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Cloud and Cloud Shadow Detection From Satellite Imagery with GeoAI and OmniCloudMask In this tutorial, you’ll detect clouds and cloud shadows, compute cloud statistics, clean segmentation outputs, convert raster masks to vectors, smooth boundaries, and generate a cloud-free mask for downstream remote sensing analysis. This workflow works with imagery... show more

Qiusheng Wu

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11,527 Aufrufe • vor 4 Monaten •via X (Twitter)

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Create Stunning Time-Series Satellite Images in Seconds! The GEE Data Catalogs Plugin v0.5 for QGIS is now available and it's a powerful upgrade. You can now create time-series satellite imagery with just a few clicks using a simple interface. The new version also supports direct downloads to your computer, making the workflow faster and more efficient. Key Features: - Access over 80 petabytes of satellite and geospatial datasets from Google Earth Engine - Generate animated time-series imagery effortlessly - Export results directly from QGIS to your local machine Useful Links: QGIS Plugin Page: GitHub Repository: Video Tutorial: #QGIS #geospatial #EarthEngine #Python #datascience #satellite

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A view from a plane... What is happening! Are we living in a parallel universe? How is it possible for anyone to look out of a plane window at 38k feet and see this, above the cloud cover, and just go with the propaganda narrative that it's only condensation?

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Exploring the Future of Legal Data Infrastructure Iagon, in partnership with Cloud Court, is pleased to announce that Ford Motor Company Motor Company will serve in an advisory capacity for this exploratory project, which seeks to evaluate the use of the Cardano blockchain and Iagon's decentralized cloud storage technology as a potential solution for the secure storage and management of legal documents and data. As a major corporation with sophisticated legal operations, Ford brings valuable perspective to this exploratory initiative based on their experience managing complex legal data infrastructures at scale. Ford is interested in exploring whether blockchain-based distributed storage could address persistent challenges in legal data infrastructure. In particular, Ford sees merit in exploring how blockchain technology might deliver economically efficient storage and audit solutions for legal data management. More insights 👉

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Traditionally, sensor fusion is a software-heavy process prone to temporal lag and parallax errors. With Rev8, we’ve moved that complexity to the silicon. This SLAM sequence is powered by Ouster’s our new L4 MAX chip. Because the lidar and color data are fused at the moment of measurement, the point cloud is born with 48-bit color. No extrinsic calibration, no drift, and zero latency. For perception teams, this means better classification of safety vests, traffic signals, and road markings without the compute overhead of post-processing. Learn more about Native Color and the OS1 MAX:

Traditionally, sensor fusion is a software-heavy process prone to temporal lag and parallax errors. With Rev8, we’ve moved that complexity to the silicon. This SLAM sequence is powered by Ouster’s our new L4 MAX chip. Because the lidar and color data are fused at the moment of measurement, the point cloud is born with 48-bit color. No extrinsic calibration, no drift, and zero latency. For perception teams, this means better classification of safety vests, traffic signals, and road markings without the compute overhead of post-processing. Learn more about Native Color and the OS1 MAX:

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Breaking: Hubble Telescope Opens a "Window" into the Dark Universe — First-Ever Detection of a Starless Dark Matter Cloud! In a groundbreaking discovery announced by NASA, the Hubble Space Telescope has revealed an entirely new class of cosmic object: a vast, starless cloud dominated by dark matter, rich in gas but completely devoid of stars. Nicknamed Cloud-9, this enigmatic structure lies about 14 million light-years away, on the outskirts of the spiral galaxy Messier 94 (M94).This is the first confirmed observation of such a "failed galaxy" or relic from the early universe — a massive dark matter halo that gathered hydrogen gas but never triggered star formation. Hubble's deep imaging ruled out any hidden faint stars, confirming it's truly dark and star-free.Key highlights from the discovery:Massive dark matter core — estimated at around 5 billion solar masses, anchoring a compact reservoir of gas. A cosmic fossil — likely a survivor from the universe's infancy, halted during reionization (when the first stars and galaxies lit up and ionized surrounding gas). Implications — This "phantom" object provides direct evidence for the existence of low-mass dark matter halos that never evolved into full galaxies. It suggests the universe could be filled with similar invisible structures, offering a major boost to our understanding of dark matter and galaxy formation. For decades, theorists predicted these "dark galaxies" or RELHICs (Reionization-Limited Hydrogen Clouds), but Cloud-9 marks the first solid proof caught in the act.Scientists are buzzing — this rare "wow" moment could rewrite parts of cosmic history and hint at countless other hidden building blocks of the universe lurking in the shadows.(Source: NASA/ESA Hubble release, January

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Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

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256,388 Aufrufe • vor 1 Monat

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.

