kooi

Not a security person, but are there standards for PII encrypted at rest?

Surely this is common? Surely?

As long as you make sure the end points are valid, this is most intuitive for me. Just using i as a backwards step.

for (int i = 1; i <= N; i++) {

    array[N-i]

}

Thanks for the reference.

From the abstract:

The alcohol ethoxylates present in the rinse aid were identified as the culprit component causing the epithelial inflammation and barrier damage.

Its outrageous.

Thing which are public law should be publicly accessible.

There's an online portal for NEC code book, but they made it awful, no search, no hyper links, the bare minimum.

I used codex in computer mode to collect the relevant pages to my project. (Electricity to shed).

Looks really neat. I'm keen to try this on my clicks communicator when that arrives.

It's mediatek NPU claims to handle ONNX. That plus the 12GB ram makes the wonder if I can get this phone to be a mini-agent.

Watch out, this paper has scary ~math notation~

;)

Coming from Rust:

Why is does implementation still have a the generic T? Should it not be concrete on i32?

/// i32 forms a sum reducer under 0 and addition const sum_trait = impl(Reducer(i32), struct { pub const empty = 0; pub fn combine(x: T, y: T) T { return x + y; } });

Wavelength = 1/f

Yep, the context switching let's me do 2 or 3 active agents, but I have to slow down if real issues come up.

Maybe the agent maxing dudes are doing their 12+ agents on a monster vibe project and all they say is "fix it", "make it work" all day

Design Patterns by Gamma et al.

Recently helped me solve a non-obvious code-stink design issue.

Gezz so much vitriol.

> Freeze frame photos show what appears to be a lightweight hollowed out car shell prop, likely using magnets, suction, and compressed gas to help the country's richest nazi once again lie to the gullible American public and boost the valuation of a car company that stopped being innovative more than a decade ago.

It's obviously a stunt. But IMO it's a real stunt probably developed by the controls engineers at SpaceX to show off some pretty crazy control capabilities.

This is NASA SkyCrane on steroids.

Gotta pay if i make more than 20 mil a month?

Sounds like a great problem to have.

My question: What kind of dynamical system observability does Timestral provide? Is it a blackbox y = F(x, params) model for offline use? Can it provide the actual dynamical system equations? Or are you stuck forever querying the Timestral server (which is bad, think about if you're grid system operator doing day ahead, month ahead forecasts, and Timestral goes down)

Typically, in linear, non-linear, and NN models, we fit some noisy data to A) An underlying model with unknown parameters or B) A model free search which attempts to construct combination of "basis" functions or C) A purely black box model which we only care about Input/Output error.

A) is useful because often we know an underlying model we want to match, but don't know the parameter values. If we can determine the parameters, we get insights about the real system. E.g, how are these two variables coupled and how strongly -> We can make business decisions about what happens if we adjust some parameters.

So what exactly does this timeseries foundation model do and provide?

Maybe, the observability is that you have a forecast, and you adjust the covariates and see what happens. But one would want much more insights than a "executive summary" of the forecast.

The important parts for me as a signals processing R&D engineer.

> Classical models such as ARIMA and linear regression remain well-established tools for forecasting time series. These models are usually fitted to a single series and learn only from that series' history.

> Deep learning models such as PatchTST add the capacity to learn nonlinear patterns across many related series, but they are still typically trained from scratch on each dataset, so what they learn doesn't carry over to new domains.

> Time series foundation models bring the pre-training approach to time series. Trained on a large and diverse corpus, our model learns patterns that transfer across series, forecasting zero-shot on data it has never seen, even in domains where data is scarce.

Edit: Looks like it's a pure black box which you can run on premise.

> Output: Probabilistic forecasts, 21 quantiles

> Inputs: Multivariate targets, past-only and known-future covariates

Goodness, someone posts a great article which IMO accessible math, and people get upset.

If you think that is crazy, ohh boy, just wait til you see all the other papers underlying LLMs, AI, and optimization in general.

I appreciate this post because starting from linear algebra is the correct way to form an intuition of the higher dimensional semantic spaces we're dealing with.