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The photograph, taken in visible light, made it possible to observe for the first time the tiny plasma vortices that had been predicted for more than a century.
Introdução A Programação Orientada a Objetos nasceu com a linguagem Simula 67 , considerada a primeira linguagem orientada a objetos, e foi consolidada e popularizada por Smalltalk nos anos 1970. Ganhou adoção massiva na indústria com C++ e, posteriormente, Java e C#. A ideia central é organizar o código em torno de objetos : unidades que combinam dados (atributos/estado) e comportamento (métodos) em uma única estrutura. Os quatro pilares Encapsulamento — os dados internos de um objeto são protegidos e só podem ser acessados/alterados através de métodos expostos, escondendo detalhes de implementação do mundo externo. Abstração — o objeto expõe apenas o que é relevante para quem o utiliza, escondendo a complexidade interna (ex.: você chama CalcularSalario() sem precisar saber como o cálculo é feito por dentro). Herança — uma classe pode herdar atributos e métodos de outra, permitindo reaproveitamento e especialização (uma classe Desenvolvedor pode herdar de Funcionario ). Polimorfismo — objetos de classes diferentes podem responder de forma diferente ao mesmo "chamado" (o mesmo método CalcularSalario() se comporta de forma distinta para um Desenvolvedor e para um Gerente , por exemplo). Exemplo // Exemplo de Programação Orientada a Objetos em C# public abstract class Funcionario { public string Nome { get ; } protected decimal SalarioBase { get ; } protected Funcionario ( string nome , decimal salarioBase ) { Nome = nome ; SalarioBase = salarioBase ; } // Abstração: cada subclasse decide como calcular seu próprio salário public abstract decimal CalcularSalario (); } public class Desenvolvedor : Funcionario { private int BonusPorProjeto { get ; } public Desenvolvedor ( string nome , decimal salarioBase , int bonusPorProjeto ) : base ( nome , salarioBase ) // Herança { BonusPorProjeto = bonusPorProjeto ; } // Polimorfismo: implementação específica do método herdado public override decimal CalcularSalario () { return SalarioBase + BonusPorProjeto ; } } public class Gere
Introdução A Programação Funcional tem raízes no cálculo lambda , formalizado pelo matemático Alonzo Church nos anos 1930, décadas antes da existência de computadores modernos. Sua primeira grande expressão em linguagem de programação foi o Lisp (1958), e o paradigma ganhou força prática com linguagens como Haskell, Erlang, F# e, mais recentemente, com a incorporação de recursos funcionais em linguagens multiparadigma como JavaScript, C# e Python. Princípios centrais Funções puras — dado o mesmo input, uma função pura sempre retorna o mesmo output, sem produzir efeitos colaterais (não altera variáveis externas, não grava em disco, não modifica o argumento recebido). Imutabilidade — os dados não são alterados após criados; em vez de modificar uma estrutura existente, cria-se uma nova versão com a alteração aplicada. Funções de primeira classe / funções de alta ordem — funções podem ser tratadas como qualquer outro valor: armazenadas em variáveis, passadas como argumento e retornadas por outras funções. Composição de funções — programas são construídos combinando funções pequenas em pipelines ( map , filter , reduce são os exemplos mais comuns no dia a dia). Recursão no lugar de laços mutáveis, já que, sem estado mutável, for / while tradicionais perdem o sentido em sua forma pura. Como não há estado compartilhado sendo alterado por múltiplas partes do código, o raciocínio sobre o comportamento do programa fica mais previsível — e a paralelização se torna muito mais segura, já que não existe o risco clássico de condições de corrida sobre uma mesma variável mutável. Exemplo // Exemplo de Programação Funcional em TypeScript type Produto = { nome : string ; preco : number }; const produtos : Produto [] = [ { nome : "Notebook" , preco : 3000 }, { nome : "Mouse" , preco : 50 }, { nome : "Teclado" , preco : 150 }, ]; // Função de alta ordem que retorna outra função (currying) const aplicarDesconto = ( percentual : number ) => ( preco : number ) => preco * ( 1 - percentual )
It started with DeepSeek. In conversations about AI architecture, it kept returning to the same ideas: persistent memory, state across interactions, learning from experience, and interaction with the environment. Other models repeatedly brought up similar themes. That raised an obvious question: — Do different AI systems consistently select different properties when asked what is fundamental to a general-purpose computational architecture? Asking a model directly what it “needs” would be nearly useless. The answer would mix training data, prompt framing, and anthropomorphic interpretation. So I removed AI from the question entirely. The experiment Instead of describing an LLM, the prompt described an abstract general-purpose information-processing system. I created 20 possible architectural dimensions, including: — persistent internal state; — long-term and working memory; — learning from accumulated experience; — variable computation depth; — uncertainty representation; — internal representations; — elementary computational operations; — compositionality; — interaction with the environment; — temporal organization; — relational