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@nmk.wtf

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mathematician, quant intern, reader of things https://nmk.wtf I give 10% of my income to GiveWell and so should you

From Money Stuff: We talk from time to time around here about my favorite postmodern comedians, the people who design user interfaces for Citigroup Inc.’s internal software. If you work at Citi, you come in every day and open up some proprietary software to do trades and send money and stuff, and that software has a bunch of fields you can fill in and buttons you can press to do things, and some of them are pranks. Like there was allegedly a field in some payments screen that “came pre-populated with 15 zeros, which the person inputting a transaction needed to delete”; if you forgot, apparently typing “$1” in that field would send the customer $1,000,000,000,000,000? Just hilarious Dada-esque comedy. Citi appears to do this stuff out of genuine commitment to the bit, but pitfalls like this exist at every bank. Every bank has software with fields that you can fill in and buttons you can press, and you are tired and distracted and you might, say, put the dollar amount of your trade in the “shares” field and sell 7,548 times as much stock as you intended to sell. That really could happen anywhere, though in fact it happened at Citi.
Wikipedia: Randomization A randomized variant of the BML traffic model, called BML-R, was studied in 2010.[9] Under periodic boundaries, instead of updating all cars of the same colour at once during each step, the randomized model performs L 2 {\displaystyle L^{2}} updates (where L {\displaystyle L} is the side length of the presumably square lattice): each time, a random cell is selected and, if it contains a car, it is moved to the next cell if possible. In this case, the intermediate state observed in the usual BML traffic model does not exist, due to the non-deterministic nature of the randomized model; instead the transition from the jammed phase to the free flowing phase is sharp.
In quantum mechanics, spin is an intrinsic property of all elementary particles. All known fermions, the particles that constitute ordinary matter, have a spin of ⁠ 1 / 2 ⁠.[1][2][3] The spin number describes how many symmetrical facets a particle has in one full rotation; a spin of ⁠ 1 / 2 ⁠ means that the particle must be rotated by two full turns (through 720°) before it has the same configuration as when it started.
A 2x3 grid of time-series line charts titled "R2 - Leave-one-predictor-out (train 2000-2019)," comparing actual versus predicted values from 2010 to 2026. Each panel shows a black solid "Actual" line and a blue dashed "Predicted" line, with a green-shaded training region before 2020 and an orange-shaded post-2020 test region split by a red dotted vertical line. The six panels show a full base model and five ablations, each dropping one predictor: full base set (R2=0.81), drop cpi_yoy (0.70), drop unemployment (0.49), drop gas_yoy (0.74), drop home_price_yoy (0.73), and drop mortgage_rate (0.80). In every panel the model tracks actual values closely during training but diverges after 2020, with predictions running persistently above the declining actual series. Post-2021 gap annotations range from about -13 to -29, largest when cpi_yoy or gas_yoy is dropped, indicating those predictors matter most for post-2020 fit.
i'm doing my part
Screenshot of a tweet by user "rain" (@__ghostfail) posted 18 hours ago, reading: "taking 35 tabs of acid and listening to this on loop every night until im claude". The tweet quote-retweets an Anthropic (@AnthropicAI) post from 22 hours earlier announcing that Claude's Constitution is now an audiobook read by two of its authors, Amanda Askell and Joe Carlsmith, including a Q&A on the writing process and the philosophies that shaped the document. The quoted post embeds a video (paused at 0:38) showing a title card that reads "Claude's Constitution" in orange, with the partial quote: "Models are learning about their past selves on the internet. It's kind of a fascinating and very hard situation for them to be in, actually," with "be in, actually," highlighted in black. The tweet has 12 replies, 40 reposts, 761 likes, and 34K views.
Jarred 7 hours ago | unvote | next [–] I work on Bun and this is my branch This whole thread is an overreaction. 302 comments about code that does not work. We haven’t committed to rewriting. There’s a very high chance all this code gets thrown out completely. I’m curious to see what a working version of this looks, what it feels like, how it performs and if/how hard it’d be to get it to pass Bun’s test suite and be maintainable. I’d like to be able to compare a viable Rust version and a Zig version side by side. reply
Screenshot of a tweet by Keller Jordan (@kellerjordan0) posted May 1. Text reads: "New modded-NanoGPT optimization benchmark result: @wen_kaiyue has improved upon both the Muon and AdamW baselines, by replacing their weight decay with hyperball optimization. The new record is 3325 steps." Below the text is a line chart titled "Modded-NanoGPT Optimization Benchmark as of 2026/04/30" plotting validation loss (y-axis, 3.2 to 4.0) against training step (x-axis, 0 to 6000) for four optimizers, with a dashed horizontal target line at 3.28. Best step counts to reach the target: Muon 3500 (orange), AdamW 5625 (blue), MuonH 3325 (green), AdamH 4875 (purple). MuonH reaches the target first; AdamH outperforms AdamW. Tweet has 7 replies, 48 reposts, 421 likes, 55K views.
In the annals of the American Revolutionary War, we often focus on the military tactics, political maneuvering, and battlefield heroics that secured independence. However, one of General George Washington’s most consequential decisions had nothing to do with troop formations or artillery placement. Instead, it involved a bold medical intervention that may have saved the revolution. In February 1777, Washington ordered the mandatory inoculation of Continental Army troops against smallpox, implementing what historians consider the first mass immunization policy in American history. This decisive action against an invisible enemy, a testament to Washington’s strategic foresight, demonstrated his leadership as a military leader and public health advocate.
CCS + Evolution Strategy Refinement (PGR=0.93). Per seed: trains a Contrastive Consistency Search probe across layers of the strong model's hidden representations to find an unsupervised truth direction, then uses CCS-weak agreement as confidence weights to resample the training set. After an SGD warmup pass on the resampled data, runs gradient-free Evolution Strategy optimization of LoRA parameters, using unsupervised swap-consistency as the fitness signal — perturbations are rewarded for producing predictions that are both confident and symmetric (p(A>B) ≈ 1 − p(B>A)). Aggregates 16 seeds via swap-consistent inference and trimmed-mean in logit space.