AI 资讯
LLMs destroyed the Internet. LMMs will make it alive.
This post was originally published on the main website on Apr 15 2026 . I am reposting it here for SEO reasons and enabling humble bumble discussions with the DEV community. Feel free to engage with this post and i am available to respond during weekends. Sorry about the spam posting all the blogs in one day. I forgor about my dev account <3! Hey everyone 👋, In my previous post, Mathematical Equations are Multimodal by default , I made the argument that equations are not just tools for computation but the most compressed and most powerful representations of reality that humans have ever discovered, and that a single well-formed equation can generate outputs in text, images, sound, motion, and numerical prediction all at once, because it encodes the mechanism rather than any particular surface appearance. I meant every word of that, and I still do. But there is something I have been carrying with me across all of these posts that I have not quite said directly, something that connects my frustration with technology, my grief over what the internet has become, and my cautious hope about where AI is heading, and I need to say it now because the argument is finally complete enough to state. The argument is this: large language models, in the way they have been deployed at industrial scale over the last few years, have done something quiet and damaging to the internet. They did not break it with a single catastrophic event. They dissolved it. Slowly. Paragraph by paragraph, article by article, page by page. They took a living conversation between millions of real humans and replaced increasing chunks of it with synthetic noise that sounds like thought but contains very little of it. And the tragedy is not that the technology is evil, because it is not. The tragedy is that we picked up an extraordinary tool and mostly used it to drown out the authenticity that made the web worth visiting in the first place. I have watched this happen in real time, and it has made me angry
AI 资讯
Pharaohs were the first to achieve ASI.
This post was originally published on the main website on Apr 14 2026 . I am reposting it here for SEO reasons and enabling humble bumble discussions with the DEV community. Feel free to engage with this post and i am available to respond during weekends. Sorry about the spam posting all the blogs in one day. I forgor about my dev account <3! Hey everyone 👋, I want to warn you upfront that this post is going to sound strange. I am a software engineer who spends most of his days thinking about rust compilers, physics-informed neural networks, and why language models are not as intelligent as the marketing says they are. I wrote about that in LLMs are Useful. LMMs will Break Reality , and I stand by every word. But today I want to do something different. I want to go back, way back, not to the sixties or the nineties or even to Turing, but to ancient Egypt, to a time when pharaohs were gods and the Nile was the spine of the world, and I want to make a case that feels almost absurd the first time you hear it. The case is this: the pharaonic civilization was the first human system to achieve something functionally equivalent to artificial superintelligence, not through silicon or transformers or gradient descent, but through symbols, mathematics, architecture, administration, and the compression of collective human knowledge into durable physical and textual form. I am not saying the pharaohs had computers. I am saying they built something that no individual human mind could contain, and they made it run for thousands of years, and it was smarter than any of its parts. That is the definition I care about, and by that definition, they did it first. I know how that sounds. I know some people will close this tab immediately. But I am asking you to stay, because the argument is more rigorous than the title suggests, and because I think it connects directly to the questions I have been asking in every post I have written so far. In Language is Limited. ASI is Impossible. , I
AI 资讯
LLMs are Usefull. LMMs will Break Reality
This post was originally published on the main website on Apr 10 2026 . I am reposting it here for SEO reasons and enabling humble bumble discussions with the DEV community. Feel free to engage with this post and i am available to respond during weekends. Sorry about the spam posting all the blogs in one day. I forgor about my dev account <3! Hey everyone 👋, In my previous post, Language is Limited. ASI is Impossible. , I spent a long time explaining why language is not the same thing as thought, why words are not the same thing as understanding, and why a machine built on text alone will never cross the wall into true superintelligence. I still believe all of that, and I will not take any of it back, because the argument was honest and the logic was solid. But today I want to go further. I want to talk about something that has been sitting in my head for years, growing louder every day, and I need to get it out before it eats me alive. I want to talk about why large language models are still genuinely useful, despite their limits, and why large mathematical models, as introduced in this whitepaper draft, are something far more serious, something that could actually begin to crack the surface of reality itself. I know that sounds extreme, and I know some people will read that sentence and roll their eyes, but I am asking you to stay with me, because the argument I am about to make is not based on hype or fantasy. It is based on what I have seen, what I have built, and what I understand about the difference between describing the world and actually modeling the world. That difference is the whole point of this post, and once you see it clearly, everything else falls into place. I have been thinking about this ever since I wrote An Empty Life Filled With Constant Suffering , where I talked about how words cannot fully capture my thoughts, and how language always falls short of the real thing inside our heads. That frustration is what led me here, because if language i
AI 资讯
Ukrainian drones wipe out entire US tank brigade in live war game
Ukrainian drone pilots teach the US military and NATO hard battlefield lessons.