Gipp 🦅

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ISRO

94,728 Aufrufe • vor 1 Jahr

Today we're launching Sailboxes: the first cloud environment purpose-built for long-horizon AI agents. Sailboxes are full machines with persistent state that auto-sleep, built for agents that live forever, priced so you only pay for what they actually use. From $0.015 per active vCPU-hour, over 70% cheaper than other providers. Under the hood: novel architecture from Nirvik Baruah and Charley Cunningham that live-migrates VMs based on actual resource usage. To show how robust it is, we ran a Minecraft server in a Sailbox and forced it to migrate every 2 minutes--players never noticed.

Today we're launching Sailboxes: the first cloud environment purpose-built for long-horizon AI agents. Sailboxes are full machines with persistent state that auto-sleep, built for agents that live forever, priced so you only pay for what they actually use. From $0.015 per active vCPU-hour, over 70% cheaper than other providers. Under the hood: novel architecture from Nirvik Baruah and Charley Cunningham that live-migrates VMs based on actual resource usage. To show how robust it is, we ran a Minecraft server in a Sailbox and forced it to migrate every 2 minutes--players never noticed.

Sail Research

56,468 Aufrufe • vor 17 Tagen

I’ll share a small part of Back in med school, I became obsessed with augmenting memory and dreamed of a Notion or Obsidian that completes itself. Today, we’ve built something close. My self-awareness is sharper and everything feels connected. I genuinely believe AI does not replace humans. It amplifies us. Huge respect to our engineers and designers who made this crazy thing real. Bubbles are the episodic units of my life that the system interprets from my raw data. Clouds are the system’s questions, its hypotheses about who I am. When I answer a cloud, it becomes a bubble again. There is so much personal data that I cannot fully demo it. Wish I could. This system understands me more deeply than anyone. Want to try it? Retweet and comment “memory.” I’ll DM you an access code to skip the waitlist.

I’ll share a small part of Back in med school, I became obsessed with augmenting memory and dreamed of a Notion or Obsidian that completes itself. Today, we’ve built something close. My self-awareness is sharper and everything feels connected. I genuinely believe AI does not replace humans. It amplifies us. Huge respect to our engineers and designers who made this crazy thing real. Bubbles are the episodic units of my life that the system interprets from my raw data. Clouds are the system’s questions, its hypotheses about who I am. When I answer a cloud, it becomes a bubble again. There is so much personal data that I cannot fully demo it. Wish I could. This system understands me more deeply than anyone. Want to try it? Retweet and comment “memory.” I’ll DM you an access code to skip the waitlist.

Daniel Park

658,436 Aufrufe • vor 8 Monaten

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

Oren Melamed

29,555 Aufrufe • vor 6 Monaten

AN AWS ENGINEER QUIETLY BUILT A 2 PETABYTE HOME SERVER FOR $9/MONTH THAT KILLS A $3,400/MONTH CLOUD STORAGE BILL the lenovo thinkstation pgx ships nvidia's gb10 grace blackwell superchip and 128gb of unified memory in a box the size of a mac mini at 1.2kg it runs an 80b qwen3 coder model at 25 to 40 tokens per second and a 196b step-3.5-flash moe model at 20 tokens per second locally the gb10 packs 6,144 cuda cores, 192 fifth-generation tensor cores and rates at 1 petaflop of fp4 with sparsity from a single 240 watt usb-c power supply fine tuning qwen 2.5 7b with lora took 18 minutes and 41gb of unified memory while the gpu pulled 65 watts and peaked at 77 degrees the box pulls a docker container from nvidia's registry and serves a frontier model on your local network with tool calling and zero data leaving your desk bookmark this and read the article below

