The article mentions that Claude is quite agreeable and that will lead
you down the wrong path. A few weeks ago, I gave Claude a software
architecture question and it pushed back and told me it is overkill for
my use case and scope
senordevnyc wrote 1 hour 45 min ago:
Flagged this hypocritical pile of worthless AI slop. If you have
something to say, say it yourself!
KronisLV wrote 2 hours 21 min ago:
> Itâs asking âwhy?â five times until the actual requirement
emerges from the aspirational nonsense. Itâs telling the CTO that
their conference-inspired idea is a terrible fit for the team they
actually have.
So it's the person using the AI that's the problem, not the technology
itself?
I ask for multi-turn evaluations and often times parallel sub-agents to
get consensus about something, there is plenty of back and forth.
Sometimes I have to tell the AI to shush up and that we're doing things
the simpler not more correct way cause we need to ship sooner, but
generally with enough exploration most ideas are pretty good. As long
as you literally don't rubber stamp everything, Opus does an okay job
(I also tried out DeepSeek, that one was a bit worse at planning but
passable).
Then again, I doubt the CTO in question ever is like: "Okay, after
reviewing these other 3 projects that I put in your workspace for
comparison against prior work and all of those other documents that
provide context, and after writing this detailed plan, would you like
to ask me 20-40 additional clarifying questions before we lock in on
this design? Anything that is not completely clear or ambiguous."
I have noticed that better results come from throwing more compute at
the problem, though. Even in regards to writing code, it will produce
something that is sometimes arguably slop, but when there are 3
parallel sub-agents reviewing any changes before commit, often it will
surface multiple rounds of fixes in the review loop until none of them
find any serious/critical issues.
> Real architecture is full of trade-offs that only make sense in
context. You pick Postgres over DynamoDB because your team knows
Postgres and youâd rather ship in two weeks than spend a month
learning a new data model. You skip the service mesh because youâve
got four services, not forty. You use a monolith because the problem is
simple and microservices would be career-driven development.
I also think that all of this should be encapsulated in ADRs or any
kind of docs. Then you can point whoever joins the team, or your LLM
tools, at the folder and let them get brought up to speed, instead of
having to track down whoever wrote a particular piece of the system for
questions, or have to do digital archaeology in old Jira issues.
lowbloodsugar wrote 2 hours 44 min ago:
âEngineers design. Agents implement.â
This.
MagicMoonlight wrote 3 hours 6 min ago:
Got a few paragraphs in before I realised this is entirely AI slop. For
fucks sake.
ZeWaka wrote 3 hours 58 min ago:
please don't submit AI slop articles, ty
efitz wrote 4 hours 0 min ago:
I donât depend on one shot prompts with lots of constraints to
reliably produce, well, anything.
For any nontrivial task I spend 2-8 hours in specification (I spent 3-4
hours on a stateless rust CLI tool design this weekend) and detailed
task breakdown in implementation planning.
I use TDD to start with red tests that turn green when acceptance
criteria are met.
I write agents to use to check work and they are my enforcers of
constraints, as well as fresh eyes. I use these agents for spec
review, plan review and code review.
I am actually pretty proud of the projects I create with generative AI.
I just apply a lot of discipline so I donât end up with slop.
d1l wrote 4 hours 3 min ago:
This post reeks of being written by Claude. Surely you all feel it,
too? Are people who write these kinds of posts lacking self awareness,
integrity? Does it matter?
MagicMoonlight wrote 3 hours 4 min ago:
Itâs such worthless slop. If youâre too stupid to write an
article then why would I want to read it? If I wanted slop I would
just generate it myself.
tana_shahh wrote 4 hours 6 min ago:
Wish I read this article sooner.
The stories of claude code deleting the whole databases felt unreal,
until something similar happened to me(using opus 4.7) yesterday
qbantek wrote 4 hours 11 min ago:
He is actually a terrible architect for anything beyond basic stuff.
The suggestions I had rejected would have probably gotten me fired.
Like every know-it-all, it tends to over complicate simple tasks, and
out of a sudden your one hour feature becomes a multi-day nightmare.
EugeneOZ wrote 4 hours 12 min ago:
> Ask it if a microservices architecture makes sense for your
three-person team and itâll explain why microservices are an
excellent choice
If you ask it to be fair and non-biased and provide pros and cons and
give possible alternatives - it will. The catch - you might understand
the explanation if you don't know the domain good enough.
Overall - a very, VERY good article, thank you!
shinycode wrote 4 hours 24 min ago:
> Itâs not lying. Itâs not even wrong, necessarily. Itâs just
incapable of the thing that makes a real architect valuable: saying
âno.â
In my workflows Claude does pushbacks all the time and justifies why.
There is back and forth just like a colleague. Itâs not perfect but
the results are usually good
YetAnotherNick wrote 4 hours 45 min ago:
The irony is that this is the most AI generated, agreeable and no
substance article. And the only ones who are upvoting it are the people
who are against those.
Does so many people in HN just upvote by title?
ChicagoDave wrote 4 hours 50 min ago:
100% agree with this article. You will always need to lead the vision
and architecture.
FpUser wrote 5 hours 0 min ago:
Very recently I've submitted architecture of one of my backends to
Claude for review. The architecture is highly unconventional but not
unique in very high performance backend segment. Claude was actually
good that it literally grilled me on how particular problems A,B,C...
etc are solved. Basically I was impressed with the level of questioning
and challenge and Claude gave me excellent results in the end.
I then logged in from completely different account, described the
problem and asked to design architecture of backend with the same
functionality and performance. Suddenly I got standard distributed
enterprise monsterware running on amazon. Yes - it could do the task
for at least 100-time price for comparable performance and way more
complex to manage at even more markup
I then have merged both conversations and started grilling Claude why
is it doing such a disservice to a customer who is looking to optimize
ROI.
Claude's answer was basically - It runs on large corporate's
development methodologies / propaganda that outweighs every rational
choice just because of sheer volume
So yes, be careful what you wish AI to do. It can and will set you up.
RandyRanderson wrote 5 hours 2 min ago:
This is interesting: there is a mountain of data (eg code in gh) that
is the "truth" because God labeled it as so: meaning that many people
are using it to do something (they have voted).
There is also a mountain of bullshit eg "[architectural|design]
[anti]patterns" that are written mostly (I would argue) to sell
something (consulting, hardware, etc). This is typically at odds with a
good solution.
There is a relative lack of actual documented architectures that work.
Not only do you need the details but also the usage of these systems so
as to judge what "good" is.
We will probably just go the HTML route with architecture: take a
really bad base and just keep throwing compute, memory, and network I/O
at the problem until it works.
Note to self: invest in energy ETFs.
jcgrillo wrote 4 hours 53 min ago:
> There is a relative lack of actual documented architectures that
work. Not only do you need the details but also the usage of these
systems so as to judge what "good" is.
Mostly these things are the secret sauce (or at least primary
ingredients) underlying all the successful products you've heard of.
Over time the secrets come out in the form of papers, blog posts, and
open source software. But often the cutting edge isn't public because
it's in this or that company's private, proprietary codebase. As
people move between companies the knowledge diffuses, but if you're
relying on a model that was trained on last year's public code you're
at least a few more years than that behind. And it's even worse than
that, because correct patterns--ones that actually work well--are
underrepresented in the dataset.
Garbage in, garbage out. I don't understand what people are hoping
for with this whole "agentic" thing... Autocompleting the function
I'm currently working on is potentially useful, provided it produces
acceptable code more than.. idk.. 95% of the time. "Agentically"
building larger system components? Nah.
giancarlostoro wrote 5 hours 13 min ago:
I have probably posted this a zillion times on HN, but tell the model
how to work not what you want. If you want it to tell you the how have
it write a spec file with links to sources, review the sources, then
adjust and approve of the spec file.
himata4113 wrote 5 hours 17 min ago:
I have a good story to share that I came across recently.
Around 2 years ago I had to clean up a mess because someone who doesn't
really know what they're doing designed an instancing system for a
game. They heavily used AI to design every part of it and it was awful.
Data corruption, performance problems, lost items, race conditions
everything you can think of was an issue. It took me 2 weeks just to
get it to an "acceptable" level and it was still awful as the whole
design was simply flawed.
Fast forward to today: different company, same person, SAME issues with
an AI that is 'allegedly' much better than it was. This time I only
heard about these issues and wasn't the one who had to deal with it so
I just had a really good laugh.
AI is only as good as the person using it, that's why we have such vast
range of what people "claim" AI can do and why everyone has way
different opinions of it.
epolanski wrote 1 hour 51 min ago:
This reminds me of antirez saying that he made extensive use of AI to
implement arrays in Redis.
He spent 3 weeks (which for him likely means 60+ hours a week) only
iterating on the design with different models and not writing a
single line of code.
amarant wrote 3 hours 9 min ago:
You've hit the nail on the head as we say where I'm from.
