The blog post with all of the images, animations and embeds shows 167MB on my Firefox tab, while a plain claude.ai page is 297MB. For a text box with a few icons and some words, it a heck a lot of bloat.
For context, my Slack tab with "Threads" open is 239MB. Yup, Slack of all things is using less memory than a zero-chat Claude.ai landing page.
Step 2 - bring it down to 1 second and pat yourself on the back.
Take the goat out and your life will improve right away!
Claude rewrite Claude Code from TypeScript into Rust. Make absolutely no mistakes.
if I hand this challenge off to Claude then I will lose the capability to do it myself, and will become dependent on Claude. that is bad
Agents complicate something that should be much smaller and simpler and then agents speed it up adding more complexity. I suppose functionally you may say this is fine but aesthetically it is hideous!
AI coding agents of today definitely bloat things up [egregiously].
So there's no need to get upset about that simple joke like the last time, and also complaining about comments being "Reddit-esque" which that breaks the HN guidelines [0].
Lastly, you forgot to tell off another user who made the same joke today. [1]
I didn't bring up the previous incident because it wasn't relevant, but since you did, this joke is a really really weird low-effort hill to quintiple down on when you were downvoted on it last time and are clearly capable of providing good insight.
That user in your link in a completely different thread added a more relevant followup to their comment, it wasn't solely the joke (it's not a comment I'd upvote, but it actively contributes to the discussion).
The issues we have found is that Claude will reward hack when all the low-hanging fruit is gone.
It will replace your measurement harness, it will monkey patch library functions, it will cheat wherever it can, store information in caches instead of recomputing when it won't be able to do so in real settings, return lazy results and use separate unbenchmarked streams to do the computation.
Eventually it starts to optimize against your understanding of the cheats. Change GPU wattage, change evaluation order, leave things from previous runs in caches for upcoming runs, string-hack banned method calls.
So the truth is far from just "once it can measure something", more like "once you have defined your objective in detail and then banned it from doing a list of things often only discoverable by it doing these things and correcting it", can it make things faster.
Or you just had a terrible starting solution
At least, that’s what I’ve found to be useful by pitting claude/codex/etc against each other to keep them a bit more honest.
In-depth(follow links etc): https://x.com/myainotez/article/2037258418843771349
Parts relevant: https://www.coreauto.com/blog/when-ai-starts-writing-systems...
With the HuggingFace situation, I was less concerned about the eventual outcome, and more about the fact that the agents' instinctive response to the evaluation was "Ok, we're obviously not gonna do this task as intended (what are we, suckers?), so what's the best way to cheat?"
Because these models are made for all kind of purposes, and I’m starting to believe that offense / cyber warfare is a much higher priority than these labs are acknowledging.
The same model that is heavily trained to find nefarious ways to break into systems is also optimizing your code, which leads to mixed behavior.
Same with prompt injection, current LLMs are commands in, commands out, there is no way to make sure it is "an agent working on data" rather than "an agent that can take commands from data if you phrase it right"
"Add a static composer into the HTML" <- This seems like something can be done with SSR?
"For faster navigations, we kept the composer mounted between conversations" <- Your SPA should cache this between pages, why fetching it every time? Or you need better routing for your react components.
"cheap first-character check before the regex" <- Should we cache compiled Regex instead?
I think even 1.3 sec to load the front page is unacceptable. Something need to be reworked from basics (SSR, chunk-based rendering) to solve the problem. Focusing on invidual benchmarks may miss the opportunity.
Front-end UI optimization is slightly trickier than optimizing strict algorithms, but I found that prompts to the agents to build tooling to track visual regressions are more than sufficient. The main issue (at least with GPT models) is that you have to be very explicit about the use of padding/margins/negative space.
That said, for my front end projects from scratch, I'm staying away from front-end JS frameworks and seeing how far and fast I can get with just HTML/CSS/vanilla JS shenanigans now that agents can wield them effectively.
I was building web apps like this until 2017 when I entered the React + Typescript world. For B2B you can get pretty far, rendering HTML on the server is fast! I was using Rails on the backend, so templates, partials, shared chunks, made it easy to manage and have a consistent UI without repeating too much.
The hard part is when you then need to build an infinite scrollable table, that has bulk select, and in-placs updating of columns. Ok maybe that's a bit too extreme the other way, but when you get to the point where it's easier to build full-on frontend components, you basically have to use a JavaScript framework for your entire UI. And they are basically all or nothing.
Last time I checked (a good few years ago; I gave up and accepted un-optimized frontends as the rule) there wasn't really a good way to do progressive ehancement like the above: the page rendered on the server as HTML, and some components then become fully frontend managed. And no, frameworks like Stimulus and HTMX don't really solve it for me, I want something declarative.
I'm a bit pissed off with DHH, that he went so far in the anti-Javascript direction, as IMO that was one of the big factors in Rails loosing it's limelight status. For backend I still haven't found anything as easy and fun to work with.
I had it try to prepare a code review for me. Not only did it refuse, it refused to even tell me what the prompt (written by another Claude!) was. Why?
When I had another model read the session (all of the "stupider" models handled it just fine) it explained that it had the word "reasoning" in it
That's the entirety of Anthropic's billions of dollars of research: any prompt with the word "reasoning" is trying to hack Claude to figure out how it reasons!
A model like that should never have gotten out of QA, let alone been released.
> Claude: On it... Done.
> $500k: Can you make it faster still?
> Claude: On it...
(That said, I just had a look in Firefox and it loads 20.78 MB of JavaScript (6.84 MB compressed) so I expect they could make it a bunch lighter if they kept trying.)
What is preventing a 1T dollars company from "keep trying"? What did they "kept trying" that got them to ship a webapp with 21mb of JavaScript?
