crvdgc

Thank you. Video from the thread: https://www.youtube.com/watch?v=x9XDsiG40Uk

(comment itself: https://www.reddit.com/r/space/comments/1usdigz/comment/owna...)

That language should be called Fusion and this language Fission.

Some providers in the UK issue debit cards with limited interest-free overdraft and charge back features. So they are basically credit cards if you squint your eyes enough.

Vision has been in A/B testing for a while now (at least in China). Is there an official announcement that this will be available for everyone?

Nix also needs the build output to be deterministic to calculate the hash. It also has the problems of timestamps etc. The build environment tries to be hermetic by setting the time to be epoch among other things.

In his later years (late 1980s), he also advocated for AI and human superpower research.

The superpower thing turned out to be pseudoscience later. As a result of being lumped together, for a long period of time, AI was regarded as pseudoscience in China as well.

Although to be fair, during the same period, the US and the USSR were researching superpowers as well.

While in Japan, I heard an urban legend that, it typically takes 5 accumulated years for a foreigner to acquire hay fever in Japan.

In the long term, the biggest problem is that space data centers are very hard to defend against missiles.

Another angle is that most donations to Linux kernel are in the form of paid employees doing kernel work. I wonder how much the kernel need besides that.

comptime is a restricted form of dependent typing.

In addition to the normal value to value, type to type, and type to value functions, in comptime, you can write static value to type functions.

In full dependent type, you can in addition write dynamic value to type functions, completing the value to type corner.

So in terms of typing strength, plain Haskell < Zig < dependent type languages.

I didn't find anything particular, but in general it should apply to anyone under the jurisdiction. I think it's illegal to drink underage in the US, even if the person is a tourist and they are allowed to drink by their own country's law.

Unfortunately, the discussion focused on the somewhat click baity title "proved this program correct". It's unclear what "this program" is. If it refers to the core algorithm with a proof, then there's no bug. If it includes the runtime and the header parser, then Lean didn't prove it correct.

That being said, using a coding agent to direct fuzzying and find bugs in the Lean kernel implementation is the big news here. (After all the kernel's implementation is not proved.)

The moral of the story is to push for more verified code not less and try AI bug hunting.

For what it worth, there's uBlacklist for Google.

https://ublacklist.github.io/docs/getting-started

Sovereign AI adds another layer of meaning now that sovereignties are at war.

GitHub does not and does not plan to include advertisements in GitHub

They already did! https://github.com/orgs/community/discussions/65245

People, we just solved the LLM watermarking problem.

Even alternatives like GrapheneOS relies on AOSP. I wonder if it's possible for regulators in certain countries to pressure Google to kill it in the future.

Even if that's not the case, I'd imagine attestation apps like banking apps would require some kind of identity verification in exchange for trusting Graphene's keys.

In principle it doesn't make sense to leave any escape hatch, but I guess as always, it boils down to economy.

In theory, it's possible to have a third party (other than Google or Apple) to provide attestation on third party hardware.

You can have a separate core and kernel to run such code. They don't have to be powerful, but they'll need to be small enough to be verified by the said provider. For most of the code that doesn't need attestation, they can be executed on normal hardware.

The provider also has to convince the regulator or banks to trust them. However, if that's solved, the user should feel no difference between pure Android and alternative platform plus attestation.

How long before people realize that they too have limited context length and adopt:

- memory/yyyy-mm-dd.md

- MEMORY.md

- SOUL.md

At least for this article it's more about the job, or to be precise, the past where job and passion coincided:

Ultimately if you have a mortgage and a car payment and a family you love, you’re going to make your decision.

Nothing is preventing the author from continuing to write code by hand and enjoy it. The difference is that people won't necessarily pay for it.

The old way was really incredible (and worth mourning), considering in other industries, how many people can only enjoy what they do outside of work.

Among the six patterns identified, it's interesting that "Iterative AI Debugging" takes more time (and possibly tokens) but results in worse scores than letting AI do everything. So this part really should be handed over to agent loops.

The three high score patterns are interesting as well. "Conceptual Inquiry" actually results in less time and doesn't improve the score than the other two, which is quite surprising to me.

Some valid points, but I hope the authors had developed them more.

On the semantic gap between the original software and its representation in the ITP, program extraction like in Rocq probably deserves some discussion, where the software is written natively in the ITP and you have to prove the extraction itself sound. For example, Meta Rocq did this for Rocq.

For the how far down the stack problem, there are some efforts from https://deepspec.org/, but it's inherently a difficult problem and often gets less love than the lab environment projects.

This specific example to me is less likely a consequence of model collapsing, but the "personality" adjustment about how aggressively it should read into the user's intention.

From time to time, I enjoy the model guessing what I meant rather than what I wrote. For example, "Find the backend.py" can be auto-corrected into "find the app.py".

But let's hit the random button on wikipedia and pick a sentence, see if you can draw a picture to convey it, mm?

The inverse is also difficult. Pick a random 15 second movie clip, how to describe it using text without losing much of its essence? Or can one really port a random game into a text version? Can a pilot fly a plane with text-based instrument panel?

Text is not a superset of all communication media. They are just different.

how do you verify the verification program?

The program used to check the validity of a proof is called a kernel. It just need to check one step at a time and the possible steps can be taken are just basic logic rules. People can gain more confidence on its validity by:

- Reading it very carefully (doable since it's very small)

- Having multiple independent implementations and compare the results

- Proving it in some meta-theory. Here the result is not correctness per se, but relative consistency. (Although it can be argued all other points are about relative consistency as well.)

Checking the validity of a given proof is deterministic, but filling in the proof in the first place is hard.

It's like Chess, checking who wins for a given board state is easy, but coming up with the next move is hard.

Of course, one can try all possible moves and see what happens. Similar to Chess AI based on search methods (e.g. MinMax), there are proof search methods. See the related work section of the paper.

imagine a folder full of skills that covers tasks like the following:

Where to get US census data from and how to understand its structure

Reminds me of my first time using Wolfram Alpha and got blown away by its ability to use actual structured tools to solve the problem, compared to normal search engine.

In fact, I tried again just now and am still amazed: https://www.wolframalpha.com/input?i=what%27s+the+total+popu...

I think my mental model for Skills would be Wolfram Alpha with custom extensions.

Some Chinese language source claims that it's a reaction to the Pakistan-US rare earth deal.

My pet theory is that this is intended as an attack to the concept of long-arm jurisdiction itself, due to

1. This is the first ever long-arm jurisdiction policy from China.

2. Diplomatically, China usually advocates for the total sovereignty of each country within its border.

3. The recent chip entity list has been a huge headache.

4. Notice how the language mirrors the US justification for the chip restriction: dual use, national security.

From the title I thought they solved math! Turns out to be a framework to use SMT solvers for decision-based proof. For additional types, you still need to write the bridging part. Interesting nonetheless.

I believe Codex CLI also auto compacts when the context limit is met, but in addition to that, you can manually issue a /compact command at any time.