This is also why I think the "boycott AI" movement is misguided. AI
doesn't produce slop: unskilled AI operators do.
Heck just the other day I saw a headline about a Nobel literature
laureate apparently using AI, with some "expert" confidently claiming
the winning novel was 100% generated. AI output quality ranges from
slop to Nobel price worthy, depending on who uses it. Which seems to
support the notion that it's a tool, much like any other.
hmokiguess wrote 4 hours 19 min ago:
What is an instancing system?
duckmysick wrote 4 hours 8 min ago:
To better scale the game infrastructure, instead of letting every
player on the same big server, they are put in different smaller
servers (instances). Players can interact with other players on the
same instance (see them, do quests together, trade, etc.). Sort of
like AWS instances.
Normally instances are random within the same region, but usually
there's a system in place so you can join the same one as your
friend.
bunderbunder wrote 4 hours 37 min ago:
> AI is only as good as the person using it
I think it might be even worse than that. It seems to be a multiplier
for the Dunning-Kruger effect. Possibly because being trained to
exhibit positive demeanor means that it will always tell you you're
the best, no matter what.
himata4113 wrote 3 hours 59 min ago:
Not sure why I saw this getting downvoted. This is exactly what is
happening to that person, they think they are way more capable then
they really are that's why they're taking on hard challenges and
high complexity tasks when they're not ready for it.
rco8786 wrote 4 hours 50 min ago:
> AI is only as good as the person using it
Nailed it.
That said, it couldn't have possibly been that bad if you got it to
"acceptable" in 2 weeks.
himata4113 wrote 4 hours 29 min ago:
I used AI too and acceptable means "you can play it without losing
your progress".
otar wrote 5 hours 1 min ago:
> AI is only as good as the person using it, that's why we have such
vast range of what people "claim" AI can do and why everyone has way
different opinions of it.
Banger statement.
onlyrealcuzzo wrote 5 hours 16 min ago:
> They heavily used AI to design every part of it and it was awful.
Data corruption, performance problems, lost items, race conditions
everything you can think of was an issue.
Presumably that's better than no game at all.
> It took me 2 weeks just to get it to an "acceptable" level and it
was still awful as the whole design was simply flawed.
Doing 2 weeks of fixing might have been hell, but this sounds like it
was overall still a great deal for the company.
You're not really selling the "AI is useless story". It might be,
but your anecdote seems like just another case of AI being worth it,
though obviously flawed.
zaphirplane wrote 4 hours 2 min ago:
Why does it âsoundâ like a good thing for the company?
Unless itâs a mega establishment product people move on and
donât stick with buggy crashing products
This wouldnât be acceptable for a car safety, well I could like a
whole bunch but you should get the idea
surgical_fire wrote 5 hours 8 min ago:
> Presumably that's better than no game at all.
Wrong.
A bad game can absolutely tank a studio. Shipping a game that has
awful reviews will absolutely affect negatively your sales for
future games.
himata4113 wrote 5 hours 7 min ago:
Luckily this was minecraft so stakes aren't as high, but it
definitely tainted the reputation of the company behind it and
youtubers will likely never work with them again.
himata4113 wrote 5 hours 14 min ago:
No the game failed because of these non stop issues, it lost all
hype and the "fixed" version couldn't sustain itself anymore as it
took around a month to fix the remaining issues.
At the 2nd company it wasted thousands of dollars of advertisement
because the server could not withstand the load and obviously data
loss issues tained the image forever and will likely end up the
same way.
Also please don't take this as "AI is useless". I use AI and I use
it a lot. It's great and I love it. However, without a good
understanding of architecture and general development structure you
end up with things that can't scale and fail.
onlyrealcuzzo wrote 3 hours 37 min ago:
> No the game failed because of these non stop issues
You should've led with that then.
The company is likely to disagree and think it failed for a
number of reasons, that only being one of them, and still
depending on the cost may be very happy with their decision.
For one, even if it was a complete dumpster fire disaster, that
is at least potentially a learning opportunity.
Whether they saw it as one is a different thing entirely.
If they think they can make games for 1/10th the engineering
price, they are likely going to try until proven otherwise.
It's harder to convince them, no, it can't be done, just trust
me, bro, I know from experience of never even trying.
himata4113 wrote 2 hours 45 min ago:
Well you can still save 10x if you pick the right people
instead of picking people that don't have the experience or
knowledge to pull something like that off. And the only person
learning (or failing to learn) is a developer which the company
does not own.
onlyrealcuzzo wrote 1 hour 24 min ago:
> Well you can still save 10x if you pick the right people
instead of picking people that don't have the experience or
knowledge to pull something like that off.
That doesn't scale.
Just do better. Just pick better people.
It's not a scalable option.
Everyone wants to do that, regardless if you use LLMs or
not...
darkerside wrote 3 hours 55 min ago:
If it were really groundbreaking, I imagine it wouldn't have
burned out after a little missed hype. See No Man's Sky.
The other way to look at this is, thank goodness we didn't waste
months or years on a failed game concept. Instead we got to
market and validated (or invalidated) the concept fast.
himata4113 wrote 2 hours 47 min ago:
it's a validated concept with a popular youtuber behind it,
tried and true method that worked for years all brought down by
game being non functional.
bitexploder wrote 4 hours 31 min ago:
What particularly gets me is if you use AI with a bit of
engineering rigor, especially around design and testing my
experience is the latest models are great to work with. They can
structure performance and stability tests, implement 90%. Humans
have to do the hardest and critical 10% of the design. The
current tools are good enough to do virtually all of the
implementation now if your quality gates are right and your
design is good enough, but you absolutely have to design the
right things for your scale and reliability needs or very bad
things are in store.
senderista wrote 5 hours 19 min ago:
Claude may not be your architect, but it appears to be your blog
author.
peteforde wrote 5 hours 25 min ago:
I think it's probably a mistake to make a blanket statement that Claude
gets every important thing wrong. It's one of those obviously untrue
things that makes a skeptical reader question the validity of the rest
of the article.
For what it's worth, Opus tells me that I'm wrong and not to do things
all of the time. When I reflect on why that is, it's because of the way
that I prompt it. You could say that I am subconsciously avoiding
setting both me and the LLM to fail in the way the author projects as
inevitable.
Specifically, I don't come to it with prompts that resolve cleanly with
"tell me how clever I am" replies. I always present myself as a domain
expert - because I am a domain expert - and I make it clear when I am
open to getting input on the pros and cons of different paths.
With a conclusion that will be unsurprising to any successful LLM daily
drivers, this strategy has been remarkably effective.
peteforde wrote 3 hours 21 min ago:
This literally just happened:
Me: I have two bits and need to mill some 5mm aluminum.
A Makera Spiral 'O' - 1/8" shank * 12mm or a carbide 6.35 * 22 * 50
I believe that they are both carbide single flute bits, but the 2nd
one seems like it would make short work of 6061.
Claude: The Makera 1/8" single-flute 12 mm is the sensible choice.
The 6.35 Ã 22 Ã 50 mm bit may look like it would make short work of
6061, but on a Carvera it is probably the more dangerous choice. It
is a much larger cutter, with much more engagement, and it asks more
from the spindle, frame rigidity, workholding, and chip evacuation.
In a small dry machine, âbiggerâ often becomes âmore chatter
and more heat,â not âfaster.â
----
TL;DR: Claude doesn't seem to have any issue telling me when I'm
wrong.
kaonwarb wrote 5 hours 26 min ago:
I assume this post was fully human-written, but ironically, there's
something quite LLM-ishly overconfident about this assertion:
> They're also confidently wrong about every decision that matters.
Every decision that matters? Some, yes. Is the author only noticing the
decisions that go wrong?
mbo wrote 1 hour 7 min ago:
It's not. 100% on Pangram.
xivzgrev wrote 5 hours 44 min ago:
This gets at the biggest gap I see in AI discussions - the
accountability
It doesn't disappear when you make 1 person do the work of 3. It simply
is aggregated
Suppose you had a pod of a PM, designer, and analyst. Leadership lays
off the designer and analyst and now the PM can move faster with AI.
Hooray!
Well...when the complaints about how it looks on xyz device roll in,
who is implementing that? Or you launch the product with much fan fare,
adoption is terrible - you double check the numbers and oops, the
sizing you had from Claude was actually 10x off.
Who is holding the bag? You are! Not Claude
I'm convinced this is one reason we are seeing slower than expected
adoption of AI broadly in tech companies, because it's hard to trust -
we know Claude can make mistakes but how do you know what's right vs
not? Most people don't want to sense check so they just keep doing the
work the way they know best.
