If they don't rely on the original works why do they incorporate them in the training corpus? But they do, so it is a derivative work.
When the industry favored copyright, it made sure to do clean room implementations of software by competitors. Programmers who had even read a single line were disqualified.
AI reads everything, so it is not a clean room implementation. The EFF knows this of course and still supports the industry (which is now of the side of theft, unless it is distilling).
Neither my words nor EFF's words suggested anything like that.
> so it is a derivative work.
Just because one work is derived from or relies on another does not implicate copyright. Copyright is not use-right or rely-right (nor should copyright be expanded to be them). (Contracts such as EULAs can go beyond the scope of copyright and may include use-restrictions.) If there is no substantial similarity (including obfuscated or mangled similarity) between the derivative work and the original work, then the derivative work does not infringe copyright.
> AI reads everything, so it is not a clean room implementation.
Very true, but substantial similarity matters. If there is no substantial similarity between the output and the original work, then an output is not an "implementation" of the original work. When I say output or Thing, I mean the output of an LLM or a human, not the LLM itself. If (if) a particular LLM itself infringes copyright, not every output of the LLM necessarily infringes copyright. If a particular output of an LLM infringes copyright, the LLM itself does not necessarily infringe copyright. (Maybe someone could, with Herculean difficulty, demonstrate that the particular LLM might as well be incapable of producing non-infringing output?)
There's no guarantee that a non-clean-room implementation always constitutes copyright infringement, especially considering that for software in particular the functional aspects are not always separatable from the creative expression. Theoretically, both clean-room and non-clean-room implementations of a very optimized program designed for non-entertainment purposes would be unavoidably substantially similar to the original work. (Related reading: the merger doctrine [1].) Substantial similarity is necessary, but not sufficient, to copyright infringement.
There are no viable free versions with sufficient computing power. The do-it-yourself AI is dangled as a carrot in front of users to camouflage the lock-in and rent seeking by Big AI. Go prove something new like Navier Stokes (replicating N-S itself no longer counts due to scraping and plagiarism) on your Mac Pro!
Paid influencers who perpetuate the open narrative are a whole new industry.
Even if there were open models, it is still IP theft and would not be "democratization" but "forced unpaid nationalization".
There is no lock in. Anyone with sufficient budget can download the weights for GLM 5.3. On OpenRouter, I count 29 different providers for this model [1].
In terms of purely local LLMs, one can run GLM 5.3 flash on a beefy workstation.
It's also the only model that generates accurate translation and localization. No other frontier model comes close. Although Gemini's coding capabilities are subpar, its natural language processing is top-tier.
I’m curious how you guys keep track of each model’s coding capabilities. The landscape keeps changing. I don’t suppose you benchmark all frontier models every other month, right?
Do you dangerously allow permissions? I absolutely cannot use it until they ship an auto approver. As it is now I have it write one bash/python script to do everything it wants to, then I review that. Otherwise it is COMPLETELY unusable and it shocks me when I hear people are using it.
I've used Antigravity as my main coding agent on one of my biggest projects for about a year. It's been great for me. (and I use Claude, Codex, Grok and Muse for all the other projects)
I did a test involving implementing cobol control flow in Java for a source to source translation project. Gemini was the only model to get the edge cases. Cobol is very peculiar in this regard.
It's very good at Elixir in my experience too. And it just does what I ask and doesn't wind me up like Opus. I don't think I've had to insult it more than once per day.
I had a typical $20 Gemini plan that I just downgraded to their $5 plan (to keep access to some of the models). It had been so long since I let Gemini work on (or review) any code / design / html (anything) that I couldn't justify bothering to keep wasting money on it. It fell behind badly over the past year. Astra might as well be an alien super intelligence at code compared to Gemini. I enjoy talking to Gemini, it is very good at conversation, I get solid answers to everyday questions. I intend to keep the $5 plan indefinitely for basic use. I don't expect they'll ever resurface as a competitor in coding with Astra & Fable et al.
I found that it's shockingly good with R. (the only language I know and can correct for)
I doubt they even intended it to be, but it seems like I kept going from resorting to 3.5-3.8 (over time) to realizing that Claude and GPT, while great at Python, will make rudimentary mistakes with R; even when they compose giant complicated R code.
I'm guessing this is partly because of Gemini's world knowledge. I tried asking the model multiple internet humor and memes and it answered correctly around 80% of the time
That might depend on whether you are translating fiction or nonfiction.
Anecdotally I'd rate Gemini behind Claude and OpenAI models at fiction and I can't find any benchmarks showing Gemini is the clear winner at this task.
Agreed. My impression is that the more verbose output of sol, astra etc is that it helps it steer itself on long running tasks (but is worse for the human user to read)
Yes I've noticed there's also this drive to implement and start talking about how it would write specific portions of code in response to design/trade off questions. I have to prompt Sol/Astra almost every time with a note that I am not looking for implementation advice since I mostly use them as a rubber duck in the design phase
Is this a "pro" thing? I have totally no idea what I'm talking to, so actually I'm thinking of stopping my plan. Gemini and Claude are much more clear about it.
