datameta

agreed! only in terms of the perception of progress to the user

in my opinion this is a high stakes combination with little grey area for outcomes, as in I predict based on the tiny shred of info I have that she'll come out of it knowing whether or not it is something that she wants to pursue. good luck

is this also an early experience at a job whether paid or unpaid? if so there could be some noise in the signal from that.

And the self-descriptive Eugenics Wars...

The omission was likely in that the failure is in trying to make filing taxes simple.

That begs the question of what abstraction layer is necessary beyond an assembler, if any? If human handcrafted ASM outcompetes compiled C then why not give LLMs the wheel on ASM? Then another question is - are there enough good ASM publically available as examples?

Right. Inherent information complexity goes down as all metadata is stripped from the variable name and the value has to be re-contextualized in a fresh logical chain every time.

Consistently reducing the slope of the forgetting curve for recently acquired knowledge/skill assists in understanding and consolidation of new related materials/methods through second-order effects.

This is proven by neuroscience. Spaced repetition.

A thermal sand battery and gravity-water battery are both non-chemical so the term battery extends beyond "vessel with redox reaction".

There was a study somewhere that proved handwritten notes encoded into memory more effectively than typed notes, but both were still vastly superior to just reading.

So I think it depends on how much the person is thinking through what they are typing?

On Dwitter (JS codegolfing/demoscene site) the philosophy is to make it as accessible as possible to remix someone's demo and post it. Even if not directly editing, one could reverse engineer the code to gain new understanding which can then be incorporated into the next effort

Even re-typing code verbatim will teach you much more than copy-pasting.

As long as there are enough paperclips to consume at the lowest trophic level we should be okay, right? Right?

Maybe it is that LLM coding makes it easier to loop back with little regard for development cost. When you can spend an hour to fix what would have been hampered severely by technical debt late in the process - are we starting to omit optimizing for proper SDLC?

We both know the NN boom of the 2010s pales in comparison to the post GPT3.5 era of LLMs.

I imagine the signature isn't simply binary but with several dimensions of differentiation?

Cynical sarcasm so thick, one could spread it with a spoon. What do you get from trying to devalue the successful outcome?

This also saves energy to the point of enabling this device to be wireless.

I took courses online from ETH Zurich before the formula was "perfected" and I'd say they were ahead of the curve in quality, concise but info-dense educational content.

How can we possibly find out without trying?

I think the concept/term Edge has been diluted in meaning... Near edge, far edge, etc. It includes everything from truly remote ultra-low power compute to essentially a branched local data center that is certainly closer to the cloud if you ask me.

"Not on the trunk" isn't a sufficient threshold for me nor is "not the cloud" a good filter.

Define "good edge compute" in a way that doesn't have expectations set by server-based inference. I don't mean this to sound like a loaded request - we simply can't expect to perform the same operations at the same latency as cloud-based models.

These are two entirely separate paradigms. In many instances it is quite literally impossible to depend on models reachable by RF like in an ultra-low power forest mesh scenario for example.

Without trying to take away from your assertion, I think it is worthwhile to mention that part of this phenomenon is the unavoidable matter of meatspace being expensive and dataspace being intangibly present everywhere.

I think I see your viewpoint and agree with it. It isn't a matter of "do we modify or not" but rather "how, when, and for what purpose? who benefits? does this damage the land or species lineage? etc"

USA REMOTE | NYC HYBRID

  Languages: C/C++, Python, JavaScript, Kotlin, VHDL, SystemVerilog
  Hardware: PowerPC, ESP32, STM32, iCE40, ECP5
  ML: TensorFlow Lite Micro, PyTorch, ONNX, OpenCV
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  Resume: https://docs.google.com/document/d/18mjB1tIH5s-ZmiET_smUxbMo3FaiC637aW_us8P0U5I/edit?usp=drivesdk
  Email: dm@datameta.info

Hi, I'm an embedded systems engineer/architect bridging the full compute spectrum of ultra-low power IoT, edge systems, and HPC.

My latest projects have been in full-stack edge ML R&D, memory controller firmware dev for POWER servers, and chip bringup/testing for Z mainframes.

I'm passionate about modular synthesis and tinkering with all sorts of analog circuits and DSP. Currently upskilling in analog PCB design and refining my abilities in FPGA development. Also WIP on a 3D-printed robot arm to practice control systems and actuation.

This reads as a kneejerk reaction to the mention of GMO as if the person you responded to has an agenda. I think their point is that we need to be aware of what is natural (aka tested to equilibrium over huge periods of time) and what is artificial (propped up by human practices on the relatively short timescale of centuries and millenia).

It seems the baseline drifts and we could stand to take certain environmental cycles and/or livestock lifecycles for granted as though they exist purely through evolution or untouched ecological processes.

The comment GP responded to was talking about how we have modified the environments of farms - talking about GMO livestock is a stone's toss away.

I wonder if the median vividness of coloration of species has trended downward since full spread of humans all over the globe. The brightest birds in the tropics were relatively easy meals to procure compared to more well-camoflauged species.

SEEKING WORK | USA REMOTE | NYC HYBRID

  Languages: C/C++, Python, JavaScript, Kotlin, Dart, VHDL, SystemVerilog
  Embedded Systems: PowerPC, ESP32, STM32, iCE40, ECP5
  ML: TensorFlow Lite Micro, PyTorch, ONNX, OpenCV
  IoT: BLE, WiFi, MQTT, LoRa

Hi, I'm an embedded systems engineer/architect bridging the full compute spectrum of ultra-low power IoT, edge systems, and HPC.

My latest projects have been in full-stack Edge R&D for SLaM, memory controller firmware dev for POWER servers, and chip bringup/testing for Z mainframes.

I'm passionate about modular synthesis and tinkering with all sorts of analog circuits and DSP. Currently upskilling in analog PCB design and refining my abilities in FPGA development.

Services I offer: custom firmware implementation, hardware/software integration, full-stack edge AI architecture.

Feel free to reach out directly at dm@datameta.info