How can you say it's a 100% faithful recreation if you've never programmed DSP before?
Indeed but to be fair I'm not sure anybody claimed much "creativity" only that it worked... but that itself is still problematic. What does it mean to claim it even manage to implement an alternative if we don't have an easy way to verify?
If you look at my other comment in this thread, my project is about designing proprioceptive touch sensors (robot skin) using a soft-body simulator largely built with the help of an AI. At this stage, absolute physical accuracy isn’t really the point. By design, the system already includes a neural model in the loop (via EIT), so the notion of "accuracy" is ultimately evaluated through that learned representation rather than against raw physical equations alone.
What I need instead is a model that is faithful to my constraints: very cheap, easily accessible materials, with properties that are usually considered undesirable for sensing: instability, high hysteresis, low gauge factor. My bet is that these constraints can be compensated for by a more circular system design, where the geometry of the sensor is optimized to work with them.
Bridging the gap to reality is intentionally simple: 3D-print whatever geometry the simulator converges to, run the same strain/stress tests on the physical samples, and use that data to fine-tune the sensor model.
Since everything is ultimately interpreted through a neural network, some physical imprecision upstream may actually be acceptable, or even beneficial, if it makes the eventual transfer and fine-tuning on real-world data easier.
This honestly though does not help me to estimate if what you claim to be is what it is. I'm not necessarily the audience for either project but my point remains :
- when somebody claims to recreate something, regardless of why and how, it helps to understand how close they actually got.
It's not negative criticism by the way. I'm not implying you did not faithfully enough recreate the DSP (or the other person the NES). I'm only saying that for outlookers, people like me who could be potentially interested, who do NOT have a good understanding of the process nor the initial object recreated, it is impossible to evaluate.
I do understand your point, and I think it’s a fair one: when someone claims to "recreate" something, it really helps readers to know how close the result is to the original, especially for people who don’t already understand the domain.
I was mostly reacting to the idea that faithfulness always has to be the primary axis of evaluation. In practice, only a subset of users actually care about 100% fidelity. For example with DSP plugins or NES emulators, many people ultimately judge them by how they sound or feel, especially when the original artifact is aesthetic in nature.
My own case is a bit different, but related. Even though I’m working on a sensor, having a perfectly accurate physical model of the material is secondary to my actual goal. What I’m trying to produce is an end result composed of a printable geometry, a neural model to interpret it, and calibration procedures. The physics simulator is merely a tool, not a claim.
In fact, if I want the design to transfer well from simulation to reality, it probably makes more sense to intentionally train the model across multiple variations of the physics rather than betting everything on a single "accurate" simulator. That way, when confronted with the real world, adaptation becomes easier rather than harder.
So I fully agree that clarity about "how close" matters when that’s the claim. I’m just suggesting that in some projects, closeness to the original isn’t always the most informative metric.
One reason I find my case illuminating is that it makes the "what metric are we optimizing?" question very explicit.
Sure, I can report proxy metrics (e.g. prediction error between simulated vs measured deformation fields, contact localization error, force/pressure estimation error, sensitivity/resolution, robustness across hysteresis/creep and repeated cycles). Those are useful for debugging.
But the real metric is functional: can this cheap, printable sensor + model enable dexterous manipulation without vision – tasks where humans rely heavily on touch/proprioception, like closing a zipper or handling thin, finicky objects – without needing $500/sq-inch "microscope-like" tactile sensors (GelSight being the canonical example)?
If it gets anywhere close to that capability with commodity materials, then the project is a success, even if no single simulator configuration is "the" ground truth.
What could OP’s next move be? Designing and building their own circuit. Likewise, someone who built a NES emulator might eventually try designing their own console. It doesn’t feel that far-fetched.
So on "So I fully agree that clarity about "how close" matters when that’s the claim. I’m just suggesting that in some projects, closeness to the original isn’t always the most informative metric." reminds me of https://en.wikipedia.org/wiki/Goodhart%27s_law
That being said as OP titled " I used AI to recreate X" then I expect I would still argue that the audience has now expectation that whatever OP created, regardless of why and how, should be relatively close to X. If people are expert on X then they can probably figure out quite quickly if it is for them "close enough" but for others it's very hard.
So on "So I fully agree that clarity about "how close" matters when that’s the claim. I’m just suggesting that in some projects, closeness to the original isn’t always the most informative metric." reminds me of https://en.wikipedia.org/wiki/Goodhart%27s_law
That being said as OP titled " I used AI to recreate X" then I would still argue that the audience has now expectation that whatever OP created, regardless of why and how, should be relatively close to X. If people are expert on X then they can probably figure out quite quickly if it is for them "close enough" but for others it's very hard.
And I'm not doing it based off of my ears. I know the algorithm, have the exact coefficients, and there was no guesswork except for the potentiometer curves and parts of the room algorithm that I'm still working out, which is a completely separate component of the reverb.
But when I put it up for sale, I'll make sure to go into detail about all that so people who buy it know what they're getting.
Consider reaching out to Audiority - I know they have some virtual recreations of Space Station hardware.
How is this different?
I don't try to hide my AI tracks. I'll gladly tell anybody that AI helped me do it because it did such a fantastic job. I mean, that's literally what this post is about!
My plug-in sounds way better than the UM-282 which was hand-coded before AI was getting popular. That's all that matters!
Honestly I think you should re-examine your own position. I see you've written plugin software in the past and I'm sure you spent a long time on DSP algorithms and learning and understanding.
Well I did the same thing with web-based software for the last 25 years. The world doesn't give two shits man. The world is going to do what the world is going to do.
You're free to have your own opinion
I tried this for laughs with Gemini 3 Pro. It spit out the same ZDF implementation that is on countless GitHub repos, originating from the 2nd Pirkle FX book (2019).
Since there is a Ursa Major project on github, made by an owner, who reimplemented this also based on observation, made into a plugin, I wonder how much was regurgitated by the AI agent.
The video claims: "It utilizes the actual DSP characteristics of the original to bring that specific sound back to life." The author admits they have never programmed DSP. So how are they verifying this claim?
Also its target market is not a technical crowd but people who make music. I'm optimizing more for what they want to see (which are sound demos) rather than what a programmer would want to see.