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[#박지훈#] ⠀ 지훈이로 가득해 천국 같던 Hello82 LIVE 윙생샷이 팬카페에 업로드되었습니다! ⠀ ✔ Hello82 LIVE 촬영장의 지훈이가 궁금하시다면 링크를 통해 확인해 주세요💛💚💖 ⠀ #PARKJIHOON# #윙생샷# #Hello82# #Build_A_Show#

[#JIHOONNow#] #Hello82# 에서 지훈이와 재미있는 시간 보내셨나요?! 지훈이와 메이가 함께 소통하며 만들어간 #Build_A_Show# !! 역시 지훈🐰 + #메이# 🌸 = 에너지 MAX🔥 #박지훈# #PARKJIHOON# #MyCollection# #Gallery#

Most people seriously underestimate what Grok Build can do They assume an AI coding agent is only useful for building apps or writing complex software But some of its most valuable work is handling the boring, obvious tasks that quietly consume 10 minutes here, 20 minutes there, every single day For example, I gave Grok Build a SpaceX video posted on 𝕏 and asked it to extract the clearest frames and turn them into a clean collage It pulled the video, selected the strongest images, cropped them, arranged the layout and produced the final collage shown in the post below I simply described the result I wanted, then let it handle the entire process while I worked on something else And that is only one small example You can use Grok Build to: • Organize and intelligently search files across your computer • Diagnose and fix audio, display and system-setting problems • Download, install and configure apps or tools • Extract images or audio from videos • Add music, trim, compress and convert media • Resize, crop and enhance images • Batch rename and organize hundreds of files • Clean spreadsheets, merge datasets and build dashboards • Write scripts and automate repetitive tasks • Fix broken configurations and connect tools together Even something as annoying as an audio setting not working becomes much easier Instead of hunting through menus, searching forums and trying random fixes, you can explain the problem to Grok Build It can inspect the setup, identify the likely cause, change the relevant configuration and test whether the fix worked It is basically an agentic operator living inside your computer, working within the permissions you give it I also keep a simple instruction file telling Grok how I prefer tasks to be completed, including automatically opening the finished result after a successful run so I do not forget about it Configure the workflow once, and Grok keeps working the way you prefer That is what makes Grok Build so useful It is not only building software It turns all the small computer tasks between an idea and the finished result into work you can simply describe and delegate

opus 5 is a VERY interesting release for a few reasons 1. it showed that the general benchmarks we use today are almost completely useless now opus 5 is nowhere near fable in practical use, not even close. anyone who’s used it meaningfully can tell this very quickly after a few tasks. yet opus beats fable on many benchmarks i now trust domain specific benchmarks built with private datasets a lot more than the popular ones. perhaps the future is everyone running their own evals because the public ones are really not telling us much 2. it seems with the 5 series, anthropic is trying a new way of training models previously, the same generation of sonnet and opus were often released at the same time or sonnet comes out before opus, which indicates sonnet and opus were trained by separate pipelines in parallel with the 5 series, it was very clear that they trained mythos first, and then distilled it into sonnet and opus. it seems this approach has a big influence on the models seeing sonnet 5 being a flop and opus 5 getting pretty mixed reviews already, i’m not sure this is working out 3. “how pleasant is it to work with the model” used to be a strength in claude, but now it’s not. honestly, grok is my favorite right now on the “pleasant” dimension. kimi is not bad either it feels like both anthropic and openai are giving RLHF less care, in favor of scalable RL that’s machine verifiable this almost looks like AI is directing humans to build a world that’s more friendly for machines rather than humans, and most humans don’t even realize they are being manipulated to help with that almost every new generation of frontier models now talk more jargons, need more steering to do what you want, and are just less fun to work with if this continues, AI will start to speak their own language that looks like English but average humans can’t understand. they will choose to do things that their human user never asked for. are we already failing at alignment?

