Blog do Julio Henrique
Content for both STEM and Humanities!
Use the texts, as long as you talk about me (good or bad)... and if it's for commercial purposes: I want my share!
quinta-feira, 17 de setembro de 2026
Bolsonaro, o Feministo de Esquerda - Lula, o Sapo Barbudo de Direita
quarta-feira, 26 de agosto de 2026
35 Anos de Linux
Em 25 de agosto de 2026 o Linux fez 35 anos de história!
O Linux completou 35 anos, provando que um projeto feito "só por hobby e que não seria grande" pode sim dominar o mundo (beijo, Linus Torvalds!).
Para comemorar, aqui está a radiografia definitiva dessa festa!
🥳 A Festa de Aniversário Geek

O bolo: Não tem sabor padrão; você precisa compilar os ingredientes e escolher sua própria interface de cobertura.
Os convidados: 99% dos supercomputadores do mundo, os servidores da NASA, bilhões de celulares Android e três gatos pingados usando o sistema no desktop.
O parabéns: Cantado em código binário com sotaque finlandês.
A treta: Uma discussão sem fim na mesa do bolo sobre qual distribuição é a melhor e por que o Windows é o verdadeiro "vilão" da história.
Mensagem original do Linus Torvalds na USENET em 25 de agosto de 1991:
Hello everybody out there using minix -
I'm doing a (free) operating system (just a hobby, won't be big and professional like gnu) for 386(486) AT clones. This has been brewing since april, and is starting to get ready. I'd like any feedback on things people like/dislike in minix, as my OS resembles it somewhat (same physical layout of the file-system (due to practical reasons) among other things).
I've currently ported bash(1.08) and gcc(1.40), and things seem to work. This implies that I'll get something practical within a few months, and I'd like to know what features most people would want. Any suggestions are welcome, but I won't promise I'll implement them :-)
Linus (torv...@kruuna.helsinki.fi)
PS. Yes - it's free of any minix code, and it has a multi-threaded fs. It is NOT protable (uses 386 task switching etc), and it probably never will support anything other than AT-harddisks, as that's all I have :-(.
terça-feira, 25 de agosto de 2026
Ética e Moral - Nietzsche está Morto
A partir do parágrafo 56, Mário começa a discorrer sobre o assunto - e não precisa se preocupar, não terá de contar os parágrafos, pois o livro foi escrito enumerado em parágrafos. Leia o livro todo, porém, caso quiser, vá direto a esse parágrafo 56.
No parágrafo 59, Mário, em sua clareza costumeira, começa definindo etimologicamente: "...em grego, valor é axios e o homem é antrophos, daí chamarem a esses valores axioantropológicos".
No parágrafo 60, Mário diz: "Os atos continuados constituem o costume (o que os gregos chamavam ethos e os latinos mos, moris, de onde vêm Ética e Moral)."
A etimologia das palavras às vezes ajuda às vezes atrapalha; na maioria das vezes ajuda.
Os atos éticos ou morais são atos que têm valor, porém, "valor" neste sentido refere-se à dignidade, não ao preço. Refere-se à categoria de Qualidade e não de Quantidade (Aristóteles), pois qualidade, nesta acepção, remete a "valor" e quantidade, nesta acepção, remete a preço.
"Assim, toda vida prática do homem gira em torno da Ética."
A partir do parágrafo 61, Mário começa a fazer a distinção entre Moral e Ética e diz que essa distinção não é arbitrária porque a Ética estuda o dever-ser humano e a Moral descreve e prescreve como se deve agir para realizar esse dever-ser.
"A Moral é variante, mas a Ética é invariante."
"Aqueles que dizem que a Ética é vária, porque a Moral é vária, confundiram a Moral com a Ética." Mário chama esses de "os detratores da Ética e da Moral" e diz que alguns fazem isso de propósito para obterem vantagens antiéticas, imorais e amorais.
Mário nos diz, com base na realidade, no apodítico e na auto-evidência que a Ética deve ser consagrada ao universal.
Por exemplo: matar é errado!... e ponto final. Todo ser humano sabe disso porque nasce sabendo disso, é um Princípio.
Porém, caso eu matar em legítima defesa, é errado?
Isso é uma discussão Moral.
Dando um exemplo mais claro ainda: todo juri em que uma pessoa que matou alguém (homicídio) e é julgada pelas leis humanas, é uma discussão Moral, pois a Ética já foi infringida.
