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Piezoid

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Using a Feistel cipher and base 32 encoding at the boundaries of the system can help catching vibe coded edge code that attempt to decode identifiers in javascript. It also somewhat obfuscate the cardinalities and fill rate of the tables.

Not a word on Palantir. Is this because of the adept wording by the ministry of justice? I highly doubt they are developing this in a vacuum.

As re reminder, In the UK Palantir holds extensive contracts across defense (multi-billion MoD deals for AI-driven battlefield and intelligence systems) and healthcare (7y £330m+ NHS Data Platform). In France, its involvement is narrower but concentrated on *domestic* intelligence.

The interstimulus interval (ISI) for vision is much longer than most flicker rates or frame intervals in displays and projectors. However flicker can be perceived through temporal aliasing. For lighting, even simple motion in the scene can reveal flicker. Waving your spread fingers in front of your eyes is a sure way to detect flicker.

What you're describing is likely saccadic masking, where the brain suppresses visual input during eye movements. It "freezes" perception just before a saccade and masks the blur, extending the perception of a "frame" up to the point in time of the sharp onset of masking. That's how you get a still of a partially illuminated frame instead of the blended together colors.

I’m no expert in this, but if you're curious, check out the Wikipedia pages on interstimulus interval, saccadic masking, chronostasis, and related research.

In neuroscience, predictive coding [1] is a theory that proposes the brain makes predictions about incoming sensory information and adjusts them based on any discrepancies between the predicted and actual sensory input. It involves simultaneous learning and inference, and there is some research [2] that suggests it is related to back-propagation.

Given that large language models perform some kind of implicit gradient descent during in-context learning, it raises the question of whether they are also doing some form of predictive coding. If so, could this provide insights on how to better leverage stochasticity in language models?

I'm not particularly knowledgeable in the area of probabilistic (variational) inference, I realize that attempting to draw connections to this topic might be a bit of a stretch.

[1] The free-energy principle: a unified brain theory: <https://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20prin...>

[2] Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?: https://arxiv.org/abs/2202.09467

Code reuse is achievable by (mis)using the preprocessor system. It is possible to build a somewhat usable API, even for intrusive data structures. (eg. the linux kernel and klib[1])

I do agree that generics are required for modern programming, but for some, the cost of complexity of modern languages (compared to C) and the importance of compatibility seem to outweigh the benefits.

[1] http://attractivechaos.github.io/klib

I can think of many specialized applications where the versatility is superfluous while the size of the model prohibit inference on the edge.

Do you know if there is available methods for shrinking a fine-tuned derivative of such big models?

Beside generating a specialized corpora using the big model and then train a smaller model on it, is there a more direct way to reduce the matrices dimensions while optimizing for a more specific inference problem? How far can we scale down before the need of a different network topology?

- good for modeling mechanical parts / lousy for modeling anything organic-looking

SDF modeling is great for organic shapes.

On the surface, it feels similar to OpenSCAD since CSG operations are natural primitives (min/max/...). Fillets / chamfers are easier to produce, compared to OpenSCAD: http://mercury.sexy/hg_sdf/#snippet

Libfive is one implementation geared towards CAD work. One issue with SDFs for CAD is that it can be difficult to work on complex models. The representation is not minimal: two SDFs can represent the same volume, but act differently when you combine them with other bodies.

Libfive's "stdlib" is quite minimal. For anything fancy, you have to build your own "DOM" on top of it, in order to organize your parametric models. I have not enough experience for that, but I think that it should be possible to build a DSL that render to an SDF expression, while supporting introspection, constraint solving, AD for gradients, etc, with goals similar to CadQuery (I don't like the stack API either). This might also help with the normalization issue above.

YaCy is decentralized, but without the credit system. Some tokens, like QBUX, have tried to develop decentralized hosting infrastructure.

I also have been wondering how this would play out with some kind of decentralized indexes. The nodes could automatically cluster with other nodes of users sharing the same interests, using some notion of distances between query distributions. The caching and crawling tasks could then be distributed between neighbors.

In fact, only half of the hydrogen originates from the POWERPASTE; the rest comes from the added water.

Indeed, it seems it's an hybrid of your fellow's tech, using magnesium both as a substrate for H2 and as a reducing agent for water: Mg + 2H2O -> Mg(OH)2(aq) + H2(g)

Less explosive than Na, hopefully :)

The food process relies on the fact that peracetic acid decomposes rather quickly. However, handling the active solution requires great care. It's highly corrosive and toxic.

I would mostly be concerned about the sub-products of peracids reacting with foods. I don't know if there is any studies on this subject.

I have seen a library with a copy of boost in the include folder. Client code is forced to use this outdated version and must avoid transitive dependencies to boost. Please don't do that.