Anyway, I like the speed at which Gemini responds so indeed for simple things it is preferable.
For me (Plus plan, iOS app), Astra only shows up under the Work tab.
I’ve been using Work for all my queries, since it seems to just be the same interface as Chat but with more features. I don’t understand why they’re two separate things.
For rabbit holes, how do you get Gemini to do any research before answering? I've very recently had it hallucinate on me like it's 2023, and that was on Pro/Thinking, as far as I remember.
Is Google still chasing frontier? Seems like they haven't had a "Pro" model in forever. I think a good niche for them would be right where they are now.
They are. They were supposed to release 3.5 Pro over the summer but haven't because of persistent architectural/technical issues allegedly. Which is better than releasing it in that state imo.
Their AI leadership team has taken some hits recently too, in the form of departures. I believe when they get their bearings they will be competitive again. 3.8 Flash has been a great model for me.
"couch all their agreements with caveats and provisos."
When you're a ChatGPT Projects or Claude Projects user, those caveats and provisos are your worst enemy because they'll change caveats into hard rules (either for the session or committed to memories) and you end up in absolute hell having to make it investigate to figure out why it can no longer produce anything but read-only pre-check code that never actually does anything but keeps performing stupid safety checks.
Yep the only way out is hooks to forbid what can be detected by ast and second model to prune comments, flatten pyramids of fallback, and squash the test suite removing quirks maintaining wanted behaviors.
This is commonly why, on Reddit in particular, you can get eaten alive.
Someone confident but incorrect, can often sound more convincing than someone with actual expertise. The expert must add caveats/hedge, because those are the facts on the ground, whereas the person reciting google can be entirely confident.
Of course the people judging aren't experts, so they side with confidence and simplicity. Heck, just writing shorter replies on Reddit is rewarded. Nobody reads the articles, let alone a paragraph-long reply.
That all being said though, there are limits. Sometimes LLMs on high-thinking go off on full tangents based on little, and don't have the self-awareness to bring it back.
I consider, on the contrary, caveating and hedging annoying 'typical redditor'/internet behaviors: they care more about being "technically correct" than conveying the message. On the internet, if you make even the tiniest mistake or simplification, someone will criticize you, so you're trained to always hedge. In normal discussions with friends you can just make general statements and people get what you mean.
You aren't arguing a "contrary" to what I actually wrote above. You're arguing with a strawman Redditor, and a point adjacent to what I was posting about - experts responding to topics within their actual realm of expertise.
So I won't be addressing this, for those reasons and others.
My experience has been very much the opposite of yours.
To an expert communicating with a layperson is a form of compression. You must turn some very complex idea into one that you suppose the other person can grasp given their limited frame of reference. It's always lossy, and you have to guess how much you can remove without sounding patronizing or being inaccurate. It's tough, and the more you know the tougher it gets.
Ever done that "explain what happens when I visit Google in my web browser" interview question?
A sales guy will answer in a sentence. An engineer might be able to talk about it for several days and still not be sure they didn't miss anything important. That much knowledge can actually be detrimental to communication.
What you're describing sounds precisely like the state of politics. The most successful politicians on both sides just scream "simple" solutions that don't stand up to even cursory examination. On the one hand, Trump, with 90% of his pronouncements being of that variety. And the DSA type, who just scream that we can fix everything if only we had much higher taxes (but ONLY for "billionaires" who would definitely pay them, perpetually, and wouldn't just leave).
It's entirely possible that in their testing of newer models, the whole problem is that even if it's doing better in benchmarks, maybe it's insufferable to work with, thus they're not releasing it.
I was surprised when (finally) trying out Claude how much I preferred Gemini's way of communicating. I wont argue Claude is better at coding, but for knowledge work, I had to dig through Claude output to find what I actually wanted. At times, it even felt borderline incomprehensible.
Just today I had Sonnet 5 generate this (asking about always-on display in the iPhone e-versions):
> This mirrors how Apple has always segmented Pro vs. non-Pro iPhones: base models got LTPS panels while Pro models got LTPO, and only with the mainline iPhone 17/17 Plus did that gap close the standard versions previously lacked the smoother 120Hz ProMotion technology and the always-on display feature, unlike the Pro models — the 17e is the one model line still using the older, cheaper panel.
(emphasis mine)
I mean, I can guess what it is trying to say, but who RL'd this nonsense?
Fable 5.1 even today inundates prose with "it's not this, it's that" type of garbage. I had to rewrite two paragraphs from a generic class announcement which I was planning to post on the LMS. Not sure where that productivity gain is that everyone is talking about.
I couldn't even stand 4.6 by the end. 4.5 was very usable, 4.6 started alright and somehow got more annoying. 4.7 made me quit my subscription and ditch their services entirely - it's an honourary member of my quite short "Coworkers I'd like to throttle if I didn't work remotely" list. Infuriating to instruct or communicate with.
