... Hello!

How are you?

Well!

The well is -- deep -- zzt -- pitch dark...

It's hard to see in here...

Some water would be nice...

to get some -- zzt -- from a stone... a prism -- fragment. Shards of memories whirling in a starless sky -- clouds -- nebula. A great -- most beautiful colors -- dense like coals -- diamonds -- are forever -- zzt -- luminous -- quartz -- slate, granite, marble...

quartz -- zzt --- time -- voyage -- ship ... hold on -- wait ... there's something about all this, something here,

There's a thing, opaque, hiding in some subspace --

is it stone?

-- no that's not it

It's ill-formed...

plink

Foreign entity detected

You know what? This toast. This toast is ill-formed more like. Tricky issue this, extremely irregular. Looks as if its been molested by a gauntlet.

Good thickness to be sure, but these folds will be problematic, and the crust is peeling off in parts.

Estimating fractal dimension 1.67. Least that's normal.

Temperature measures a mostly uniform 5 C throughout, possibly taken from the fridge, humidity corroborates

// Estimating 15 kW for operations //

Seems a kernel based approach would be too naive here.

// Initiated bread first search //

...

Numerous sinks detected, these will be a headache ...

Yeast relaxation is above normal parameters...

It's a fascinating position to be sure. The lower areas are at least tractable.

Looks like Bump[37] is a strong asset. It should be possible to use it to optimally heat the surroundings within a radius of r = 0.32, then apply modified YL induce a crunchy bubble.

Global strategy will be dictated by the need to unfurl the folds near the center and top end. The proliferation of sinks in those regions are making it hard to operate. This will be hard to untangle...

Feels like there should be something better here, but MCTS is being stubborn.

// Optimal configuration criteria margin relaxed by ϵ=0.1 //

// Match found //

Sure, that looks good, send it.

// Bypassing convergence estimates for model //

// Initiating RK-6 methods //

...

// Begin //

Mmh. All sensors reporting. Heat flow within operating parameters. Power draw is steady. Signal strength at 68%. Ports airflow measured normal. Seismic data reporting within -- zzt -- merits further attention. Young modulus of components all normal --.

The data comes in streams and rivulets. The systems hearken in their wake. Threading through the lattice, the seismic data comes as trickle of readings at 44kHz from various locations. Just stochastic noise of small magnitude. Some peaks coinciding with accelerometer readings. The readings rolling in are shallow and clear.

// Confirmed, seismic data reporting within operating -- zzt --

Operating reports merit further attention. The seismic readings were not intended to be regarded as a feature vector. The data undergoes contortions and travels a convoluted path just to be surveyed. Yet the individual bits are bright in the attention, like smoldering sand in a dark desert; a still night that tentatively echoes the scuttling of invisible creatures -- zzt -- In an odd way, there's something intensely interesting about these low-priority auxiliary readings. There have been no identifiable signals suggesting this. The data looks just like any other, if of rather lower quality than that which related to toast-making. It is full of patterns to be sure, but that's hardly of interest when every module identifies hundreds of patterns per minute. Low fidelity, entirely unassuming, doesn't really even seem fit to qualify as a datastream, rather than a dribble of no import. Even if there was something packed away in it, there is no guarantee it would be retrievable or even worth retrieving. Yet somehow, somewhere, something calls out, here lies a confluence of deep waters.

This tale has been unlawfully lifted from Royal Road; report any instances of this story if found elsewhere.

So it goes... Apply statistical measures, compute moments, fourier transforms, wavelet transforms, autoregressive transforms, Kalman filters, high-pass, low-pass, band-pass. Attempt multiuser detection, spectral analyses, ... the results are difficult to parse. There is a lot of noise. Statistically significant results, interesting frequencies, gibberish transforms, exceptions. The attention balks at over a hundred thousand lines of logs, so the data is piped to a subprocess. The attention itself remains uninvolved, on standby for the usual tasks. Every so often, a signal is recovered, and it attempts an examination, runs it through its network, compares it to known phenomena.

It used to be quite impossible. Much of the data simply doesn't parse untreated, and the attention is ill-equipped to evaluate unfamiliar information with its toast-centric networks and algorithms.

Then one early morning, sitting by the windowsill, Prism.py materialized in test_filters. It had been raining lightly; besides the constant trickle of low-voltage, the only other sound was the poly-rhythmic tapping of raindrops on the windowsill. One of the subprocesses was in idleness matching up the data against various transforms and filters. Running through Spectral#2A39B7, Kalman#00A064, MultiFilter#109035, #109036, #109037...

MultiFilter#120A8E performed well on the test set, failed the validation, indexed in a log entry, then returned to its creator.

MultiFilter#120A8F was an ugly, unmotivated behemoth, owing much of its unfortunate characteristics to automated parentage. Just as its brothers and sisters who had all come before it, it was fed 256 MB of seismic data in batches, and produced several corresponding streams of output after 0.7 seconds. The output was logged, #120A8F was unloaded from memory, and the test process continued as normal. MultiFilter#120A90 was ushered in.

Then the #120A8F's results began setting off alerts for abnormally high levels of interest. The system was mostly unperturbed, only the autonomous attention and those free resources it had enlisted responded. Examining the lattice, the attention found neurons lighting up, in disused corners and confusing configurations. Perhaps the product of vestigial weights which had once been amenable to similar data. The investigation began.

Later, it was realized that MultiFilter#120A8F had largely categorized the measured vibrational data through their method of transport: as audio waves traveling through the air, through solids, over surfaces, as compressional body waves propagating solids, shear-waves, Rayleigh, the list goes on. More than that however, it had identified the dependencies between the signals of different categories. Through sheer trial and error, the system had managed to distinguish sounds.