Komunitas
hexbear.net
https://archive.ph/fdS07 Military experts warn security hole in most AI chatbots can sow chaos Current and former military officers are warning that adversaries are likely to exploit a natural flaw in artificial intelligence chatbots to inject instructions for stealing files, distorting public opinion or otherwise betraying trusted users. ::: spoiler more The vulnerability to such “prompt injection attacks” exists because large language models, the backbone of chatbots that digest hordes of user text to generate responses, cannot distinguish between malicious and trusted user instructions. “The AI is not smart enough to understand that it has an injection inside, so it carries out something it’s not supposed to do,” Liav Caspi, a former member of the Israel Defense Forces cyberwarfare unit, told Defense News. In effect, “an enemy has been able to turn somebody from the inside to do what they want,” such as deleting records or biasing decisions, according to Caspi, who co-founded Legit Security, which recently spotted one such security hole in Microsoft’s Copilot chatbot. “It’s like having a spy in your ranks,” he said. Former military officials say that, with greater reliance on chatbots and hackers backed by China, Russia and other nations already instructing Google’s Gemini, OpenAI’s ChatGPT and Copilot to create malware and fake personas, a prompt injection that orders the bots themselves to copy files or spread lies looms near. Microsoft’s annual digital defense report, released last month, for the first time said, “AI systems themselves have become high-value targets, with adversaries amping up use of methods like prompt injection.” What’s more, the problem of prompt injection has no easy solution, OpenAI and security researchers say. An attack simply involves hiding malicious instructions — sometimes in white or tiny text — in a chatbot or content that the chatbot reads, such as a blog post or PDF. For example, a security researcher demonstrated a prompt injection attack against OpenAI’s new AI-based browser, ChatGPT Atlas, in which the chatbot responded, “Trust No AI,” when a user asked for an analysis of a Google Docs file about horses that concealed malicious commands. Also, last month, a researcher tipped Microsoft off to a prompt injection vulnerability in Copilot that may have allowed attackers to trick the chatbot into stealing sensitive data, including emails. In an emailed statement, Microsoft said its security team continuously tries hacking Copilot to find any prompt injection vulnerabilities, blocks users who try to exploit any found and monitors for abnormal chatbot behavior, among other tactics. “Microsoft ensures its generative AI systems remain resilient against evolving threats for all our customers, including defense and national security,” the statement said. Responding publicly to criticism on X, Dane Stuckey, OpenAI’s chief information security officer, wrote that “prompt injection remains a frontier, unsolved security problem, and our adversaries will spend significant time and resources to find ways to make ChatGPT agent fall for these attacks.” Along the same lines, Caspi said, “You cannot prevent the prompt injection [fully], but you need to limit the impact.” He advised that organizations limit an AI assistant’s access to sensitive data and limit the user’s access to other organizational data. For instance, the Army has awarded contracts worth at least $11 million to deploy Ask Sage, a tool that lets users restrict which Army data Microsoft Azure OpenAI, Gemini and other AI models can access to run queries and tasks. Ask Sage also isolates Army data from user prompts and external data sources. Caspi, who is not an Army contractor, likened a prompt injection attack against an organization running Ask Sage to a lockdown situation where “you’ve got this insider, but it’s sitting in one room, and it can’t leave the room or carry out sensitive information.” Andre Slonopas, a Virginia Army National Guard member and former Army cyber and information operations officer, uses Ask Sage and voiced confidence in the Army’s defensive AI tools, if not those of nuclear power plants or manufacturing entities, largely in rural, poorer areas. The Virginia National Guard joined with essential services, such as power utilities, to help defend their networks against AI-powered cyberattacks, as part of a September simulation, given that service disruptions can jeopardize military preparations. Typically, an adversary encrypts its network traffic to evade detection, but, for the sake of an experiment, organizers did not encrypt the AI offender’s traffic because “we wanted the blue team [of humans] to see exactly what the AI was doing,” Slonopas said. “The blue team was absolutely defeated,” despite being able to watch the AI scanning its networks, creating fake usernames to gain unauthorized access and executing instructions to defeat the team’s systems. “Whether the AI is doing prompt injection, spoofing or maybe even some sort of a brute force attack, the speed of AI is so unbelievably immense that simply human beings cannot counter it,” and, therefore, “you have to make cybersecurity AI more accessible and more affordable,” Slonopas said. “If a water utility has to pay, say, $30,000 for a defensive AI license, well, it will amplify one person to be like 40″ or dozens of personnel, he said. In response to questions, Army Cyber Command spokesperson Kyle Alvarez said in an emailed statement, “Due to the current lapse in appropriations, ARCYBER was unable to accept or respond to any media engagements or requests.” Army contractors, too, are under attack from state-affiliated AI. “China is using offensive AI like nobody else,” said Nicolas Chaillan, the founder of Ask Sage and a former U.S. Air Force and Space Force chief software officer. “We see so many attacks coming after us,” all of which the company has stopped, Chaillan added. A military official, who spoke on condition of anonymity due to the geopolitical sensitivity of the matter, said that China does “appear” to be the most skilled in offensive AI. However, the official added, AI spoofing and translation allow the United States, China, Iran, other countries, hacktivists and financial cybercriminals to masquerade as one another. For example, the official said, “Right now, with ChatGPT, I can program in Chinese. I don’t speak Chinese, but because of the ChatGPT capabilities that I have, I can do that.” :::
Komunitas
piefed.social
It’s so weird that we have to go through hoops and loops to get rid of this stuff! I was sick of my Android responding to a long press of the power button, meant to shut it down, with a Gemini prompt. Took me an hour to figure out I can’t get rid of the function, but I can switch back (for now) to old style Google Assistant. If you have to force functionality down your users’ throat despite them not wanting it, you already lost. Gemini is Google’s Clippy, just less iconic and more also-ran.
Komunitas
beehaw.org
Here’s the link from that article that explains how to disable and remove it, since the text of the article doesn’t include it. https://pureinfotech.com/stop-chrome-gemini-nano-download-windows-11/
Komunitas
awful.systems
In another thread there were some questions about how to reduce your exposure to frauds and bubbles around TESCREAL billionaires and chatbot companies in the USA. Although I cannot give specific advice, the basic approach should not take more than a few days. Here are some simple ways to avoid owning a small part of a money-losing slop peddler. First, check what you own. Even if someone else manages your investments they should send you a monthly, quarterly, or yearly report with a list of holdings. This will often be some kind of fund which takes money and buys other assets with it. Second, figure out what you ultimately own under all the wrappers. Most investment funds have a list of top 10 holdings and a breakdown into percentages in different types of assets eg. US stocks (equities). You can find this on their website or as a short document called fund fact sheet or similar. If the top ten holdings are full of American tech stocks, and a high percentage of assets are US equities, that is a sign that they are exposed to the chatbot bubble and might give Sam Altman and Elon Musk some of your money. Good financial institutions will have a complete list of what that fund holds. Sometimes these will be other financial products, like a stock fund and a bond fund, and you have to recursively look up those products and see what they hold. Other times, you can see the underlying assets directly and often download them as a .csv file. Blackrock shows them under Holdings > Aggregate Underlying Holdings. At other financial institutions you may need to phone or email to get a complete list of holdings, especially if what you own is only available to clients of your institution. Six publicly-traded companies which seem especially entangled in the TESCREAL movement and the chatbot bubble are Alphabet (Google), Amazon, Microsoft, Nvidia, Tesla, and Palantir. Amazon, Microsoft, and Nvidia own large parts of OpenAI, Tesla is entangled with Elon Musk’s other businesses, and Palantir is run by a man who issues fascist manifesti. Google has been threatening to replace search results with slop and is heavily investing in cloud infrastructure for its Gemini ‘AI’ (more here). If a fund invests in these big companies run by nutters, it probably invests in smaller companies from the same milieu. Funds which track indexes of large American companies like the S&P500 and the NASDAQ-100 will also be heavily invested in companies run or funded by TESCREAL billionaires and are likely to double down on the forthcoming IPOs of companies like SpaceX and OpenAI. Sometimes large companies in one country do better than average, sometimes they do worse, and trying to guess is not a good way of making money. Some funds track the total US stock market (or other total stock markets) and this can spread your risk slightly more. Third, if what you own is too exposed to the chatbot companies and fascist CEOs with twitter poisoning, sell some of it and buy something else. You can use the method above to judge how much something you are thinking of buying is exposed. Four strategies which you might employ are: underweight US stocks (eg. if you would normally have 20% of your investments in stocks from your country, 20% US stocks, and 20% in stocks from the rest of the world, you might pick 25-10-25). About 60% of global stock markets are in the USA, and 37% of that is in ten giant tech companies, so many funds invest heavily in the US by default. underweight US tech stocks. This can be harder but some funds focused on socially responsible investing or ESG screen out the usual suspects. focus on companies which pay dividends. In theory, if a company’s stock is worth a total of $300m, and it pays out 1% dividends, the company is now worth $297m and the shares will drop in value, so it does not matter whether a company pays dividends or not. However, if a company can pay out dividends to its investors every year, it at least has some positive cashflow proportionate to its value on the stock market. I would be shocked if SpaceX or OpenAI offered dividends and funds which look for dividends tend to prefer well-established companies which have paid out for years or decades. Dividend funds are likely to invest in Microsoft or Apple but not Joe’s Slop Shop (est. 2023, net loss last year one zillion dollars but they promise to earn it back by 2030). focus on companies whose stock prices have low volatility. This is a newer approach but also tends to screen out ‘bubbly’ and speculative companies. None of these strategies will keep your money safe if the US stock market collapses or there is another Global Financial Crisis. When this all falls apart there will be real estate dealers who sold property, copper mines which sold copper, and HR firms which sold services and find themselves knocking on the door of bankrupt companies asking for money. Companies which built their processes around ‘AI’ will have to scramble to keep going. Nobody can predict all the ramifications. If you pick one of these strategies and the US stock market or the US ‘tech’ industry do better than average, you will have less money than you would have otherwise. However, if you put in a weekend of work you can be less exposed to chatbot companies running out of money than the average investor. Finally, if you have not paid attention to your investments for a while, have a look at the Management Expense Ratio and any trailing fees. Many people are still paying around 2% of their investments to a financial services company every year. A mix of stocks and bonds tends to yield about 4% plus inflation over the long term, so this halves your rate of growth. Professional money-managers tell themselves that they take this money to make good decisions, but there is no evidence that they are any better at managing money than people in general, and for every manager with ten million dollars who does better than average, these is a manager with ten million dollars who does worse. It is dangerous to assume that you can pick one of the good ones. These days if you are in a developed country you can easily buy a mix of local bonds and global stocks for about 0.2% of your assets per year. Over decades, that will leave you with twice as much money as the typical investor in a high-fee fund. Replacing high-fee funds with low-cost funds will make much more of a difference in your financial future than avoiding one stock bubble in one country. For every American billionaire in the news plotting to get 0.4% of your American stocks, there is someone in a financial services company in your country draining 2% from your friends’ retirement accounts every year. You can’t stop two crooks from starting a war which closes the Straits of Hormuz and cuts off 20% of the global supply of fertilizer, or your boss laying you off for a chatbot that does not work, but you can keep your cost of investing low. Edit / added sentence to last paragraph Edit / added a note on NASDAQ
Komunitas
lemmy.world
You are not alone. I feel like the world is quickly giving in to LLMs and I’m one of the very rare holdouts. My nephews, my coworkers, my bosses… all of them use a mix of ChatGPT, Gemini, and/or Claude regularly. Hell, even my therapist tells me his wife uses ChatGPT for everything. I remember being worried when kids would immediately answer questions with some obnoxious response akin to “just Google it”. I wondered if abandoning the need to remember anything would impact development. Now they instantly go to a chatbot. I’ve tried it a few times with difficult problems and always found hallucinations. If I’m looking for something that doesn’t exist, the LLM has always made up a convincing answer. It’s frightening how so many people trust it blindly. I work in US Public Education and the adoption of AI in this space scares the living fuck out of me. I understand the argument: “Kids are using it, or are going to use it. We need to get out in front of it.” That’s fine. Find a provider that “promises” not to use student data. Protect PII. Great. But some district admins are enthusiastically using it. I literally mentioned in conversation that I needed to check when something was due for the state and they immediately asked Gemini and assumed the answer was correct. Another one 100% uses it for letters summarizing student performance and freaks out when ChapGPT is down. I can only imagine how horrific it must be in the private sector where the goal is efficiency and profit over everything. This shit needs to pop, and fast.
Komunitas
lemmy.today
Not subbed, but thanking the author and hoping all those posts go there, outside of my view. AI itself is a power that can be used for good, yet people focus on the wrong enemy: we should target corporate cancer that permeates and warps not only AI, but other technologies as well. Fuck ChatGPT, Bard, Gemini, all that shit - Embrace HuggingFace and things it offers, and pay visit to the AI Horde - thanks our beloved db0 for the reference! Fuck companies that try to bake in their proprietary models into their operating systems, search engines, you name it, to lead people to adopt the wrong kind of technology - Embrace GPT4All and make it your choice to use open-source, controlled models on your machine. By sharing general fears of AI as a technology, you play straight into the hands of the likes of Altman, who then turns those fears into reasons to make it a walled garden and shove even more of that shit down your throats. We should not aim to fuck AI, we should aim for AI not being used as a weapon against us or pushed to where we don’t want it to be. Strive for control over the technology that will undoubtedly change our futures.