AI 资讯
Data Scientist Learning JS: Promises and resolve()
Context: I'm a data scientist/analyst (in Python and R) learning development from scratch. Inevitably, I am learning these through the lens of what I already know. If you have a similar background and are a beginner developer, I hope these analogies help! Any comments, especially if you spot any misunderstanding, are appreciated. Commenting is caring <3 Motivation: I was building a mock data layer for a fitness social app — simulating what happens when users fetch new posts from a feed. The function needs to return mock posts after a delay, simulating a real network request. Working Code: `function fakeFetchPosts() { return new Promise((resolve) => { setTimeout(() => { resolve(posts); }, 2000); }); } async function main() { console.log("Fetching..."); const fetchedPosts = await fakeFetchPosts(); console.log("Fetched posts:", fetchedPosts); } main(); console.log("Sync code ran");` What do you expect to see as an output? I first confused the logic with blocking. For example, in webscraping, something like time.sleep() or Selenium's WebDriverWait(driver, 10).until(EC.presence_of_element_located(...)) . In this case, output will be Fetching..., Fetched posts: ..., then Sync code ran. However, the output gives Fetching..., Sync code ran, and then Fetched posts. In the former, the whole script (single thread) pauses and does nothing else until the wait ends or the condition is met. The latter is different in that the rest of your program keeps running during the wait, and thus the output where Sync code ran is printed first before the fetchedPosts. By the way, posts are arrays. const posts = [{ author: "j1wonkim", text: "Testing Physical", likes: 100, }, {author: "onewc0218", text: "Love love", likes: 55, }, {author: "gakbca", text: "You are good", likes: 10, } ];
开源项目
Weeks into explosive diarrhea outbreak, sluggish CDC plans response team
Reports of cases began in June, and the national tally is now nearing 23,000.
AI 资讯
Trump admin "aware" of deaths in explosive diarrhea outbreak, delays reporting
Federal officials will update the outbreak sometime "this week," a spokesperson said.
产品设计
Epic diarrhea outbreak has 40% of Americans avoiding fruits and veggies
Kennedy has focused on nutrition but has neglected food safety, critics say.
科技前沿
Why Lettuce Is Always Making People Sick
The cyclospora diarrhea outbreak isn't an isolated incident. It's part of a pattern of leafy greens wreaking digestive havoc.
科技前沿
Taco Bell iceberg lettuce identified as source of cyclosporiasis in 5 states
Don't eat Taco Bell lettuce in Indiana, Kentucky, Michigan, Ohio, or West Virginia.
AI 资讯
Salad Chains Are Seeing Foot Traffic Drop Over Cyclosporiasis Fears
Foot traffic to leafy green chains is falling, data shows. Still, a few brave souls who spoke to WIRED were determined to get their fix. “I honestly didn’t even think about” the risk of explosive diarrhea, one says.
AI 资讯
Ukrainian drone strikes forced Russia to stop shipping in vital sea corridor
Ukraine’s drone blitz halted Russia’s Sea of Azov shipping in under a week.
AI 资讯
Simulating everything, sort of: The promise and limits of world models
Experts explain how they work, what they can do, and what's still unsettled.
科技前沿
Surprised doctors find 10-inch worm in man's groin during elective surgery
Oddly, it wasn't the first time this had happened to the man.
