And yet I(and many others publicly on x) chucked Claude for codex back in June. Codex is my workhorse. The main reason I use codex is because of its language. I just got really tired of reading long weirdly worded prose that I had to fix with some skill(though some people like matt pocock and dex horothy have good ideas on this, buts it's just wasteful). This is triply bad for learning newer stuff because it goes up and down the abstraction layer on any topic like mad. One moment it would be explaining a high level detail and then cite contrasts and then point indirectly to an implementation detail as an example. It's idea of explaining more abstractly was also weird in a different way.
Fable was better but I don't have 1000$/day to spend on it.
Hi thanks for the insight. Do you see a role for Control Systems(i.e. ones analogus to Instrumentation engineering) playing a role to modulate certain parts of continual learning? One very important way we learn are lived experiences, it's like telling memory:this part is more important( for emotional or social utility values), pay attention. Good or bad lived experiences both count. I guess is that a path that practical research is considering?
The core or the problem is also what you are describing was bought up by Yuval Harari in his interview with the economist.
To paraphrase some of his lines: We make decisions using our emotions and our thoughts. What makes us different from the AI is that we can be afraid.
To portray a guy as ordering "beef bulgogi", in the same breath as "email this rocket design marketing", while it _might_ seem appealing and resolute, though oddly fast paced, seems pretty ignorant of the human _quality_ that make most practical decisions messy.
All of this will be besides the point. Here is what's gonna happen. The frontier labs are just gonna keep building powerful models. AGI or not, open models in a year will be as powerful as Fable and Astra — probably by using em — and at a very soon enough point after that some one (a state or a few dozen people) with a few 100 GPUs is going to launch an unconscionable attack(if they have not already) that's gonna do a lot of damage.
Please for the love of god, just sit in a room with the government and put some restrictions around AI use before it harms a lot of people. Like tell the government to impose a minimum spend on frontier lab AI's spend on cyber defense and building every country's capabilities. The post-training mask for "I am a good assistant" is going to become a very sad joke when many people literally lose everything.
When one writes jupyter notebooks for DS you are not writing python. If you ask 10 DSs explain to me what python's attribute lookup model is and why is it different from other OO languages like say Java or C++, they would not care about it. The only thing DSs care about is the rich DS Library support and fast speed of protoyping. To a DS using jupyter this is almost the same feedback loop as a type system at compile time.
Have you tried using `uv`'s newer tools? They help a lot e.g. with linting speed, lock management, package dependency separation, correct python version mgmt and no need to fudge with venv.
Does it have something like litestream to backit up for specific production usecases (i.e. a single webserver is enough and downtime of a few mins is tolerable)?
I am in the final stages of adding this based on your comment. Just wrapping up doc updates and will push a new release with hot copy functionality tonight!
What is being made is "what are our core values?" argument. One does not need to be a mathematician to know how poorly this worked for large communities when incentives are misaligned...
If a subset of mathematicians, use AI to condense timelines focusing on goal 6.2 exclusively and make rapid progress and reach a proverbial inflection point — one where value proposition of the using this new normal is too enticing to give up — everyone will ask: "This thing is so awesome. Why should I care about your values?"
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