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Turning YouTube into a hard drive, translating DOOM from C to Rust without writing a line, running a European centre's weather model on a desktop card. None of these questions gets answered by reading: they have to be built. Every piece ends with something you can run or download.

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Article2026-09-26A model that does not write, it only decides: I had Intern-Decision predict the rain without training itIntern-Decision came out on 26 September 2026: 0.8B to 4B models that do not generate text and return one probability per question in a single forward pass. I installed it the same day, ran into three failures along the way, one of which made it between 2 and 3.2 times slower, and had it predict tomorrow's rain over 27,256 days in seven Chilean cities without training it. The 2B reaches an AUC of 0.823, almost the same as a Naive Bayes trained on 74,145 rows, and stays below logistic regression.#Models#Benchmarks#GPUTutorial2026-09-16Neural network explained: it matched boosting with 2,177 weights, and changing the seed moved its AUC by 0.004What a neural network is, what each layer does and when it fails, measured on daily weather from seven Chilean cities between 1984 and 2026. One hidden layer of 128 units, 2,177 weights and 81 KB, reached AUC 0.883 on the 2016-2026 test period: the same figure as a 2.1 MB boosting model, at 0.40 ms per row against 3.09. Training it cost five times more, and repeating the training with only the seed changed moved the validation AUC between 0.881 and 0.884.#Machine learning#Algorithms#PythonTutorial2026-09-16Transformer explained: it landed below three simpler models, and among the top candidates the grid ranked them backwardsWhat a Transformer is, what attention does and when it fails, measured on daily weather from seven Chilean cities between 1984 and 2026. The chosen network has 9,633 parameters and 49 KB, and landed below boosting, the forest and a one-layer network. The ranking is not the lesson: among the six architectures that competed, the validation order inverted on the test period.#Machine learning#Algorithms#PythonTutorial2026-09-15KNN explained: training took 0.007 s, predicting 5.5 s, and with k = 1 it memorized what it sawWhat k-nearest neighbors is, why you must scale, how to choose k and when it fails, measured on daily weather from seven Chilean cities between 1984 and 2026. Fitting on the 74,145 rows took 0.007 s and scoring the 2016-2026 test period 5.5 s on one thread; with k = 1 it scored AUC 1.000 on its own training days and 0.69 on validation. With k = 100 it reached 0.870, below a boosting model and above logistic regression.#Machine learning#Algorithms#PythonTutorial2026-09-15Naive Bayes explained: it trained in 0.008 s and weighs 1.3 KB, but 62 % of its probabilities landed at the edgesWhat Naive Bayes is, what the independence assumption means and when it fails, measured on daily weather from seven Chilean cities between 1984 and 2026. Fitting on 74,145 rows took 0.008 s and the model weighs 1.3 KB, at AUC 0.822; on binned variables it reached 0.844, with no conclusive difference from logistic regression. 62 % of its probabilities fell below 0.01 or above 0.99, and calibrating it took log loss from 1.09 to 0.39.#Machine learning#Algorithms#PythonTutorial2026-09-15SVM explained: pressure in Pa sank AUC to 0.555 and the exact kernel took 51 secondsWhat a support vector machine is, what the margin, C and gamma do, and when it fails, measured on daily weather from seven Chilean cities between 1984 and 2026. Unscaled, switching pressure from kPa to Pa dropped validation AUC from 0.835 to 0.555; on all 74,145 rows the RBF kernel took 51 s and, on the 2016-2026 test set, still ranked below a boosting model that trained in 2.6 s.#Machine learning#Algorithms#PythonTutorial2026-09-14Decision tree explained: 15 nodes you can read, 8,524 leaves that memorizeWhat a decision tree is, how it picks its questions and when it memorizes, measured on daily weather from seven Chilean cities between 1984 and 2026. With no depth limit it got everything it saw right and dropped to AUC 0.689 on new days; at depth 7, chosen on validation, it reached 0.862.#Machine learning#Algorithms#PythonTutorial2026-09-14Gradient boosting explained: 594 chained trees that train 9 times faster than a forestWhat gradient boosting is, how each tree corrects the previous ones and when it fails, measured on daily weather from seven Chilean cities between 1984 and 2026. With rate 1.0 it destabilized and after 3,000 rounds ended at AUC 0.681; with rate 0.03 and 594 rounds chosen on validation it reached 0.883, with no conclusive difference from a 200-tree forest.#Machine learning#Algorithms#PythonTutorial2026-09-14K-Means explained: I asked Chile's weather for four kinds of day and it found SantiagoWhat K-Means is, how Lloyd's algorithm works and when it misleads, measured on daily weather from seven Chilean cities between 1984 and 2026. Unscaled, humidity accounts for 76% of the separation; with k = 8, only 18% of random starts end within 0.1% of the best inertia found; and no criterion agrees on how many groups there are.