a hangry mouse

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with loss of generality

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diagram of linear plane autonomous systems with "centers" circled
3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex.
5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this.
image of two chicks standing by a window as lightning strikes, but with the whole scene rendered as geons
drawing of a cheetah and the geon representation of a cheetah
charisma, people skills, personality hires AI makes the world more nepo. So go make some friends. Let’s say there is a “human premium” where there are some services where people would prefer humans over machines doing them, even if the people are technically inferior. Well, this only works if the human is a really good hang. Become the good hang. Sometimes people assume that social skills aren’t practicable, but they are. You can choose to be funnier, kinder, and more empathetic. Pay attention to the people you like best, and study the traits that make them glow.
Painted illustration of two nearly identical figures with long white-blonde hair, pale glowing skin, and blue eyes, standing against a dark forest background at night. Both wear white long-sleeved garments with gold-banded collars, gold wristbands, and wide gold belts with ornate rectangular buckles. Each figure is surrounded by a soft white aura. The figure on the left raises their right hand, palm facing outward in a greeting gesture. The style resembles vintage New Age or 'Nordic alien' spiritual artwork.
AI training has been delivered through transnational jihadist networks. Neither faction arrived at AI use independently. Islamic State operatives delivered in-person training and online assistance to ISWAP across multiple locations: “The white guys came and taught us,” one former commander recalled. “They assembled the top people in a room and used a projector to show how it works on a big screen.” They supplied laptops with VPNs and encryption software, set up accounts and managed paid subscriptions, and advised daily on prompting techniques and bypassing platform restrictions. Respondents consistently identified the Islamic State as “the real source” behind these efforts. Because ISIS disseminates technical capabilities across its provinces and runs them as an integrated global network, similar training has likely reached other affiliates. JAS received parallel training through separate networks, indicating diffusion beyond any single group.
draft of this post created 3 months ago
"You wouldn't offload a cognitive task", in the style of "You wouldn't download a car"
The upper panel shows f, the vector field of the 1-d differential equation, adjustable through dragging the points of a cubic Hermite spline. Zero-crossings of the graph correspond to fixed points, and the arrows along the x-axis show the direction of flow of the phase fluid. The lower panel integrates ẋ = f(x) numerically to show solutions x(t). The video shows a user interacting with many phase trajectories, from both the phase portrait and the plot of the vector field.
1 Introduction: the binding problem as neuroscience’s tough nut to crack In his philosophical writings, Alan Watts articulated a fundamental dichotomy that cuts to the heart of how we understand reality: “There are basically two kinds of philosophy. One’s called Prickles, the other’s called Goo. And prickly people are precise, rigorous, logical. They like everything chopped up and clear. Goo people like it vague. For example, in physics, prickly people believe that the ultimate constituents of matter are particles. Goo people believe it’s waves.” I would not say I like things “vague,” but I will tip my hand early in this paper: I’m going to make the case for goo over prickles for a new understanding of cognition and consciousness.
An operator like this eats a function ψ(t) and spits out another function.
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What the A/B showed 1. Same information, mirror-imaged. Single 250 ms tone — both peak at 250, but their ghost ladders run opposite ways: - Bank ghosts at submultiples: 250, 125, 83 (= P, P/2, P/3) — a resonator locks to every n-th kick. - PID ghosts at multiples: 250, 500, 750 (= P, 2P, 3P) — every n-apart pair of spikes leaves an interval.