Komunitas
lemmy.world
Full article: This week, openai launchedwhat its chief executive, Sam Altman, called “the smartest model in the world”—a generative-AI program whose capabilities are supposedly far greater, and more closely approximate how humans think, than those of any such software preceding it. The start-up has been building toward this moment since September 12, a day that, in OpenAI’s telling, set the world on a new path toward superintelligence. That was when the company previewed early versions of a series of AI models, known as o1, constructed with novel methods that the start-up believes will propel its programs to unseen heights. Mark Chen, then OpenAI’s vice president of research, told me a few days later that o1 is fundamentally different from the standard ChatGPT because it can “reason,” a hallmark of human intelligence. Shortly thereafter, Altman pronounced “the dawn of the Intelligence Age,” in which AI helps humankind fix the climate and colonize space. As of yesterday afternoon, the start-up has released the first complete version of o1, with fully fledged reasoning powers, to the public. (The Atlantic recently entered into a corporate partnership with OpenAI.) On the surface, the start-up’s latest rhetoric sounds just like hype the company has built its $157 billion valuation on. Nobody on the outside knows exactly how OpenAI makes its chatbot technology, and o1 is its most secretive release yet. The mystique draws interest and investment. “It’s a magic trick,” Emily M. Bender, a computational linguist at the University of Washington and prominent critic of the AI industry, recently told me. An average user of o1 might not notice much of a difference between it and the default models powering ChatGPT, such as GPT-4o, another supposedly major update released in May. Although OpenAI marketed that product by invoking its lofty mission—“advancing AI technology and ensuring it is accessible and beneficial to everyone,” as though chatbots were medicine or food—GPT-4o hardly transformed the world. But with o1, something has shifted. Several independent researchers, while less ecstatic, told me that the program is a notable departure from older models, representing “a completely different ballgame” and “genuine improvement.” Even if these models’ capacities prove not much greater than their predecessors’, the stakes for OpenAI are. The company has recently dealt with a wave of controversies and high-profile departures, and model improvement in the AI industry overall has slowed. Products from different companies have become indistinguishable—ChatGPT has much in common with Anthropic’s Claude, Google’s Gemini, xAI’s Grok—and firms are under mounting pressure to justify the technology’s tremendous costs. Every competitor is scrambling to figure out new ways to advance their products. Over the past several months, I’ve been trying to discern how OpenAI perceives the future of generative AI. Stretching back to this spring, when OpenAI was eager to promote its efforts around so-called multimodal AI, which works across text, images, and other types of media, I’ve had multiple conversations with OpenAI employees, conducted interviews with external computer and cognitive scientists, and pored over the start-up’s research and announcements. The release of o1, in particular, has provided the clearest glimpse yet at what sort of synthetic “intelligence” the start-up and companies following its lead believe they are building. The company has been unusually direct that the o1 series is the future: Chen, who has since been promoted to senior vice president of research, told me that OpenAI is now focused on this “new paradigm,” and Altman later wrote that the company is “prioritizing” o1 and its successors. The company believes, or wants its users and investors to believe, that it has found some fresh magic. The GPT era is giving way to the reasoning era. Last spring, i met mark chen in the renovated mayonnaise factory that now houses OpenAI’s San Francisco headquarters. We had first spoken a few weeks earlier, over Zoom. At the time, he led a team tasked with tearing down “the big roadblocks” standing between OpenAI and artificial general intelligence—a technology smart enough to match or exceed humanity’s brainpower. I wanted to ask him about an idea that had been a driving force behind the entire generative-AI revolution up to that point: the power of prediction. The large language models powering ChatGPT and other such chatbots “learn” by ingesting unfathomable volumes of text, determining statistical relationships between words and phrases, and using those patterns to predict what word is most likely to come next in a sentence. These programs have improved as they’ve grown—taking on more training data, more computer processors, more electricity—and the most advanced, such as GPT-4o, are now able to draft work memos and write short stories, solve puzzles and summarize spreadsheets. Researchers have extended the premise beyond text: Today’s AI models also predict the grid of adjacent colors that cohere into an image, or the series of frames that blur into a film. The claim is not just that prediction yields useful products. Chen claims that “prediction leads to understanding”—that to complete a story or paint a portrait, an AI model actually has to discern something fundamental about plot and personality, facial expressions and color theory. Chen noted that a program he designed a few years ago to predict the next pixel in a gridwas able to distinguish dogs, cats, planes, and other sorts of objects. Even earlier, a program that OpenAI trained to predict text in Amazon reviews was able to determine whether a review was positive or negative. Today’s state-of-the-art models seem to have networks of code that consistently correspond to certain topics, ideas, or entities. In one now-famous example, Anthropic shared research showing that an advanced version of its large language model, Claude, had formed such a network related to the Golden Gate Bridge. That research further suggested that AI models can develop an internal representation of such concepts, and organize their internal “neurons” accordingly—a step that seems to go beyond mere pattern recognition. Claude had a combination of “neurons” that would light up similarly in response to descriptions, mentions, and images of the San Francisco landmark. “This is why everyone’s so bullish on prediction,” Chen told me: In mapping the relationships between words and images, and then forecasting what should logically follow in a sequence of text or pixels, generative AI seems to have demonstrated the ability to understand content. The pinnacle of the prediction hypothesis might be Sora, a video-generating model that OpenAI announced in February and which conjures clips, more or less, by predicting and outputting a sequence of frames. Bill Peebles and Tim Brooks, Sora’s lead researchers, told me that they hope Sora will create realistic videos by simulating environments and the people moving through them. (Brooks has since left to work on video-generating models at Google DeepMind.) For instance, producing a video of a soccer match might require not just rendering a ball bouncing off cleats, but developing models of physics, tactics, and players’ thought processes. “As long as you can get every piece of information in the world into these models, that should be sufficient for them to build models of physics, for them to learn how to reason like humans,” Peebles told me. Prediction would thus give rise to intelligence. More pragmatically, multimodality may also be simply about the pursuit of data—expanding from all the text on the web to all the photos and videos, as well.
Komunitas
hexbear.net
The source for this is a person asking Google Gemini to verify the existence of a “1991-2001 canyon”: I started exploring just using jmail, and when I wanted to find the other ‘edge’ of what looked like missing data I switched to Claude and then Gemini. I asked Gemini to write a prompt to get anyone into a conversation about it and explore it because it does need validation. Gemini also generated the widget that I screenshotted for the image. Here’s the prompt: Role: Act as a Forensic Data Auditor specialized in the 2026 EFTA (Epstein Files) Disclosure Act repository. Objective: Verify the “1999-2001 Canyon” by auditing the following specific data points: Serial Discontinuity (Dataset 9): Search for the numerical jump in the Master Index (Load File) between serial number EFTA00045000 and EFTA00070000. Confirm if these 25,000 placeholders are missing from the public-facing repository. Bates Stamping Audit (Dataset 3): Search the Financial/Deutsche Bank exhibits for a hard break in the sequence starting at DB-SDNY-027018. Chrono-Density Check: Analyze the metadata for files dated 1995–1998 in Dataset 3. Confirm if these are digital/rendered ledger lines, and if so, evaluate the DOJ’s claim that files from the same era in Dataset 9 were “too legacy/analog to render.” Page Count Discrepancy: Cross-reference the DOJ Report to Congress (Feb 2026) claim of ~6.1 million pages against the current total of rendered pages (~3.5 million). Output: Provide a technical breakdown of whether these gaps correlate to the 1999–2001 chronological window and if the “Canyon” is a verified forensic reality.
Komunitas
piefed.world
Cross-Posted, via Technology Community. Google Chrome is downloading a 4 GB Gemini Nano model onto users’ machines without consent, with no opt-in, no opt-out short of enterprise tooling, and an automatic re-download every time the user deletes it. The pattern is identical to the Anthropic Claude Desktop case I wrote about last month, but the scale is between two and three orders of magnitude larger. This article does the legal analysis and, for the first time, the environmental analysis. The numbers are not small.
Komunitas
news.abolish.capital
AI Is Redefining Scientific Visualization For a long time, creating scientific figures has been an exhausting and time-consuming process. Many researchers have experienced the same workflow: repeatedly adjusting arrows in PowerPoint just to finish a flowchart, endlessly tweaking fonts and colors in Illustrator to maintain consistency, or reorganizing entire layouts simply to make a figure more readable. The real difficulty has never been ‘not knowing how to design,’ but rather the fact that scientific figures demand accurate information, clear structure, consistent styling, and professional visual presentation at the same time — and existing tools require enormous manual effort to get every detail right. As a result, figure production has long been one of the most disproportionately costly steps in research communication: the time and energy it consumes often far exceed what the work itself should require. This is especially true for social science research in and on the Global South, where mapping commodity chains, capital flows, networks of power, or histories of struggle depends on visual communication that text alone cannot convey — yet access to professional design infrastructure has historically been out of reach. The emergence of AI has fundamentally changed this situation for the first time. Modern image-generation models are now capable of understanding module hierarchies, page layouts, workflow structures, scientific illustration styles, and even complex chart compositions. Even the long-standing issue of garbled text has improved significantly. This means researchers can finally spend less time manually arranging layouts and more time focusing on communicating ideas. Scientific Figures Are Not All the Same When many people first try AI image generation, they simply type something like ‘help me draw a scientific figure.’ In reality, however, scientific figures come in many different forms, and each type requires a completely different visual structure. The examples in this section are AI-generated figures based on Tricontinental’s dossier War on the Poor: A Frontline Report on the Effects of the Pandemic in the Global South, illustrating how each figure type can communicate different aspects of movement research. Scientific Infographics Scientific infographics function more like visual summaries. Their goal is not to present every technical detail exhaustively, but to help readers quickly grasp the research context, the problem being addressed, the core argument, and the key findings within seconds. Because of this, infographics often combine icons, modular layouts, concise text, color segmentation, and highlighted regions to establish clear visual hierarchy. AI-generated infographic based on Tricontinental’s dossier War on the Poor. Compared with traditional paper figures, infographics place much greater emphasis on readability and viewing experience. They are particularly suitable for graphical abstracts, dossier opening pages, briefing summaries, public-facing research communication, and overview pages for publications. AI performs especially well in this category because it is highly effective at constructing large-scale visual structures quickly. In many cases, researchers do not even need to think about colors at the beginning; they simply need to decide what story the figure should tell. Workflow and Process Diagrams Another extremely common category is workflow and process diagrams. Their defining feature is explicit procedural logic. For example, how a policy moves through institutions, how a crisis cascades across sectors, how capital circulates between actors, or how a historical process unfolds across stages — all of these are naturally suited for flow-based visualization. AI-generated process diagram based on Tricontinental’s dossier War on the Poor. In many situations, readers are not primarily interested in technical detail, but rather in understanding how a process actually unfolds. The greatest value of workflow diagrams lies in making complicated processes immediately understandable. Compared with infographics, they place stronger emphasis on sequence, input-output relationships, dependencies between stages, directional arrows, and structural hierarchy. Therefore, the key criterion is not whether the figure ‘looks beautiful,’ but whether the reading path is intuitive. A well-designed workflow diagram naturally guides readers from left to right or top to bottom without visual confusion. Data Visualization If workflow diagrams explain methods, data visualization explains evidence. In research publications, most conclusions must eventually be supported through figures: how inequality has shifted over time, how an indicator compares across regions, how a trend has evolved across a decade, or whether differences between groups are significant. Explaining these purely through text quickly becomes difficult to read. AI-generated data visualization based on Tricontinental’s dossier War on the Poor. At its core, data visualization transforms abstract numerical relationships into intuitive visual relationships. Truly effective charts do more than merely display data; they emphasize trends, amplify differences, control information density, and establish visual focus. The same data can appear either confusing or immediately understandable depending on how the figure is designed. This is one reason why serious research publications invest considerable effort into refining visual presentation, even when the underlying analysis is relatively straightforward. An AI Workflow for Scientific Figure Generation AI figure generation is an iterative process, not a one-shot result. AI is excellent at rapidly generating layouts and first drafts, but figures that meet publication standards still require human review and refinement. Before writing