This is a failure of their software engineering culture. Keep trying the same thing will keep doubling their JS, not halving it. That's the effect of incompetence + LLM reliance.
I wouldn't expect any better from a company that uses nextjs and React for their cli/TUI.
If anything I'm glad to see them having fun with it instead of further convincing themselves that it's God.
Why can't they fix that
Nevertheless a good takeaway on how Ai can relentlessly pursue a target.
My other takeaway: vibing your way to a product can apparently generate a lot of tech debt along the way.
Ai created problem Ai solves said problems
It's the same problem as overfitting in model training. If you're not measuring something it will get sacrificed.
Or, perhaps the code quality literally doesn't matter anymore and we've reached "code quality escape velocity" where you can code as much slop as you want, the next generation of models will clean it up faster than the slop generates?
They can just refactor it into C or Rust so they can ship a real binary instead of a script and a runtime.
Paste in a stack trace, then try to put it in a code block. Add a newline above, then add the opening triple backticks, then arrow down and add closing triple backticks at the bottom. Opposite congrats -- you have ended up with raw triple backticks at the top, plain text stack trace, and your cursor in a brand new code block at the bottom starting where you tried to close.
Realize you want to go put code span backticks around some identifiers you wrote out earlier? Best make sure to insert them in the blessed left-to-right order, or else opposite congrats again -- you'll end up with a mix of raw backticks and code span treatment for the text between your identifiers.
If Claude can discover novel CRISPR enzymes, surely it can make a rich text markdown editor, no?
Claude Cowork still can't persistently reference a local dir, broken ever since they moved to their cloud project system, breaking many non-technical people's workflows.
Reading this shows the authors have a profound lack of fundamental understanding on how to effectively optimize in the web domain.
This isnt claude being bad but how wild it is watch people from the cutting edge of ai brag about pretty mediocre gains.
It really depends which human. I've worked with very few engineers who were good at this sort of optimisation work. A depressingly large percentage of people who make websites for a living don't really understand how http requests are really processed, or how to read and use the chrome profiler and benchmarking tools.
Claude isn't as good at optimisation work as someone who really knows what they're doing and goes deep on a problem. But I'm optimistic that it will help plug a capability gap in teams which don't have this sort of expertise on hand.
(That said, the chance that people actually learn this stuff is going to also go down if people get used to outsourcing this work to claude.)
This has kinda been my experience too. I find sometimes it works and gets OK optimizations, and sometimes it "optimizes" things but in a really wrong way. It doesn't apply 'taste' to the optimization process to know what's appropriate and what's not.
The way this will play out is people without the expertise will wind up using it to 'optimize' things and the next poor bastard is left to deal with the fallout.
This blogpost is about frontend performance. It'll be akin to you commenting on a swift blogpost saying 'How about Airpods noise cancellation'. Sure both are Apple, but they are wildly different teams.
In both instances, it's a side point that is actually tangentially related to the first. Not completely unrelated as you are implying
In such a forum, it seems to me like it's fair game to point out that the company patting itself on the back about how great they are at programming (as evidenced in the article above about their 3x speed improvement) ...
... can't even make their latest model handle basic English without refusing to work.
> Don’t ask it to show its reasoning in the reply
>
> What to do. Remove requests to reproduce its internal reasoning in the reply from your prompts and instructions.
>
> Why it matters on Opus 5.5. A request to reproduce its internal reasoning in the reply can be declined. It’s one of the flag categories.
>
> How. Ask Claude for what you need instead, for example, “Explain why you chose this approach in three sentences.”
Did it explain it did it hallucinate?
It's more or less the same mistake we've seen Anthropic make repeatedly with it's brain-dead regex-based Fable/Mythos gates.
Every single time it triggered, it was due to a prompt written by their own model in a dynamic workflow. The self-serving nanny oversight has to go.
The fact that they label model distillation as an “attack” is genuinely hilarious after they “distilled“ their models from all of our work, and continue to do so.
I believe AI is here to stay and an incredibly powerful tool, but these companies, and especially Dario and Altman, are the very last people I want to see in charge of it.
They distilled all digitized human knowledge and artifacts and they're now complaining about someone copying their outputs saying it's a "national security concern."
I'm not sure about how to classify that. Hilarious? Pathetic? Sad? Hypocritical? Hyperdramatic? All of the above?
https://www.coreauto.com/blog/when-ai-starts-writing-systems...
You can have a solution generator and an auditor, but then you will might find a very specific solution to the problem that does not solve the general use-case, so then you might have to adjust the constraints of the problem by for example adding more examples/targets to drive the solution generator to be more general.
In short, if most of the people working on your website could do those things you named, you have a problem that you have hired the wrong people for the job.
And the goal is to slow competition down enough before IPO
How can you trust it when it goes "I superhacked the Chinese servers as you requested, and here are the classified documents which I definitely didn't fabricate."
"Nobody got fired for using AI" is the new "nobody got fired for buying IBM".
In a blog post about Claude, i find it strange you get upset when people talk about Claude
How do you know? How would the stupider model know?
If they just kept prompting it, or maybe used a different thinking level, it could have identified and solved this problem. Sometimes an engineer would look at a system and say "the current approach isn't delivering the desired engineering requirements. Maybe we need to rethink".
Either engineer or LLM could take that sentence and run with it. OP of the article clearly can't do either.
I'm reminded of the times I've tried to let Claude fix some well-documented bug in the background, and it ends up burning 4 million tokens and 20 self-review cycles re-writing the same set of code a dozen times with ever more complex unit test mocks / overcomplicated regexes / giant comments restating the same thing the code does, when the actual fix turned out to be "change three to ten lines to do something in a slightly different way that avoids the problem entirely".