AN AWS ENGINEER QUIETLY BUILT A 2 PETABYTE HOME SERVER FOR $9/MONTH THAT KILLS A $3,400/MONTH CLOUD STORAGE BILL the lenovo thinkstation pgx ships nvidia's gb10 grace blackwell superchip and 128gb of unified memory in a box the size of a mac mini at 1.2kg it runs an 80b qwen3 coder model at 25 to 40 tokens per second and a 196b step-3.5-flash moe model at 20 tokens per second locally the gb10 packs 6,144 cuda cores, 192 fifth-generation tensor cores and rates at 1 petaflop of fp4 with sparsity from a single 240 watt usb-c power supply fine tuning qwen 2.5 7b with lora took 18 minutes and 41gb of unified memory while the gpu pulled 65 watts and peaked at 77 degrees the box pulls a docker container from nvidia's registry and serves a frontier model on your local network with tool calling and zero data leaving your desk bookmark this and read the article below

starmex

192,758 Aufrufe • vor 1 Monat

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Varun

308,655 Aufrufe • vor 3 Monaten

If you need McKinsey-style slides, try this prompt: I asked Kimi to conduct a comprehensive analysis of the GenAI video model market, focusing on leading players (e.g., Seedance 2.0, Sora, Kling, Veo, Luma). My prompt: Conduct a comprehensive analysis of the GenAI video model market, focusing on leading players (e.g., Seedance 2.0, Kling, Veo, Luma). Compare their core architectures, temporal consistency, and prompt adherence to identify current industry benchmarks. Use the latest information Requirement: A professional, high-density consulting presentation slide, designed in the style of a top-tier strategy firm (McKinsey/BCG) blended with high-end editorial aesthetics. Core Content & Layout: 1. Rich Data Visualization: The slide is populated with complex, precise charts (stacked bar charts, waterfall charts, or line graphs) and detailed data tables with rows and columns. 2. Structured Frameworks: Includes strategic diagrams or 2x2 matrices constructed with thin, clean lines. 3. High Information Density: The layout is sophisticated and multi-column, mimicking an actual business analysis deck, not just an empty cover page. Visual Style: 1. Aesthetic: Tech-minimalist but information-heavy. Clean, sharp, and authoritative. 2. Typography: Serif fonts (like Times New Roman) for the main headlines to give a premium financial report feel; clean Sans-serif for chart labels and data numbers. 3. Color Palette: Clean white background. Text is sharp black. Charts and graphical accents use Deep Royal Blue and distinct shades of grey for data hierarchy. 4. Graphics: Use fine hairline borders for tables and precise vector lines for graphs.

If you need McKinsey-style slides, try this prompt: I asked Kimi to conduct a comprehensive analysis of the GenAI video model market, focusing on leading players (e.g., Seedance 2.0, Sora, Kling, Veo, Luma). My prompt: Conduct a comprehensive analysis of the GenAI video model market, focusing on leading players (e.g., Seedance 2.0, Kling, Veo, Luma). Compare their core architectures, temporal consistency, and prompt adherence to identify current industry benchmarks. Use the latest information Requirement: A professional, high-density consulting presentation slide, designed in the style of a top-tier strategy firm (McKinsey/BCG) blended with high-end editorial aesthetics. Core Content & Layout: 1. Rich Data Visualization: The slide is populated with complex, precise charts (stacked bar charts, waterfall charts, or line graphs) and detailed data tables with rows and columns. 2. Structured Frameworks: Includes strategic diagrams or 2x2 matrices constructed with thin, clean lines. 3. High Information Density: The layout is sophisticated and multi-column, mimicking an actual business analysis deck, not just an empty cover page. Visual Style: 1. Aesthetic: Tech-minimalist but information-heavy. Clean, sharp, and authoritative. 2. Typography: Serif fonts (like Times New Roman) for the main headlines to give a premium financial report feel; clean Sans-serif for chart labels and data numbers. 3. Color Palette: Clean white background. Text is sharp black. Charts and graphical accents use Deep Royal Blue and distinct shades of grey for data hierarchy. 4. Graphics: Use fine hairline borders for tables and precise vector lines for graphs.