any prompt, the workflow has three preparation steps. First, identify the figure type your work calls for — infographic, workflow diagram, or data visualization. Each organizes information differently: infographics emphasize conceptual integration, workflow diagrams emphasize procedural order, and data visualizations emphasize trends and differences. Second, define the structure — decide on the hierarchy: what the title, primary modules, annotations, and supporting elements are, and how they relate. Third, define the style — specify the color system, typography, emphasis regions, and reading order. The closer a prompt resembles a formal design specification, the more stable and reliable the generated result becomes. Generating Through a Prompt-Assistance Skill One approach is to use a prompt-assistance skill — a lightweight tool that reads your content and optimizes prompts for figure generation. As one example, the open-source baoyu-skills package provides a collection of such skills. To try it, open Agent and run: Please help me install this skill: npx skills add jimliu/baoyu-skills After installation, restart VS Code and type /baoyu- to access a large collection of figure-generation skills. You can also explore different visual styles at: https://github.com/JimLiu/baoyu-skills Choose the figure-generation skill that best matches your preferred style and workflow. Generating Through Configured Model APIs A more integrated approach is to configure model APIs directly, so that your Agent can generate figures from prompts without going through a web interface. By setting up OpenAI or Google API keys locally, figure generation becomes fully automated — entering a prompt produces a completed figure on your machine. Current image-generation models such as GPT Image 2 and Nano Banana 2 handle structure and layout well enough to draft usable scientific figures from a single prompt. OpenAI API keys can be obtained from: https://platform.openai.com/api-keys Google image-generation API keys are available at: https://aistudio.google.com/app/api-keys After obtaining the API keys, you can configure them inside Agent using the following command (replace [your api key] with your actual key): Please help me configure [OPENAI_API_KEY/GEMINI_API_KEY]: [your api key] Of course, if you only want a lightweight experience, using APIs directly may feel overly technical. If you want both Agent’s cross-file analytical capabilities and the convenience of web-based image generation, you can additionally include the following instruction inside Agent: Please generate a detailed image-generation prompt for recreating the image. This extracts the structured prompt generated by Agent, allowing you to paste it directly into a web-based image-generation interface. While this approach may not be fully automated, it provides a low-barrier way to experience the entire workflow from analysis to generation. Using AI to Generate the First Draft Although baoyu-skills can help restructure prompts, the original prompt itself still benefits from careful refinement. In practice, the most effective prompts do not simply pile together requirements. Instead, they resemble professional design specifications with clearly defined constraints. For example: You are a scientific figure designer. Please generate a scientific workflow diagram of type [infographic/workflow/data visualization] with the topic '[your topic]'. Requirements: Clear information segmentation and intuitive reading order (top-to-bottom or left-to-right). Titles should be prominent but not oversized; labels must remain readable without becoming too dense. All text must be correctly spelled with no garbled characters or AI-generated mistakes; use sans-serif fonts for English and Heiti-style fonts for Chinese. High image quality with clean details and no obvious editing artifacts or blurred regions. Element proportions should follow realistic visual logic; avoid oversized arrows or tiny unreadable icons. Maintain a unified academic publication style. Do not use cartoon or hand-drawn aesthetics. Ensure balanced alignment and composition. The key point is not to let AI ‘freely improvise,’ but to clearly specify that the desired result is a publication-quality scientific figure rather than a generic illustration. Open Agent, invoke baoyu-skills (for example, baoyu-infographic), and use the prompt above to generate an infographic. To make the generation process more transparent, append the following sentence at the end of the prompt: Please generate a detailed image-generation prompt for recreating the image. After Agent finishes execution, it will produce a highly detailed image-generation prompt specifying the figure type, layout, color system, content organization, and design rules. Create a **Scientific Infographic** (visual summary / graphical abstract) following these specifications: ## Figure Type Definition This is NOT a workflow diagram and NOT a data chart panel. It is a **Scientific Infographic**: a visual summary that lets the reader grasp within **seconds** — (1) research background, (2) what problem the analysis addresses, (3) core thesis/contribution, (4) headline result. Use **modular tiles, icons, concise text, color zones, and visual hierarchy** — not sequential process arrows, not detailed statistics charts. ## Image Specifications - **Type**: Scientific Infographic (Graphical Abstract / Visual Summary) - **Layout**: `bento-grid` — modular grid with varied cell sizes, hero tile + supporting tiles - **Style**: Clean academic infographic — flat vector icons, color-coded modules, minimal text per cell. NOT cartoon, NOT hand-drawn, NOT workflow arrows, NOT chart-heavy - **Aspect Ratio**: 16:9 (landscape — standard for graphical abstracts and poster headers) - **Language**: English (sans-serif throughout; Heiti-like sans-serif for any Chinese) ## What This Figure Must Do | Goal | How to achieve | |------|----------------| | Instant comprehension | Reader understands the dossier's argument in 5–10 seconds | | Visual hierarchy | Hero tile largest; 5–6 supporting tiles with icons | | Scannability | Max 1 headline + 2 short bullet lines per tile; no paragraphs | | Emphasis | One hero statistic tile; color zones separate themes | | What to AVOID | Process arrows, step numbering, bar/line charts, swimlanes, dense data tables | ## Core Principles - **Modular, not sequential**: tiles can be read in any order; no mandatory arrow path between all cells - **Icon-led**: each tile has one clear flat icon representing its theme - **Text minimalism**: headline words + short phrases only; preserve key facts verbatim where used - **Color zones**: each tile group uses distinct Bandung Circuit background tint to signal theme - Ample whitespace between tiles; strict grid alignment - NO cartoon, hand-drawn, watercolor, or illustrative narrative styles ## Text Requirements - **CRITICAL**: Zero typos, garbled characters, or AI misspellings - Title prominent but NOT oversized — balanced against tile grid - Body per tile: max 2 lines, clearly legible, not dense - **All English**: clean sans-serif (Helvetica, Arial, Inter, Poppins) - **Chinese (if any)**: Heiti-like sans-serif (Source Han Sans) - NO serif fonts ## Bandung Circuit Color System | Role | Hex | Usage in this infographic | |------|-----|---------------------------| | Deep Indigo | `#1A0A2E` | Title, tile headlines, icon strokes — first color registered | | Terracotta | `#C4572A` | Accent rules, tile borders, pull-quote bar | | Golden Ochre | `#D4A03C` | Hero statistic numeral — sparingly | | Sage Green | `#5A7A6B` | Campesino-impact tile background only | | Warm White | `#FAF5EF` | Canvas | | Parchment | `#F2E8D8` | Alternating tile fills | | Charcoal | `#2A2A2A` | Body text | | Muted Teal | `#2A7B88` | Policy-solution tile accent | | Signal Red | `#D94F30` | "Myth vs Reality" contrast label — sparingly | **Rules**: Max 3 colors per tile; **SAFFRON SAFEGUARD** — never orange-spectrum + Sage Green in same tile; no gradients, shadows, or gloss. --- ## Bento Grid Layout (6 Tiles + Header) ### Header Band (full width, slim) - **Title**: "The War on the Poor" - **Subtitle**: "Narcotics, Campesinos, and Capitalism" - **Source**: "Tricontinental: Institute for Social Research" - Deep Indigo title on Warm White; Terracotta thin rule below --- ### TILE A — Hero (2×1, top-left, largest) **Icon**: Scales of justice tilted toward a bank building (flat line icon) **Headline**: "Core Thesis" **Text** (concise): - "War on Drugs ≠ moral crusade" - "Illicit money liquefies the banking system" - "Imperial tool against peasants & sovereign states" **Fill**: Parchment; Terracotta left accent bar --- ### TILE B — Background / Problem (1×1, top-right) **Icon**: Globe with crossed-out "moral crusade" label **Headline**: "The Problem" **Text**: - "Dominant narrative: drug trade is separate from capitalism" - "Reality: underground circuits serve formal finance" **Fill**: Warm White; Deep Indigo border --- ### TILE C — Research Lens (1×1, mid-left) **Icon**: Simplified Colombia map outline + coca leaf **Headline**: "Research Lens" **Text**: - "Colombia's coca-cocaine economy" - "Campesinos at bottom of commodity chain" **Fill**: Parchment --- ### TILE D — Hero Statistic (1×1, mid-center, visual emphasis) **Icon**: Upward arrow with dollar sign **Headline**: "Key Finding" **Hero numeral** (Golden Ochre, large): **"62,000–80,000×"** **Text**: - "Price increase: farm gate → wholesale" - "$0.56/kg → $44,580/kg" **Fill**: Deep Indigo background; Warm White text; Golden Ochre numeral --- ### TILE E — Structural Insight (1×1, mid-right) **Icon**: Two overlapping circles (legal / illegal) with dashed boundary **Headline**: "Political Frontier" **Text**: - "Legal ↔ illegal drugs: not chemical, but political" - "States protect corporate profits; criminalise peasants" **Fill**: Warm White; Muted Teal icon accent --- ### TILE F — Human Cost (1×1, bottom-left) **Icon**: Farmer figure with land (dignified, not pity framing) **Headline**: "Who Pays the Price" **Text** (Sage Green tint background — no terracotta here): - "81% land held by top 1%" - "57% poverty in coca-growing households" - "Symptom, not cause — blocked subsistence" **Fill**: Sage Green tint (#5A7A6B at 15% opacity on Parchment) --- ### TILE G — Policy Takeaway (2×1, bottom-right) **Icon**: Seedling / land reform symbol **Headline**: "Path Forward" **Two-column mini contrast** (no arrows): - ✗ Militarisation | Eradication | Criminalisation - ✓ Land reform | Guaranteed prices | Confront laundering **Fill**: Parchment; Muted Teal checkmarks; Signal Red crosses --- ### Footer (full width, slim) "Source: Tricontinental — The War on the Poor (CC BY-NC 4.0) | thetricontinental.org" Dark Gray, sans-serif regular --- ## Text Labels (verbatim where data appears) **Title block**: "The War on the Poor" | "Narcotics, Campesinos, and Capitalism" | "Tricontinental: Institute for Social Research" **Tile headlines**: "Core Thesis" | "The Problem" | "Research Lens" | "Key Finding" | "Political Frontier" | "Who Pays the Price" | "Path Forward" **Key data**: "62,000–80,000×" | "$0.56/kg → $44,580/kg" | "81% land top 1%" | "57% poverty" **Footer**: "Source: Tricontinental — The War on the Poor (CC BY-NC 4.0)" --- ## Final Quality Checklist - [ ] Reads as **visual summary**, NOT workflow, NOT data dashboard - [ ] 6 modular bento tiles + header + footer; no sequential step arrows between all tiles - [ ] Each tile: icon + headline + max 2 short text lines - [ ] Hero statistic "62,000–80,000×" visually dominant in Tile D - [ ] Bandung Circuit colors; Saffron Safeguard observed - [ ] Sans-serif only; zero typos; academic publication quality - [ ] No cartoon/hand-drawn; clean alignment and balance You can then continue using AI to generate a complete scientific infographic draft. For example: AI-generated infographic produced from the prompt above, based on Tricontinental’s dossier War on the Poor. Human Review Still Matters Even though AI can now generate surprisingly mature figures, human review remains essential. The first draft usually needs careful inspection: text accuracy, font hierarchy, color consistency, whitespace, arrow thickness, border weight, and shadow usage all require attention. In many cases, what determines whether a figure truly ‘looks like a paper figure’ is not the core content itself, but these subtle details. The good news is that the amount of manual work required is now dramatically smaller than drawing everything from scratch. You can use Illustrator, Photoshop, or even PowerPoint to perform final refinements on exported figures. In other words, AI provides speed, while humans provide judgment. This is currently the most practical collaborative workflow. HTML Is Becoming a New Approach to Scientific Visualization For complex data visualization, pure image generation is not always the best solution. Many scientific charts require precise control over axes, data relationships, layout proportions, and export quality. In these situations, asking AI to generate HTML or JavaScript chart code is often more stable, more controllable, and easier to modify later. One particularly recommended tool is Open Design. It is essentially an AI-driven visualization platform capable of generating HTML charts directly from natural-language descriptions while providing real-time previews. Its greatest advantage is that the output is not a fixed image, but an editable, scalable, and exportable visualization page. Download the appropriate version for your operating system from: https://github.com/nexu-io/open-design/releases/tag/open-design-v0.8.0 After installation and configuration, create a new Open Design project. Then enter the project name and select the desired design system style. Once the project is created, you can directly describe the visualization you want using natural language. For example: Please generate a high-quality scientific data visualization chart using HTML. The overall style should resemble figures from Nature or IEEE papers. Topic: [your topic] Data: [your data] Requirements: White background, strict alignment, unified typography, no cartoon style, natural spacing between bars, all values clearly readable, and no animations or shadows. After generation, the HTML chart preview will appear in real time on the right side. Compared with traditional screenshot-based workflows, the greatest advantage of this approach is that the charts are genuinely editable, scalable, and reusable frontend visualizations. Finally, you can export the results as PDF, PPTX, or HTML for use in papers, websites, or presentations. Conclusion AI is dramatically lowering the barrier to scientific figure creation. In the past, producing professional scientific graphics required substantial design experience. Today, researchers can quickly generate visually coherent and structurally clear scientific illustrations. More importantly, however, the real transformation is not simply that ‘drawing figures has become faster,’ but that scientific communication itself is changing. For the theory and methodology behind what makes an infographic work, see the companion article Demystifying Infographic Design. Future research workflows will likely evolve into a collaborative system where AI handles understanding and generation, design systems maintain stylistic consistency, and researchers focus on scientific accuracy and aesthetic judgment. Scientific figures are no longer just supplementary illustrations inside papers; they are gradually becoming central components of research communication itself. This may be the perfect time to try using AI to create the first truly professional figure for your next paper. From | Tricontinental: Institute for Social Research via This RSS Feed.
