"When I came back a few minutes later I saw my machine open a browser window in my regular Firefox and then navigate to the dialog in question. I had not told Claude Code to use any browser automation".
Yup, tokens are eaten, money are paid. I am wondering how much energy/money is being burnt everyday by all of those LLM Agents on some useless activities like trying to recreate web application just to fix CSS bug.
And I would not call it proactive, proactive would be to ask for a CSS + HTML file in question, not trying to recreate them from screenshots.
Monetization is coming. They'll tell companies, AI is replacing your workers, so it is still worth to pay 100K/year for the license, as those AI are not going to jump to other job, get sick, be late, complain, require free coffee and so on.
Soon the times of AI for $20/$200 a month will be long gone.
Get people hooked, tell them spending time coding is no longer needed, let their skills deteriorate, tell them they need cough up for a licence to do their job
Forcing developers to pay for models that were build on code they scraped scott-free
A tax to do their job that developers are jumping at the chance to pay
Everybody's finally realising that node dependencies are a threat, but letting these AI companies gatekeep the industry is a bandwagon people are scrambling towards
> Forcing developers to pay for models that were build on code they scraped scott-free.
Yes this makes me sad behound explanation. Specially when I see open source developers happily using these tools. These companies stole your, free, hard work and charge you a subscription!! Not to speak about them torrenting books and (most likely) training on private repos.
This and devs paying a subscription to use a tool that is marketed as trying to replace them.
I had 150$ monthly budget thatbI used for various open source projects and I've cut that entirelly.
I don't get what you're saying. You're frustrated that Open Source projects were used to build these AIs and that OS devs (or devs in general) are paying to use AI.
Then you say you had money that you used to donate(?) to OS and have cut that because of the frustration?
Open source just means sharing the source code for people to learn off or have the ability to customize on their own. I don't think there is any need to be frustrated about that (now if it was copyright/private of course).
> To summarize the analysis that now follows, the use of the
books at issue to train Claude and its precursors was
exceedingly transformative and was a fair use under Section 107
of the Copyright Act. And, the digitization of the books
purchased in print form by Anthropic was also a fair use but not
for the same reason as applies to the training copies. Instead, it
was a fair use because all Anthropic did was replace the print
copies it had purchased for its central library with more
convenient space-saving and searchable digital copies for its
central library — without adding new copies, creating new
works, or redistributing existing copies.
> Forcing developers to pay for models that were build on code they scraped scott-free
That's also caused by some very smart (even brilliant) developers (you can see many of them in this very thread) choosing to be oblivious about all this and bury us all under, hoping that they'll be among the last ones to go. Writing this down I realise that they maybe aren't all that smart.
I've been saying this since the beginning, the rug pull is coming. If these models can eventually replace a human worker, there is no reason these companies won't charge (and get away with it) very close to a typical SWE salary.
It would not surprise me one bit to see anywhere from $80k-$100k/seat pricing.
A Ferrari will likely lap you when you’re racing, though, and the market and the economy is a race. You’ll be facing a question soon, or your employer will, whether to spend a significant chunk of free cash on fable-class tokens or on literally anything else instead - wages and salaries included.
<< You’ll be facing a question soon, or your employer will
Maybe? If you talk to executives, the impression that I am getting is that they tend to be somewhat misinformed at best, which, yes, is bound to result in some really bad decisions down the road. But, and it is not a small but, the ones I did talk to ( and, amusingly, those are the ones with strong opinions ) don't seem to have a lot, um, practical exposure to this tech beyond what they heard at the watercooler. Honestly, it is kinda infuriating. And all this before we get to how companies want to say they use AI, but also keep cost down.
"Without safeguards, Fable 5’s capabilities in areas like cybersecurity could be misused to cause serious damage"
What does it mean? That they have to add "safeguards" not do erase user disc, or, conversely, they are telling the audience that this model COULD be made so powerful to do some crazy stuff that can hurt governments, etc.? Are they showing off or threatening that if government X would not purchase the license the adversaries might do and what's then!