GeoLibre v2.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings a legend that writes itself from your symbology, a new GeoLens catalog browser, and 200+ GeoLibre Rust geoprocessing tools running entirely in the browser. What's new in v2.3.0 - Automatic on-map Legend: the legend builds itself from your visible layers, with class rows for graduated, categorized, rule-based, and expression styling, gradient bars for heatmaps and raster colormaps, and land-cover labels from a Raster Attribute Table. Rename, hide, reorder, or add your own entries, and it saves with the project. - Symbology swatches in the Layers panel: every row shows a dot, line, square, or image glyph in the layer's own color, so a tall layer stack reads at a glance. - GeoLens catalog browser: connect to a self-hosted GeoLens server, search its catalog, and add datasets as vector tiles, GeoJSON, or rendered raster tiles. - Emerging Hot Spot Analysis: build a space-time cube from timestamped points and classify every cell as a new, intensifying, persistent, diminishing, sporadic, oscillating, or historical hot or cold spot, all client side. - Mosaic time series: the Time Slider now steps through MosaicJSON and STAC collections of many COGs per date, on either a GPU or a WASM rendering engine. - Copy and paste layer styles: give a whole set of layers one consistent look without restyling each in turn. - Shareable tool links: deep-link any Whitebox tool with a ?tool= URL that opens the dialog preselected and pre-fills the form, with a Copy link button to build it for you. - Smarter data loading: pick which layers to load from a multi-layer GeoPackage, import CSVs whose coordinates are in any projected CRS, and read a raster's real CRS, pixel size, and extent from the metadata dialog. - Multiple AI profiles: define several provider, model, and credential setups, pick a default, and switch between them from the assistant panel. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS# #Geospatial# #OpenSource# #RemoteSensing# #MapLibre# #GeoLibre#

A 24-YEAR-OLD MED STUDENT BUILT AN OBSIDIAN VAULT THAT KNOWS HIS MEDICAL DEGREE BETTER THAN HE DOES He started the way most people do — one outline note called "Emergência," branching out into ER protocols, pediatric cases, infection pathways, cardiac systems. Nothing organized, just wherever the next lecture took him. Two years in, the outline had turned into something else entirely. Hundreds of interconnected notes, color-coded by system, dense enough that zooming out on the graph looks less like a notes app and more like a star field. He filmed it once, just to show his friends. It's the third time this exact "watch my vault turn into a galaxy" clip has gone viral this month. But the graph was never the point, even if it's what got the views. Two weeks before a cardiology exam, he pointed Claude at the vault and asked it to find every note connected to arrhythmias — scattered across four different semesters, written in four different note-taking phases, never once cross-referenced by him. Fifteen seconds later, he had the entire topic laid out with sources, including a contradiction between two notes from a year apart that he'd never caught. That's the difference between a vault and a graveyard with good lighting. A graph this size isn't something a human rereads before an exam. It needs a system that reads it for him, on schedule, surfacing what's actually connected instead of what just looks impressive zoomed out. He didn't build a study system. He built something that catches what he's already forgotten, before the exam catches it for him.

NASA used a supercomputer to build a mind-bending simulation of falling into a black hole. Once you cross the point of no return, you have 12.8 seconds left to exist. To visualize these final seconds, NASA scientists ran the math of general relativity on a supercomputer, generating 10 terabytes of data in 5 days. The simulation shows a camera plunging into a supermassive black hole where light warps and space flows inward. The actual descent takes 3 hours, but to a distant observer, your image would freeze at the edge forever. A century of Einstein's equations, finally playing as video.

Huawei (@huawei) just made a big AI announcement at WAIC 2026. But what does it actually mean? Most people won’t care about the technical terms like “SuperNode” or “Atlas 950 SuperPoD.” Here’s what it means in simple English. Imagine AI as a giant factory. Instead of having one powerful machine doing all the work, Huawei has connected thousands of AI chips together so they behave like one giant AI supercomputer. This allows AI models to be trained and run much faster and at a much larger scale. Why is this important? For years, the world’s most advanced AI has depended heavily on NVIDIA’s GPUs. Huawei is now showing that China can build a large-scale AI computing platform using its own Ascend AI chips instead of relying on U.S. technology. What does this mean for everyone else? ✅ AI development becomes less dependent on a single supplier. ✅ Competition will accelerate, leading to faster innovation. ✅ AI services should become cheaper and more widely available. ✅ More countries will be able to build their own AI infrastructure instead of relying on foreign hardware. This isn’t just another hardware launch. It’s another step toward a future where AI is built on multiple technology ecosystems, not just one. Whether you’re an executive, investor, developer or business owner, one thing is becoming increasingly clear: The AI race is no longer just about building smarter models. It’s about who owns the computing power behind them. The companies and countries that control AI infrastructure may shape the next decade of innovation. Follow me for practical insights on AI, business and the future of technology. P.S. At 10xme, we’re helping executives understand and adopt AI through practical playbooks, AI diagnostics and weekly insights. Learn more at

The Build with Gemini @xprize Hackathon is live through August 17, 2026. Dream big, build a startup that solves real-world problems, and compete for $2M in prizes. This video shows you how to use different Google products and tools, like Gemini Deep Research, Gemini Spark, and @GoogleAIStudio, to bring your project from idea to launch 💡