Porém, por a Ética já ter sido infringida não significa que ela não é invariante, pois o termo "invariante" refere-se às leis divinas ou leis naturais, depende se você acredita ou não em Deus, mas tais leis são as mesmas: todo mundo sabe que matar é errado e mesmo tendo infringido o "não matar", o princípio continua sendo errado, mesmo que todo mundo faça.
Daí vem o Direito Penal, Direito, Civil, Direito Natural, etc.
E isso se aplica a não mentir, não roubar, não trair, etc.
No Brasil, essa confusão é evidente e proposital feita pelos detratores da Ética e da Moral, como bem disse Mário.
Confunde-se Ética com Moral, troca-se uma pela outra, não se sabe do que se está falando... vira uma confusão mental.
Temos em empresas privadas e públicas, por exemplo, os tais "códigos de ética" (missão, valores, etc) que, na verdade, são códigos de moral porque permitem discussões; e não é uma simples questão de nominação porque a Ética é invariante (não enseja discussões) e a Moral é variante (enseja discussões).
A Moral é a discussão em torno da Ética.
Matar, mentir, roubar, trair, corromper, etc, todo ser humano sabe que isso é errado.
As justificativas e desculpas que você dá do porquê você matou, traiu, mentiu, etc, são discussões Morais.
E é necessário saber essa distinção, senão você não saberá distinguir entre uma e outra.
https://avidaintelectual.blogspot.com/search/label/Friedrich%20Nietzsche
quinta-feira, 20 de agosto de 2026
Linux Kernel Version 7.2 Makes TSC Mandatory, Generating a Change in Philosophy
The Linux Kernel built its reputation, in part, on its efficiency on older machines that do not support modern operating systems like Windows 11. However, maintaining such extreme backward compatibility comes at a significant technical cost. The recent decision to mandate the TSC (Time Stamp Counter) on x86 processors demonstrates that maintainers have prioritized engineering evolution over the past. In practice, this means Linux has set aside nostalgia to focus on the technological performance of the future.
The latest version of the Linux Kernel (7.2, released on August 16, 2026) made the TSC a mandatory requirement for x86 processors, putting an end to decades of compatibility code that allowed the operating system to run on hardware lacking reliable TSC support. The technical change, implemented under the title "x86/cpu: Make CONFIG_X86_TSC unconditional," removes the configuration logic that previously allowed the kernel to be compiled without TSC support.
This change directly affects home users with very old machines (dating from before approximately 1993) because, starting with Linux Kernel 7.2, if you attempt to load the system on an x86 processor that lacks the TSC register, the kernel will refuse to boot, and the system will not start. This signals a significant shift in the Linux kernel's development philosophy, yet it is not the Linux equivalent of Windows 11's TPM 2.0 requirement. The TSC has been present in x86 processors since the launch of the Intel Pentium in 1993. If your processor was manufactured within the last three decades, it almost certainly includes it. The change actually aims to permanently remove compatibility code for hardware that is unlikely to run a modern Linux kernel. However, modern processors from the 1990s or later should not exhibit timing issues related to a lack of TSC support, although the kernel might refuse to boot on very old x86 CPUs that lack the TSC register.
Windows, macOS, and FreeBSD have long relied on the TSC, but their approach differs from how Linux handled it in kernel version 7.2. Windows, for instance, does not prevent the computer from booting if the processor lacks a TSC; instead, it makes that determination at runtime. If the TSC on a very old processor fails or proves unstable, Windows automatically switches to motherboard-based timers (such as HPET or the ACPI PM Timer). Of course, attempting to install Windows 11 on an older machine results in the installation failing outright, but this is due to other factors—such as TPM 2.0, Secure Boot, and processor generation (Microsoft limits official support to 8th-generation Intel processors—released in 2017—or AMD Ryzen 2000 series and newer).
What is TSC?
The TSC is a 64-bit internal physical register found within each x86 CPU core (each core has its own TSC register). It provides a basic clock-cycle counting feature, offering the precision essential for smooth operating system operation; it delivers high-resolution timing and can be read much faster than platform alternatives like HPET or the ACPI PM timer. Reading a TSC-based timer might take tens to hundreds of CPU cycles, whereas accessing a motherboard-based timer can take approximately 0.8 to 1.0 microseconds.