#3 Trillion, or even more, will be achievable. We are at the very beginning of the monetization of AI. It all started with the free Chat GPT, and a few others. Now the standard is $20 a month if you are not using AI tools too much and you don't need anything fancier. Otherwise you need to pay more, like $200 a month.
Unless you are not company and you don't have some "enterprise deal". And you need an enterprise deal, as 1) it guarantees that your (and your customers) data will not be sold to someone else 2) you are scared that your competitor will have such deal and become much more productive.
This is what we have now. What will be the future?
Well, soon, if you want something like financial advice or medical advice or job search/CV polishing you will be told, that your $20/$200 is not covering that, you need to purchase additional model to have that. Will you do that? It depends how much you are desperate to get medical advice or find a job.
Anthropic Mythos is an example. Soon, if you are programmer and you will ask AI Agent to spot a bugs, AI Agent will tell you that you need to buy extra model for this. Same with performance analysis, same with the design using tool X, Y or Z.
This is pretty scary, as it will put our well-being, productivity in the hands of few corps. It will be event worst that Google Search monopoly we used to have (until AI chats broke this, replacing Google Monopoly with a few other vendors monopoly).
Can this be prevented? Surely. Hopefully we will have capable open models and consumer-level hardware will catch up. But I think this is the place where governments should step in, invest into alternative models which will be at least comparable with flagships.
Chinese models shows that this is doable, DeepSeek is worst than Chat GPT/Claude/Gemini, but not that much and is clearly better than Grok (which is a huge disappointment for me). I guess India would join this game (especially with nationalist like Modi as the leader).
Europe could join this game, the problem is it kills its capabilities with high energy prices and inability to come out with some reasonable, well financed solution. So the only thing EU was able to come up with is some set of regulations that are blocking fast AI development in Europe...
There is French Mistral, but it is French, it is under-financed, it is only-French, as France would not like to lose control over it.
Germany have totally different strategy, they invest into manufacturing oriented AI, what makes a lot of sense, but does not help with the dangers we are facing.
The rest of the Europe is just too poor to spend billions on AI.
There is still time to buckle up for Europe, but given the course of events, stupidity of Brussels elites who does not see the storm coming I am not optimistic.
The only thing you’re neglecting is that personal computers will be able to run “good enough” open source models locally within 3 years. Can already run it today.
Extremely sorry for this unrelated rant. Got triggered by the keyword "Modi".
Quoting Modi is a joke which one cannot even remotely relate w.r.t to AI and don't even feel sorry for saying that since that man blabbers on every stage about non-sensical / non-existent stuff! Watching his videos is the best timepass one can have!
Having said that, the infra and mindset is definitely not there in India to even remotely to innovate or compete in AI race!
Academia is a huge BS where every other person is a backstabber!
A lot of talent is there for sure, but all wants to work for some company or another since there is absolutely zero support for entrepreneurs . No real innovation.
All copy cats as they have proven with mobile and robotics. Just copying or masking Chinese products with the local brand names and reselling. That's all they are good at and that's the irony.
So far, nothing has come from that country which is a real innovation or ground breaking. The day it happens probably one can consider that they are good.
But otherwise, they are good at selling / reselling and scamming the world and nothing else! They cannot produce anything or whatever they produce is taken control by a handful of big corporates from the western region. That's a narcistic corporate monopoly!
Extremely bad tax structure, endless corruption and useless and unqualified ministers occupying worst portfolios, people are really struggling to survive!
Where will they innovate or compete in the global AI race?
Everyone at every level just want to scam and make money to surive that's all!
Why this is surprising? LLM-s are good in text generation on the base of the stuff they were trained on. Software is text generation, translation is text generation, LLMs can answer questions since billions were spent on tuning foundation models, that is people were collecting in (semi)automatic way questions with answers to the point we might think that LLM-s are "thinking".
