posnet

In the streaming SQL space, I feel like at this point it's essential to start off with saying what you bring to the table compared with Materialize.

No matter what you think about how the Materialize, the company, has executed, differential dataflow should still be the standard that all streaming SQl hold themselves to.

If you can't do recursive CTE's streaming, then you are worse than Materialize and you need to show that you add something above and beyond.

Note, I don't work for, or have any affiliation with the company, I just enjoy and wish more people have read, 'Scalability! But at what COST?'.

I assume the anti-porn crowd will still dislike this on the principle that porn is inherently bad, even though one of their main arguments is how the porn industry exploits the performers.

And presumably the sex work positive crowd will dislike this because it is taking jobs away from real women.

The crypto/cloud equivalent is letting market makers connect directly to the exchange front end servers via AWS VPC peer or equivalent rather than going via the public load balancers.

Many of the recent ones are as you say, "not leaks".

However the Challenger Tank one was real https://ukdefencejournal.org.uk/classified-challenger-tank-s...

It can be detected if your adversaries are clever enough: https://lukespademan.com/blog/the-dangers-of-curlbash/

They also directly broke Levine's second rule of insider trading.

2. Don’t do it by buying short-dated out-of-the-money call options on merger targets [0]

[0]: lawsofinsidertrading.com

No, it's more of a Judge Dredd type situation.

According to the GSA themselves, the value of the helium there is ~$100,000,000 [0] though that is 2019 prices, I believe it has gone up since then.

Apparently for diving purposes it's as high as $2.0 per cubic foot. [1]

[0]: https://pubs.usgs.gov/periodicals/mcs2020/mcs2020-helium.pdf

[1]: https://gue.com/blog/the-price-of-helium/

Not a problem here. Read the top level comment.

"static key-value data. ... a const (i.e. a hash table)"

Also similar to the design of the Luajit IR.

"Linear, pointer-free IR: The typed IR is SSA-based and highly orthogonal. An instruction takes up only 64 bits. It has up to two operands which are 16 bit references. It's implemented with a bidirectionally growable array. No trees, no pointers, no cry. Heavily optimized for minimal D-cache impact, too." [0]

[0] http://lua-users.org/lists/lua-l/2009-11/msg00089.html

Everybody knows relational databases don't scale because they use joins and write to disk. Relational databases weren't built for Web Scale. MongoDB is Web Scale. You turn it on, and it scales right up. Shards are the secret ingredient in the Web Scale sauce.

I was very confused, I thought this was about the polars, data science/data analytics toolkit. I know names are hard, but maybe pick something more distinct.

The question isn't whether satellite links can beat deep-sea cables, it's whether they can beat shortwave.

https://sniperinmahwah.wordpress.com/2018/05/07/shortwave-tr...

Also of interest are the Nightwatch planes for use as mobile command posts in the event of a nuclear strike. They are able to trail a 5 mile long cable to act as a massive transmitter to also contact submerged ballistic missile submarines.

https://en.wikipedia.org/wiki/Boeing_E-4#Lower_lobe

I am not scared of tax avoidance techniques.

AIs or Super-intelligence wiping out their creators is not a great filter (at least in the context of the fermi paradox), unless you are making the claim that ALL said AI or SI will then shutdown and not attempt to use the rest of the galaxy/universe for resources.

A Frank Herbert fan then.

I know they aren't optimized for truth, that was the point of my comment. But based on my own experience of showing people ChatGPT, without explaining the flaws they tend to treat the output as if it was.

I don't understand the second question.

This actually gets at the heart of the problem with the current batch of LLMs.

They have no concept of truth, and so will gladly generate tokens that are "likely", but are at best clearly wrong, or worse look correct to a non-expert, but are subtly wrong.

With software at least you can run it and inspect the behavior for flaws. Or have the results be validated by an actual programmer for correctness. And hopefully the developer using the LLM understands the limitations, and understands how it can hallucinate incorrect but convincing results.

However even software engineers I know, who are aware of all of the above issues, will ask ChatGPT questions about fields they aren't familiar with and view the results as authoritative. It's the same effect as when you read a news article about a topic you're familiar with and can immediately see flaws, shortcuts and exaggerations; but then you turn the page to a topic you aren't an expert it and say to yourself "what an interesting article".

The example from the article is even worse, since the author asked for an explanation of the code, and it hallucinated a clear and convincing but completely wrong explanation. Again to a programmer it is obvious, but to a layperson it's actively deceptive.

I could easily imagine a scenario where some asks ChatGPT, what is the best combination of chemicals to clean mold from a ceramic surface, and it responding with "Bleach and Vinegar are the perfect way to clean a ceramic surface." and following up with an explanation like "Vinegar de-greases the surface allowing the the disinfectant power of the bleach to penetrate into the mold". Which all sounds reasonable, unless you know beforehand that those chemicals mixed produces toxic chlorine gas.

Now that was definitely a contrived example, but unless you know and constantly question the output of an LLM you could easily be misled. Especially if you are asking about topics outside of it's training data or with minimal training examples.

Maybe with enough training data or some combination of transformers with reinforcement learning that has a "truth" metric, hallucinating completely incorrect information can be reduced to an acceptable level. But at this point it seems intractable.

Unfortunately as we have learned from open source software, it doesn't matter if it's write or wrong. The only way to enforce licensing is to litigate.

The decades of GPL violations have taught us the only way to get commercial interests to not abuse copyleft licenses is to force them via the courts, and in doing so dis-incentivize other businesses from violating the licenses.

But it isn't 100% effective by any means, and usually relies on a large commercially successful organization to already have aligned interests in enforcing their GPL/copyleft licenses.

Anything else is just ethics paper fodder.

"virtualization overhead is less than 5%, which is the lowest overhead in the industry that we’re aware of"

Is it really lower than AWS bare metal instances? Amazon are fairly reticent on details of their nitro platform, but it's pretty clear they've implemented most of their hypervisor in FPGA/Silicon.

It would be interesting to see it benchmarked against the highway qsort[1] Google published last year.

[1] https://github.com/google/highway/tree/master/hwy/contrib/so...

Because it's actually a new browser, not just a wrapper/reskin around webkit, gecko or blink.

It works on metal instances.

Would love to see this integrate with magic trace [1]. I'll need to look at the code for the flamegraph plugin, because handling nanosecond timestamps in flamegraphs seems to break most tools due to float precision.

(1) https://github.com/janestreet/magic-trace

From my experience in HFT, the answer was "unhinged template meta programmer" with a healthy dose of assembly/reverse engineering thrown in.

The title is very misleading, it is just one of their micro-services that has that uptime.

GPT-3 is surprisingly good at generating dungeon crawler style adventures. It even successfully tracks page numbers and the "turn to page X" prompts.

Remote 0-day onto all google internal infra?

I always feel that so many of the 'digital humans' narratives sci-fi are just approximations of the depth of world achieved in permutation city or diaspora.