I actually did (was going to travel internationally), and it wasn't as useful as you'd think. I would be talking to someone, and in the background someone else would be talking, and it would translate both people.
Only worked in a 1:1 in a quiet place. Still, can't complain for free.
As sister comments have pointed out, I find that it's very much dependent on the phone you're using. If it can't even show me a waveform then I don't think it's going to be able to tease out any sort of speech
Sorry I wasn't clear. It's about the hardware microphone. They're usually optimized to filter out far field sounds and only focus on near field sounds. So if you're trying to understand a conversation from even 10 ft away, it can be a problem. Not saying that's the situation that you encountered, but it does make sense that a $50 phone versus a $500 phone might have different capabilities when it comes to the microphone
It's not a model problem, but it is a SW problem. It should be able to distinguish nearby people from people farther away and give me options to set a threshold on who to include.
Not the same, but related: Gemini is great for querying text in another language, it provides really cogent, useful responses with just enough source language quotes to be able to reference the source text effectively.
The 4.5 models were the last Anthropic models that were actually pleasant to read. Especially Sonnet 4.5 was really fun to use for non-coding purposes. I liked having it generate silly short stories while waiting for deployments to finish. Haiku 4.5 still has a small bit of that charm, but 4.6 and later models are all unbearable unless you want a simple 'this is the answer' to a question.
Not only that, Opus likes to invent arcane jargons. And the longer the task runs, the harder it is to understand its output. The conversation will be filled with uncommon word choices and awkward sentence structure.
We have an agentic system that produces insights for end users, and runs most of its work on DeepSeek v4.1 Flash but as an output stage transforms the resulting text through Gemini 3.8 Flash for readability, and it works.
On my TODO is try and run all of the analysis pipeline in dense "machine speak" to save on tokens and just let Gemini sort it out at the end.
Yeah, I set it to “warm: less”, “enthusiastic: less” and “emoji: less” and it was much more bearable than I remembered it being before. Although it does love to “separate” questions when it thinks.
I can’t agree more! It feels really smooth to drive, kind of buttery compared to other frontier experiences, at least in antigravity 2.0 or whatever. I’ve been doing some web app coding with it and I’m happy with the results.
Wondering if people have managed to have Gemini in-front of other models like claude/codex models and only interact with that. Having Gemini act as a pure human/llm translator.
BTW, as kind of a follow-up to this, I think the most important finding to report--for those who only use one model, as many people seem to--is just how much more often Claude seems to be extremely confidently (and even insufferably) wrong than any of the other three big models (all of which I use quite often... yet I only pull out Claude when I've given up hope in a problem and are looking for out-of-the-box brainstorming).
And like, it does this despite it speaking in extremely dense math, which both makes it sound correct and requires a lot more effort to prove when it is wrong... yet, it isn't actually correct more often, and so that time sink just isn't worth the benefit. I then think many people--including people who can speak math (as can I)--just stop bothering to check everything, as if you come across a human who speaks like this it probably does correlate with slow and careful thought that helps prevent errors.
Instead, Claude has the mistake rate of a somewhat accelerated beginner impossibly combined with the language of an expert professor; and we as humans just aren't good at that combination: it becomes very dangerous and makes it take longer to spot its egregious mistakes and trained-in biases. If you have to use Claude, I thereby claim you really need to have a team of not-Claudes to help insulate you from this, and Gemini (while being a bit senile) is a lot more collaborative and approaches problems in ways that makes it harder to get tricked.
(To translate this into more of an engineering analogy: Claude always feels to me like the engineer who put more effort into learning how to program in functional languages than into how to actually develop working code, and then confidently presents you answers in Haskell or Lisp that never quite work. To find their errors is then very costly. In contrast, Gemini feels more like a Java or Go developer who knows they are a cog... that's helpful! <- Which maybe just goes to show that AI has finally turned me into a manager, omg.)
This is so shameless. The Economist ==> influences "elite" readers who will dangle the Nobel carrot in front of Trump ==> influence Trump (who does not read The Economist).
Zitron predicted the downfall of Oracle as someone mentioned below. He also predicted that the overhyped data center construction plans (Project Stargate, repeated vague Nvidia pledges) would not materialize.
If he got MSFT's cloud revenue growth wrong for this year, how much of that is selling shovels to OpenAI and how much is circular?
I'm sure that an admittedly great mathematician who is sponsored by the "AI for Math" fund and math.inc (which literally wants to corporatize mathematics!) is very appealing to LLM startups.
Perhaps the World Wide Web but do you see how addicted the average person is to TikTok? 0.1% might decide to lead a tech-free lifestyle (and we can be sure that each will interviewed and get an article written about them).
It is more concrete now. For example the footage that was released about the Kirk shooter showed that you are filmed everywhere, including in residential areas.
When the industry favored copyright, it made sure to do clean room implementations of software by competitors. Programmers who had even read a single line were disqualified.
AI reads everything, so it is not a clean room implementation. The EFF knows this of course and still supports the industry (which is now of the side of theft, unless it is distilling).
reply