Now people want to handle car rental. What are the relevant data that models were trained on for this kind of application? For Python code there is kirjillion examples on Github, for mathematical proofs there is endless stream of papers, books, etc. But for car rental? Mostly adds in the internet that want to trick you into a bad deal. So yes, LLM will be a disappointment, as it tries, well, to trick you into a bad deal. In addition, data are rather scarce so there will be a lot of hallucination, as it gets mixed up with yacht rental, bikes rental, ski equipment rental, etc.
The performance of specific tasks will depend on either those tasks having been included in the training (which Apple could work on), or added by ways of fine tuning, and context sourced from userland.
For any category of tasks, there's a ton to be gained still in terms of how context is populated more effectively (relevance) and efficiently (token use). See software engineering harnesses and the skills architecture of OpenClaw for example. SWE harnesses make all the difference in how well Claude Code and OpenAI Codex perform. OpenClaw can't do shit without loading skills from the filesystem into context JIT.
I'll be very curious to find out how Apple is feeding context in their new AI approach. Part of it appears to be an 'index' that my iPhone started building (visible in main Settings screen) after installing the iOS 27 Developer Beta.
I was teaching a lot of stuff to students: physics, math, statistics (during my university times) now I teach programming and Machine Learning.
I am torn between instructional based approach, which has this advantage that gives people a set of minimal skills to start doing stuff by themselves and the project-based approach, which is probably more interesting, but is very hard to squeeze in a relatively short classes time and also might left gaps, even in the base areas, as there is no time to cover everything end-to-end (think of teaching people about for loop, as it helps working with lists, but do not mention a while loop).
So, there should be some ideal holy grail in between both ways of teaching: show them everything versus let them explore and invent everything by themselves.
The crux is that instructional-based approach works great if it is well tuned to the student's needs. The problem is that every student has different needs and capabilities, so it is hard to do something that will work for everyone. So something is too difficult for some people, while being too easy for others.
That's why we have Bloom's 2 sigma problem - 1:1 learning works orders of magnitude better than in-class learning.
Now, LLM AI enters the scene, as the article is mentioning - individualized instruction could be finally achievable and I am much less skeptical about that than the author, as I tested that on myself, the good thing is I can ask and ask for more and more details if I am not able to grok something and my "teacher" is always patient, has as much time as I need.
It does not mean that teachers are not needed, just the opposite, because the key problem is to know what to learn, LLM will just do what you ask for, nothing more, so one need to know what to ask about. But once someone is on the specific topic and problem, you can really go quite far with LLM as a tutor.
I am not exactly waiting for Linux that will have obligatory ads and will take screenshots of my desktop and send them somewhere. Sorry Bill, but now, I've been through this already, I saw how superior DR DOS goes down because your mom was IBM board member, I had to use Windows 98 Millenium Edition, I was lucky to skip Windows Vista. So, again, no, thanks, never again.
Same with your cloud offering, ridiculous solutions like Azure Service Bus that has pathetic performance, pathetic API and high price.
Well, campfire method sounds easy in the blog post only.
You need an NDA from a candidate (many would not like to risk - what if someone gets job on the competing company and is sued for revealing some secrets from the campfire interview process).
In many cases interviewee would need to get a laptop from the company, as there are specific requirements about data security (disc encryption, usage of solutions like Fortinet or Zscaler), be added to company SAP to get access to the resources (Office, Teams, etc.), company need to purchase licenses for Office, Github, Jira, etc.
Surely hiring is hard, surely there are false positives and false negatives, but fixing this requires hell a lot of resources and organizational changes and costs.
Yup, tokens are eaten, money are paid. I am wondering how much energy/money is being burnt everyday by all of those LLM Agents on some useless activities like trying to recreate web application just to fix CSS bug.
And I would not call it proactive, proactive would be to ask for a CSS + HTML file in question, not trying to recreate them from screenshots.
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