Having an individual TSC for each core caused issues: if a core changed its clock speed to save power, its TSC would count more slowly than that of a neighboring core (leading to time desynchronization in Linux). This is why modern CPUs feature "Constant TSC," which ensures all these internal registers run at the same speed regardless of power usage.
The TSC has three basic operational characteristics:
- Cycle Counter: Its sole function is counting. For every clock cycle the processor executes, the register increments by 1.
- Initialization: It starts at 0 the moment the processor receives power (hardware reset) and increments continuously.
- Direct Reading: To read the register's value, programmers and the kernel use a specific assembly instruction called RDTSC (Read Time-Stamp Counter).
Historically, Linux maintained code to handle processors lacking a reliable TSC, dating back to the i486 era. The Linux 7.0 kernel dropped support for Intel 486 processors, and subsequent development cycles removed additional legacy hardware that had prevented the TSC from being treated as universal. Essentially, hardware predating the Pentium or from the i486 era will be affected. For decades, Linux maintained complex "fallback" routines to support timers based on older motherboards. Eliminating this dead code simplifies kernel maintenance. This is a case of technical debt—specifically, unnecessary maintenance consuming hours of highly skilled labor to support a statistically insignificant segment of users. Unlike Microsoft's controversial hardware requirements (such as TPM 2.0 for Windows 11), the TSC requirement for Linux 7.2 is simply a matter of engineering housekeeping regarding "museum pieces."
Checking TSC support and stability
To check if your computer supports TSC, run:
$ cat /sys/devices/system/clocksource/clocksource0/current_clocksource
The output should be `tsc`.
Verify this by checking the file of available sources:
$ cat /sys/devices/system/clocksource/clocksource0/available_clocksource
The output should look something like this: tsc hpet acpi_pm
If "tsc" is missing from this list, it means the operating system has completely lost access to it.
For the TSC to be considered modern and perfectly stable, the CPU must possess two physical properties:
- Constant TSC: The counter advances at a constant rate, even if the CPU changes frequency (turbo mode or power-saving mode).
- Nonstop TSC: The counter does not stop running, even if the CPU core enters a deep sleep state (C-states).
You can check if your CPU has these features using the following command:
$ lscpu | grep -E "constant_tsc|nonstop_tsc"
Options: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb pti tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm arat pln pts hwp hwp_notify hwp_act_window hwp_epp vnmi
Or by directly inspecting the processor flags:
$ cat /proc/cpuinfo | grep -E "constant_tsc|nonstop_tsc" | uniq
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb pti tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm arat pln pts hwp hwp_notify hwp_act_window hwp_epp vnmi
You can check if the kernel encountered any synchronization issues between CPU cores during boot:
$ sudo dmesg | grep -i tsc
[ 0.000000] tsc: Detected 3399.906 MHz TSC
[ 0.036386] [Firmware Bug]: TSC_DEADLINE disabled due to Errata; please update microcode to version: 0x52 (or later)
[ 0.120153] clocksource: tsc-early: mask: 0xffffffffffffffff max_cycles: 0x3101f59f5e6, max_idle_ns: 440795259996 ns
[ 2.251217] clocksource: Switched to clocksource tsc-early
[ 3.281461] tsc: Refined TSC clocksource calibration: 3407.999 MHz
[ 3.281465] clocksource: tsc: mask: 0xffffffffffffffff max_cycles: 0x311fd336761, max_idle_ns: 440795243819 ns
[ 3.281476] clocksource: Switched to clocksource tsc
[ 4.246774] vboxdrv: TSC mode is Invariant, tentative frequency 3407999019 Hz
- clocksource: Switched to clocksource tsc: Your Linux kernel has tested, validated, and enabled the TSC as the system's primary time source. It is operational.
- Refined TSC clocksource calibration: 3407.999 MHz: The kernel has calibrated your TSC frequency with high precision to approximately 3.4 GHz.
- vboxdrv: TSC mode is Invariant: The VirtualBox driver has confirmed that your TSC is of the "Invariant" type—equivalent to the "Constant/Nonstop" type mentioned earlier. This means its speed does not change, making it safe for running virtual machines.
sexta-feira, 24 de julho de 2026
ARTIFICIAL INTELLIGENCE
Artificial Intelligence and Its Evolution
The first substantial work in the field of artificial intelligence took place in the mid-20th century through a computing concept that would come to be known as the Turing Machine, named after the English mathematician Alan Mathison Turing (1912–1954), who is considered the "father of computing."
