Because you'll slowly start building the individual pieces of the database over the file system, until you've just recreated a database. Database didn't spawn out of nothing, people were writing to raw files on disk, and kept on solving the same issues over and over (data definitions, indexes, relations, cache/memory management, locks, et al).
So your question is: Why does the industry focus on reusable solutions to hard problems, over piece-meal recreating it every project? And when phased in that way, the answer is self-evident. Productivity/cost/ease.
Could you go into more details about why their "harness sucks?" This feels like a shared conclusion, but I've used several and theirs is better than many.
I generally agree that the harness isn't good, but it works and gets the job done and that seems to be the singular goal of the top 4 or 5 companies building them.
We saw what Claude Code looks like inside, and it's objectively bad-to-mediocre work, but the takeaway seemed to be 'yeah but it works and they've got crazy revenue'.
That's where we're at. The harness is kind of buggy. The LLM still wanders and cycles in it sometimes. It's a monolithic LLM herding machine. The underlying model is awesome and the harness works well enough to make it super effective.
We can do so much better but we could also do worse. It's a turbulent time. I'm not super pleased with it all the time, but it's hard to criticize in many ways. They're doing a good job under the circumstances.
I see it kind of like they're at war. If they slow down to perfect anything, they will begin to lose battles, and they will lose ground. It's a highly contentious space. The harness isn't as good as it could be under better circumstances, but it's arguably a necessary trade off Anthropic needs to make.
I've been using OpenCode until yesterday (with some plugin to let me use their model until they implemented what it seems very sophisticated detection to reject you).
It just has a sane workflow it's easy to use, doesn't bother you with 1000 questions if you allow this or that to run and generally it feels like the model is dumber and makes more mistakes since yesterday since I have to use claude code.
Then you can publish the public Code Signing certificate for download/import or publish it through WinGet.
Using Azure Trusted Signing or any other certificate vendor does not guarantee that a binary is 100% trustworthy, it just means someone put their name on it.
Both are great, where they differ is: Claude Code has a better instinct than Codex. Meaning it will naturally produce things like you, the developer, would have.
Codex shines really well at what I call "hard problems." You set thinking high, and you just let it throw raw power at the problem. Whereas, Claude Code is better at your average day-to-day "write me code" tasks.
So the difference is kind of nuanced. You kind of need to use both a while to get a real sense of it.
I think the way I and others use it is code with clause, review or bug hunt with codex. Then I pass the review back to Claude for implementation. Works well. Better than codex implementation and finds gaps versus using Claude to review itself in my opinion.
Codex just changed the way they calculate usage with a massive negative impact.
Before a Subscription was the cheapest way to gain Codex usage, but now they've essentially having API and Subscription pricing match (e.g. $200 sub = $200 in API Codex usage).
The only value of a subscription now is that you get the web version of ChatGPT "free." In terms of raw Codex usage, you could just as easily buy API usage.
edit: This is currently rolled out for Enterprise, but is coming to Pro/Plus soon. The people below saying "I haven't had this issue" haven't yet*.
> e.g. $200 sub = $200 in API Codex usage [...] In terms of raw Codex usage, you could just as easily buy API usage.
I don't think it's made out like that, I'm on the ChatGPT Pro plan for personal usage, and for a client I'm using the OpenAI API, both almost only using GPT 5.4 xhigh, done pretty much 50/50 work on client/personal projects, and clients API usage is up to 400 USD right now after a week of work, and ChatGPT Pro limit has 61% left, resets tomorrow.
Still seems to me you'd get a heck more out of the subscription than API credits.
This. ChatGPT Pro personal at $20/month and using GPT 5.4 xhigh is the best deal currently. I don't know if they are actually losing money or betting on people staying well under limits. Clearly they charge extra to businesses on the API plans to make up for it.
In the future, open models and cheaper inference could cover the loss-leading strategies we see today.
Right, because you're on the old and not new structure.
They just rolled it out for new subscribers and existing ones will be getting it in the "coming weeks." Enterprise already got hit with this from my understanding.
Unfortunately many believe they can, and it is impossible to disprove. So now real people need to write avoiding certain styles, because a lot of other people have decided those are "LLM clues." Bullets, EM Dash, certain common English phases or words (e.g. Delve, Vibrant, Additionally, etc)[0].
Basicaly you need to sprinkle subtle mistakes, or lower the quality of your written communications to avoid accusations that will side-track whatever youre writing into a "you're a witch" argument. Ironically LLM accusations are now a sign of the high quality written word.
Someone with native fluency in American English can (should) be able to tell the difference between human writing and unpolished AI copy-paste.
Essentially 0 people use emoji to create a bulleted list. Nobody unintentionally cites fake legal precedents or non-existent events, articles, or papers. Even the “it’s not X, it’s Y” structure, in the presence of other suspicious style/tone cues signals LLM text.
I'm going to ask the qustion I ask everyone who makes the claim that they wrote like that for years: Can you show us a link from prior 2022 that you wrote like that?
Sure, but, look, we have seen these claims so many times, that if it were true by now someone would have linked at least one archived blog post to show that it is, indeed, how humans used to write.
So are you saying that anyone with native fluency in English but who is not from the US can't tell the difference between human writing and unpolished AI copy-paste?
I don't agree.
Given that US-based LLM models tend to default their output to American English, its arguably much easier for "the rest of us" to spot the "US" language patterns...