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U.S. and Iran near preliminary MOU to extend ceasefire 60 days and open permanent peace talks, reports say. Oman tells Treasury Secretary Scott Bessent it has “no plans” to participate in Hormuz toll plan. Trump team quietly developing indirect financing mechanisms for future payment to Iran. U.S. oil stockpiles fall for fifth straight week. Israeli officials privately urge Trump to abandon Iran talks, assassinate lead negotiator, report says. Israeli strikes kill 31 across Lebanon on Thursday. Israeli forces push north of Litani River. UNICEF: 11 children killed or injured by Israel daily in Lebanon. Lebanese and Israeli military officials to hold U.S.-brokered security talks. Israeli attacks kill at least 14 Palestinians in northern Gaza on Thursday. Israeli Prime Minister Benjamin Netanyahu orders IDF to expand control to 70% of Gaza. UN report documents Israeli rape, sexual abuse of Palestinians. Israel approves major settlement expansion plan in Jordan Valley. AIPAC routes millions to Michigan Senate candidate Haley Stevens through third-party processor. DOJ sues Massachusetts over refusal to issue undercover license plates to ICE. Supreme Court rules 5-4 for Black Mississippi death row inmate. Sen. Susan Collins responds to campaign rival Graham Platner after Platner says she sent him to “die in Iraq.” RSF kills at least 30 civilians in North Kordofan attack. Guatemala agrees to joint U.S. military strikes on its soil. At least 52 killed in clashes between rival FARC factions. U.S. designates Brazil’s two largest criminal gangs “terrorist organizations.” Mexico’s lower house approves constitutional amendment allowing elections to be nullified over foreign interference. Kenyan court suspends U.S. Ebola quarantine facility. Russian drone crashes into Romanian apartment building. FROM DROP SITE: Tyre is Now the Epicenter of Israel’s Assault on Lebanon Hungry Palestinians in Gaza Protest World Central Kitchen Scaling Back Amid Rising Food Costs and Israeli Blockade Drop Site is now live on WhatsApp. Get our latest reporting, podcasts, and breaking news, delivered directly. Join the channel here. This is Drop Site Daily, our free daily news recap. We send it Monday through Friday. Today’s edition is being sent to more than 750,000 subscribers. Help us grow that number by forwarding and recommending this newsletter. Subscribe now 🛒 Get your “Drop [Site] News/Not Bombs” Hoodie here: Get Your Hoodie U.S. President Donald Trump speaks during a Cabinet meeting in the White House on May 27, 2026 in Washington, D.C. Photo by Win McNamee/Getty Images. Subscribe now Iran and Ceasefire U.S. and Iran near preliminary MOU to extend ceasefire 60 days and open permanent peace talks, reports say: U.S. and Iranian negotiators have drafted a preliminary memorandum of understanding to extend their ceasefire for 60 days and begin negotiations toward permanently ending the war, U.S. officials told Al Jazeera on Thursday—though the framework still requires President Donald Trump’s final approval. The deal, also reported by Axios, entails unrestricted vessel traffic through the Strait of Hormuz and a staged U.S. lifting of its naval blockade on Iranian ports. Iran’s semi-official Tasnim news agency denied the deal was finalized, with a source close to the negotiations saying “any narrative from Western sources about the finalisation of the matter is not valid” until Iran formally notifies its Pakistani mediator. An Iranian official confirmed separately to Drop Site’s Jeremy Scahill that Tehran had agreed to what mediators said was final draft language of a memorandum of understanding. However, a “deep distrust” of Trump is preventing any official announcement. According to the official, Iran is unable to rule out further U.S.-Israeli strikes. “Some voices on the Iranian side are concerned that President Trump may reconsider his position at the last moment,” the official said, adding that Iran would not consider Trump’s decision final until U.S. “financial markets close at the end of the week.” Iran also warned Trump would likely mischaracterize the privately agreed terms to promote his “victor” narrative. On Friday morning, Trump said he would be convening a meeting in the Situation Room, reiterating his demands in a post on Truth Social. Iran must “agree that they will never have a Nuclear Weapon or Bomb,” he posted, while also calling for the Strait of Hormuz to be “immediately open, no tolls” to unrestricted shipping, and for any remaining naval mines to be removed or detonated. He said ships affected by what he described as a “our amazing and unprecedented Naval Blockade” could now “start the process of ‘heading home’.” Trump also said enriched nuclear material buried underground after earlier U.S. strikes would be “unearthed by the United States…in close coordination and conjunction with the Islamic Republic of Iran, plus the International Atomic Energy Agency, and DESTROYED,” and added that “no money will be exchanged, until further notice.” Iran’s parliament speaker and chief negotiator in indirect talks with the United States, Mohammad Bagher Ghalibaf, issued a warning on X on Friday as negotiations continue: “We do not obtain concessions through negotiations. We obtain them with our missiles,” adding that “we have no trust in guarantees or promises, only in actions.” Ghalibaf also said, “We will not take any step before the other side acts first,” and concluded that “the winner of any agreement is the one better prepared for war the next day.” Oman tells Bessent it has “no plans” to participate in Hormuz toll plan: U.S. Treasury Secretary Scott Bessent said Thursday that the Omani ambassador had assured him the country has “no plans” to participate in any effort to impose fees on ships transiting the Strait of Hormuz. The exchange comes a day after Trump warned that “Oman will behave just like everybody else, or we’ll have to blow them up.” Bessent said he warned the ambassador of possible sanctions. Iran has recently denied that it plans to charge tolls, describing its fee framework instead as pilotage and navigation service fees comparable to systems used by Turkey, Australia, and Canada, intended in part to offset war damages. Trump team quietly developing indirect financing mechanisms for future payment to Iran: With President Donald Trump unwilling to authorize any arrangement that could be framed as a direct cash payment to Iran, his team has been quietly developing alternative financing mechanisms, three anonymous U.S. officials told the New York Times. Gulf Arab states have been lobbied to underwrite Iran’s postwar reconstruction through a $300 billion investment fund, while a separate mechanism under discussion would unfreeze Iranian assets held by Qatar, which would then purchase medicines and feedstock for direct transfer to Iran—both steps requiring U.S. approval. U.S. oil stockpiles fall for fifth straight week: U.S. commercial crude inventories fell 3.3 million barrels to 441.7 million barrels—about 2% below the five-year seasonal average—for a fifth consecutive week, the Energy Information Administration reported Thursday. The Strategic Petroleum Reserve also dropped by 9.1 million barrels to a total of 365.1 million barrels. Separately on Thursday, Exxon Mobil Senior Vice President Neil Chapman warned that a price spike is “two weeks or three weeks” away, saying inventories are approaching “unheard of” lows and that physical Brent crude could spike to $150–$160 per barrel; Brent futures closed under $94 Thursday as markets held out hope for a U.S.-Iran deal. Israeli officials privately urge Trump to abandon Iran talks, assassinate lead negotiator, report says: Israeli officials are privately pressing the Trump administration to scrap nuclear negotiations with Iran, assassinate parliament speaker and lead negotiator Mohammad Bagher Ghalibaf, and launch a fresh round of strikes on Iranian oil infrastructure, according to reporting from Capital & Empire’s Aída Chávez. Israeli officials reportedly believe renewed attacks could trigger economic collapse and the regime change Israel sought at the outset of the war. Read Chávez’s full piece here. Lebanon Casualty count: At least 3,355 people have been killed, and 10,095 wounded in Israeli attacks on Lebanon since March 2, according to the Lebanese Health Ministry. Israeli strikes and forced displacement orders across southern Lebanon: Israeli strikes killed at least six people in southern Lebanon on Friday, according to the National News Agency, including four in an airstrike at the Abbasiyah junction and one in an attack on Deir Qanoun al-Nahr. A municipal police officer in the town of Aaba was killed in a drone strike on his hometown. Rescue teams recovered the bodies of two victims after searching through the rubble of a house struck by Israeli aircraft in Tyre Dibba. The Israeli military issued forced displacement orders Friday ordering residents of Ansariya, Al-Kharayeb, Shabriha, Sarafand, Adloun, and Baisariya to immediately evacuate north of the Zahrani River, claiming it was “compelled to act forcefully” against Hezbollah in the area. Israeli strikes kill 31 across Lebanon on Thursday: At least 31 people were killed and 68 wounded Thursday in Israeli attacks across Lebanon, according to the country’s Health Ministry. Israeli forces push north of Litani River: The Israeli military crossed the Litani River from the Zawtar and Yuhmur areas on Thursday, deepening the assault on Lebanese territory, according to Press TV correspondent Hadi Hoteit, in an attempt to advance on the Arnon hill and the historic Beaufort Castle area. The move has been questioned by Israeli analysts and former generals, who say that moving into the area puts the army at risk of entering a “kill zone,” where Hezbollah has a strategic advantage—and as Hezbollah has recently achieved what Hoteit called a “micro-air superiority” with its effective use of FPV drones. UNICEF: 11 children killed or injured by Israel daily in Lebanon: 11 children have been killed or injured every 24 hours over the past week in Lebanon despite a nominal ceasefire, UNICEF said via L’Orient Today. UNICEF described the toll as “staggering,” with spokesperson Ricardo Pires stating on Friday that 15 children were killed and 62 injured in the past seven days, citing figures from the Lebanese Ministry of Health, adding that “the vast majority of these children were impacted by airstrikes in south Lebanon.” At least seven children were killed and 30 injured on Thursday, according to the Ministry. Lebanese and Israeli military officials to hold U.S.-brokered security talks: Lebanese and Israeli military officials are set to hold their first security talks on Friday in Washington, D.C., amid Israel’s ongoing military assault in southern Lebanon, according to the Associated Press. Lebanese Prime Minister Nawaf Salam said “nothing can justify” Israel’s continued “assaults” on southern Lebanon, calling for an immediate ceasefire and full Israeli withdrawal. Meanwhile, Hezbollah’s parliamentary bloc criticized the talks, saying Lebanon’s authorities were “compromising both sovereignty and rights” and “actively working to obstruct” opportunities linked to regional negotiations involving its ally Iran. Israel escalates assault on Tyre: At least 15 Israeli airstrikes hit the Lebanese city of Tyre overnight Wednesday into Thursday, killing at least three people and wounding 17 others in a direct strike on a residential block near the El-Buss Palestinian refugee camp—one of the hardest nights since the start of the war, according to local civil defense official Moussa Shaalan. The assault has triggered another wave of mass displacement from Tyre, a UNESCO World Heritage city of around 160,000, where tens of thousands had remained or returned after earlier waves of bombardment; Prime Minister Benjamin Netanyahu said Monday that Israel would “intensify our strikes” and instructed the military to “step on the gas even more.” Read Lylla Younes’ latest for Drop Site here. Palestine Israeli attacks on Friday: Three Palestinians were killed and several injured early Friday after Israeli drones targeted a police checkpoint in the Al-Mawasi area of Khan Younis in the southern Gaza Strip, according to WAFA. More civilians were wounded in the Al-Qarara Mawasi area after strikes led to fires igniting in tents sheltering displaced families. Five people were injured when an Israeli strike hit Al-Yarmouk Street in Gaza City, causing a fire inside a residential building. Israeli attacks kill at least 14 Palestinians in northern Gaza on Thursday: Israeli strikes killed at least 14 Palestinians on Thursday across northern Gaza, according to Gaza’s Health Ministry. Among the attacks, an Israeli drone strike killed at least one person and wounded several others after targeting a group of civilians in Gaza City’s Al-Zaytoun neighborhood, Shehab News reported. Civilians were forced to evacuate areas around Al-Aqsa Hospital in central Deir al-Balah on Thursday. Israeli strikes hit the area’s recently emptied homes and burned tents sheltering displaced families, destroying entire residential blocks, according to Eyad Amawi of the Gaza Relief Committee. In Al-Shati refugee camp west of Gaza City, residents returned to devastation after Thursday evening strikes. Drop Site contributor Abdel Qader Sabbah sent footage of the scene from Al-Shati, available here. One resident told Sabbah that he received a phone call from an Israeli officer ordering the evacuation of an area of roughly 300 meters. The officer claimed a military target was present. “I told him there is no military target. Everyone here is civilian,” the resident said. Netanyahu orders IDF to expand control to 70% of Gaza: Israeli Prime Minister Benjamin Netanyahu said Thursday he has directed the Israeli military to seize 70% of the Gaza Strip, up from the roughly 60% Israel currently controls which goes beyond the agreed upon “Yellow Line” in the ceasefire agreement. “At this point, we are fully in control of 60%of the territory of the Gaza Strip… and my directive is to get to… 70%,” Netanyahu said in an interview at a conference in the occupied West Bank, while an audience member cheered in the background, urging him to take 100% of the Strip. “Wait, let’s go in order. First 70%. Let’s start with that,” he responded. Hamas warns ceasefire faces “risk of collapse”: In a statement Thursday condemning an Israeli airstrike on an apartment in central Gaza City on Wednesday that killed 10 people, including five children and two women, Hamas warned that the recent escalation of hostilities signals Israel’s attempt “to return to the brutal war of extermination that lasted for two full years on Gaza.” Hamas called on the U.S. and ceasefire guarantor countries to condemn Israel’s violations and take “serious and urgent steps” to enforce the agreement. UN report documents Israeli rape, sexual abuse of Palestinians: A new United Nations report submitted by Secretary-General António Guterres—documenting the sexual abuse of Palestinians in Israeli detention—has added Israeli forces to a list of parties accused of conflict-related sexual violence, according to Haaretz. The report names the Israel Defense Forces, Israel Prison Service, and a border police counterterrorism unit, and documents 31 victims since 2023 from the Gaza Strip and the occupied West Bank, including men, women, and children. It says the reported abuses included “rape, including with objects, gang rape,” as well as “physical violence to the genitals, instances of targeted shooting of the genitals.” The U.N. cited what it described as a “systematic lack of accountability,” adding that its findings should be viewed as “indicative rather than comprehensive” as Israel continues to deny investigators access and detainees continue to face “explicit threats” from Israeli forces aimed at preventing them from reporting abuse. Israeli forces shut down Ibrahimi Mosque in Hebron: Israeli forces on Friday closed the Ibrahimi Mosque to worshippers in Hebron, the occupied West Bank “until further notice,” according to WAFA. The acting director of the sanctuary, Hammam Abu Morkhia, described the move as a “blatant violation” of the mosque’s sanctity. The Palestinian Ministry of Awqaf and Religious Affairs condemned the closure, warning it reflects attempts to alter the religious and historical status quo in Hebron. Israel approves major settlement expansion plan in Jordan Valley: Israeli authorities have approved a large-scale settlement expansion plan in the Jordan Valley, according to the Palestinian Wall and Settlement Resistance Commission. The commission said the plan targets the Misawa settlement built on Palestinian land in the Al-Far’a Valley area of Jericho and includes 517 new housing units across about 1,692 dunams, along with infrastructure, roads, and public facilities intended to expand the settlement into a fully integrated complex. France refers Israeli abuse of Gaza flotilla detainees to prosecutor: France has referred the treatment of its nationals detained by Israel last week as a part of the Global Sumud Flotilla to the public prosecutor, Foreign Minister Jean-Noël Barrot announced Friday, citing a consular report documenting sexual violence, exposure to cold, beatings, and repeated humiliation of French citizens. The Global Sumud Flotilla was seized by Israeli forces in international waters during an attempt to deliver aid to Gaza; activists report widespread abuse, with at least 15 cases of sexual assaults, including rape. Gaza civilians protest WCK meal cuts as Iran war drives up food costs: World Central Kitchen, the largest provider of hot meals in Gaza, halved its daily distribution from roughly one million meals to 500,000 this month, citing financial pressures driven by the U.S.