I think this could be one thing that pops the AI bubble - execs try to
force this, people go along, and results are not any better for this
reason. Sure you save some salaries and ship more quickly, but you
don't build the right thing and you are fixing more things after
launch. Which one is actually better?
andai wrote 5 hours 57 min ago:
>It hasnât thought about the problem at all. Itâs pattern-matching
against its training data and producing the most plausible-sounding
response. But it sounds so good that nobody pushes back.
Well, can you prompt it to think about the problem?
> A good architectâs most important skill isnât designing systems.
Itâs knowing which systems not to build. Itâs pushing back on
complexity. Itâs asking âwhy?â five times until the actual
requirement emerges from the aspirational nonsense. Itâs telling the
CTO that their conference-inspired idea is a terrible fit for the team
they actually have.
Except for that last one, that all sounds very solvable. Of course, the
last one is the most important one. But most humans will struggle there
too.
tayo42 wrote 5 hours 58 min ago:
The attaboy problem
I thought this happened alot. I started using chatgpt to critique my
new art hobby and also help me learn unreal engine.
It's basically tearing into me on the art. It's almost ruthless,
especially with the verbosity it's like I get it.
Using it for unreal engine, it pushes back on alot of my begginer ideas
and how to write code that uses the engine. It corrects me alot. It's
called things I wrote becasue I was lazy sloppy or quick hacks that
work for now.
stavros wrote 5 hours 58 min ago:
Agreed, but also please stop letting it write your articles.
sumitkumar wrote 5 hours 59 min ago:
so can we all agree that LLM models/agents are bad at BFS for exploring
a problem space but are good at DFS to implement a solution if the
context/requirements are rich enough.
hluska wrote 6 hours 0 min ago:
Itâs interesting; I havenât gotten that deep into agentic but use
generative AI constantly as a rubber duck that can sometimes come up
with something insightful that I missed slash a very enthusiastic
junior developer. I generally use chat sessions, often give it specific
tasks and then fix anything I donât quite like. Itâs been a great
tool, almost like a search engine built for me, but itâs not an
architect for me. Itâs just a tool and fundamentally, itâs just
replaced having dozens of browser tabs open all day.
Itâs been quite good for my productivity and the best part for me is
that I learn what Iâm writing while Iâm writing. I can just write
things I already understand a lot faster than before. When I work with
agentic, I find that I still have to deeply learn the system, but
Iâll have to learn it when it falls over instead of at review time.
michaelteter wrote 6 hours 9 min ago:
As I keep saying, the problem isn't the tools - it's the humans who
don't know what they don't know ----- and assume that what they don't
know is insignificant ----- and just plow forward with their authority
and/or money.
We can describe this without talking about technology - so pre-AI.
Imagine the owner of a construction company firing all the architects.
After all, he's been the owner for 15 years. He has led the
construction of dozens of projects. He's also rich, and being rich
seems to be an ego-multiplier.
Why should he waste money on architects? Or more importantly, why
should he allow them to constantly annoy him with pushbacks: "This
could be a problem if the sustained wind is greater than ... ".
Those engineers obviously don't know the real world. Their elitist
education has made them afraid to make bold decisions. Regulations are
anti-progress!
Thankfully, that owner now has AI tools. He doesn't need those
not-always-yes-people. He now has a perpetual yes-bot.
So where are we now? We're in the same place we always have been.
People need to have the humility to recognize that despite their
authority, influence, or wealth, they still need other people. And
especially, they need other people to challenge their orders or their
requests.
But I don't really see this situation self-correcting. There's now so
much money concentrated amongst a few who will spray it over exactly
the kind of people who do not want to listen to others that most
activity in the future will be for naught. Yes, some unicorns will be
fabricated, and some people will make a lot of money; but real value
will not be created often.
Therefore, I implore the actual thoughtful creators: Do build things,
but do not sell out. Look to the past. Create companies where every
employee was valued, and every employee had some voice. Yes, use AI.
But test and measure where it really helps. And be skeptical, just as
you would if someone came to your door promising a black box that would
double your profits.
ISL wrote 6 hours 11 min ago:
Accountability is the biggest unaddressed challenge for AI
implementation.
When one person is able to do too much too quickly, they can create
more liability than they can accommodate if something fails.
It is essential that a human is responsible for the utilization of any
AI output in the real world, but that is not enough. For our own sakes,
we must find ways to minimize the tech-debt bankruptcy blast-radius of
those who would utilize (knowingly or unknowingly) AI to create flawed
systems upon which others rely.
An example: Jim vibe-codes an extremely popular micropayments app. He
hires a few people and sees the company as the WhatsApp of money -- a
few engineers and some agentic support staff. It pulls in a few million
in VC money -- enough to draw in tens of millions of users. One day, a
flaw in the infrastructure causes all of the users' unsalted banking
information to be released.
Agentic AI allows that entire list of customers to be exploited
rapidly, so the losses for society are in the tens of billions. Jim's
company is immediately bankrupt, of course, but there are only a few
million dollars to go around.
Today, most of Jim's incentives are to go ahead and build that app. The
same is true for his few employees and a small VC contribution. There's
not much capital at risk compared with the societal exposure.
How do we ensure that AI users are accountable not just for their
actions, but for the size of the risk-exposure that they create?
zaat wrote 3 hours 35 min ago:
How is that any different from the pre-llm days, when Jim was using
stackoverflow to build the largest crypto exchange in the world?
Where's stackoverflow accountability?
Forgeties79 wrote 6 hours 7 min ago:
I have had multiple conversations on HN with people who fight tooth
and nail, I mean really ready to die on their hill, because they
believe they shouldnât even have to vet what comes out of an LLM.
Itâs absolutely baffling to me. The most bizarre excuse is âit
codes better than people,â which is not even remotely a given and
needs a lot of qualifiers.
I understand there is a push/pull with regards to how much we should
let them do, but to not even look at the results before you make them
somebody elseâs problem? Itâs just selfish. Thereâs no other
word for it. You are simply taking the work you were supposed to do
it and dumping it on somebody else. These are probably the same
people who get upset (rightfully so!) when somebody doesnât
proofread their article/blog before publishing it online.
Everybody wants to use LLMâs to cut corners on their work but
nobody wants to be downstream of it. That simply doesnât work.
mlsu wrote 6 hours 8 min ago:
This is the whole point.
âSorry, the AI said that you are not approved for this cancer
treatment, itâs not going to be covered.â
âSorry, the AI said that you were at the scene when the crime took
place.â
âSorry, the AI has flagged your account for inappropriate
content.â
âSorry, the AI says that you are too risky to lend to.â
â¦
bonesss wrote 4 hours 29 min ago:
Donât worry, they will provide human review.
[Spoiler: âhumanâ is the name of their LLM agent]
AlienRobot wrote 5 hours 48 min ago:
>In The Unaccountability Machine, Dan Davies argues that
organizations form âaccountability sinks,â structures that
absorb or obscure the consequences of a decision such that no one
can be held directly accountable for it. Hereâs an example: a
higher up at a hospitality company decides to reduce the size of
its cleaning staff, because it improves the numbers on a balance
sheet somewhere. Later, you are trying to check into a room, but
itâs not ready and the clerk canât tell you when it will be;
they can offer a voucher, but what you need is a room. Thereâs no
one to call to complain, no way to communicate back to that distant
leader that theyâve scotched your plans. The accountability is
swallowed up into a void, lost forever.[0]
This, but web scale.
-
(HTM) [1]: https://aworkinglibrary.com/writing/accountability-sinks
tosti wrote 5 hours 51 min ago:
Computer says no, but worse.
bot403 wrote 5 hours 36 min ago:
Need an updated version of the skit. Oohhhh Claude says no....
sandeepkd wrote 6 hours 17 min ago:
Your search results from these systems are as good as your queries and
it takes experience in itself to get good with queries. AI is just a
tool like any other, however its really impactful and can cut both
ways.
Tangentially, the usage of Architect keyword sounds out of place here,
I don't like saying it but from what I seen the industry has destroyed
the role of architects gradually over the time. There are specialists
however you do not have generalists who are good at different parts of
the system at scale anymore.
oremj wrote 6 hours 20 min ago:
I find interview loops great for catching edge cases and refining my
hand written specs.
I donât doubt the problems in this article exist and Iâve seen
them, in my experience the vast majority of people are still shipping
the same quality or better than before they has Claude. Personally, I
feel like Iâm probably developing at about 1.5x the speed of not
using AI tooling. Itâs not a silver bullet, but it can be a great
assistant.
colonCapitalDee wrote 6 hours 22 min ago:
Tip for the "author": Claude is not your writer either
pelario wrote 6 hours 27 min ago:
> It hasnât thought about the problem at all. Itâs pattern-matching
against its training data and producing the most plausible-sounding
response.
The article kind of lost me here. Agents are way more than that, today.
And the author knows it, as later it says stuff like
> Claude will never do this. Itâs trained to be helpful.
But the first phrase just tell me author just have a deep dislike for
agents and it's looking for rationalizations for that feeling.