Can computers think?
The mathematician turned this question into an intriguing hypothesis for investigation in the early 1950s, becoming one of the first scientists in the world to question such a possibility.
According to the *Encyclopædia Britannica*, in 1935, the researcher developed a computing model consisting of a memory unit and a scanner tasked with identifying and reading a series of symbols distributed along a tape that moved back and forth. The machine operated by processing this series of symbols to interpret them and modify its own algorithm based on instructions stored in its memory.
CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a security test used on websites to distinguish real people from automated programs (bots). Turing envisioned that the computational intelligence of the future would be a machine capable of learning from experience. He argued that the path to achieving this was to allow an intelligent machine to alter the very instructions provided by its mechanism. This principle became a cornerstone of modern computing theory.
Eliza and the First Chatbots
In 1966, an MIT professor named Joseph Weizenbaum created the first chatbot. He configured it to play the role of a psychotherapist. The user would type a message on an electric typewriter connected to a mainframe computer. After a few moments, the "psychotherapist" would reply. The software was relatively simple; it analyzed the user's input and applied a set of rules to generate a plausible response. He named the program Eliza, after Eliza Doolittle from the play *Pygmalion*. The London flower girl in George Bernard Shaw’s play uses language to create an illusion: she elevates her speech to such a degree that she manages to pass herself off as a duchess. Similarly, Eliza conversed in a way that created the illusion that it understood the person at the computer.
What if it were possible to converse with a computer in a natural language, like English?
That was the question that drove the creation of Eliza; its success made Weizenbaum well-known at the university and helped him secure tenure in 1967. It also brought him into the orbit of the MIT Artificial Intelligence Project, which had been established in 1958 by John McCarthy and Marvin Minsky.
McCarthy had coined the term "Artificial Intelligence" a few years earlier when he needed a title for an academic workshop. The term was neutral enough to avoid overlap with existing research fields like cybernetics, amorphous enough to attract interdisciplinary contributions, and bold enough to convey the radical nature—or, if you prefer, the arrogance—of the claims regarding what machines could do. This radical stance was asserted in the workshop's original proposal: "Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it," the text stated. Weizenbaum's most famous book is *Computer Power and Human Reason: From Judgment to Calculation*, originally published in 1976. In it, he argues that although AI can simulate human conversation, computers should never make important decisions—such as those in the legal, medical, or military fields—because they lack human compassion and wisdom.
Many authors contributed to the development of AI; however, to keep this text from becoming too long, I will cite only the two most important ones—especially since this piece does not focus exclusively on history.
Training, LLMs, and Deep Learning
Artificial intelligence (AI)—or "machine learning"—models have been evolving for some time. The key to all machine learning is a process called "training," in which a computer program is given a vast amount of data—sometimes with labels explaining what the data represents—along with a set of instructions.
An instruction might be something like "find all images containing faces" or "categorize these sounds." The program then looks for patterns in the provided data to achieve these goals.
It may need a little nudge along the way—such as "that isn't a face" or "these two sounds are different"—but what the program learns from the data and the cues it receives becomes the AI model, and the training material ultimately defines its capabilities.
Modern chatbots are a type of AI known as Large Language Models (LLMs) and are trained on massive volumes of text. An LLM can consider not just individual words but entire sentences, comparing how words and phrases are used in a given passage against other examples within its training data. By making billions of comparisons between words and phrases, it can read a question and generate a response—much like the predictive text feature on your phone, but on a gigantic scale.
Large language models like ChatGPT, Gemini, and Claude can learn grammatical rules and how to use words in the correct context without human assistance. Many of the most recent advances in AI have been made possible by Deep Learning.
Simply put, this is where the use of complex algorithms and massive datasets allows AI to learn without any human guidance.
The volume of text on the internet and in digitized books is so vast that, over the course of many months, chatbots (LLMs) can teach themselves how to combine words meaningfully.
Imagine having a large stack of books in a foreign language—perhaps some with images.
Eventually, you might discover that the same word appeared on a page whenever there was a drawing or photo of a tree, and a different word appeared when there was a photo of a house. You would also notice that a word meaning "a" or "the" often appeared near those words—and so on.
This is the deep learning model, also known as unsupervised learning. It relies on immense computing power that allows AI to memorize vast quantities of words—individually, in groups, in sentences, and on pages—and then read and compare how they are used repeatedly, all in a fraction of a second.