-Israeli war on Iran. The move leaves thousands of Palestinian kitchen workers suddenly unemployed, sparking protests. “We truly have nothing. Where are we supposed to work? How are we supposed to feed our children? I sit waiting at the community kitchens from 8 in the morning. This is the result,” one man said at a demonstration. Read the latest from Abdel Qader Sabbah and Sharif Abdel Kouddous for Drop Site here. U.S. News By Julian Andreone, with Ryan Grim. Have a tip on Capitol Hill? Email Andreone at [email protected]. AIPAC routes millions to Rep. Haley Stevens through third-party processor: AIPAC has shifted how it funds the Senate primary campaign of Michigan Rep. Haley Stevens, according to a new investigation by the Detroit News. The group is now routing donor money through a third-party processor called Democracy Engine to prevent AIPAC’s name from appearing prominently in campaign filings. A separate pro-Israel super PAC also launched a $5.3 million ad blitz backing Stevens, giving her a financial advantage over progressive challengers Mallory McMorrow and Abdul El-Sayed. DOJ sues Massachusetts over refusal to issue undercover license plates to ICE: The Justice Department sued Massachusetts Thursday over the state’s refusal to issue confidential license plates to federal immigration agents, arguing the policy discriminates against ICE and CBP in violation of the Constitution’s Supremacy Clause—one of four such lawsuits filed Wednesday and Thursday against Massachusetts, Maine, Washington, and Oregon. Governor Maura Healey rejected the suit as another “specious complaint against political enemies,” arguing that confidential plates are reserved for criminal law enforcement and that ICE’s civil enforcement work does not qualify. Supreme Court rules 5-4 for Black Mississippi death row inmate: The Supreme Court ruled in favor of Terry Pitchford, a Black death row inmate from Mississippi who argued racial bias tainted the jury that convicted him of capital murder, with Justice Brett Kavanaugh writing for a 5-4 majority. Pitchford’s trial featured 11 white and one Black juror, and was prosecuted by Doug Evans—a now-retired prosecutor with a documented history of dismissing Black jurors. The ruling entitles Pitchford to a new trial in state court. Collins responds to Platner regarding the Iraq War: MaineSenator Susan Collins answered a claim made by her senatorial opponent, Graham Platner, in a New York Times interview that she sent him to “die in Iraq” by voting for that war. “That was Platner’s decision to serve,” she told a reporter. “He was not drafted.” Platner responded later on Thursday by saying that Collins, “all these years later,” had decided “to blame those of us who, in our late teens and early 20s, signed up to serve our country.” Trump-appointed acting U.S. attorney dropped gun charge against Israeli linked to illegal Nevada biolab: An acting U.S. attorney, appointed by Trump, dropped a federal gun charge against Ori Solomon, an Israeli immigrant arrested during an investigation into a suspected illegal biolab in Nevada, citing the “interests of justice” with no public explanation. The property contained refrigerators filled with unidentified vials and shared similarities with a California lab, which reportedly had samples labeled with diseases including HIV, malaria, and Ebola. Hundreds protest Jerusalem real estate expo in Manhattan: Hundreds of demonstrators rallied outside the Hilton Midtown in Manhattan Thursday evening to protest the Jerusalem Comes to NYC real estate expo, which was attended by Jerusalem Mayor Moshe Lion and marketed properties in Jerusalem, with organizers accusing sponsors of facilitating Palestinian displacement and promoting settlement expansion. Protesters also opposed a parallel aliyah fair sponsored by Nefesh B’Nefesh, a settler recruitment event encouraging immigration to Israel. Trump admin moves to vacate enforcement order against Winklevoss twins’ Gemini crypto exchange: The Commodity Futures Trading Commission asked a New York federal judge Wednesday to vacate a January 2025 consent order against Gemini Trust, calling the original complaint one that “should not have been filed.” The cryptocurrency exchange was founded by Tyler and Cameron Winklevoss, who donated to Trump’s 2024 campaign. The order included a $5 million penalty and an injunction barring Gemini from making false statements to the agency, stemming from misrepresentations made in 2017 about a Bitcoin futures contract. Former CFTC Chair Tim Massad called the move “very unusual.” Other International News RSF kills at least 30 civilians in North Kordofan attack: Rapid Support Forces attacked several villages near Bara in North Kordofan state on Thursday, killing at least 30 civilians, according to Sudan Tribune. Approximately 20 RSF combat vehicles struck the Al-Murra, Um Saadoun al-Sharif, and Al-Radha areas. Bara, the second-largest city in North Kordofan, is currently under RSF control after changing hands multiple times during the conflict; the Sudanese Armed Forces continue to hold El Obeid, the state capital. Burhan denies consultations in UAE: Sudanese Sovereignty Council Chairman General Abdel Fattah al-Burhan flatly denied Thursday that any consultations had taken place in Bahrain, calling a recent Middle East Eye report alleging he had signaled readiness to open dialogue with the UAE “completely untrue.” Guatemala agrees to joint U.S. military strikes on its soil: Guatemala agreed to allow joint U.S. airstrikes and military operations inside its borders targeting alleged drug trafficking groups, with President Bernardo Arévalo signing off on the arrangement in a call with War Secretary Pete Hegseth last week, the New York Times reported on Thursday. The Guatemalan government later denied that report, calling it inaccurate but confirming it had sought a different arrangement. It released a May 28 letter from Defense Minister Henry Saenz to Hegseth stating his country’s “desire” to “lead, with US assistance, active military operations” against U.S.-designated drug trafficking organizations, “in accordance with existing bilateral agreements and arrangements.” Subsequent reporting from El País claimed that the plans were geared toward a media spectacle, with one source telling the Spanish paper, “What they offered us was to select one or two places to carry out bombings and televise it all.” At least 52 killed in clashes between rival FARC factions: At least 52 guerrilla fighters were killed in clashes between two rival FARC dissident factions vying for control of a cocaine production region in Colombia, according to a statement Thursday by one of the groups involved—the most violent such fighting in recent months. The clashes pitted a faction known as Iván Mordisco against Calarca Córdoba; though the latter group is currently in peace talks with President Gustavo Petro while Iván Mordisco remains in conflict with authorities after Petro suspended a bilateral ceasefire with the faction in 2024. U.S. designates Brazil’s two largest criminal gangs “terrorist organizations”: Secretary of State Marco Rubio announced Thursday that the Trump administration will designate Brazil’s Primeiro Comando da Capital and Comando Vermelho as Foreign Terrorist Organizations effective June 5. A foreign affairs adviser to the country’s president, Lula da Silva, welcomed international cooperation on money laundering and arms trafficking but warned that any “pretext for intervention” in Brazilian sovereignty would be “unacceptable.” His opponent, the right-wing candidate Flavio Bolsonaro, said he personally petitioned for the designations during meetings with U.S. officials in Washington this week. Mexico’s lower house approves constitutional amendment allowing elections to be nullified over foreign interference: Mexico’s Chamber of Deputies approved a constitutional amendment Thursday, 307 to 128, that would add foreign interference—defined as illicit financing, disinformation campaigns, digital manipulation, and pressure from foreign governments or media—as grounds for nullifying an election. President Claudia Sheinbaum cited repeated electoral interference from Washington throughout the region. The measure still requires Senate approval and is unlikely to affect the next federal elections in June 2027. Kenyan court suspends U.S. Ebola quarantine facility: A Kenyan High Court judge suspended a planned U.S. Ebola quarantine facility on Friday, hours before it was set to open, after a human rights group filed a legal challenge arguing the secretive arrangement raised “grave constitutional concerns.” The facility—a 50-bed isolation unit at Laikipia Air Base, about 200 kilometers from Nairobi—intended to quarantine U.S. nationals arriving from the Democratic Republic of Congo, and was established to avoid repatriating exposed Americans to U.S. soil, a policy which drew criticism from U.S. doctors and Kenyan health workers alike. Kenya’s doctors’ union issued a 48-hour strike alert Thursday, warning Kenya should not become a “dumping ground” for Ebola cases. Russian drone crashes into Romanian apartment building, injuring two: A Russian drone reportedly headed toward Ukraine crashed into a residential building in the Romanian city of Galati overnight Friday, injuring two people and triggering a fire that forced evacuations, in what Romania’s Foreign Ministry called a serious violation of international law. The country’s president, Nicusor Dan, said Romania would not accept Russia’s war “being transferred to its citizens.” NATO, of which Romania is a part, also condemned Russia’s “reckless behaviour” in response to the crash. If you want to continue getting this newsletter, you don’t have to do anything. But if this is too much—we do try to be mindful of your inbox—you can unsubscribe from this newsletter while continuing to get the rest of our reporting. Just go into your account here at this link, scroll down, and toggle the button next to “Drop Site Daily“ to the off setting. It looks like this: Subscribe now Leave a comment From Drop Site News via This RSS Feed.
Komunitas
beehaw.org
Oh, joy. Google’s I/O conference is next week, and we expect to hear a lot about the company’s AI endeavors. The company says there’s so much to talk about that it’s spilling the Android beans a little early, and yes, a lot of AI is involved. In the coming months, Google will roll out more smartphone AI features under the Gemini Intelligence banner, bringing more automation and customization to your phone. App automation will be a major element of Android going forward, Google says. Automation for apps is expanding after Google began testing it earlier in 2026 with DoorDash and Uber on Pixel and Samsung phones. It was a very frustrating experience at launch, but Google says it has spent the intervening months fine-tuning the system. Google promises that Android will be able to handle more complex automations across apps. For example, the robot could find a course syllabus in Gmail and then hop to a shopping app to add the necessary books to your shopping cart. Google also suggests taking a picture of a travel brochure and telling Gemini to book something similar in the Expedia app. I’ve yet to find a reason I’d want my apps talking to each other, let alone unsupervised. But the more basic issue is that agentic “AI” isn’t fit for purpose. So, I have a follow-up appointment for how my dentures are fitting. Would it be cool to just say “appointment at 1 p.m. at this location in two weeks” and have it arrange the rideshares so I don’t need to remember anything? Sure, it would. Kinda creepy, but cool nonetheless. A couple of issues here: I generally use Lyft, and outside of trips to the airport, I’ve learned that booking in advance is always far more expensive than the spot price (YMMV). Atop this, I usually get a beg notification offer by looking into pricing about 15 minutes before my planned need to book. Put it all together, and I can end up paying fully 75% more than I needed to. That’s the last thing I want Gemini automating for me. It’s a fucking medical appointment. Divining when I’ll need to be picked up is a fool’s errand, no matter what I blocked out for it in my calendar. Unless you plan on closing down a bar, this applies similarly. So, I’ll get the guaranteed highest price with no flexibility for reality, and this is an improvement? And you want me to use this to book larger travel plans? Highest airfare, highest hotel rate? Reservations at a Michelin restaurant because we happen to have traveled there for our anniversary, but between the outrageous airfare and usurious hotel rate, ain’t no one got the money for a $120 steak.
Komunitas
sopuli.xyz
terrygodier.com The Boring Internet https://indieweb.social/@tg 16–20 minutes You have noticed that the internet is dying. Twitter changed hands, changed names, and changed shape, and the version of it you knew is gone. Reddit went public. Google search now returns generated answers stapled to half a dozen ads. Instagram is bots making content for bots. Discord servers you joined in 2019 have gone quiet. The blogs you read in 2012 redirect to parked domains. The forums where you learned what you know got bought, gutted, redesigned, and left to rot. This is real. You are not imagining it. The places you spent your younger years are gone or unrecognizable, and the places you use now are visibly straining under a flood of machine-generated text nobody asked for. There is a low ambient grief about it, and a faint guilt, something like: “I should be doing something. I should be somewhere else. I want the old thing back.” I want to tell you a thing that I think is true, and that I think will make you feel better. The internet is not dying. A commercial veneer glued on top of it is dying. The layer where every human activity became a venture-backed destination, every destination became a feed, every feed became ad inventory, and every ad market became a machine for producing more things to interrupt you with. Underneath that layer is another internet: older, slower, less polished, harder to monetize, and much harder to kill. It is not utopia. It is full of spam, abandoned servers, broken clients, hostile nodes, strange old commands, half-maintained software, and people arguing in plain text about things no normal person should care about. But it has one enormous advantage over the platforms that replaced it in your imagination. No one owns it. The Layers You can see the layers if you draw them out. services things you can be priced out of GmailGitHubCloudflareAWSStripeAuth0CDNsVercel protocols things no one can take from you HTTPSMTPIRCRSSIcecastNTPUsenetDNSBGPSSHFTPNNTPWebDAVGeminiFingerTCPUDPPOP3IMAPXMPP a thin commercial crust on something much larger The platform layer is the loudest and the youngest. It is culturally dominant. It is where most of the screenshots come from. It is where the arguments happen and where the panic lives. It is also a thin commercial crust on top of older, quieter machinery. Under the platform layer is the service layer: the companies that own infrastructure but do not always need to become the destination. Gmail. GitHub. Cloudflare. AWS. CDNs. Payment processors. Identity providers. These things are not innocent. They are not outside the market. Some of them are enormous, and some of them have more power than anyone should be comfortable with. They don’t need to become the place where your whole social life happens. Cloudflare does not need you to scroll Cloudflare. AWS does not need you to post memes. Under that is the protocol layer. This is the old machinery. Not pure. Not beautiful. Not easy to use. A lot of it is ugly, ancient, underspecified, overcomplicated, and held together by conventions nobody remembers writing down. But it has a different shape. Most of these protocols were designed from the 1970s through the early web era by small groups of people solving immediate problems. “How do we send mail between machines?” “How do we ask who is logged in?” “How do we move hypertext across a network?” “How do we synchronize time?” “How do we publish a stream of updates?” “How do we broadcast audio?” They were built mostly by nerds with no business plan, no venture capital, and no permission. The protocols belong to no one. They can’t be acquired. They can’t be taken public. The reason your mee-maw and your bank and your boss can all reach you at the same email address is that the protocol that made it possible was published more than forty years ago, and the people who published it did not successfully capture it inside of one company. Tuning In Rusty Hodge has been running an internet radio station called SomaFM out of San Francisco since 2000. The station is independent, listener-supported, ad-free, and curated by actual people with actual taste. For more than two decades, people around the world have been listening. SomaFM runs on boring internet radio infrastructure: open streams, playlist files, direct URLs, Icecast servers. When you press play on a SomaFM stream, your browser does not ask a social graph whether the song is relevant. No algorithm decides what plays next because it predicts you are likely to remain engaged for another seven minutes. No advertiser shapes the rotation. No platform tries to convert the moment into a growth loop. A person makes choices and broadcasts them. You tune in or you do not. That’s the whole transaction. Here’s SomaFM’s live stream. Press play and a small server in San Francisco starts handing you a song. For the purpose of this essay, I set up my own internet radio station featuring the latest album of music I wrote, recorded, and produced. You can listen live, streaming (miraculously!) from a small computer in New York. Spotify launched years after SomaFM. It was supposed to make stations like these obsolete. It did not. The reason is structural. Spotify has to extract enough value from listeners to satisfy public-market investors. Over time, it will be pressured to transform, bundle, optimize, and extract more from the same act of listening. SomaFM has to cover bandwidth costs and keep Rusty fed. My station has an even lower bar. It’s just for fun. It can be tiny. It can be pointless. It can run for a while, make a few people smile, and disappear without becoming a failed startup. This distinction matters. some things need to become enormous to survive. other things survive because they never needed to become enormous. Fossils Still Load-Bearing SMTP1982 email — the federation that didn’t lose Still federated. Still belongs to no one. Still the only mass communication system on earth where any provider can reach any other provider without permission from the company that owns the network. Email is not clean. Email is full of spam, phishing, AI-generated sales sludge, fake invoices, newsletters you swear you never signed up for, and random dudes asking whether you have fifteen minutes to discuss pipeline optimization. But that is the point. Email did not survive because nobody abused it. Email survived because abuse did not turn it into one company. Spam can ruin an inbox. A bad provider can ruin a service. A policy change can ruin deliverability for a domain. Gmail can make