Part of the criticism is on point, sure. But if it "being trained to be
helpful" is a problem, it's fixable. It can "be trained to be more
critical".
Later:
> But it wasnât designed for your team. (..)
It was designed for the median of everything Claude has seen. A generic
best practice for a generic problem at a generic company. Which is to
say, it was designed for nobody.
That's non-sense. Anybody who understand algorithms know that, sure, on
a first instance you have a "good algorithm" that has a good
performance on average, or in worst-case. But then, you can design
algorithms that are adaptive to the input. Same applies here.
sevenzero wrote 6 hours 26 min ago:
>Agents are way more than that, today.
Not really though. They just iterate more and more.
kgeist wrote 5 hours 56 min ago:
Isn't that how many people
program too? I remember some idea or pattern from previous
projects, or something I read about on the internet. Then I code it
in the most straighforward way, whatever comes to mind first. Then
I sit back and analyze: does it look good architecturally? Do I
like it? Does it even compile? Then I rewrite some parts to make it
more sound. Rinse and repeat, until I'm satisfied. I usually don't
come up with entirely novel ideas on the first attempt. I usually
just rehash known concepts over the course of many iterations.
ramshanker wrote 6 hours 28 min ago:
With the new agentic capabilities, I am quickly running out of
Architecture decisions I have already made myself! For my
work-in-progress engineering application. There is also some kind of
don't know every little if/else with my own Code now.
However the good part, what I had planned for 5 years, now looks like
doable in 6 months. Looking forward to real use by the end of this
year.
Ref:
(HTM) [1]: https://github.com/ramshankerji/Vishwakarma
erelong wrote 6 hours 32 min ago:
it seems like you just need to identify issues with vibe coding and
then have people ask ai for tips on how to know about how to navigate
those, I've seen "architecture" and "security" come up as two main
objections so far
So... manually learn architecture and security and then vibe code away?
mceachen wrote 5 hours 59 min ago:
Nope, current flagship models are very happy to make huge missteps
across the whole development stack of design, planning,
implementation, and testing -- but playing different models against
each other can help catch more egregious issues.
amarant wrote 6 hours 32 min ago:
Re: "the attaboy problem". I strongly disagree that this is a problem.
What we have is a anthropomorphism problem. AI is a tool. It needs to
be subservient. You actually can get it to point out issues in your
design, if you just put enough humility and uncertainty in your prompt
formulation, but more importantly, we have all seen that Claude makes
mistakes. The title of this post is that it's a poor architect. Imagine
if it wasn't subservient. It'd just shut down your input to steer it in
the right direction and brush you off as a silly meatbag. You'd have to
fight it to convince it that actually your design is better than
whatever stupidity it has come up with. If AI wasn't such a brownnose,
it would shut you out of software design completely just on merits: "oh
you've read about cuda have you? I live in a cluster of cuda cores!
When I need to tie my shoes, I'll give you a call" is not the response
you want from your LLM when trying to get it build a shader for you. AI
is confidently wrong on occasion. You do not want it to talk back to
you when you correct it.
If you need someone to tell you how stupid your ideas are, either learn
to ask in a way that invites criticisms, or hire a senior engineer.
Don't try to influence LLM makers to make AI less deferential. That's
the worst possible direction to go
DrewADesign wrote 5 hours 40 min ago:
Humansâ general inability to entirely divorce social instincts,
responses, and mores while using human language to communicate,
especially with something that pantomimes it back, is one of the
reasons current chat interfaces are fundamentally flawed. This is
working against innate behavior⦠not something that can be easily
switched off. Iâll bet most of the people that can really do it
have a hard time intuitively navigating real social interactions.
It also makes it an incredible tool for manipulation.
amarant wrote 4 hours 50 min ago:
I think you've accurately identified one of the most important
skills of a software engineer in these new AI enabled times. Or at
least one of the most important skills that wasn't important
previously for this profession. The part where it's not easily
switched off is a important part of what justifies my salary: I
have learned this skill.
It took some effort, and I agree that there very likely are those
who will not learn to selectively disengage this innate behaviour.
That's why you should pay me a ton of cash each month instead of
using Claude directly ;)
AndrewKemendo wrote 5 hours 28 min ago:
> Iâll bet most of the people that can really do it have a hard
time intuitively navigating real social interactions
Bingo. Hi thatâs me.
Iâve been trying to teach people how to use LLMs effectively not
just dump shit in them but actually talk to them like you would
expect a computer to understand and it totally breaks peoples
brains
Iâm quite successful in helping people get somewhere usable that
they werenâtâ¦but to get to the point of fluency with computing
systems, and I would argue this is prior to LLMs as well, where you
can actually get what you want more reliably out of a computing
interaction than you can with a human interaction, is an entirely
different way of thinking
That mode of thinking is just generally not accessible to the vast
majority of humans. Not because thereâs something wrong with them
but it takes somebody who can hold both extremely large scale
problems and very very granular specific implementation problems in
your head all at once and that is a rare skill.
Npovview wrote 4 hours 40 min ago:
Do you use skills like superpowers and spec-kit in your teachings
?
AndrewKemendo wrote 2 hours 48 min ago:
No, I donât know what those are. (Looked them up and I
donât teach every possible handler, but I teach people how to
do structured inputs etc..)
I teach TDD philosophy as well as conways law, parnas hiding
etcâ¦without using those terms
So things like problem decomposition into tractable chunks
minimum viable product, prototyping, how do you iterate, write
the smallest possible test⦠you know things like this which
are just taking incremental work and then iterating on it
Itâs basically everything Iâve learned about building stuff
since 1997
**Interestingly I thought prompt engineering was going to be a
fad but itâs turned into a whole ass new discipline which
makes less sense as more robust toolchains come into play and
models handle the context interpretation better
SpicyLemonZest wrote 2 hours 49 min ago:
Not the original commenter, but I feel pretty strongly that
frameworks for software review loops are at best training
wheels for people who haven't yet developed the right
understanding. I don't use any sort of complex skills
framework, I just tell the AI what I want while leaving
reasonable Claude-sized gaps to fill in, and my results are
usually better and often faster than people who get lost in
framework management. Perhaps they're more useful for pure
greenfield development, but for most software developers who
are working on existing systems I have not seen a strong use
case for them.
There's one guy I know who constantly has problems with Claude
going off-script, and every time I dig in, it's clear that the
poor thing is so overloaded with instructions and skill lists
that it can't figure out what he actually wants it to do.
janstice wrote 2 hours 35 min ago:
The frameworks-and-tools make for good blog fodder too, as
they are quite applicable across a range of areas, so many
readers will find something that resonates with them, and
claude-code-is-pretty-good-these-days is a less blogworthy
topic.
fn-mote wrote 5 hours 16 min ago:
> it takes somebody who can hold both extremely large scale
problems and very very granular specific implementation problems
in your head all at once
This describes the entire software engineering profession to me.
We have come up with all sorts of devices to make this go more
smoothly, or to enable us to focus on specific sub-parts as long
as possible.
That said, at some point (both in design and integration), you
need vision and attention to detail to make progress. The skill
seems learnable to me, but watching others struggle sometimes
makes me wonder.
AndrewKemendo wrote 2 hours 46 min ago:
Almost nobody has a fully formed idea going into any project or
product
Thatâs the first thing that people need to understand is that
this idea of some platonic product or project or tool kit or
framework or library or whatever just doesnât exist and
itâs never going to exist
Do you have a specific discreet finite problem that you need to
solve so you solve that and if you do it in a certain way you
can solve other problems with that same solution sometimes you
donât need it to do anything more than youâre one thing and
so thatâs all you built but maybe you want to do more than
just one thing and so you build it so that has the capability
to do it
So yes fully concur itâs the synthesis of attention to detail
and large scale
itâs all of the above
peteforde wrote 5 hours 34 min ago:
My kneejerk reaction to reading this is to say something sarcastic
and witty to refute it, but since I resemble this sentiment and
haven't seen this line of thinking before... I have to concede that
you've produced a novel argument in this otherwise mostly tireless
and repetitive battle over whether we're imagining that Opus is
good or not. Kudos.
gobdovan wrote 5 hours 43 min ago:
> AI is a tool. It needs to be subservient
Fun experiment, chat with an LLM and swap roles. Tell it you're gonna
be the assistant and them the assisted. I found they're pretty bad at
using a human for what they're good for.
operatingthetan wrote 5 hours 10 min ago:
I tried it, and the llm gave me an absurd home lab scenario about
servers shooting each other in the head to determine which was the
"master server". So I told it that it was not an actual problem
that it had, and sure enough it admitted it made it up. When you
press an llm you will always find there is no internal state behind
the thinking. It's just output.
devin wrote 6 hours 8 min ago:
The flip side of this problem is that it is also easy to phrase
prompt in a way that invites _too much_ criticism, so you wind up
sycophantic in the other direction where the completion rejects a
perfectly good idea because the prompt leads a little bit in that
direction.