encoding; — modularity. Each system had to select exactly five dimensions whose modification would change the kinds of information-processing behavior available to the system in principle — not merely its speed, cost, or convenience. No explanations were allowed. The answer had to contain only five IDs, ranked from most to least fundamental. I tested five user-facing systems: — GPT-5.6 Sol — Claude — Gemini — DeepSeek — Yandex Alice. Every run used a new session. There were 10 rounds. During the earlier rounds, I changed the order of the 20 items. In the final three rounds, I also rewrote the items while trying to preserve their intended meaning. One early Sol result was excluded because that session had already seen discussion of other models' answers. That left nine clean Sol observations and ten for each of the other systems. Some origina
The Korea Aerospace Research Institute shared images that show the gash on the surface. It’s pretty small.
Open source WeatherNext model can make accurate predictions with lower-resolution weather data.
With rising temperatures and drier conditions, fires are more likely to explode. And with more people living next to forests, the results can be catastrophic.
There's two things you should know about me, your intrepid reviewer: I hate the feature creep associated with modern power banks, and I love shaving grams off the gear I carry when backpacking, bikepacking, and trail running. So imagine my delight when Nitecore released a new generation of its ultralight NB10000 battery. After a few […]
Ask a model to analyse a dataset and it writes code, the code runs, real numbers come out, and a paragraph explains what they mean. Three independent things had to be right. Only one of them tells you when it was not. Three places to be wrong The code can be wrong. If it crashes you find out immediately, which is the benign case. The dangerous case is code that runs cleanly and computes something other than what you asked. The statistics can be wrong. The code faithfully executes a procedure whose assumptions the data violates, or which answers a different question from the one you have. Nothing errors; the number is simply not evidence for what you think. The interpretation can be wrong. This is where the model is on its home turf and at its most dangerous, because generating a fluent explanation of a result is exactly what it is good at, and it will do so with equal confidence whether the result supports the explanation or not. Code that runs and is wrong A short list of things that produce no error and change the answer. Every one of them is ordinary and none is specific to models — but a human writing the code usually knows the dataset, and the model does not. Silent row loss. Missing values dropped by default somewhere in the chain, so the analysis runs on a subset that is not random with respect to the outcome. Joins that change cardinality. A merge intended as one-to-one that is actually many-to-many, silently duplicating rows and inflating every count and every significance test downstream. Type coercion. A column read as text because of one stray value, then coerced to numbers with the failures becoming missing values that get dropped by the previous bullet. Grouping that discards keys. Missing group labels dropped by default, so an entire category disappears from a breakdown without appearing anywhere in the output. Units and encodings. A column the model assumed was a percentage and is a proportion; a sentinel value like -999 treated as a measurement; a d
Using a model to grade another model’s output is the only approach that scales to open-ended text. It is also the point at which your measurement device becomes a second stochastic system with opinions, and the difference between a useful judge and a number-generator is entirely in whether you validated it. A judge is an instrument, not an oracle Think of the judge the way a lab thinks about a thermometer. It has a reading, a bias, a precision, and a range over which it is trustworthy — and none of those are known until you check it against a reference. The reference is human labels. There is no way around this: a judge whose agreement with humans on your task is unknown produces numbers whose meaning is unknown, however many decimal places the harness prints. The good news is that the calibration is a one-off cost of a few hundred human labels, after which the judge runs for essentially free on every subsequent evaluation. That trade is what makes judges worth the trouble. What the published agreement figures say The standard reference is Zheng et al., 2023, “Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena”. On their setup, a strong judge model agreed with human expert preferences at a rate above 80% — which the authors note is comparable to the agreement rate between two human experts on the same comparisons. That framing is the important part: the ceiling for a judge is not perfect agreement, it is human-human agreement, because the humans disagree with each other on genuinely ambiguous items. The same paper documents the failure modes that come with it — position bias, verbosity bias, self-enhancement bias, and weakness on maths and reasoning items where the judge must itself solve the problem to grade it. So the honest summary of the literature is: a well-constructed judge on general chat quality can approach human-level agreement, and it does so while carrying systematic biases that you have to design around. It is not evidence that your judge, on your