I think that’s a RLHF issue - if you ask people “which looks better”, they too-frequently picked the emoji list. Same with the overuse of bolding. I think it’s also why the more consumer-facing models are so fawning: people like to be praised.
> Unfortunately many believe they can, and it is impossible to disprove. So now real people need to write avoiding certain styles, because a lot of other people have decided those are "LLM clues." Bullets, EM Dash, certain common English phases or words (e.g. Delve, Vibrant, Additionally, etc)[0].
I think people will be able to detect the lowest-user-effort version of LLM text pretty reliably after a while (ie what you describe; many people have a good sense of LLM clues). But there's probably a *ton* of LLM text out there where some of the instructions given were "throw a few errors in", "don't use bullet points or em dashes", "don't do the `it's not this, it's that` thing" going undetected.
And then those changes will get built into ChatGPT's main instructions, and in a few months people will start to pick up on other indicators, and then slightly smarter/more motivated users will give new instructions to hide their LLM usage... (or everyone stops caring, which is an outcome I find hard to wrap my head around)
This is the correct answer. We’re at a point where it will soon be safer to assume a human or someone with agency and their approval wrote the text, than to completely dismiss it as “written by LLM” or a human.
So judge the content on its merit irrespective of its source.
Staccato (too may short sentences with periods) is also a telltale for me. Most humans prefer longer sentences with more varied punctuation; I, for example, am a sucker for run-on sentences.
Indeed, isomorphic plagiarism by its nature forms strong vector search paths that were made from stealing both global websites, real peoples work, and LLM user-base input/markdown.
However, reasoning models adding a random typo to seem less automated, still do not hide the fairly repeatable quantized artifacts from the training process. For LLM, it is rather trivial to find where people originally scraped the data from if they still have annotated training metadata.
Finally, reading LLM output is usually clear once one abandons the trap of thinking "I think the author meant [this/that]", and recognizing a works tone reads like a fake author had a stroke [0]. =3
And I'm sure we've all seen what happens if you run the Declaration of Independence or the Gettysburg Address or the book of Genesis through an AI "detector". They usually come back as AI.
Only for poor quality systems. Unfortunately there are many systems that tried to make easy hype, but are the equivalent of an ML 101 classifier class project.
If one measures for perplexity (how likely text is under a certain language model), common text in a training set will be very likely. But you can easily create better models.
> Ironically LLM accusations are now a sign of the high quality written word.
Citation needed. The LLM accusations come from the specific cadence they use. You can remove all em-dashes from a piece of text and it still becomes clear when something is LLM written.
Can they be prompted to be less obvious? Sure, but hardly anyone does that.
It's more "The Core Insight", "The Key Takeaway", etc. than it is about emdashes.
Incidentally, the only people annoyed about "witch-hunts" tend to be those who are unable to recognise cadence in the written word.
i think another part of the problem is that some people are using AI so much that they are starting to mimic its cadence in their own writing. they may have had a prior coincidental predisposition for writing somewhat similar to AI with worse grammar, and now are inching towards alignment as they either intentionally or accidentally use AI output as a model to improve their writing
Using Claude Code seems like a popular frontend currently, I wonder how long until Anthropic releases an update to make it a little to a lot less turn-key? They've been very clear that they aren't exactly champions of this stuff being used outside of very specific ways.
I don't think there is any incentive to do so right now because the open models aren't as good. The vast majority of businesses are going to just pay the extra cost for access to a frontier model. The model is what gives them a competitive advantage, not the harness. The harness is a lot easier to replicate than Opus.
There are benefits too. Some developers might learn to use Claude Code outside of work with cheaper models and then advocate for using Claude Code at work (where their companies will just buy access from Anthropic, Bedrock, etc). Similar to how free ESXi licenses for personal use helped infrastructure folks gain skills with that product which created a healthy supply of labor and VMware evangelists that were eager to spread the gospel. Anthropic can't just give away access to Claude models because of cost so there is use in allowing alternative ways for developers to learn how to use Claude Code and develop a workflow with it.
Are the Claude Code (desktop) models very different from what Bedrock has? I thought you could hook up VSCode (not Claude Desktop) to Bedrock Anthropic models. Are there features in Claude Desktop that are not in VSCode/cli?
Well, if they did, it would probably be shooting themselves in the foot, seeing that the Claude Code source is out there now, and people are waiting for an excuse to "clean-room" reimplement and fork it
Yet Codex specifically aims out to be compatible with all backends! Up until Gemma 4 though it’s been pretty solid, but totally fails with unknown tool (I’m guessing a template issue)
I think CC is popular because they are catering to the common denominator programmer and are going to continue to do that, not because CC is particularly turn-key.
not to use the cli tool. You can install it and change the settings to point to pretty much any other model.
It's an okay-enough tool, but I don't see a lot of point in using it when open sources tools like Pi and OpenCode exist (or octofriend, or forge, or droid, etc).
Windows 11's 4 GB minimum is dishonest. You cannot reasonably run it on that little, it is far too bloated at this point. Even LTSC benefits from 6 GB, and that is substantially cut-down compared to retail/enterprise.
I'd say Windows 11's real minimal is 8 GB in 2026, with the recommended being 16 GB.
PS - And even at 8 GB, it hits 100% usage and pages under moderate load or e.g. Windows Update running in the background.
So your question is: Why does the industry focus on reusable solutions to hard problems, over piece-meal recreating it every project? And when phased in that way, the answer is self-evident. Productivity/cost/ease.
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