21 posts.
Almost everyone optimises the wrong thing. Measure with cProfile and timeit instead, learn to read the output, and use the two facts that explain most slow Python: membership tests that are O(n) inside a loop, and Amdahl's law, which caps how much any one fix can buy you.
A mathematical and code-driven walkthrough for simulating plasma half-lives, receptor binding kinetics, and Loewe synergy in herbal nootropic stacks.
A step-by-step laboratory and mathematical guide to calculating extraction yields, titrating bioactive compounds, and standardizing botanical nootropics from raw biomass.
Collapsing every AI failure into one phrase obscures more than it explains. The distinctions between reliability, security, privacy, and agentic-systems failures are not academic—they determine which solutions actually work.
A logistic term from preferential attachment on a finite pool, a quadratic term from obsolescence, and a fixed point that matches a simulation to a tenth of a per cent.
Running the pipeline on synthetic nulls and known answers before touching real data, because surprise is not evidence.
A short guide for readers of this archive, so that a benchmark is never mistaken for a derivation.
The distinction that this programme kept blurring, and the checklist I now use to keep them apart.
Prefer the theory that compresses the data and pays for its own parameters. It is Occam with an invoice.
Pointing a different agent at your own framework and telling it to recompute every number from the printed formulas.
Writing down the falsification threshold before running the analysis, so that a null result still counts.
A closed door is a result. Three of the strongest outputs here are proofs that something cannot be done.
Six labels, applied to every claim. The most useful thing in this project is not a result, it is a habit.
An honest map of everyone else working on many worlds, gravity and simulation evidence, with falsification criteria for each.
If the substrate has a discreteness scale, it has a noise spectrum. Deriving the shape is the testable part.
Blinded protocols for surface codes and matter-wave decoherence, written before any data exists.
A likelihood pipeline built so that the framework can lose. That is the point of building it.
Model cost plus residual cost, with a polynomial fit you can reproduce, because the whole plateau argument runs on this.
Where the logistic term in a saturation law actually comes from, starting from individual researchers picking combinations.
A saturated domain does not stop abruptly. It follows a double-damped curve, and later in this archive I derive where that curve comes from.
Shannon counted chess games. This counts possible knowledge states. The gap between the two is the whole reason the plateau matters.