#Machine learning#Algorithms#PythonTutorial2026-09-14Random Forest explained: how a forest of trees votes, measured on 42 years of Chilean weatherWhat Random Forest is, how bootstrap sampling and feature subsampling work, and why its trees train in parallel instead of in a chain. I measured it predicting rain in seven Chilean cities: 7.5 times faster on 8 CPUs, a plateau past 100 trees, a noise column that the default importance ranks above real features, and a ceiling when extrapolating temperatures.#Machine learning#Algorithms#PythonTutorial2026-09-14Linear regression explained: the line that measured the heat in Chile, and what breaks itWhat linear regression is, how its coefficients are computed and when it stops working, measured on daily data from seven Chilean cities. The max temperature rises 0.33 °C per decade in Temuco and falls in Valparaíso, unscaled gradient descent does not reach a useful solution, 5% of rows with a misplaced decimal point multiply the error by 4.7, and the line trains about 1,100 times faster than a random forest.#Machine learning#Algorithms#PythonTutorial2026-09-14Logistic regression explained: 16 numbers to decide whether it rains tomorrowWhat logistic regression is, how it turns a sum into a probability and when it misleads, measured on daily weather from seven Chilean cities between 1984 and 2026. A straight line gives negative probabilities on 9% of days; at a 0.5 threshold logistic regression catches only half of the rain; and it promises more rain than falls.#Machine learning#Algorithms#PythonArticle2026-09-10I measured Pingora 0.9.0 six times slower than nginx. The culprit was one line of my codeI wrote a minimal reverse proxy with Pingora 0.9.0 and in a 2 vCPU container it measured 21 thousand requests per second against nginx's 126 thousand: six times slower. The culprit was not Pingora but a blocking getaddrinfo my upstream_peer ran on every request. Fixed, the real gap is 1.70x, and perf quantifies it: 1.70x in cycles per request, 119.4 thousand against 70.4 thousand.#Rust#Benchmarks#NetworkingArticle2026-09-09Hard rules against Random Cut Forest on the same stream: the model adds 13 points of recall and costs 7 times the throughputI ran Apache Flink with Kafka and measured three deterministic rules against Amazon's Random Cut Forest over exactly the same payment stream, on a four-core box with no GPU. The rules reach 0.814 recall at 173 thousand events per second; the model reaches 0.943 but drops the pipeline to 23 thousand; together they hit 0.982.#Streaming#Benchmarks#DataArticle2026-08-23The seven GEO rules: I reproduced the only test that exists, then audited my own 72 pagesA Russian study tested the seven rules the GEO industry keeps repeating against 73 pages that six engines had cited, and not one survived. I rebuilt its counts and reproduced all seven p-values exactly. Then I took the detector to my own 72 pages, and there something appeared that none of the rules looks at: four different pages declaring the same entity.#SEO#GEO#StatisticsArticle2026-08-21I translated DOOM from C to Rust without writing a line, then used the model for what it is actually good atI needed to move a program from C to Rust and my first idea was to ask a model. A tool already existed that does it in 23 seconds. The translation matched the original bit for bit across the 11,113 frames compared, except one that traces back to an uninitialised-memory bug already present in 1993's DOOM, and the model ended up doing something else entirely: textures, per-pixel relief and continuous lighting on top of the original engine.#Rust#C#Code translationArticle2026-08-17TabPFN and TabICL against tuned XGBoost: the model that does not train won on fourteen tables out of fourteenThe claim behind TabPFN and TabICL is that they predict on a table without ever training on it and still beat tuned boosting. I measured it on fourteen datasets from the Grinsztajn benchmark, with the same split and the same clock for everyone. The one that does not train wins, the advantage holds up to 32,000 rows instead of breaking, and the most-cited model can no longer be downloaded without an account.#Models#Benchmarks#GPUTutorial2026-08-16I ran AIFS 2.0, ECMWF's weather model, on my desktop card and landed 0.45 °C from the official forecastECMWF published the weights of its AI forecast model. It weighs under a gigabyte and runs on a desktop card: 48 hours of forecast in 108 seconds and 5.77 GB of memory, or 33 minutes if you have no GPU. I installed it step by step, compared it against the same centre's operational physical model, and wrote down the two stumbles that nearly made me publish nonsense.#Models#GPU#WeatherArticle2026-08-15PhantomRelay: why your automation gives itself away before the first HTTP byteA Node HTTP client is distinguishable from real Chrome in the TLS handshake, before sending a single header. How I built a browser relay with a Rust addon over BoringSSL, cost-based escalation and nearly 900 tests.#Systems#NetworksTutorial2026-08-14Agatha: how I turned YouTube into a hard drive with error correctionAgatha encodes any binary file into 4K video frames, uploads them to YouTube and recovers them with 100% fidelity using BCH error correction. How it works, what it needs, and real examples verified by SHA256.#Systems#Research

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