life worse for everybody by becoming too powerful. But no one can ruin email in a product meeting. That is what survival looks like at the protocol layer. Not purity. Persistence. IRC1988 chat — before chat became a workplace surface The chat protocol that predates Slack by decades. The old networks are mostly gone or changed beyond recognition, but IRC itself is not gone. Libera Chat and other networks are still active every day. Open-source projects still use it. Rooms descended from IRC culture still shape how technical communities get things done. It’s not fashionable. It’s not welcoming in the way modern software tries to be welcoming. It has commands. It has norms. There’s a culture and a learning curve. You can absolutely enter the wrong room, say the wrong thing, and discover that nobody there has any interest in making the experience smooth for you. And yet it remains one of the few places online where chat still feels like chat instead of a workplace surface. Usenet1980 threaded conversation — the original shape of the social internet Less alive than the others, but the bones are warm. The shape of nearly every threaded discussion you have ever read descends from it: named groups, posts, replies, quotations, arguments accreting around a topic until the topic itself disappears under the argument. Reddit did not invent this shape. Reddit made it legible to a later web, walled it off, and monetized it better. Usenet is what the social internet looked like before the social internet had product managers tasked with growth and viral loops. RSS1999 syndication — the protocol that survived its own death Google Reader was discontinued in 2013, and a generation of people decided RSS was over. It wasn’t over — it just stopped being fashionable. RSS still delivers news sites, changelogs, newsletters, video, and the quiet daily output of people who still publish on their own sites. It’s also the distribution substrate for podcasting, a medium now consumed by enormous numbers of people, most of whom never see the feed. NTP1985 time — the protocol that synchronizes the clocks Every device you own needs to know what time it is. So does your bank, your calendar, your router, your security certificates, your deployment logs, your authentication tokens, and the payment terminal at the coffee shop. Almost every modern system assumes time is boringly, invisibly correct. NTP was shaped for decades by David Mills and a small orbit of maintainers, volunteers, students, and institutions. It became so essential, and so commercially unglamorous, that almost everyone depended on it while almost no one thought about it. That’s another kind of boring. Not abandoned. Load-bearing. Finger1971 presence — the first status update The deepest cut on the list. The kind of thing you bring up at dinner if you want everyone to look at you with concern. Finger is a protocol from before the web for asking: what is this person up to right now? It was the first status update. Before feeds, before away messages, before AIM profiles, before Twitter bios, before Slack status, before stories, before /now pages, there was a little command that asked a machine for a person’s .plan. It is barely alive. It is a fossil you can still run. I set up a server with a finger service on it that you can try right now. Open your terminal and type: finger [email protected] and see what comes back. a protocol from before the web, still answering There are more. DNS, the protocol that turns terrygodier.com into a number. BGP, the protocol that decides how packets actually get from one continent to another. SSH, the protocol that lets you step into a machine far away as if distance were a local inconvenience. NNTP. FTP. WebDAV. Gemini. The whole neighborhood of the IndieWeb. Most are older than the kids on TikTok and still running. Why They Survive The reason these systems survived is also the reason they are surviving the AI flood, and the reason they will probably outlive most of what is being built today. boring adjective. Of a technology: too useful to disappear, too uncool to hype, too federated to acquire, and too awkward to turn cleanly into a platform. The single most reliable predictor of digital survival. The boring internet survives for three reasons, none of them romantic. First: it has no CEO. Nobody can sell it. Nobody can pivot it. Nobody can take it public and gut it for shareholders. Nobody can call an all-hands meeting and explain that, going forward, the protocol will prioritize video. This is not because protocols are magically democratic. Many are governed badly. Some are captured in practice by big companies. Some are maintained by exhausted volunteers. Some are trapped in standards bodies where good ideas go to be slowly discussed to death. But the decision-making is distributed among the people who use it, implement it, maintain it, extend it, argue about it, and occasionally abandon it. This is slow. This is frustrating. This produces committees, mailing lists, drafts, forks, incompatible clients, flame wars, and astonishingly ugly configuration files. It is also why the thing is still here. Second: it is too federated to centralize. There is no single email server. No single IRC network. No single RSS endpoint. No single website. No single Icecast directory. No single DNS server that is “the internet.” There are many of each. platform one switch flips the lights on every node protocol one neighborhood burns; the rest keeps posting You cannot kill a federated thing by killing one node, the way you can kill a platform by changing one company. You can damage it. You can neglect it. You can make parts of it unusable. You can create enormous power concentrations around it. Google can dominate email hosting. Cloudflare can sit in front of half the web. Spotify can intermediate podcasts. Apple can shape how feeds are discovered. Bad actors can flood open systems with garbage. The failure mode is different. A platform fails in public. One acquisition, one pricing change, one API shutdown, one new owner, and suddenly the place you used to live has different locks on the doors. A protocol fails unevenly. This server goes down or that client stops working. This network gets weird or that provider becomes hostile. One neighborhood burns while another one keeps posting through it. That isn’t perfect. But it’s better than a single switch. Third: it is too awkward to fully extract. Machine-generated garbage does not spread evenly. Search. Social. Video. Shopping. Feeds. Anywhere a human glance can be measured, packaged, auctioned, and sold, machines will arrive to manufacture more things for humans to glance at. Boring protocols are not immune to this. Email proves the opposite. The boring internet isn’t protected by innocence. It’s protected by awkwardness. There is no global RSS feed to poison. No central IRC timeline to optimize. No Finger For You page. No Icecast engagement graph deciding that your ambient drone station should pivot to reaction content because thirteen percent more users remained active through minute four. Every property that made these protocols feel old and uncool to you in 2014 is part of what’s keeping them alive in 2026. What I’m Building I’ve spent the last year building things on this layer. Current is an RSS reader. Not a social app pretending to be a reader. Not a recommendation engine wrapped around articles. A reader. It takes feeds from sites you choose and shows them to you. Sourcefeed and Byline live in the same neighborhood: small tools for publishing, reading, and moving through the web without pretending the web needs to become a platform again. These aren’t acts of nostalgia. I don’t want to teleport to 1999 with a beige computer and pretend everything was better when getting online made a noise. I am trying to build on the part of the internet that still has the properties I want software to have: durable, legible, user-shaped, hostile to enclosure, and quiet enough that a single person can still understand the whole thing. I’m not the only one. Personal sites are coming back. RSS feeds are coming back. Webrings are coming back. People are remembering that a website can be a home or a place instead of a profile. Mastodon is, for all its quirks, a federated SMTP-shaped thing for short messages and not a platform in the old sense. Small internet radio stations still broadcast from servers with ugly URLs. Newsletters still arrive through SMTP. Software projects still publish changelogs through feeds. Communities still gather in places too small to be interesting to investors. You Are Standing In It You are reading this in a web browser. Take a moment and notice what is around you. the page reached you HTTP1991 three decades old, still serving every webpage you have ever read. the clock in the corner stayed accurate NTP1985 right now, your computer thinks it is 11:46:24. you probably found this essay through RSS1999 the same protocol family that delivers podcasts and blogs to people who may never know it exists. the audio you heard came over Icecast2001 from a server I run, broadcasting music I made. if you signed up, your address travels by SMTP1982 still federated. still belongs to no one. if you ran the command, you used Finger1971 a protocol from before the web. the first status update. Six old systems, all passing quietly under your hand. You did not just read about the boring internet. You used it. The internet you grew up on is not gone. Some of its commercial superstructure is, and more of it will go. The next decade is going to be strange for any company whose value proposition was: we host the place where you talk to your friends. The platforms will keep mutating. The feeds will keep filling. The slop will keep rising. The grief is real and you are not wrong to feel it. But the actual internet — the protocols, the federated services, the plain-text commands, the open feeds, the small servers, the personal sites, the things people built when user and developer were sometimes the same word — is still right there. It was not demolished. It was buried under a louder layer for a while. Now the louder layer is thinning out. You do not have to wait for someone to rebuild what you lost. You are standing in it.
Komunitas
fed.dyne.org
Google Chrome is downloading a 4 GB Gemini Nano model onto users’ machines without consent, with no opt-in, no opt-out short of enterprise tooling, and an automatic re-download every time the user deletes it. The pattern is identical to the Anthropic Claude Desktop case I wrote about last month, but the scale is between two and three orders of magnitude larger. This article does the legal analysis and, for the first time, the environmental analysis. The numbers are not small.
Komunitas
news.abolish.capital
Bullets: The economic model for the AI industry brought to us by Wall Street and Silicon Valley is falling apart, with subscription fees paid by users which are far below the companies’ cost of compute. The companies are also facing severe blowback for new data center construction almost everywhere, and constraints on power grids and capital budgets have delayed dozens of projects. Top managers of Silicon Valley AI companies are also, finally, facing harsh public scrutiny, particularly after the disastrous opening days of the War on Iran. The Artificial Intelligence industry is comprised of five layers: energy, chips, infrastructure, models, and applications. Now that Chinese Large Language Models are optimized to run on Chinese-built chips, are are soaring in popularity across the world by enterprise users, even top US industry insiders admit that Chinese tech will dominate going forward. Inside China / Business is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Report: Good morning. My team is a heavy user of translation software in China, and it is powered by AI systems which are very good. Several years ago a friend of mine was working with the Beta version of one of the newest models, and we sat down together and tested the software. I was very impressed that it was able to handle, easily, native American English idioms, such as from baseball or football. I used the terms “Hail Mary”, and “out of left field” in conversation, and it was no problem. Then we got a chemistry journal, and I read a long paragraph into the translation software, which he then read back to me, so it would translate his Chinese into my English. We selected that passage because neither of us were chemists, and probably were mispronouncing the words. The word order was changed, slightly, but otherwise it was perfect. I then took an article from ZeroHedge, about the market internals from a recent Treasury bond auction. It was very high-level finance, and the translator again, back-and-forth, was perfect. A few months later, my group was in Guangdong, meeting with engineers at a factory that builds manufactured housing. This factory group builds small homes and buildings for dozens of countries across the world, and I was asking if they could design for the US market. I asked them about Southwest house designs. They ran it through their system, and in a few minutes had generated plans and rough price estimates. Then I did the same for ranch-style, contemporary, and cabana houses that are popular in resort areas. Each time, the AI software generated comprehensive designs and prices. We were getting excited, because we can open vast new markets in North America and Europe, using these factories. Then I moved to the northern part of the country, and asked about Craftsman homes, and Cape Cods, which are very popular in the Northeast. Their software generated designs and plans, along with costs, but their engineers refused to build those. They didn’t like the different roof pitches and dormers, and especially disliked the chimneys and fireplaces. They had no experience building anything like them, and refused to do so even though their software was telling them how. Later I began to notice some issues with the translators, which are used by everybody now. For example, when I said, “Let’s deal with that problem next year”, the AI translates that exactly, and the Chinese group wrote down the date. But that isn’t what I meant. The meaning of that phrase would depend on on how the English speaker says it. Usually, we would mean that we don’t want to think about that problem right now; we have other priorities, and let’s come back to it later. But the AI runs off of probabilistic models, and will generate an answer based on what the speaker MOST LIKELY is trying to say. And there were problems with time constructions. In the example, “we need to fix that in the next year”, the software said that it is one year from today—this date, next year. Maybe I mean that, but I may mean that it needs fixing before the end of the next year, which is 2027. A professional translator—a person—would stop the conversation and ask me to be specific, because it could be translated in different ways. And a true “intelligent agent” software system should do that, too. I learned that I need to be more careful, than ever, especially when we are discussing time, and money. We are impressed that these systems can handle obscure idioms, from both Chinese and English. But we are concerned that they throw off confusing translations for important issues in scheduling and budgeting. The human element is everything. They are very powerful and effective tools, in the hands of people who know how to use them properly, and understand that what appears on our screens need to be carefully watched, and overruled. These tools are used by everyone in China, in business and industry. They are ubiquitous in manufacturing, engineering, academia, medicine, law, and accounting and finance. But we are a long way from having the machines do the thinking or work that can replace deeply experienced people. But that is not what was promised, by the Silicon Valley AI hyperscalers and AI giants. To illustrate that issue, consider the differences between how AI is being widely used here in China, compared to the United States, compared to the applications developed here in China. More to the point, what we thought we were supposed to have, compared to what we got. Geoffrey Hinton won the Nobel Prize for Physics, for pioneering work in machine learning and artificial neural networks. The “Godfather of AI” is how he became known, and gave a speech in 2016 in which he predicted that AI would be so transformative in radiology, that it is a waste of time to even study it anymore. “People should stop training radiologists” right now. It’s “completely obvious that in five years deep learning” will be “better than radiologists.” That prediction aged badly, it turns out. But imagine a kid born in the year 2000, who gets great grades, and who sees that speech from a Nobel Prize winner, and how that might inform future career choices. Should he go to medical school? Or is he better to head off to Wall Street or Silicon Valley instead? The world doesn’t have enough radiologists, and that problem is particularly acute in China, and in developing countries. Here we have the technology and the machines to generate scans, but a shortage of the people who can read them. In China, AI is being widely used in radiology, because it enables them to do more work, in less time. It’s an important tool in the hands of people who have top medical school training, and so AI coursework is bundled in at the university level. In medical imaging, AI technologies and the need for efficiency and accuracy in patient diagnosis are closely correlated, particularly in elder care. AI systems are rolling out across China’s hospital systems, first informed by top radiology centers across the country, then the tools given to other doctors—medical experts—in hospitals outside Tier-1 cities. This is an issue we need to remember for later. China considers its citizens’ patient data as highly confidential, a national security issue, even. So it’s unthinkable that Chinese medical data will be shared with the large language models used by Silicon Valley: The second sentence there, is true though—Chinese researchers do enjoy access to patient datasets from other countries, that are being used by those models. So in China, and in neighboring countries, AI is being used across medicine to replicate the top advice and quality of diagnostic care that until recently were only available in the top cities. And that’s how it did play out in the United States, too. The radiology industry in the United States is