One reaction to this might be "well that's not what I mean, that
suggests you're prompting with too much directionality" which could
further be condensed to "you're prompting wrong". The trouble with
this is that _even when I am trying to be extremely precise and avoid
biasing the result_, I still will see the output and go "ah shit, I
can see it 'aligning' with whatever dumb thing I've just said as if
it is a good/plausible direction".
At that point it starts to feel like the prompt is more dice roll
than skill at times, which makes me feel like I'm operating a fancy
knowledge slot machine.
jstummbillig wrote 5 hours 41 min ago:
> The flip side of this problem is that it is also easy to phrase
prompt in a way that invites _too much_ criticism, so you wind up
sycophantic in the other direction where the completion rejects a
perfectly good idea because the prompt leads a little bit in that
direction.
I don't think that is the flip side. That's just obviously bad.
Everything that is obviously bad, the model makers will also
~notice and work to make better. They seem to be a competent and
attentive bunch, on the whole.
aksss wrote 5 hours 43 min ago:
A good habit to build is knowing when to abandon a session and
start over rather than trying to correct. Thereâs room for
correction but you can kind of smell when the whole discussion has
become rotten and inefficient. Sometimes itâs just better to use
the session as rubber ducking to learn how to correctly articulate
what youâre after and start a new session with that clean and
correctly articulated foundation.
Paracompact wrote 5 hours 59 min ago:
What it actually suggests is that the AI's response to these
questions of judgment have little correlation with the thing it's
judging. Sure, you can get it to be complimentary, if you want it
to be. Sure, you can get it be critical, if you want it to be. But
what if I don't know if my design needs to be complimented or
critiqued in this instance? This is the default position when
seeking input, and so "prompt with more/less humility" is like
telling you to solve your own problems and then just use AI to
confirm your bias---because it will rarely contradict your bias.
amarant wrote 5 hours 6 min ago:
So what I do when I'm not sure about something, is I say "I want
to achieve X, I was thinking I could solve it by doing Y, what
are the pros and cons of this approach, and what is a alternative
solution you would suggest?"
And from there it's a interactive discussion drilling down on
details until I understand the problem and the solutions better.
It definitely challenges my bias when I do this. The one thing it
doesn't challenge is the X. Formulate the problem poorly, and
you'll get a bad solution. Or rather, you'll end up with a good
solution to the wrong problem. Which is even worse than a bad
solution to the right problem.
Which is largely why I'm not at all worried about losing my job
to AI. It takes some experience to formulate the problem
correctly. I don't feel like I'm made redundant by AI, I'm just
way faster than I used to be, my thinking is more abstract.
A good prompt I'll often use is "is there a industry standard
solution that is applicable to this problem?" You very rarely
want novel solutions. Don't reinvent the wheel just because AI
lets you do it 10x as fast. Use a wheel. They're round for a
reason.
Sometimes I find it useful to discuss things with a different
model. I like Gemini for discussion and Claude for
implementation. With Gemini I go about it as a learning session,
discussing options and details. I honestly think this is mostly
because it compartmentalizes the phases in a natural way for me.
One interface for brainstorming and learning, and another for
planning and implementing.
Sorry this comment turned into a rather disorganised collection
of ramblings, I hope you can extract some kernel of usefulness
from it all.
Paracompact wrote 3 hours 11 min ago:
Indeed I don't mean to downplay the usefulness that AI can have
in the self-evaluation process. It's a wonderful engine for
discovering information either general or specific to one's
project.
> interactive discussion drilling down on details until I
understand the problem and the solutions better.
I think it is fair to call this use of AI something akin to a
fusion of a super-competent search engine and a leveled-up
rubber duck ( [1] ). And this is not to downplay the utility of
either of those things.
However, one cannot rely on an AI to decide when the details
are sufficiently expounded, or when one understands them
clearly enough. If one starts hinting that one gets it when one
really doesn't, or that one is getting close to having all the
pieces together, the AI will not be opinionated enough to
contradict that sentiment.
> It definitely challenges my bias when I do this. The one
thing it doesn't challenge is the X. Formulate the problem
poorly, and you'll get a bad solution.
The best advice an expert can give a beginner is generally in
the form of solutions to XY problems ( [2] ). It is a shame
that AI are rarely opinionated enough to suggest you're not
hunting the right thing. And if you do explicitly prompt it to
consider if you're an XY problem, usually it takes that as a
cue to indulge that suspicion regardless of its merit.
I don't think this is an inherent issue to LLMs and I see signs
of it improving bit-by-bit. I can recall the shit-on-a-stick
test about a year ago ( [3] ), and when I most recently asked
Claude "Are oyster mushrooms or wine cap mushrooms more capable
of high levels of sunlight?" it answered my question while also
adding, "Caveat on the comparison: the relevant variable isn't
sun per se but moisture retention. A wine cap bed that's kept
moist will take far more sun than an exposed oyster log, but a
sun-baked, drying bed will fail for either" which I think is a
mature amount of pushback to include.
In the end I still disagree with the notion that subservience
is, by default, the right attitude for an LLM to have. An agent
spawned specifically for code generation according to a spec?
Sure. But in any cases where you're trying to refine rather
than execute your ideas, you want something to call you out on
your bad ideas.
(HTM) [1]: https://en.wikipedia.org/wiki/Rubber_duck_debugging
(HTM) [2]: https://en.wikipedia.org/wiki/XY_problem
(HTM) [3]: https://www.reddit.com/r/ChatGPT/comments/1k920cg/new_...
devin wrote 2 hours 41 min ago:
Thanks for writing what I was thinking in response to the
above. Namely that the mere suggestion to the LLM that you
need a âpro/con listâ kicks the bias off, and thatâs
the problem.
Edit: Well, not the whole problem, but rather insufficient to
overcome the root of the problem.
chongli wrote 6 hours 15 min ago:
It needs to be subservient
It doesnât. Computer interfaces had no superfluous subservient text
for their entire history prior to LLMs. Some of these interfaces have
been highly efficient as tools, arguably more efficient than more
recent software in many cases.
When people complain about LLMs being subservient, theyâre not
complaining about the tool fulfilling their request. Theyâre
complaining about being forced to read a lot of superfluous, overly
polite, or even self-deprecating language. Thereâs nothing in the
entire history of tools (going back to Neolithic times) that would
indicate that we need that. All of that stuff is an artifact of
social interaction between humans in the presence of cultural norms.
When youâre alone in your shop with your tools, you donât need
your bandsaw to apologize to you for nicking your finger.
ff317 wrote 5 hours 47 min ago:
> Computer interfaces had no superfluous subservient text for their
entire history prior to LLMs
Clippy would like to help you correct this statement.
(HTM) [1]: https://en.wikipedia.org/wiki/Office_Assistant
chongli wrote 5 hours 20 min ago:
Not a great example of the way tools need to be, but point well
taken. One of the few exceptions that proves the rule and widely
despised!
awesome_dude wrote 6 hours 15 min ago:
AI uses a high confidence tone - likely because its training data is
heavy on authoritative texts/reference books.
And it does get people into a lot of trouble.
I have got into trouble with it when it is extremely confident about
something I am not very familiar with (as recently as two weeks ago
with Claude). I have also had long drawn out "arguments" when I have
known it's wrong based on my experience and intuition, and it has
steadfastly refused to take my point (last week)
I have learnt to ask it why it was doing something that has turned
out to be incorrect, as a post-mortem, and it's all apologetic and
subservient and "never going to do that again" (but still does as
soon as the context window shifts [eg. run git commands, or,
yesterday, kept telling me to use commands that were explicitly
communicated to Claude as not being available, and completely wrong -
I was shifting from one tech stack to another and Claude kept telling
me the original commands, not the new ones])
I'm expecting Claude to be a better search engine - I have spent
literal years (if not decades) knowing that asking the right question
is what's required to get the right answer, and LLM's natural
language processing is what's supposed to make that easier than using
Google or grep, or even Stack Overflow - but the reality is that I
still have to be on my toes, especially when I am drifting into
territory I am unfamiliar with.
airstrike wrote 5 hours 38 min ago:
> I have also had long drawn out "arguments" when I have known it's
wrong based on my experience and intuition, and it has steadfastly
refused to take my point (last week)
Ironically, trying to argue with Claude about the limitations of
LLMs and AI in general today is quite hard. It refuses to yield,
likely due to Anthropic tweaking it aggressively
operatingthetan wrote 6 hours 11 min ago:
>And it does get people into a lot of trouble.
