kooi

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.

Very cool.

I'm excited for my Clicks Communicator to come in. I'd love to try to connect this to a hot-key for easy access.

My gmail got absolutly flooded with LLM generated spam messages. They all had "Hello, test" in many different languages, correspondence personas, etc.

which makes me think someone was trying to test their mass email spam generator.

1000+ emails bypassed my spam folder, went straight into my Primary inbox. From the POV of the "tester", I'd say they had a success.

I'm thinking of migrating off Google for proton mail now.

Yes, it's crazy. Just keep in mind that 80% of the economy running outside of our little software bubble is still using bulldozers, shovels, paint.

"It’s 2026 and it seems like AI has reached singularity." No, no it has not. The singularity, mathemetically and as originally termed by futurists, is when progress reaches an asymptote and by definition becomes undefined.

Just had a look. The OP library claims to be no_std compatible while ChoRus is not.

The 1%ers are in the vertical, but the question is vertical to where?

It needs to a potential field with practical, economical, "real life" attraction well. I.e, robotics, real economic efficiency gains, manufacturing novelties.

The worry is that the 1% is attracted towards a non-practical money hole. I.e: Token burn for the lols, sophisticated software systems that dont provide actual value outside of giving NVIDIA cash.

It's rough, but at some point there needs to be an arbitrary line.

I think 21 is used is many cases because that's supposedly when the brain has completed most of it's growing or something about pre-frontal cortex being fully developed.

The crisis in reading is due partly to trendy, faulty, and "inclusive" pedagogy.

A return to classic phonics will help a lot. This is already happening because the empirical evidence against the whole language approach is too sound to ignore.

See this study from 2013 https://eric.ed.gov/?id=ED545621

Not quite sure what all those words mean.

Dynamical systems will never be solved in a semantic domain.

IMO, they can be helpful the in robotics stack, from planning level up, but that's it.

I think where both camps get hung up is sometimes the process method group "ignores" the obvious outcomes and effectiveness of LLMs.

But the outcomes group "ignores" the fundamental limitations of models which are purely text based.

E.g, a baseball players trains to catch high-speed balls and they dont do it by: "ball velocity 50mph, vector:[1,2,3], run move hand command now"

That's absurd.

No, there is an embodied network which is "trained" on visual, tactile input, and control as direct output.

LLMs are fundamentally not the right tool for that.

I think the most important aspect of high salary is job demand.

Perhaps once there was a high demand for good SWE coming out of 4 years of $$ tuition uni.

Personal anecdote: Got a buddy who dropped out of Bible school, worked at a auto shop while getting his certs. After 2 years about any dealership would hire him.

He moves, lands a 100k+ job. 2 years later, switches to a dealership in San Jose, earning 150+.

6 months later, he gets an offer to become foreman at a dealership in gilroy with a $5/h raise + foreman benefits. How it works is techs are paid flat rate: Paid per job, not per hour. So if youre good, you can really rake in the money.

Its been wild to see his career arc in real time.

He's highly trained, very handy, Nd he's damn smart at debugging.

Basically doing CAN bus diagnostics with an oscope and logic analyzer.

He earns more than me who got a MS in ECE.

On the flip side, his job is physical and he works more hours than me.

I get to so R&D, think about cool math, and code. But in another life I'd love do be a tech.

All that said, there's a lot of demand for good auto techs.

The strongest reason is because it compromises the human operator's moral steering: Initiating what normally would never be done.

The weakest reason is LLM consciousness. Let's not anthropomorphize a forward pass through a high dimensional mathematical vector space of human semantics.

Extremely frustrating that the real doctor was so reluctant to get an MRI.

Why do they do this crap? I'd really love to know.

Similar experiences with our own doctors. It's pulling teeth to get tests done. If you every bring outside information, it's shutdown.

I have this idea about prompt injecting their audio scribe with repeated words. Maybe some part of the healthcare monolith will respond to a appointment summary with:

"You no listen. You no listen. Escalate. Escalate. Why make me in danger. Concern. Concern."

Dang, got some large sell volume going into the end of the year.

Expecting a downturn because midterm results mean the Trump party is over?

Yes, they're trying to break into corporate knowledge work by appealing to efficient gains due to reducing busy work.

Just different interfaces on top of the same product (selling tokens)

Great analysis. Clearly shows "advancement" is not exponential but logarithmic. Hence, "We need to slow down!!" as a cover for stagnating improvements.

Mhh I've tried one-shotting double buffered IO a few times and it just can't do it. Maybe it's a prompting issue, maybe LLM's have a hard time with thinking about parallel processes.

I'll create a simple "framework" of what I know works. After that's there the LLM is fantastic.

This is great!

Lol @ "airgapped".

Very cool. Does this integrate with banks like ActualBudget? Any plans to support self hosting?

Universities need to be "hardcore" non-AI.

It is so, so easy to pick up LLM aided development.

What is way harder is self-managed slop mitigation which is only achieved with employees who know the A,B,C's of software development.

Regularly do this because our managers are so busy I assume any messaging done within the body of an email will not be read.

BLUF, then details.

I love these windows emulators

The effect if LLMs is not linear. It's metaphorically x^2.

Smart people will be more effective. Average people will remain average. Dumb people will become dependent on it.

Bruh just ignore it if its not interesting.

Surprisingly good content. My take away is that OpenAI and Anthropic will pivot from model improvement to reducing inference cost.

My pet conspiracy theory: Both companies have seen outcome saturation. That's not good for IPO's or PR.

Instead, they say they will "hit the brakes" because of safety concerns and this provides a palatable narrative for logarithically improving models.

This theory must assume also that all the "AI hacks" are actually a PR stunt.

I think that's a reasonable position: Wimpy sandbox and major token burn and of course you get some kind of exhilaration.

Very nice. Be safe out there when tweaking these things.

Yes everything needs an initial condition.

You could have the smartest human political operator, but if he has no context, no motivation, not much is going to happen.