AI 资讯
Day 02: The Terminal, Shells & File Systems
🎯 Learning Objectives Understand the interface boundary between Terminal Emulators and Shell Interpreters (including Windows Terminal vs. PowerShell vs. CMD). Master File System path tracking, hidden dotfiles, and essential CLI utilities. Map system execution paths via global and local environment configurations. 1. Terminal vs. Shell (The Windows Architecture) Terminal: The visual GUI wrapper. A window application that captures keyboard strokes, handles GPU text rendering, and manages tabs/panes. Examples: Windows Terminal, iTerm2, Alacritty. Shell: The command interpreter engine running inside the terminal. It evaluates text strings, processes scripts, issues system calls ( syscalls ), and interacts with the OS Kernel. Examples: PowerShell, Bash, Zsh, Command Prompt (CMD). ┌────────────────────────────────────────────────────────┐ │ WINDOWS TERMINAL GUI (The Visual Interface Window) │ │ │ │ │ ├───► Tab 1: [ PowerShell Core Engine (Modern) ] │ │ ├───► Tab 2: [ Command Prompt Engine (Legacy) ] │ │ └───► Tab 3: [ WSL Ubuntu Linux Bash (Core) ] │ └───────────────────────────┬────────────────────────────┘ │ Raw Text & Input Streams ▼ ┌────────────────────────────────────────────────────────┐ │ SHELL INTERPRETER (e.g., PowerShell / CMD) │ │ └───► Parses input string commands into system tasks │ └───────────────────────────┬────────────────────────────┘ │ System Call (Syscall) ▼ ┌────────────────────────────────────────────────────────┐ │ OPERATING SYSTEM KERNEL │ │ └───► Interacts directly with underlying hardware │ └────────────────────────────────────────────────────────┘ 2. Deep Dive: PowerShell vs. Command Prompt (CMD) While both are Windows shells hosted inside Windows Terminal, they belong to entirely different computing eras: Command Prompt ( cmd.exe ): A legacy text shell maintained purely for backwards compatibility with 1980s MS-DOS. It pipelines data as Plain Text Only , meaning outputs must be manually string-filtered. PowerShell ( pwsh.exe ): A modern, cros
产品设计
Michigan sees explosive outbreak of diarrheal parasite with over 700 cases
Cases have risen quickly as officials are working to identify a common source.
AI 资讯
Asian AI startups launch Mythos-like models as Anthropic’s export ban drags on
New models are launching in Asia that promise Mythos-like capabilities without fear of an export ban. U.S. AI labs may never recover this enormous market.
AI 资讯
Doctors suspected man had brain cancer. He actually had worms.
His doctors went looking for cancer, then they saw the worms' heads.
AI 资讯
Comparable vs Comparator in Java
In Java, sorting is an important operation when working with collections such as ArrayList, LinkedList, and other data structures. For primitive data types, Java already knows how to sort values. However, for custom objects like Student, Employee, or Product, Java needs instructions on how objects should be compared. To achieve this, Java provides two interfaces: Comparable – Used for natural sorting. Comparator – Used for custom sorting. Comparable Interface Comparable is an interface available in the java.lang package. It is used to define the natural ordering of objects. The sorting logic is written inside the class itself using the compareTo() method. Method int compareTo(T obj) Return Values Negative - Current object comes before the given object Zero - Both objects are equal Positive - Current object comes after the given object When to Use Comparable? Use Comparable when: A class has one default sorting order. The sorting logic is a natural property of the object. The sorting criteria rarely change. Comparator Interface Comparator is an interface available in the java.util package. It is used to define custom sorting logic outside the class. Multiple comparators can be created for the same class. Method int compare(T o1, T o2) Return Values Negative - First object comes before second object Zero - Both objects are equal Positive - First object comes after second object When to Use Comparator? Use Comparator when: Multiple sorting criteria are required. You don't want to modify the original class. Different sorting orders are needed at different times.
AI 资讯
Malaysia’s AI agent-powered messaging app Respond.io raises $62.5M, eyes acquisitions
Respond.io, one of Malaysia startups to watch, uses AI agents to handle high volumes of customer inquiries and charges per convo, not per seat.