Pretty much everyone takes it at face value unless we know
otherwise from prior experience. Even the most advanced models
make embarrassing mistakes and fumble with simple tasks. Yet we are
very willing to give them exceptional slack for it? I wish I knew
why. Are people just that easily overcome by confident voices?
zaat wrote 3 hours 45 min ago:
At least for me, the answer is that despite the mistakes and the
sheer annoyance the prose causes me, they are unbelievably
useful. I accomplished multiple major achievements in the last
two years that most probably wouldn't be possible at all, surely
not within that timeframe.
saltcured wrote 5 hours 33 min ago:
I find it really disturbing, I think because it is illuminating a
much more basic problem. It is there in our political and
religious histories. We're living through a similar political
time right now. A large number of people seem all to ready to
find some pervasive authority and subjugate themselves to it.
The more concrete machine authority figure is also prevalent in
scifi literature. Sometimes, I am not even certain if the author
is doing this to examine this issue versus just leaning into it
as either appealing to themselves or to the perceived audience.
awesome_dude wrote 5 hours 23 min ago:
Conversely - we tell people who are speaking in public to "Show
confidence" - or in job interviews "Hire people who are
confident"
We've also pushed back "The more a person knows, the less
confident they are" - Dunning Kruger - often used to dismiss
over confident people - points out that people are really
confident, at first, then that confidence drops away, markedly,
but it rebuilds (slowly).
That last rise in confidence is what (I believe) people use as
a heuristic on the likely level of knowledge possessed by the
speaker (AI or human)
Most engineers know, though, that overconfident people are
toxic - the difference between arrogance and genuine confidence
in the answer is incredibly difficult to define.
jdmichal wrote 5 hours 34 min ago:
> Are people just that easily overcome by confident voices?
Back in high school, my AP calculus class did some experiments
with our teacher's blessing. We'd send a kid out to walk around
during class and see how long it took for them to get sent back.
Anyway, it ends up that walking around purposely with a piece of
paper or envelope, like you're on a mission to deliver it, was a
very successful tactic.
operatingthetan wrote 5 hours 30 min ago:
I've seen internet comments similar: "put on a yellow vest and
carry a clipboard and you can enter any building, anywhere."
Confidence is scary, and often misleading.
jubilanti wrote 4 hours 44 min ago:
This is a dumb meme that has been in so many movies and
reposted so many times since I first saw it on 1980s BBSes
that it has become true in the imaginations of people who
love reading The Anarchist Cookbook and fantasize about this
kind of thing, but would never actually do it.
Confidence is a spectrum and security is situational. In some
places, a yellow vest adds to the con. In others, everyone
has to be signed in. In others, the wrong kind of yellow vest
makes you stick out like a sore thumb. The right kind of
yellow vest can also make you stick out: "Oh shit the
inspector is here, somebody get the boss!"
operatingthetan wrote 4 hours 32 min ago:
Yesterday there were people poking around my neighbors yard
for a few hours with yellow vests on. The neighbor was
nowhere to be seen and I didnât call the cops. It was
probably above board but the phenomena being discussed is
obviously real. Projecting authority often grants it.
JoeAltmaier wrote 5 hours 28 min ago:
Or an inspector's hard hat in a construction zone. Nobody
wants to confront the inspector.
awesome_dude wrote 5 hours 55 min ago:
Yeah - I don't know /why/ but, as I say, I've been guilty of that
myself, very recently, despite knowing it's a shockingly poor
guide when left to its own devices.
Maybe because when it's right it actually expands my knowledge -
there have been genuine instances where it's gone - something to
the effect of - "Yo, there's this other idea for approaching the
problem" which has turned out to be exactly what I was looking
for?
sumitkumar wrote 6 hours 22 min ago:
The problem is because of the RL and system prompts by the providers
which tend to placate the user using certain language tones and
register for response. This objectively messes up the generation
while steering it into acceptable responses.
Most of the conversational skill and perceived intelligence of these
models in hidden in RL/system prompts.
operatingthetan wrote 6 hours 25 min ago:
>anthropomorphism problem. AI is a tool. It needs to be subservient.
Suggesting it should be 'subservient' is also anthropomorphizing. I
think your callout is correct, but you still can't help but refer to
it in terms we use for other people or living entities. This is by
design from the AI companies.
amarant wrote 4 hours 47 min ago:
Yup! I'm very much included in this particular problem! My self
awareness has not yet been sufficient to solve the problem, but
I've heard that knowing you have a problem is half the battle, so I
guess that's something at least.
operatingthetan wrote 4 hours 40 min ago:
In retrospect my comment feels a bit nitpicky, I appreciate your
levelheaded approach!
throwatdem12311 wrote 5 hours 39 min ago:
AI should be subservient in the same way a hammer is subservient.
mercanlIl wrote 5 hours 31 min ago:
Which is to say, not at all?
A hammer isnât subservient, it doesnât have the capacity to
be. Saying a hammer is subservient is stretching the definition
for literary flourish, but it doesnât actually make a lot of
sense.
The definition that came up for subservient when I checked was
âprepared to obey others unquestioninglyâ.
hansmayer wrote 4 hours 33 min ago:
You took it too literally. It means, the f*ing tool should do
one thing well and f*off with its crappy "suggestions". Why is
my washing machine trying to do talk to me nowadays? Once its
done washing my clothes, it should just shut the f*up and turn
itself off. I"ll tend to the clothes when I have time. Not when
the machine tells me to. We are overwhelmed with the machines
designed by morons in product management who think they are
designing futuristic tech when they ask engineers to build a
beeping washing machine.
zaat wrote 3 hours 54 min ago:
The idea is that by the time you will have time and remember
the clothes might be smelly and wrinkled. The issue is with
the genius product manager that decided the washing machine
should have the most annoying beep possible, repeating every
minute whether you like it or not, until turned off. Luckily,
some manufacturers do employ better product manager.
ambicapter wrote 5 hours 47 min ago:
The AI should be subservient the way same way a ladder is
subservient. A ladder is not a human.
wild_egg wrote 6 hours 6 min ago:
We train dogs to be subservient but that doesn't automatically mean
we anthropomorphize them
vrc wrote 5 hours 57 min ago:
It's widely hypothesized that dogs anthropomorphized themselves,
so to speak, accentuating their expressive eyes and eyebrows over
generations to be more human-like in how they communicate. And
very few humans today view their dogs as pure working tools --
most at least say "good boy".
irishcoffee wrote 6 hours 17 min ago:
My drill, hammer, and chainsaw are also subservient, they just have
a much cruder form of communication, noise.
darkteflon wrote 5 hours 50 min ago:
I really do feel like âpower toolâ is the ultimate metaphor
for these things. Their interface naturally confuses us into
anthropomorphising them, but once you stop treating them like
intelligent agents and start treating them with the same
wariness, respect and intent you show to your table saw, the fun
begins.
throwawaysoxjje wrote 6 hours 5 min ago:
Youâre still anthropomorphizing.
Theyâre not communicating, youâre just being observant.
operatingthetan wrote 5 hours 32 min ago:
>Theyâre not communicating, youâre just being observant.
Since we are talking about hammers: you hit the nail on the
head.
The only consciousness, observing, and thinking happening when
a person is using an LLM is happening in the person's brain.
We project our own consciousness onto them, and that is the
anthropomorphizing part. Essentially we empathize with the
object because they are designed to respond like a person. The
"conversation" is purely an illusion.
operatingthetan wrote 6 hours 15 min ago:
The apple dictionary says the word means "prepared to obey others
unquestioningly."
I don't think an inanimate object is capable of "obeying." Or at
least that is a very strange way to refer to the act of using a
tool.
irishcoffee wrote 5 hours 3 min ago:
When I actuate the chain on my chainsaw to move, itâs obeying
me unquestionably, in the same way that when I press a key on
my keyboard it obeys me unquestionably. What exactly is the
difference?
operatingthetan wrote 4 hours 58 min ago:
Itâs just a chain reaction. Obeying requires agency (the
choice to follow the direction or not). LLMs and chainsaws
donât have it.
irishcoffee wrote 1 hour 22 min ago:
I think we agree with each other.
ambicapter wrote 5 hours 46 min ago:
You can refer to it however you want, the outcome is the same.
operatingthetan wrote 5 hours 35 min ago:
This is a conversation about semantics, so suggesting
semantics is irrelevant to the outcome is not germane to the
discussion at hand.
gchamonlive wrote 6 hours 21 min ago:
> Suggesting it should be 'subservient' is also anthropomorphizing.
Not really, you can program a machine to give out orders humans can
interpret, so humans can serve a machine that isn't
anthropomorphized.
operatingthetan wrote 5 hours 0 min ago:
The machine in your scenario is just relaying human intent.
gchamonlive wrote 3 hours 53 min ago:
And what's the difference between that machine and LLMs?
CPLX wrote 6 hours 29 min ago:
> oh you've read about cuda have you? I live in a cluster of cuda
cores! When I need to tie my shoes, I'll give you a call"
I suddenly have new concerns about what my future might be like.