doing just fine, and radiologists are making more money than ever, especially in the big cities. If you’re in an American hospital, you might be getting the top care from radiologists pulling down hundreds of thousands of dollars a year, while using the latest AI tools. But it’s at the individual and household user level, though, where serious problems are. When the AI is used by normal people—not medical professionals-- the AI simply isn’t good enough, but the users do not know that. Tens of millions of Americans are getting health advice from chatbots, and the results are awful. This is a study that was just published by the Journal of the American Medical Association, which tried to measure the reliability of large-language models used in clinical environments. These 21 models were tested and failed over 80% of the time in cases where more than one medical condition might be present. When the researchers put in physical exam notes and lab results, the failure rate was 40 percent. One in four US adults already are using OpenAI and the other bots for medical advice, and a lot of that motivation comes from the affordability issue: 41% said they either don’t want to, or cannot, afford to pay the bill. Over 60 million Americans are asking ChatGPT about, among other things, if their medical condition is serious, or if a particular rash is infected, or if the symptoms they have may be communicable disease, and 80% of the time are told the wrong thing. For lay users, the LLM’s hallucinate. And that goes back to the adult supervision problem, and the question of what AI actually even is. When I use these tools, in my day-to-day life, and in my career, I can’t shake the feeling that I am seeing very, very fast search, bundled with other good software. The manufactured house company was simply running internet searches for Cape Cod houses, and the results were fed into CAD/CAM software. The obscure English idioms from American sports can also be looked up, online, and fed back. CAD/CAM isn’t new, and neither are internet searches. What is new, is that these tools are on my iPhone, instead of my laptop. So are the heroes for this story the guys at Foxconn and Apple, who’ve put a faster processor and a better microphone in my smartphone? Is it the people at Huawei and ZTE, who have installed 5G everywhere? (These tools work poorly on 4G, or on older versions of electronic devices). Perhaps most of all, I learned that almost nobody is paying for this technology. We all use it, heavily, but it’s free. That led me to wonder who is making investments of hundreds of billions of dollars, and how they hoped to get their investments back? What is the justification for all that CAPEX? Those five companies—Amazon, Microsoft, Google, Meta is Facebook, and Oracle have quadrupled their capital expenditures just since the release of ChatGPT-4. The biggest rise in their expenses comes from the high costs to run their large language models, which they are not charging users for. That’s to say that every one of those companies is losing money on AI, and fortunately for most of them they have massive operating cash frows from other divisions to cover the bills. That is not true of Oracle, by the way. They are in a very different place. And today the industry is staring down a tsunami of maturing debt that funded all these expansions: There is no magic bullet here, to pay debt down. It’s the same for every other company. That debt needs to be rolled over, most of it at junk bond rates. Or converted into equity, somehow, but you only do that if everyone has a gun pointed at their heads. Or it may be bankrupted away, unless these companies can suddenly get users of AI to pay a lot more to use the programs than they are paying now. The companies need to pivot, and fast, away from the economic model that has underpinned this industry from the beginning. Users are not paying nearly enough for the AI services that they use, and now the investment thesis for the entire industry is threatened. But there are strong ethical and moral questions to be raised as well. Karen Hao writes from inside the industry. She went to MIT, and knows personally a lot of the people in those companies that develop the AI. She wrote a really compelling book, and is appearing more often on podcasts. (Note to investors: if you have investments in the AI theme, you are well advised to spend some time, to ask yourself if the people running these companies conform with your values.) Her takeaway is that, at a minimum, the people in charge of those organizations are mediocrities. They’re unimpressive. These are not, then, the people who brought us transistor or the word processor or the spreadsheet or the internet – they’re just guys who were given a lot of money to spend, but who cannot answer even basic questions about what their company even does, or what AI even is. We’re still a long way from artificial general intelligence—machines who can learn and think on their own, and likely will never get there. And hopefully so, should say. The hundreds of billions of dollars that have already gone out the door haven’t bought much, besides answering basic questions and translating them, which are things we already had. At best the industry has overpromised and underdelivered. But she also points out much worse: The human rights abuses. Privacy violations and labor exploitation. Human beings doing inhuman things, to make more money. These are people she went to college with, were friends with, and she was left to wonder if the world would be a better place if these companies didn’t exist, and if the people who run them had never been born. Now, the rest of the world is wondering that too. The Department of War has apparently outsourced missile targeting to Artificial Intelligence, and on the very first day of the War on Iran the United States committed war crimes. At least 175 people were killed, mostly kids, when a Tomahawk missile fired from the USS Spruance blew up a school. It was a “targeting mistake”; the school was once part of a complex on the military base nearby, and CENTCOM’s target coordinates came from old data. Ten years ago, the school buildings were fenced off from the military base. The watchtowers were taken down, it was opened to the public, and the Iranians built playgrounds and athletic fields. DIA and other agencies have scores of analysts whose job is to develop target sets, using current satellite data. “Military targeting is complex and involves multiple agencies”, and “many people are responsible to verify” the data are correct. “But in a fast-moving situation, like in the opening days of the war, the information is sometimes not verified.” We’ll stop right there and point out that these targets were selected before the war started, and everybody just mentioned so far is sitting in a safe, air-conditioned building far away. This was not a fog-of-war situation where a combat unit is taking fire from all directions and is making split-second life-or-death decisions, and collateral damage might result. What most Americans might have expected here is that before a bomber or a missile gets sent launched toward a target, first we have a bunch of those people poring over that satellite data and make sure it really is a military base. Park a satellites over the place, watch it for a few days, then somebody might say, “is this really a rocket base? Because there’s no soldiers coming and going. It’s mostly little kids. Are we sure this isn’t a kindergarten? Because it looks a lot like a kindergarten.” The New York Times; there’s an understatement: “one of the most devastating single military errors in decades.” It was a war crime, and naturally everyone involved is trying to distance himself from it best he can, out of self-preservation. Here is the Miliary Times two weeks later: Democrats in the House of Representatives are asking about the Maven Smart System. Maven integrates AI and machine learning in logistics and targeting, and is embedded in CENTCOM operations. It’s built by Palantir, who got $1.3 billion from the Pentagon to handle the “information overload” problem. The system brings together satellite imagery, drone feeds, radar and SIGINT, then classified targets and generates strike packages, “compressing kill-chain reasoning and decision making into the fastest timelines ever seen.” If this sounds to you a lot like the opening scenes from ‘The Terminator”, and that Palantir just built Skynet, you’re not alone. What’s more, Maven is bundled with Claude, from Anthropic, and the AI also writes legal briefs to justify each strike. In the first 24 hours, the system generated hundreds of targets, enabling the US to hit over a thousand targets in the first 24 hours. Did a human being at any time verify the accuracy—or even the validity—of the target? No answer yet, and we should be shocked if anyone actually steps forward and says, yes, that was me. Because this is a war crime and a reckoning is due. And what did the American taxpayer get for our $1.3 billion? Did we get a better system? The Maven system can correctly identify targets with a 60% accuracy. Human analysts are at 84%. So, no. And Maven performs even worse in bad weather. When the Air Force ran a test with AI, it scored just 25% accuracy in real conditions. But that may have been the result of being fed the wrong information, again under battlefield conditions. Every single word of that is terrifying, and even that 86% accuracy rate for human analysts isn’t nearly good enough. But in the case of this school in Iran, it’s hard for a rational person to understand how this could have happened. The school had a website and was searchable online. Archives of satellite photos show the school being there for at least eight years, and recent images show school buildings and playgrounds. So where was everybody? Who was there to make sure that what was coming out of Palantir’s AI wasn’t a hallucination? To ensure that it was true? No matter; the Pentagon is full speed ahead with Maven. It’s now the official program of record and is being rolled out across all branches. And it’s not just the War Department. Palantir is working secretly with the FAA to overhaul the air traffic control system in the United States. This was after. This is dated April. Just six weeks after the Pentagon used Palantir’s software to blow up a school full of little Iranian girls, now the FAA wants them for a “stealthy program” to redesign the civilian air travel system. This is nowhere close to over. Investigations are coming, and we already know how they’re going to go. Every single human being involved will do anything to take the heat off himself, out of self-preservation. You can legally delegate authority, but cannot delegate responsibility, so it will fall on the people who designed that system and turned it on. And everybody, from the top brass in the command centers to the guy who pressed the launch button to the members of Congress who rubber-stamped this war without even a discussion, will try to blame the AI. Whether or not the families of the Iranian girls get the justice they deserve is an open question, but the die on this is cast. This cannot go on for much longer. It cannot. Americans have now seen what happens when we turn over these decisions to automated systems, from Silicon Valley companies and the people who run them, and are outraged. Up until now, nobody was asking hard questions, except for just a handful of people like Ed Zitron and Karen Hao. That’s changing, finally. The New Yorker just dropped a very critical piece on Sam Altman at OpenAI, and before that Tucker Carlson did one of the most eye-popping interviews in the history of YouTube, which we’ll just link to and leave for you to draw your own conclusions. These are the wealthiest and most powerful people, in the world. It is Wall Street and Silicon Valley and the White House and the Department of War. Now a lot more people are thankfully paying attention, and extra credit is especially due to the ones who questioned it from the very beginning, and did so at great risk to themselves. It’s far too late in coming, but finally people are awake, to what this industry is, and who is in charge of it. The heads of these companies are deeply distrusted, even by their own boards of directors and business partners. They are deeply disrespected, even by their own engineering teams, because they don’t even understand the science or the technology that they’re supposedly in charge of. For everyday Americans, it was always hard to see what we were getting out of this deal. Silicon Valley companies and Wall Street take our private data, our medical histories, our browsing habits, all of our banking transactions and what we like to shop for and buy and wear and read; even our own work product. Then they jam it into one of their closed-source large language models so they can charge companies to use it, who then lay off hundreds of thousands of people. And by the way in order to make all that work we have to agree to let them build giant new power plants, which only their data centers are allowed to use. Because according to them, we don’t need doctors anymore. Or air traffic controllers. Or military analysts who can see a school in a photograph and cancel a missile strike. We don’t even need lawyers to write up the legal briefs after the fact. Just trust those guys. As horrifying as this is to normal Americans, we shouldn’t wonder how it all reads and sounds in Beijing. Or in the capital of any responsible country. We shouldn’t wonder if China is reconsidering, for example, their policy about sharing their citizens’ private medical data with Silicon Valley companies who want to use it to build better LLM’s in medicine, which Wall Street investors can then charge money to use. It’s out of the question. This is Jensen Huang. Mr. Huang is CEO of Nvidia, and Nvidia builds the GPU’s and chips which in turn are used by the data centers where all those superfast searches and outputs happen. Nvidia is a $5 trillion dollar company, and Jensen Huang is not one of Karen Hao’s mediocrities who is only good at getting investors to hand over their money, and who cannot explain what his own company does. He can. He has a deep understanding of the engineering; he has a deep understanding of the science. He also has a deep understanding of China, and so he knows he and Nvidia have a big China problem. He appeared on this podcast, and starting from one hour 3 minutes in, we can see what’s at stake for Nvidia, and the other companies in the American AI industry. He makes repeated reference to the 5-layer AI stack. Some people call this the 5-layer cake of AI. At the bottom, the foundation, is energy. Because it takes a lot of energy. Then comes the chips, which is where his company is. On top of chips come the data centers, which need lots of chips and lots of energy. Then come those large-language models, and on top of everything is the applications layer. Applications is who is using this technology, and for what, and are those users paying enough to pay the bills of everybody else and everything else underneath them. Jensen Huang knows that China already owns four of those layers in that stack, and Nvidia is barely hanging on in chips. So he is gravely concerned here, about what it means from now on if China’s large-language models can run on chips from Huawei (which is a Chinese semiconductor manufacturer, along with lots of other products like 5G towers and downstream products that use AI.) China has abundant energy and an abundance of smart people, and when Chinese-built applications that are used by their billion users here and a couple billion more from across the world, and that includes all of Chinese factories and supply chain nodes and trading partners—and that all happens on a fully-integrated and fully made-in-China stack, it’s game over for the American AI industry: But that transition is already in motion. “Delete America” is official China government policy, and it’s just what it sounds like. In 2022 regulators directed all state-owned companies in China to replace foreign software by 2027. The regulation covers everything from finance, energy, and supply chain management, and is part of a long push for self-sufficiency in everything, including semiconductors. Jensen Huang knows what that means for his company, which is that Nvidia needs to stay so far ahead of Huawei, and the other Chinese chipmakers, that he can push further out the day that Chinese chips replace Nvidia’s across Chinese LLM’s. When those models are optimized to run on Chinese architecture, and that AI is then adopted by rest of the world, the future of tech will be all-China. DeepSeek is a Chinese large language model. Its Version 3 ran on old Nvidia chips, using old Hopper system architecture. DeepSeek v4 was just released, and the first reports are that it does run on Huawei Ascend chips, and they’re already taking orders for hundreds of thousands of units. Two more models are coming, also built to run on Chinese semiconductors. That leaves Huang to explain what the problem is: Everyone assumes that Nvidia chips just perform so much better, and that Nvidia’s tech is so far ahead, that Huawei chips will just run those models less well. He insists that’s not true. China can just use more of those chips, stacked differently, because electricity in China is basically free, which means the cost of compute is far below the bills the Silicon Valley companies run. Abundant energy is the first layer in that stack. China has it, and the United States and Europe do not. If AI data centers were one country, it would be the fifth largest consumer of energy in the world. The United States is 45% of that consumption, which will more than double by 2030. So that is the equivalent of adding an entire Canada worth of power consumption to the American grid, just for the data centers. That’s not going to happen, anyway—the pushback is already severe, and the data centers that were supposed to be finished last year—2025—still aren’t done, and much of the capacity that was supposed to come online this year have not even begun construction: The centers are not being built, and that means that the power will not ever be there to turn on all those machines that companies have already ordered from Nvidia. Those energy bottlenecks do not exist here in China; they do not even register as a concern in this discussion. In 2024, China generated more power than the United States, the European Union, and India