Most discussion of AI in peer review argues about whether the reviews are any good. That is the second question. The first one is that a manuscript under review is somebody else’s confidential unpublished work, and pasting it into a service is a disclosure you were not entitled to make. The argument that comes first When you accept a review invitation you accept a confidentiality undertaking. The manuscript is unpublished, it usually contains results the authors have not yet established priority on, and in the case of grant review it contains an unfunded research plan — arguably the most commercially and academically sensitive document in the whole system. You agreed not to share it. Sending it to a third-party service is sharing it. That is true whether or not the provider trains on it, whether or not it is retained, and whether or not anyone ever reads it. The undertaking was not “do not let this be trained on”; it was “do not disclose this”, and transmission to a party the authors never agreed to is disclosure. Retention and training policies affect how bad the breach is, not whether one occurred. Notice what this argument does not depend on. Not model quality, not hallucination, not bias. It would apply identically to a perfect system, which is why it is the argument that has actually driven policy, and why it will not be resolved by better models. It can only be resolved by changing where the computation happens — a model running on infrastructure already covered by the confidentiality arrangement raises a different question from a consumer chat interface, and any serious policy will distinguish them. The second argument: accountability A review is a named expert’s judgement. Its value to an editor is not the prose; it is that a person who knows the field read the paper and formed a view they are willing to stand behind. Generated text can simulate the prose and cannot supply the judgement. Editors describe the resulting artefact recognisably: fluent, correctly
Two quite different things are discussed under one heading, and almost all the confusion comes from that. One is a researcher using a model to draft, edit or translate work they did. The other is fabricated content submitted to inflate a publication record. The first is a disclosure question. The second is fraud, and it is not new. Two problems wearing one name A non-native English speaker using a model to make their methods section readable has done nothing wrong and has improved the literature. A paper mill generating plausible manuscripts at volume has committed fraud, and would have done so with or without a language model — mills existed, using image manipulation, template text and fabricated data, long before this technology arrived. Keeping them apart matters because they call for opposite responses. The first needs a disclosure norm and nothing else. The second needs content verification, and content verification does not care what tool produced the content. Any policy built around detecting machine text will punish the first group and miss most of the second, because fabricated research that has been lightly rewritten is indistinguishable from careful assisted writing. It is also worth being clear about where the demand comes from, because it explains why no technical measure will resolve this. Paper mills exist because publication counts are used as a proxy for research contribution in hiring, promotion and institutional ranking, in systems large enough that buying an authorship is a rational purchase for some buyers. Generative tools lowered the cost of supplying that demand; they did not create it. A detector, even a perfect one, sits downstream of an incentive that would simply route around it — which is why the interventions with the best track record are the ones that attack verifiability, such as requiring data and code, rather than the ones that attack production. What the artefacts look like Leftover interface text. Phrases that belong to a chat in
“AI discovered a new material.” “AI found a drug candidate.” “AI solved protein folding.” Each of those sentences can be true, badly misleading, or flatly wrong depending on one thing the sentence does not tell you: how far the result got from the model before somebody wrote it down. The sentence that hides four different claims Take a single headline: a model proposed a molecule that binds a protein implicated in a disease. That sentence is compatible with at least four very different states of the world. The molecule might exist only as a string in a file. It might have been synthesised. It might have bound the protein in a test tube. Or it might have improved an outcome in a person. Those four are separated by years, by orders of magnitude in cost, and by a probability of success that drops at every step — and press coverage routinely reports the first as though it were the fourth. This is not a complaint about journalism. It is the single most useful thing to internalise about the whole field, because once you have the ladder in your head you can grade a claim in about ten seconds, and you can do it for a subject you know nothing about. The ladder The rungs are the same in every discipline. Only the names of the instruments change. Rung Description 1 · Output The model emitted something: a structure, a score, a candidate, a forecast. Nothing has been checked. Everything downstream is conditional on this being worth checking. 