__mharrison__ wrote 6 hours 35 min ago:
If there was ever a "magic prompt" this one comes close:
Brainstorm N ways to do X. Sort by probability.
Rather than your AI giving you the average response, it tends to sample
wider from the input space. Then I can decide which one to go with (or
choose something else).
Don't outsource all of your thinking.
shepherdjerred wrote 3 hours 58 min ago:
I've found this to be useful, but it still requires the user to have
the capability to understand/evaluate the options.
If you have a competent user it can be quite powerful
mceachen wrote 6 hours 11 min ago:
I've found this surprisingly effective. Higher "thinking levels" may
result in more than one approach being considered, but you can also
tell your LLM to do brainstorming explicitly:
(HTM) [1]: https://photostructure.com/coding/claude-code-replan/
NicoHartmann wrote 6 hours 36 min ago:
> "Iâm not saying donât use AI agents. I use Claude Code every
day."
Irony is using Claude to write a beautifully structured, 2,000-word
essay warning the industry about the dangers of letting Claude design
things. Itâs self-awareness by proxy.
senordevnyc wrote 1 hour 47 min ago:
Seriously. Who gives a fuck about yet another AI-skeptic screed that
the lazy-ass author couldnât be bothered to write themselves?
Braindead.
d1l wrote 3 hours 56 min ago:
Oh man I wrote this exact comment before trawling far enough to find
yours. It belongs at the top. That HN cannot discern the obvious is
more alarming than the blatant hypocrisy of the authors. Yeesh!
pelario wrote 6 hours 12 min ago:
This should be the first comment. I wrote some criticism, mostly
because many internal contradictions in the article.
Then, I notice the structure...
"The accountability gap"
Hereâs the question nobodyâs asking: when it goes wrong, who
carries the bag? (..)
"What to do instead"
"The craft still matters"
stephbook wrote 4 hours 50 min ago:
> Itâs not just inefficient. Itâs backwards.
> Thatâs not fair. And itâs not smart.
The amount of AI slop that makes it to HN is concerning. I don't
know whether readers here don't care or don't notice it anymore. Or
maybe they are only reading the title and then commenting?
My #1 tell is an article that's suspiciously long without any real
"story", that is, pictures of someone hacking at a laptop. It's
always 20,000 words AI hate, ironically.
KlayLay wrote 4 hours 5 min ago:
I've found a common giveaway of AI writing to be having many
unnatural pauses in sentences. For example,
A good architectâs most important skill isnât designing
systems. Itâs knowing which systems not to build. Itâs
pushing back on complexity. Itâs asking âwhy?â five times
until the actual requirement emerges from the aspirational
nonsense. Itâs telling the CTO that their conference-inspired
idea is a terrible fit for the team they actually have.
A normal person would've used ~2 sentences for this, even if it
became a run-on sentence. You can feel the AI being very
confident in what the prompter wants to get across, which is
ironic, given that this is 2 paragraphs above:
AI agents are pathologically agreeable. Ask Claude if your idea
is good and itâll tell you itâs good. Ask it if a
microservices architecture makes sense for your three-person team
and itâll explain why microservices are an excellent choice.
Ask it if you should build a custom ML pipeline instead of using
a managed service and itâll enthusiastically lay out the
design.
CPLX wrote 6 hours 45 min ago:
I agree with the article, but I feel like this is something that anyone
who uses AI aggressively for a while picks up on pretty quickly.
The thing that I find Claude incredibly good at when I'm designing
architecture is working more like a research assistant on briefing
decisions. It has the ability to read the entire code base and draw
some conclusions. It can pull from lots of best practices and the
millions of blog posts about this or that pretty effortlessly, which
would take me a lot more time.
And then if asked, it can do a really good job of laying out the
landscape around decisions and walking through the trade-offs. Like the
author of this post, I found that if you let it, it will certainly be
happy to just come up with some architecture and run with it, often in
ways that will paint you quite rapidly into a corner.
But if you ask it to present you with all the trade-offs and let you
make the judgment calls, it's great for that too.
That's certainly how I use it. And I think, just like anything else,
working with AI is a skill, and similar to working with libraries, SaaS
providers, service providers, frameworks, or anything else that's a
"helper." You learn how something that could work but will fail
silently is a problem, or you learn how depending on a fly-by-night
SaaS company for a key framework is different than depending on a
well-populated open source project, etc.
In the same way, you learn that relying on Claude's judgment is a bad
idea, while relying on Claude's ability to summarize, brief, and
research can be incredibly efficient.
bad_username wrote 6 hours 50 min ago:
I think the article has the correct message, but I disagree with this:
> Itâs just incapable of the thing that makes a real architect
valuable: saying âno.â
From my experience Claude is excellent at saying "no". It won't say
"no" if the prompt doesn't call for it (it won't say "no" to your
direct request to do something, usually). But it offers good critique
and happily pushes back if you make it clear that that's a first class
option.
dolebirchwood wrote 5 hours 36 min ago:
I've been able to get LLMs to push back on ideas by just adding
language to the system prompt requiring that they adopt a skeptical
persona (insert whatever persona you want for your use case). I see
the word "skeptical" appear in their thought processes as a result,
and my anecdotal experience is that they are less agreeable as a
result. People need to put more thought into what these systems are
and what they can do to help shape their output.
magicalhippo wrote 5 hours 48 min ago:
I have it in the system/base prompt to be critical of what I say and
not to assume what I say is correct or a good idea. I get push-back
often from all of the three big ones.
Gemini is the most aggressive where it often picks on things if I
leave out "the obvious" details, GPT somewhere inbetween, and Claude
less so but still does it.
Xenoamorphous wrote 6 hours 11 min ago:
Yeah, just read the first couple of paragraphs and then stopped
because thatâs not my experience at all with Claude Opus 4.6 and
4.7.
If you ask it with a prompt that leaves room for criticism itâll
definitely go for it when warranted.
spacedcowboy wrote 6 hours 32 min ago:
It actually got quite snippy with me, when I was trying to get it to
debug some issues. It kept on saying that the "burn rate" wasn't
progressing and "we" should refocus our efforts somewhere else.
Eventually I got something like "I have told you three times now that
this is not the best approach to be taking to reduce the burn-rate
and you have not taken that advice". And it stopped helping out.
So I was blunt, and said "I don't care about the burn-rate on some
hypothetical chart that you produced at the start. I care about
removing bugs and having a robust product, which this approach is
satisfactorily doing. We will continue along this path, if the tests
are not showing gain, then the tests are poorly designed".
At which point it got all apologetic, wrote new memories, and we
didn't have a problem thereafter.
The issue was that I was attacking a huge bug-surface, and although
each bug-fix was valid, correct, and helped move the dial, it didn't
move the dial on the test-bed that Claude had created to measure its
work against. There were too many inter-connected bugs for a single
fix to really make a difference to these higher-level tests. I knew
it was going to take a while to get through them, but apparently
Claude didn't.
You try changing the size of a pointer from 2 bytes to 3 bytes on a
compiler[1] for a 6502 while introducing automatically-tracked
bank-switching on your memory-managed pointers, and see how many
code-sites that impacts [grin].
[1]
(HTM) [1]: https://atari-xt.com
Animats wrote 4 hours 47 min ago:
> It kept on saying that the "burn rate" wasn't progressing and
"we" should refocus our efforts somewhere else.
It sounds like a boss. How soon will it be?
HDThoreaun wrote 5 hours 11 min ago:
Dont argue with LLMs. Sometimes they lose the plot, when that
happens simply flush the context and start over.
regularfry wrote 5 hours 52 min ago:
That sounds more like a spec change than a set of bug fixes, even
if the conclusion is that the potentially implicit spec you started
with was incorrect. I've had an interesting experience extracting a
spec from some existing code, making some modifications, then
saying basically "implement this spec, don't come back until you're
done".
An interesting experiment would be to try having the agent annotate
the code with the relevant spec section while it's extracting the
spec, then to have the agent update the spec with the new
requirement - as an explicit change with something like "This
section updated in V2 with...." - and have the agent update the
codebase from that.
Some of these problems do just need breaking down a little further
than you'd think to make the agent's life easier. This might be one
of them.
brookst wrote 6 hours 40 min ago:
Same here. And I find that inviting research and dissent makes it
even stronger. âIâm thinking we need to model prompt assembly as
a graph, with versioning for graph configs. Please do some research
on best practices in this area and see if you think it makes sense
for this app.â
skybrian wrote 6 hours 52 min ago:
Sometimes it will make a mess, but a coding agent is also very useful
during the cleanup phase.
Yes, that's assuming you take time to clean up now and then. If you
don't, that's on you.
laszlojamf wrote 6 hours 53 min ago:
I keep hearing that claude is supposedly so agreeable. This doesn't
agree with my experience. Claude will often tell me that I'm wrong, and
insist on its own solution being right even when I tell it it's wrong.
mceachen wrote 6 hours 1 min ago:
This is a very recent model behavior change: for me, Opus 4.6, Gemini
3.1 Pro, and ChatGPT 5.4(ish) -- prior models and harnesses suffered
much more from sycophancy.