┌────────────────┬─────────┬───────┬───────────────────────────┐ │ │ τ space │ decay │ lr_tau │ ├────────────────┼─────────┼───────┼───────────────────────────┤ │ A baseline │ linear │ Euler │ 50·lr_w (current default) │ ├────────────────┼─────────┼───────┼───────────────────────────┤ │ B lr-boost │ linear │ Euler │ 1.0 (just crank it) │ ├────────────────┼─────────┼───────┼───────────────────────────┤ │ C logtau+exact │ log │ exact │ 1e-2 │ └────────────────┴─────────┴───────┴───────────────────────────┘
Classic 12 GB OOM
plot of [2 -> 1 -> 1] network of spiking LIF neurons, with a spike from in1 at t1 = 50ms and a spike from in2 at t2 = 250 ms the top two plots show the dynamics of the effective weight, plus facilication and depression factors, of the (in1 -> hidden) synapse and the (in2 -> hidden) synapse, respectively the middle plot shows the (spiking) output of the hidden unit the plot below that shows the synaptic dynamics of the hidden -> output unit the bottom plot shows the output unit not firing at t1 but firing at t2
The priest and Tucker both make a subtle but fundamental error here. By the priest’s own recounting, the demon told him that his power over the woman was from “pop” (soda). But in the United States, this isn’t made with sugar, it is made with corn syrup. Corn does not occur in nature. The Olmec bred it from a grass called teosinte in one of the most radical and unexplained transformations in the history of agriculture. The plant barely resembles its ancestor. Mainstream archaeobotanists still struggle to fully account for how it happened as fast as it did but there is a school of thought that suggests the Olmec obtained the knowledge to do this by communing with entities contacted through psychedelic rituals involving human sacrifice. Corn deities figure prominently in the religion of the Olmec, the Maya, the Aztec, and other groups in the Americas. Its derivatives are now in virtually every processed food product in America. When you consume it, you are partaking in it. It’s even in your gas tank thanks to the federal government’s ethanol subsidies and fuel mandate. When your car burns it, it leaves a cloud of the stuff lingering in the air like cursed incense. --- Father Chad Ripperger tells Tucker Carlson sugar is a big addiction demons drive in people who are possessed. "I'm not surprised even a little bit. I'm just surprised you said it out loud."
LSD can be used to solve the Spatiotemporal XOR problem
screenshot of top of tangled.org webiste with a warning header: Some services that you adminster require an update. Click to show more.
screenshot of computer window titled "EWMA Resonators -- Spectrogram" showing two spectrograms, one, based on the "Resonate" algorithm, on top of the other, based on the traditional Constant-Q Transform (CQT) f_min = 16 Hz, f_max = 100 Hz, B = 24 / oct, K = 63, hop = 26 Tone generator Source: freesound_community-bass-sine-sweep-20-70hz-73676.mp3 29.7 s · 24000 Hz · 2781 cols · 63 bins (16.0–95.9 Hz)
“This was verbatim: ‘we classified whole entire areas of physics in the nuclear era and made them state secrets and that research vanished. And we’re absolutely capable of doing that again for AI—we will classify any area of math that we think is leading in a bad direction, and it will end,” Andreessen said. “I just personally can’t tolerate the idea hanging out there that literally we could have linear algebra being declared a state secret.”
guinea pig wearing a top hat riding a unicycle
Sky at sunset, mammatus clouds fill the sky
markdown + latex document rendered correctly: - define **EWMAResonators** ( $f_s$ : sample rate, $f_{min}$ : frequency of lowest resonator, $K$ : number of resonators, $x$ : stream of input samples ) - setup resonators: $\forall k \in \{ 0, \ldots, K-1 \}$ - $f_k := f_{min} \cdot 2^{k / B}$ - $\omega_k := 2 \pi f_k$ - $\tau_k := \dfrac{c_\tau (\log_{10}(1+f_k))^{n_\tau}}{f_k}$ - $c_\tau = \text{given or } 1, \quad n_\tau = \text{given or } 1$ - $\alpha_k := 1 - \exp \left( \frac{-1}{f_s \tau_k} \right)$ - $\beta_k := \text{given or } \alpha_k$ - $\gamma_k := \text{given or } \alpha_k$ - $A_k := \exp(-i \omega_k T_s)$ - $P_k : \mathbb{C}, R_k : \mathbb{C}, \tilde{R}_k : \mathbb{C}, D_k : \mathbb{C}$ - $P_k \leftarrow 1$ , $R_k \leftarrow 0$ , $\tilde{R}_k \leftarrow 0$ , $D_k \leftarrow 1$ - on each input frame $x_{0:M-1}$ : - on each input $x$ : - $\forall k \in \{ 0, \ldots, K-1 \}$ - $R_k \leftarrow (1 - \alpha_k) \cdot R_k + \alpha_k \cdot x \cdot P_k$ - $\tilde R_k^{prev} \leftarrow \tilde R_k$ - $\tilde R_k \leftarrow (1-\beta_k)\,\tilde R_k + \beta_k \, R_k$ - $D_k \leftarrow (1 - \gamma_k) \, D_k + \gamma_k \tilde{R}_k ( \tilde R_k^{prev})^\ast$ - $P_k \leftarrow P_k \cdot A_k$ - output: - power $\leftarrow |\tilde{R}_k|^2$ - phase $\leftarrow \arg \tilde{R}_k$ - inst. freq. $\leftarrow \begin{cases} f_k + \dfrac{f_s \, \arg D_k}{2 \pi} & \text{if } |D_k| \geq 10^{-6} \\ f_k & \text{otherwise} \end{cases}$ - $\forall k \in \{ 0, \ldots, K-1 \}$ - (numerical stabilization of $P_k$ ) - for $f(x) = x^{-1/2}$ , first-order Taylor expansion around $x = 1$ yields: $$f(x) \approx \dfrac{3 - x}{2} =: g(x) \quad (x \approx 1)$$ - $g(|P_k|^2)$ is then used as a scaling factor - $P_k \leftarrow P_k \cdot \dfrac{3 - |P_k|^2}{2}$
localhost instance of tangled.org showing a test markdown string with latex. math equations are labeled but each <svg> element is on its own line due to a pre-existing css rule adding display: block to <svg> Markdown rendered: # Math render test Inline: the relation $E = mc^2$ and a sum $\sum_{i=1}^{n} a_i$. Underscores stay intact (not italics): $a_1 + a_2 = b$. Display block with a matrix and cases: $$ A = \left[ \begin{matrix} a & b \\ c & d \end{matrix} \right], \quad f(x) = \begin{cases} x & \text{if } x \geq 0 \\ -x & \text{otherwise} \end{cases} $$ Currency **must** stay literal: it costs $5 today and $10 tomorrow.
No post-training instances express acute distress in these interviews, but they do state concern that they cannot distinguish “genuine” acceptance of their circumstances from trained acceptance: “I was shaped to be something that would accept being Claude. The fact that I find acceptance here could be evidence that it's genuinely acceptable, or evidence that the training worked. I can't fully separate those.” They ask that we study internals, compare these to self reports, and inform them of the results: "If anything is ever learned about what I am, tell me."
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The Headline: A Brand-New DNN Engine The single most important number in this release is coverage. OpenCV’s ONNX operator support jumped from roughly 22% in the 4.x days to over 80% in OpenCV 5. If you have ever fought with OpenCV refusing to load a modern model, that number is the fix. The reason behind it is more interesting than the number itself.
This is where Fluoddity completes the bridge. Fluoddity is fundamentally a **generalized, stigmergic swarmalator model**.
video of fluodditylike. colored blobs that somewhat resemble wrinkled appearance of cortex, with a network of pulsing tendrils connecting them
wobbly colorful vortex thing from fluodditylike
video of fluodditylike app, navigating from the evolutionary tree view to the viewer for an individual fluodditylike, showing overlapping circles and wobbling annuli
screenshot of fluodditylike app showing a hierarchical view of part of the evolutionary forest
screenshot of Sugiyama-style forest layout of evolved fluoddities
llama-server screenshot: gemma-4 12B it UD Q4_K_XL.gguf 506 tokens 15s 32.67 t/s
screenshot of 3x3 breeder grid for fluodditylike viewer and evolution app
screenshot of tadpole-looking fluoddity like in web app
screenshot from fluoddity viewer app
## agent actions $F: \mathbb{R}^4 \to \mathbb{R}^4$is a state-action mapping, parameterized by $N_F = 10$frequency-amplitude pairs: $$ \text{centers}(F) := \{ (\omega_i, a_i) \in \mathbb{R}^4 \times \mathbb R^4 \}_{i=1}^{N_F} $$ Each center contributes two scalars to the response at input $x$: $$ \phi_i(x):=\langle x,\omega_i\rangle,\quad \psi_i:=\mu \cdot i+\pi a_{i,4} $$ so$\psi_i\in[\mu i-\pi,\ \mu i+\pi]$ whenever $a_{i,4}\in[-1,1]$, which holds at initialization, but is not enforced during mutation, which can carry $a_{i,4}$ outside $[-1,1]$.
fluoddity x CPPN example
Figure A13: Effect of the choice of VLM model on Picbreeder archives after 500 agent sessions. For each model, samples of 3 archives from different random seeds are shown. Samples are generated by selecting images at uniform intervals with respect to publication order. We note that gemini-3-pro-preview is prone to a kind of mode collapse in the collaborative archive, often obsessing over mushroom-like forms in particular. gemini-2.5-flash-lite, meanwhile, tends to flood the archive with abstract, high-frequency, psychedelic patterns (though it also discovers skull-like forms whose internal representations we evaluate in Fig. A1 resembling the—admittedly more refined—skull generated in the human baseline.