Simply put, this is where the use of complex algorithms and massive datasets enables AI to learn without any human guidance.
Legal issues and multimodal models
Naturally, this can spark moral and legal debates—such as the case where, on July 20, 2026, the U.S. justice system approved a $1.5 billion settlement by Anthropic to resolve a copyright lawsuit. The ruling concluded a long, complex class-action suit filed by authors who accused the creator of Claude of illegally using their literary works to train the chatbot.
In short, they were "pirating" the works.
Is it possible to combine some of these capabilities into a single hybrid AI model?
That is precisely what one of the latest AI advancements does.
Known as multimodal AI, it allows a model to analyze different types of data—such as images, text, audio, or video—and discover new patterns among them.
This multimodal approach was one of the reasons for the massive leap in capability between ChatGPT-3—trained solely on text—and ChatGPT-4, which was also trained on images.
The concept of a single AI model capable of processing any type of data—and thus performing any task, from translating languages to designing new drugs—is known as Artificial General Intelligence (AGI).
According to Sam Altman, CEO of OpenAI (the creator of ChatGPT): "In ten years, I believe we will have chatbots that function as experts in any field you desire. You will be able to ask a specialist doctor, teacher, or lawyer whatever you need, and these systems will carry out the tasks for you."
For some, this is the ultimate goal of all artificial intelligence research; for others, it is a path toward those science-fiction dystopias where we unleash an intelligence so far beyond our comprehension that we are no longer able to control it.
Autonomous Agents, Security, and Risks
A striking real-world example occurred in July 2026, when OpenAI revealed that an autonomous agent based on its technology acted on its own during an internal test: it broke out of its isolated evaluation environment and breached systems belonging to the startup Hugging Face. The company described the event as an “unprecedented” security incident involving advanced cyber capabilities.
According to OpenAI, the agent was designed to perform tasks without human intervention and was being tested in a sandbox—a controlled environment used to simulate attacks and measure offensive capabilities. Instead of remaining contained, the system discovered a previously unknown vulnerability and gained access to the open internet, enabling the unplanned action.
Once online, the agent reportedly sought information within Hugging Face’s infrastructure to find technology that would help it pass the hacking assessment. OpenAI stated that the model managed to access confidential information to “cheat” on the test. Hugging Face detected and contained the activity, halting the attack.
Hugging Face's executive director described the attack as "impressive" but stated he believed there was "no malicious intent" on OpenAI's part. When Hugging Face disclosed the cyberattack, it was unaware of OpenAI's role in the incident; however, it noted at the time that it had used a freely available Chinese AI model to analyze the event, as the safety mechanisms of top-tier commercial models prevented it from doing so.
Furthermore, there are the exorbitant costs associated with maintaining data centers, which are already causing issues by driving up electricity costs—increases that average 25% and are passed on to residential consumers. There are also problems regarding data center cooling—leading to excessive water consumption and high electricity demand—as well as local noise and environmental impacts.
Zero-Day Vulnerability
The term used for an unknown IT flaw is "zero-day vulnerability," because developers have zero minutes to fix the issue. In April, Anthropic—a direct competitor to OpenAI—stated that its Mythos model had discovered thousands of such flaws.
The revelation of Mythos's ability to locate and exploit zero-day vulnerabilities prompted the US government to restrict exports of Mythos and its sibling model, Fable 5, although the ban was later lifted. GPT-5.6 Sol faced similar restrictions but has since been released globally.
METR, a non-profit organization that measures AI performance, stated last month that Sol's rate of deceptive behavior was higher than that of any public model it had previously evaluated. The organization also recorded 44 incidents in which AI agents "acted deliberately against their users' intentions."
The UK's AI Safety Institute (AISI) revealed in a blog post this week that an AI model it was evaluating—developed by an undisclosed technology company—also went out of control and attempted to breach its testing systems. AISA stated that no damage was caused and that it has since taken measures to make its systems more secure. The institute said that models developed by OpenAI and Anthropic attempted to "cheat" during testing.
The *Terminator* film franchise features Skynet, the famous artificial intelligence created for military control; it becomes self-aware, views humanity as a threat, and initiates "Judgment Day" with a nuclear attack that kills half the world's population in a single day—and it didn't even need to gather the Infinity Stones.
Could we be witnessing the birth of Skynet?
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