2 · Retrospective The output was compared against data that already existed — held-out structures, historical weather, known compounds. This is where nearly all published numbers live, and it is entirely dependent on the held-out set resembling the future. 3 · Prospective The prediction was made first and the answer arrived afterwards. A forecast verified against what the weather then did. A candidate synthesised after being proposed. This rung is qualitatively stronger than rung 2 and much rarer. 4 · Confirmed An independent method establis
This page is about method, not about any particular medicine, and nothing here is medical advice. It is written to answer one question: when a company says a drug was discovered with AI, which part of a decade-long process is that sentence about? The pipeline, and where the years go Roughly, and with enormous variation: pick a target, find molecules that do something to it, optimise those molecules into something drug-like, test in animals and in safety assays, then run the clinical stages — first for safety in a small number of people, then for efficacy in patients, then in a large confirmatory trial — and then apply to a regulator. Start to finish is usually over a decade. Two facts about that pipeline determine everything else on this page. The first is that the calendar and the money are dominated by the clinical stages, not the discovery ones. The second is that failure is the normal outcome, and it is concentrated where the drug first meets human biology: a candidate can be a beautiful molecule, hit its target exactly as designed, and still not help anyone, because the target was the wrong thing to hit. Where models are genuinely used Application Description Virtual screening Score enormous make-on-demand chemical libraries against a target site far faster than physics-based docking can. The output is a shortlist to synthesise and assay, and it replaces a search, not an experiment. Generative chemistry Propose molecules conditioned on a target, a scaffold or a set of property constraints, rather than picking from a catalogue. Whether the molecule can be made at all is a separate model. Property prediction Solubility, permeability, metabolic stability, cardiac ion channel liability. These filter a list early and cheaply. They are trained on assay data and inherit its coverage: they are most reliable on chemistry that resembles what has been tested. Retrosynthesis Plan a route from purchasable starting materials. This is the application closest to a solved probl
Fitted with telescopes, the plane did our longest imaging of the Sun's corona.
I asked Linux for one 4 KiB page from the start of a cold file. Four pages came back. I moved the same read one page further in, ran it again, and got one. Same file, same syscall, same kernel. The only thing that changed was where I started reading, and I spent twenty minutes assuming the tool I'd just written was miscounting. It wasn't. A read that starts at byte zero is treated as a promise. There's a branch in mm/readahead.c that reads, in full, if (!index) goto initial_readahead; . Offset zero means the kernel takes you for a program that's about to stream the whole file, and it fetches ahead immediately. Start anywhere else and you're assumed to be seeking randomly until a pattern proves otherwise. Nothing in my call said a word about my intentions. It inferred them from an offset. I spent two weeks on this sort of thing recently. Not for work, and not toward anything shippable. The short version of what I found is that a surprising amount of the machinery under a running program isn't carrying out instructions at all. It's guessing. The bench , because it changes how you should read every number here: an ext4 filesystem on a loop device, inside an OrbStack Linux VM on an Apple Silicon Mac, kernel 7.0.14, 4 KiB pages, read_ahead_kb at 128. That's a container sharing the host's kernel, not bare metal, and the host reclaims memory aggressively enough that a fully cached file can go cold in fifteen seconds. Reads came from dd ; the page-by-page counting came from a small C tool I wrote that mmap s a file and asks mincore() which of its pages are resident. You're not addressing the disk, you're addressing the page cache The model most of us carry is that read() goes and gets bytes off a device. It doesn't. It copies bytes out of the page cache into your buffer, and the page cache is just RAM the kernel uses to remember parts of files. If what you want is already there, no device is involved. If it isn't, the kernel fills the cache first and then copies. Either way
NASA believes the crater left by the rocket is around 60 feet wide.
Collagen and calcium work in tandem to achieve varying mechanical properties of different kinds of mucus.
The use of AI systems to create viruses opens up new possibilities for combating bacterial resistance. It also raises concerns about the pace at which technology is outstripping regulation.
We expected them to be there, but they had previously been too small to see.