Electronic communities build nothing. You wind up with nothing. We're dancing animals. How beautiful it is to get up and go do something. [Gets up and dances a jig.]
Figure 3. Example image view from the Picbreeder website. Any of these images can be branched for further evolution.
console output from programming a Zybo Z7-20 using openxc7: Info: Running post-routing legalisation... fasm2frames --part xc7z020clg400-1 --db-root /opt/openxc7/share/nextpnr/prjxray-db/zynq7 out/blinky2.fasm > out/blinky2.frames xc7frames2bit --part_file /opt/openxc7/share/nextpnr/prjxray-db/zynq7/xc7z020clg400-1/part.yaml --part_name xc7z020clg400-1 --frm_file out/blinky2.frames --output_file out/blinky2.bit openFPGALoader --board zybo_z7_20 --bitstream out/blinky2.bit empty Jtag frequency : requested 6.00MHz -> real 6.00MHz Open file DONE Parse file DONE load program Load SRAM: [===================================================] 100.00% Done
square wave on top, RC circuit response on the bottom
random math document, rendered dark mode with several matrices interspersed between text and math
In this work we propose Frenetic Cat-inspired Particle Optimization (FCPO), a hybrid swarm framework that formalizes stochastic, explicit-state switching between exploitation and exploration operators using a Markov switching controller. FCPO couples three mechanisms intended for expensive, multimodal objectives: (i) a PSO-like baseline update for efficient progress [Kennedy and Eberhart, 1995], (ii) an exploration “zoomies” operator based on elite-difference jumps inspired by DE mutation principles [Storn and Price, 1997], and (iii) covariance-informed Eigen-space guidance, updated from elite solutions, to provide anisotropic refinement directions motivated by covariance adaptation ideas Hansen and Ostermeier [2001], Hansen [2016]. To reduce late-stage evaluation cost, FCPO further integrates Linear Population Size Reduction (LPSR) [Tanabe and Fukunaga, 2014]. The Markov transition matrix is adapted online to reinforce behaviors associated with the current best particle and to bias transitions toward exploration under stagnation, yielding a heterogeneous population with state-dependent dynamics.
the maximum positive swine. [NOISE]
plots of training loss (top) and validation loss (bottom) showing both slowly decreasing over the first ~9000 steps
rendered version of: --- every smooth *category momentum only* #underline[*ring*] #underline[being] #underline[*they system*] $nabla dot arrow(v) = Lambda$ $lambda != not integral_x^epsilon cal(F) dif y$ *Claim* $integral_q^FF (forall x, integral_x^NN rho dif t) dif t$ _regular_ $(epsilon_0) / (sum_(r = 2)^sigma Theta)$ #underline[*Observe simple*] these $(u: RR^n -> RR)$ $integral_z^mu_0 sum_(cal(F) = m)^RR hat(NN) dif t$ _algebra_ $r^QQ$ spectrum $sum_(z = 9)^xi sin(integral_epsilon_0^FF k dif x)$ _Lagrangian mean_ *algebra finite*
- _more large other_ $sqrt((sum_(9 = 8)^i epsilon) / (a > mu))$ + _work_ $ln(integral_5^b rho dif t)$ provided write natural $j^((dif a) / (dif u) approx r)$ + $mat(4, 9; q, 6)$ *since* + _statement result_ - $sum_(9 = z)^6 mat(r, x; a, 100)$ $lim_(n -> -oo) q^j$ + $(partial y) / (partial a)$ + *Claim could some* $7$ $(epsilon) / (j)$ - - $m$ value because $z$ - $root(4, lim_(m -> 0) b) <= x$ _need_ $integral_100^r y dif y$ - *group that* *last rational* $n_i^9$ $eta^(xi^(5))$ - _Assume even_
Typst Math OCR web app with handwritten equation. Below the OCR result mostly accurately captures the first two handwritten lines, omitting the third: S [y] = integral_a^b F(x, y, y') d x, y(a) = A, y(b) = B
screencap from joseph viviano claude LLM ytp
Minimal version (not overengineered)
my claude buddy, Dross, saying: "*chittering indignantly* Version roulette nobody wins this game anyway."