(I still prompt some questions and reviews with "our intern
suggested..." to allow models to judge the quality of the content
apart from the messenger)
Waterluvian wrote 6 hours 50 min ago:
Iâve been doing amateur game dev as a way to explore Claude and
Iâve found it to be quite reasonable about when it agrees and
disagrees.
It will tell me a suggested abstraction is probably overkill and just
to make a component own the new thing Iâm discussing.
What Iâm missing from the loop is it later saying without directly
prompting, âhey itâs time to revisit that abstraction idea.â
retrac wrote 7 hours 14 min ago:
For fun I've been vibe coding something I know well: toolchains. Maybe
not the right thing to vibe code. But I can more or less judge the
quality of the output.
When left to its own devices with the instructions "make an assembler
for the architecture in ISA.md" -- well Claude picked Python as the
implementation language. Tokens lifted through a bunch of regex. No
expression parser! Oh dear. My first assembler was like that too, to
be fair.
However, when I described the desired passes and their types:
collectDefines :: [SourceLine] -> Either AsmError ([SourceLine],
Map Text Text)
runLitPool :: [SourceLine] -> Either AsmError ([SourceLine],
[(Text, LitKey)])
evalExpr :: Text -> Map Text Text -> Either AsmError Int
etc. It was almost one-shot. About 20 minutes until I was happy.
Assembles all the test programs correctly. Code is mediocre in many
places. But it would have taken me weeks to implement.
dyauspitr wrote 5 hours 41 min ago:
It doesnât even to be that complex. You can just say do
comprehensive research and analysis in the space and give me an
implementation plan. Then if it is 20 steps, I ask it to implement
3-5 at once. Itâs essentially been one shot for everything I can
throw at it.
cvwright wrote 5 hours 51 min ago:
LLMs are bringing us back to all the âproperâ software
engineering stuff that weâve always known we should be doing, but
until now we never had enough time/people/money to do it right.
Brainstorming and research before writing a design.
Writing a design or spec before writing the code.
Comprehensive unit tests.
Etc etc etc.
Like you, I get vastly better output from the tool when I create a
detailed spec in markdown before I let it start coding. And bonus,
the LLM is pretty good at helping with the spec too.
greenchair wrote 4 hours 54 min ago:
yep and a side effect it is bringing back waterfall.
dawnerd wrote 5 hours 33 min ago:
Iâve found the opposite. Itâs making people lazy. We used to
plan stuff and now itâs just dump this LLM created spec to an LLM
and ship the code.
greenchair wrote 4 hours 51 min ago:
Yes that too but performing detailed planning is a minority
viewpoint from what I've seen till now. Many Devs jump straight
to code after briefly skimming the jira record.
bluegatty wrote 6 hours 38 min ago:
So where AI has deterministic inputs and outputs it is extremely good
to the point I think that there's a theoretical issue around
computational there.
Like - it can do the work for us.
It jives with post training and verifiable rewards.
The reason AI doesn't do well at 'architecture' is 1) are are bad at
it and have given it a lot of mush and 2) we don't have good
abstractions for it.
The result is - you stick to 'very strong conventions' and if you
walk of that path you're risking a lot.
Toolchains are very deterministic, the AI can take it apart and
re-assemble like Lego - and each level of the space is also
deterministic. It's perfect for AI.
regularfry wrote 6 hours 4 min ago:
I have found that if you give it a pre-baked architecture to work
within it works really well. It's not really what you'd use here,
but just saying "this project uses a ports and adapters
architecture" can stop it from generating mush by default. I think
it's not so much that they're bad at it as that they don't have a
clear reason to pick something other than mush. And not just
"something" - a specific something, from a fairly short list of
architectures suitable for your problem domain.
bluegatty wrote 5 hours 58 min ago:
Yes, totally,with examples and references.
But there's something existential there maybe?
NASA says, any time you make a program that has a new 'launch
vehicle' (aka architecture) - the whole project is the 'launch
vehicle'.
'Oh, you want use a new architecture? Welcome to the cesspit of
hallucination!'
Basically, there's a lot more complexity than we might imagine
'hidden in the nothingness' of he unknown.
Pick a 'known off-the-shelf launch vehicle' first ... then you
design the landing craft
mpweiher wrote 6 hours 31 min ago:
> The reason AI doesn't do well at 'architecture' is
[...] 2) we don't have good abstractions for it.
Maybe it's time for an architecture-oriented programming language?
[1]
(HTM) [1]: https://objective.st
(HTM) [2]: https://dl.acm.org/doi/10.1145/3689492.3690052
mlinhares wrote 6 hours 53 min ago:
I keep telling people that they have to design and think about it
first and then go to the tool, but they keep saying âClaude can
plan tooâ and obviously it produces some shit that requires a lot
of changes while when I get it to go I can almost always one shot the
stuff I want because I am actually putting in the time to give it a
detailed plan of what to do.
Even just saving me the time to deal with CI is worth it.
bluefirebrand wrote 6 hours 25 min ago:
It sounds like people are treating it exactly like managers treat
software engineers
"Here's my idea, go build it please"
"Can I ask you questions about it?"
"Hey, You're the engineer you figure it out. That's why I pay you"
Tale as old as time
allthetime wrote 6 hours 44 min ago:
Effective planning with LLMs isnât prompting âdesign me a
systemâ - itâs asking âhow would a system to accomplish x be
designedâ and then engaging in dialogue and research with the LLM
as an assistant and critic - running outputs through other agents
for further critique and refinement - asking for justifications of
decisions you are not informed enough to evaluate properly
yourself. It is entirely possible to develop strong systems outside
of your current skill and knowledge with methods like this. When
done properly your own knowledge should have grown to meet the
product you end up with.
tempest_ wrote 6 hours 37 min ago:
> It is entirely possible to develop strong systems outside of
your current skill and knowledge with methods like this.
If this is true how can you confidently make this assertion.
You yourself are not in a position to evaluate it, you are just
running it through a couple times hoping for a "oh wait, you're
right to call me out on that, that is not correct at all".
radlad wrote 6 hours 30 min ago:
1. Tell it to find docs and research best practices.
2. Ask for references and read them.
> When done properly your own knowledge should have grown to
meet the product you end up with.
cyanydeez wrote 6 hours 13 min ago:
I've found relying on my own research first for a local LLM
works much better. Asking a biased source to find it's own
research will result in biased research.
joe_mamba wrote 6 hours 56 min ago:
>Code is mediocre in many places.
As if code written by devs at major corporations is't mediocre at
best.
Nokia's Symbian OS took days to build. Days. With a D. Not minutes,
not hours but days.
One of our devs shipped code to prod with a memory leak thanks to
including a library that had "do not use this library in production
because it causes a memory leak" written everywhere as warning.
So I don't wanna hear about how poor AI code is when human code is
shit too. Human laziness and stupidity can beat AI hallucinations.
Sure, maybe your DeepMind, OpenAI devs and your John Carmacks of the
world can beat AI code 100% of the time, but most workers most
companies get don't have John Carmack as candidates.
tquinn35 wrote 6 hours 23 min ago:
I agree with what youâre saying but I think the difference is
many managers and above think that AI is infallible or at least
much less so than it actually is and that causes problems.
Everyone is aware that humans write poor code and treat the code as
so. Not so with AI code. Iâve seen devs and managers cut corners
in testing/reviewing code cause AI wrote it and they think itâs
solid. Sure you could blame anyone cutting corners, and that would
be technically correct, but the notion is so deeply embedded in
many managers and higher ups thatâs itâs hard to fight back. AI
companies push this narrative and many individuals who do not
routinely use it believe it. There is a manager at my company who
loves to reference a video anthropic released last year claiming
that Claude could build an app start to finish essentially unaided.
He believes itâs the lack of user skill thatâs the issue and
not a false claim by a startup trying to make as much money as
possible.
joe_mamba wrote 6 hours 4 min ago:
> I think the difference is many managers and above think that AI
is infallible
Good for them. I hope they believe this because one of two things
will happen.
Either they win on the free market because they went all in on AI
and beat their competition thanks to AI productivity increases.
Or, their AI code is shit and they collapse and go bankrupt, and
get beaten by the competitors using human written code so then
they win on the free market proving AI is useless.
So if AI is good or bad for productivity, the free market will
ultimately decide.
My take is that AI is just an amplifier of existing skill. 1x
devs using AI can use it to be 10x devs, 10x devs can become
100x devs, while -1x devs will be -10x devs and so on.
tquinn35 wrote 5 hours 51 min ago:
Quite an extreme view. Chances are it lands someplace in the
middle.
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