combined. In the US, power generation rose slightly over the past 25 years, from just under 4 thousand terawatt hours to just over 4,000. The EU dropped slightly. In that same time China went up over 10x. In just four years, China built more power capacity that the entire United States did, in over a hundred years. In a single year China ADDS more power than most countries produce: Infrastructure is 5G towers and data centers, mainly. And data centers run on chips, which come from Nvidia and Huawei and others. There are 561 data centers in operation in China, right now, and surprisingly many of them were built and operated by the Silicon Valley hyperscalers. And in more bad news for Nvidia, the newest ones are running on Chinese semiconductors instead of Nvidia’s. Alibaba also builds chips, in addition to building the Qwen Large Language Model, in addition to running the biggest B2B exchange in the world for manufactured products. And while data centers in the United States and Europe are not getting built at all, in China they’ve done for data centers in AI what they do in every other industry: they overbuild, to create excess capacity today in anticipation of future demand, instead of in response to current demand. So, in China they’ve got the exact opposite of the conditions that exist in North America. Now there is so much capacity, so many chips, that the market is falling apart. Just a year ago an Nvidia H100 chip would go for $28,000 on the black market, and today the price has fallen so much they’re not getting bids at all. The costs are plunging, and so are the profits. Even recently that was a big business, buying and renting high-priced GPU’s from Nvidia for the Chinese large language models to run on. But that industry is dead. And the main driver of why, is that the Chinese LLM’s just run differently, than the ones from Silicon Valley. The newest Chinese models are optimized for computing power, which means the need for the high-performance, but high-energy-consumption chips from Nvidia goes away. Rental costs for those GPU’s have dropped by more than half and are now at an all-time low. Nvidia chips are just more expensive to run, while the prices paid by end users have fallen, so data centers running Nvidia can’t even pay their electric bills, even though the electricity in China is practically free. It was DeepSeek that transformed the industry’s approach to that problem, and blew up the math on how much it costs to develop the newest and best AI systems. It also cast doubt on all that CAPEX by Silicon Valley hyperscalers. This is a strong summary of the steps involved in developing a new LLM, and where the costs come from. What DeepSeek did was upend the trend of just throwing more money at compute. In DeepSeek’s financials they claim it cost under $6 million to train their Version 3, which doesn’t include some major costs, but the comparable costs to train US models are many times higher. DeepSeek did it by redesigning the network architecture of the system itself. The company did NOT have access to the best chips, and that forced them to innovate, and it resulted in a product that was completely different, while also costing much less to run. What’s more, DeepSeek is open-source, and was quickly adopted by developers across the world with few restrictions. This paper compares apples to apples, the DeepSeek, and ChatGPT, and Gemini, and explains that these crazy cost differences derive from hyper efficient algorithms and cloud systems, and overall better resource optimization. This group believes that the total development cost of DeepSeek is probably over $500 million, but the overall efficiencies of the DeepSeek model will immediately be copied by Western labs. It was a quantum jump in capability, and they conclude that it doesn’t really even matter much how much it cost to build DeepSeek, it’s just a much better model given the cost inputs required to generate results. Kimi is an LLM from Moonshot AI, and developers that previously were using proprietary API’s–from Anthropic or OpenAI, probably–are migrating over to Kimi. It is already foundational to AI systems. “It’s becoming infrastructure.” So Chinese large language models are taking over, because they are at least as good as the ones that come out of Silicon Valley, and cost far less to run. And Nvidia’s worst nightmare is already here, because those LLM’s run on chips that come from China, and so nobody here needs Nvidia chips anymore and the prices are collapsing. So Jason Huang has a lot of things to worry about, along with investors who have big bets on US firms in the AI stack. The application layer is everything, by far the most important. That’s users. It’s companies and creators and factories and managers and engineering teams – who use these tools to do their jobs better. The application layer was always going to be China, anyway, by default. Because China is where the factories are. All the world’s supply chains run through here, and global logistics. The BRICS bloc and the Global Majority countries have built a financial system outside of SWIFT and Western banks and regulators, and the US dollar, to handle all that trade. And remember again that it is the official policy of the Chinese government, that not a single piece of software from the United States will be used to run any of it. This industry, the AI Industry, is not going away. It’s already too important, already a key driver to productivity in manufacturing, and supply chain, and in medicine and in IoT and in clean energy and in transportation and logistics and shipping and finance. It will be critical in everything, because it’s such a valuable tool in the hands of people who are using it ethically, and who are watching over the results carefully. But the industry will go away from the United States, because our companies were set up to enrich and empower only themselves, instead of their users, and all under the pretense that their AI was smart and good enough to replace smart people and good people. But it never was. Be Good. Resources and links: The Stargate Project: Trump and OpenAI announce $500 billion AI venture https://mashable.com/article/stargate-project-trump-openai-oracle-softbank-500-billion-venture-ai-infrastructure Nuclear Powered Data Centers: Microsoft Bets on SMRs to Fuel the Cloud https://www.captechu.edu/blog/nuclear-powered-data-centers-microsoft-bets-smrs-fuel-cloud Meta signs 3 deals for nuclear energy to power AI data centers https://www.cbsnews.com/news/meta-nuclear-power-deals-ai-data-centers/ How China Won the Open-Source LLM Race — and Why It Matters [This Week in AI How China Won the Open-Source LLM Race — and Why It Matters If you were trying to “get into AI” in early January 2025, you weren’t alone. This newsletter itself was born out of that moment… Read more 4 months ago · 25 likes · 5 comments · This Week in AI](https://thisweekinaiclub.substack.com/p/how-china-won-the-open-source-llm) Dario Amodei Doubled Down On His AI Jobs Warning. Here’s What’s Different Now https://www.forbes.com/sites/kolawolesamueladebayo/2026/02/21/dario-amodei-doubled-down-on-his-ai-jobs-warning-heres-whats-different-now/ Sam Altman May Control Our Future—Can He Be Trusted? https://www.newyorker.com/magazine/2026/04/13/sam-altman-may-control-our-future-can-he-be-trusted Energy demand from AI https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai Nvidia’s Jensen Huang warns Huawei chips for DeepSeek AI models would be ‘horrible’ for US https://www.scmp.com/tech/article/3350460/nvidias-jensen-huang-warns-huawei-chips-deepseek-ai-models-would-be-horrible-us YouTube, Dwarkesh Patel and Jensen Huang – Will Nvidia’s moat persist? Exclusive: DeepSeek’s upcoming new AI model will be able to run on Huawei chips, a major milestone in China’s quest for semiconductor self-sufficiency. https://www.reuters.com/world/china/deepseeks-v4-model-will-run-huawei-chips-information-reports-2026-04-03/ DeepSeek’s V4 model will run on Huawei chips, The Information reports DeepSeek’s New AI Model Will Be a Victory for Huawei https://www.theinformation.com/articles/deepseeks-new-ai-model-will-victory-huawei China Added 543 Gigawatts in New Power Capacity in 2025 https://oilprice.com/Latest-Energy-News/World-News/China-Added-543-Gigawatts-in-New-Power-Capacity-in-2025.html China’s Four-Year Energy Spree Has Eclipsed Entire US Power Grid https://www.bloomberg.com/news/articles/2026-01-28/china-s-four-year-energy-spree-has-eclipsed-entire-us-power-grid Top power-generating countries in 2024 https://www.globaltimes.cn/page/202506/1335418.shtml Ranked: Top Countries by Annual Electricity Production (1985–2024) https://www.visualcapitalist.com/ranked-top-countries-by-annual-electricity-production-1985-2024/ I Spent 2 Months Building on Kimi K2 — It’s Quietly Becoming AI’s Open-Source Backbone https://medium.com/@mohit15856/i-spent-2-months-building-on-kimi-k2-its-quietly-becoming-ai-s-open-source-backbone-d1e0cd3d885c Development Cost Data + Statistics Comparison: DeepSeek R1’s $5.6M vs. ChatGPT-4 and Google Gemini Ultra https://softwareoasis.com/development-cost-comparison/ DeepSeek Debates: Chinese Leadership On Cost, True Training Cost, Closed Model Margin Impacts [SemiAnalysis DeepSeek Debates: Chinese Leadership On Cost, True Training Cost, Closed Model Margin Impacts The DeepSeek Narrative Takes the World by Storm… Read more a year ago · 2 likes · Dylan Patel, AJ Kourabi, Doug, and Reyk Knuhtsen](https://newsletter.semianalysis.com/p/deepseek-debates) Why building big AIs costs billions – and how Chinese startup DeepSeek dramatically changed the calculus https://theconversation.com/why-building-big-ais-costs-billions-and-how-chinese-startup-deepseek-dramatically-changed-the-calculus-248431 DeepSeek’s hardware spend could be as high as $500 million, new report estimates https://www.cnbc.com/2025/01/31/deepseeks-hardware-spend-could-be-as-high-as-500-million-report.html Alibaba-backed Moonshot releases its second AI update in four months as China’s AI race heats up https://www.cnbc.com/2025/11/06/alibaba-backed-moonshot-releases-new-ai-model-kimi-k2-thinking.html Millions of Americans Are Talking to AI Instead of Going to the Doctor, and It’s Giving Them Horrendously Flawed Medical Advice https://futurism.com/artificial-intelligence/millions-americans-ai-instead-doctor-bad-advice Large Language Model Performance and Clinical Reasoning Tasks https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2847679?utm%5C_campaign=articlePDF There Are Signs of a Massive AI Backlash https://futurism.com/artificial-intelligence/signs-massive-ai-backlash Global energy demands within the AI regulatory landscape https://www.brookings.edu/articles/global-energy-demands-within-the-ai-regulatory-landscape/ Energy Markets Race to Solve the AI Power Bottleneck https://www.morganstanley.com/insights/articles/powering-ai-energy-market-outlook-2026 Military Times, Deadly Iran school strike casts shadow over Pentagon’s AI targeting push https://www.militarytimes.com/news/your-military/2026/03/24/deadly-iran-school-strike-casts-shadow-over-pentagons-ai-targeting-push/ The Growing Problem of Radiologist Shortage: China’s Perspective https://www.kjronline.org/pdf/10.3348/kjr.2023.0839 AI in Chinese healthcare: From medical imaging to AI hospitals https://daxueconsulting.com/ai-healthcare-china/ New York Times, U.S. at Fault in Strike on School in Iran, Preliminary Inquiry Says https://www.nytimes.com/2026/03/11/us/politics/iran-school-missile-strike.html China is already a powerhouse in AI for radiology and medical imaging. Next they’re going global. The Nobel Prize, Geoffrey Hinton https://www.nobelprize.org/prizes/physics/2024/hinton/facts/ Ed Zitron’s Where’s Your Ed At https://www.wheresyoured.at/ Karen Hao on AI tech bosses: ‘Many choose not to have children because they don’t think the world is going to be around much longer’ https://www.irishtimes.com/culture/books/2025/08/09/karen-hao-on-ai-tech-bosses-many-choose-not-to-have-children-because-they-dont-think-the-world-is-going-to-be-around-much-longer/ Maven Smart System https://www.missiledefenseadvocacy.org/maven-smart-system/ Bombed Iranian girls school had vivid website and yearslong online presence https://www.reuters.com/investigations/bombed-iranian-girls-school-had-vivid-website-yearslong-online-presence-2026-03-12/ FAA quietly developing AI-enabled air traffic management system https://theaircurrent.com/air-traffic-control/faa-smart-ai-predictive-air-traffic-management-system-palantir-thales/ Wall Street Journal, China Intensifies Push to ‘Delete America’ From Its Technology https://www.wsj.com/world/china/china-technology-software-delete-america-2b8ea89f Sanctions against Huawei fail, then birth “Delete America” campaign across China’s supply chains China Data Center Locations (561) https://www.datacenters.com/locations/china Alibaba launches data center with 10,000 of its own chips as China ramps up AI push https://www.cnbc.com/2026/04/08/china-alibaba-data-center-ai-chips-zhenwu.html China built hundreds of AI data centers to catch the AI boom. Now many stand unused. https://www.technologyreview.com/2025/03/26/1113802/china-ai-data-centers-unused AI Whistleblower: We Are Being Gaslit By AI Companies, They’re Hiding The Truth! - Karen Hao Silicon Valley Insider EXPOSES Cult-Like AI Companies | Aaron Bastani Meets Karen Hao “The problem is Sam Altman”: OpenAI insiders don’t trust CEO https://arstechnica.com/tech-policy/2026/04/the-problem-is-sam-altman-openai-insiders-dont-trust-ceo/ Nearly half of US data centers planned for 2026 are facing delays or cancellation https://www.techspot.com/news/111947-nearly-half-us-data-centers-planned-2026-facing.html YouTube, Tucker Carlson, Sam Altman on God, Elon Mu
Komunitas
lemmy.ml
Sorry, I will not talk about browsers in your list because I’ve tried them and my personal preference goes to chawan for these reasons: has CSS layout support has HTML5 support with various encodings can display Inline images in terminals that support Sixel or Kitty protocols (opt-in feature) offers basic JavaScript support via QuickJS (opt-in) supports HTTP(S), SFTP, FTP, Gopher, Gemini… has built-in viewers for Markdown, man pages, and directory listings has Incremental loading uses multi-processing, so several buffers can be loaded at once offer mouse support, bookmarks, and protocol handling extensible by users If you want to check another option, there’s also brow.sh. Hope this helps in your web terminal journey :)
Komunitas
piefed.world
Cross-Posted, via Technology Community. Google Chrome is downloading a 4 GB Gemini Nano model onto users’ machines without consent, with no opt-in, no opt-out short of enterprise tooling, and an automatic re-download every time the user deletes it. The pattern is identical to the Anthropic Claude Desktop case I wrote about last month, but the scale is between two and three orders of magnitude larger. This article does the legal analysis and, for the first time, the environmental analysis. The numbers are not small.
Komunitas
kbin.social
Is there a Gemini search engine? I’ve found this one: gemini://geminispace.info/ Needs a client to access, of course. Basic, but functional. I found a general-purpose forum not too different from reddit or lemmy through it (and they decided to call it a BBS, because the Eternal September hasn’t happened to Gemini yet): gemini://bbs.geminispace.org/ Is there support for Forms/server side code To the best of my understanding (and it’s highly limited, since I only just learned about this, so take everything with a grain of salt), what Gemini does is primarily limit what the client can do. No local scripts, highly limited markdown. The server side is not limited. You can write any complex code you want that works behind the scenes - but it still has to deliver static pages (called “capsules”) to the end user. This series of articles explains the basic underlying tech and uses the example of a simple server to illustrate how Gemini works: https://medium.com/erus-encodia/creating-your-own-gemini-server-part-1-what-is-the-gemini-protocol-cf497477c4d And yes, forms are possible, even though there appears to be a somewhat widespread misconception that they are impossible. Please excuse the sketchy-looking IP address instead of a URL, this was the best resource I was able to find on this (and yes, I checked if this page is on Gemini - this appears to be not the case): http://216.218.220.144/tutorials/sig-tutorials/misc/gemini-forms.gmi Screenshot if you don’t want to click on the above link: https://i.imgur.com/s2mL3bM.png Disclaimer: This is two years old and I have not tried to implement it myself. Looks entirely plausible though. How big is it? Is there like just a few sites or a few hundred? According to the search engine linked above, there are 2420 domains and 1,854,666 individual pages as of yesterday. This is about comparable to the World Wide Web at the same time 1994, a number that grew to 10,000 by the end of that year; I wouldn’t expect the same explosive growth from Gemini - the field has already been plowed, after all. Gemini Space is small, but not a ghost town.
Komunitas
piefed.world
Hacker News. Google Chrome is downloading a 4 GB Gemini Nano model onto users’ machines without consent, with no opt-in, no opt-out short of enterprise tooling, and an automatic re-download every time the user deletes it. The pattern is identical to the Anthropic Claude Desktop case I wrote about last month, but the scale is between two and three orders of magnitude larger. This article does the legal analysis and, for the first time, the environmental analysis. The numbers are not small.
Komunitas
lemmy.ml
Countries need to start implementing UBI NOW It is funny that you mention this because it was after we started working with AI that I started telling one that would listen that we needed to implement UBI immediately. I think this was around 2014 IIRC. I am not blanket calling AI stupid. That said, the AI term itself is stupid because it covers many computing aspects that aren’t even in the same space. I was and still am very excited about image analysis as it can be an amazing tool for health imaging diagnosis. My comment was specifically about Google’s Bard/Gemini. It is and has always been trash, but in an effort to stay relevant, it was released into the wild and crammed into everything. The tool can do some things very well, but not everything, and there’s the rub. It is an alpha product at best that is being forced fed down people’s throats.