Crystal

704,782 Aufrufe • vor 5 Monaten

JENSEN HUANG UNVEILED A BOARD THAT RUNS 1 TRILLION PARAMETER AI MODELS. THE $249 NVIDIA BOX UNDER YOUR DESK KILLS A $200/MONTH AI BILL FOR $5 IN ELECTRICITY jensen held it up on stage with one hand and called it the architecture that runs the future of ai. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

JENSEN HUANG UNVEILED A BOARD THAT RUNS 1 TRILLION PARAMETER AI MODELS. THE $249 NVIDIA BOX UNDER YOUR DESK KILLS A $200/MONTH AI BILL FOR $5 IN ELECTRICITY jensen held it up on stage with one hand and called it the architecture that runs the future of ai. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

54,309 Aufrufe • vor 2 Monaten

Storyboard sheet of this prompt created with GPT image 2 And Animate this Storyboard with seedence 2.0 The journey from fearless to legendary starts with one ride. All work done on OpenArt Prompt: Storyboard sequence, 10 cinematic frames: A stylish young woman relaxes in a modern desert-view room while drinking from a glass bottle, then stands and gazes out the window with determination before casually tossing the bottle aside. She exits the building and walks toward her FMX motocross bike, gears up, and puts on her helmet. The action escalates as she races down a giant mega ramp, launches into the open sky, performs a breathtaking backflip high above the desert landscape, and lands in a dramatic cloud of dust. In the final frame, she removes her helmet, her hair falling free as smiling friends run toward her cheering, all captured in ultra-realistic cinematic storytelling, golden-hour lighting, dynamic camera angles, emotional sports-drama atmosphere, and high-end action photography.

Storyboard sheet of this prompt created with GPT image 2 And Animate this Storyboard with seedence 2.0 The journey from fearless to legendary starts with one ride. All work done on OpenArt Prompt: Storyboard sequence, 10 cinematic frames: A stylish young woman relaxes in a modern desert-view room while drinking from a glass bottle, then stands and gazes out the window with determination before casually tossing the bottle aside. She exits the building and walks toward her FMX motocross bike, gears up, and puts on her helmet. The action escalates as she races down a giant mega ramp, launches into the open sky, performs a breathtaking backflip high above the desert landscape, and lands in a dramatic cloud of dust. In the final frame, she removes her helmet, her hair falling free as smiling friends run toward her cheering, all captured in ultra-realistic cinematic storytelling, golden-hour lighting, dynamic camera angles, emotional sports-drama atmosphere, and high-end action photography.

𝗦𝗮𝗻𝗶𝗮

18,471 Aufrufe • vor 1 Monat

PhD Students - How to detect AI text in your writing? We often use ChatGPT for writing. However, this leads to AI-plagiarized text. This can be problematic in many scenarios. For example, if you use AI text in your papers. Your research paper can get desk rejected. 🍁How to detect if there is AI text in your writing? 1. Go to and log in. 2. Click on 𝐴𝐼 𝑑𝑒𝑡𝑒𝑐𝑡𝑜𝑟 from the left menu 3. Insert your text and click on 𝐴𝑛𝑎𝑙𝑦𝑧𝑒. 4. will generate AI detection report This report shows the following. → Percentage of AI generated text → Options for converting AI text into non-AI text 🍁How good is this AI detector? SciSpace conducted a benchmarking study. In this study, the detection capability was compared with other AI-detectors. SciSpace AI detector was tested with 4000 samples. It showed an accuracy of 96%. This means it can detect AI-generated text with 96% accuracy. The study showed that SciSpace AI detector has outclassed AI detectors like GPTZero, ZeroGPT, and Grammarly. 🔴Anything you'd like to add?

PhD Students - How to detect AI text in your writing? We often use ChatGPT for writing. However, this leads to AI-plagiarized text. This can be problematic in many scenarios. For example, if you use AI text in your papers. Your research paper can get desk rejected. 🍁How to detect if there is AI text in your writing? 1. Go to and log in. 2. Click on 𝐴𝐼 𝑑𝑒𝑡𝑒𝑐𝑡𝑜𝑟 from the left menu 3. Insert your text and click on 𝐴𝑛𝑎𝑙𝑦𝑧𝑒. 4. will generate AI detection report This report shows the following. → Percentage of AI generated text → Options for converting AI text into non-AI text 🍁How good is this AI detector? SciSpace conducted a benchmarking study. In this study, the detection capability was compared with other AI-detectors. SciSpace AI detector was tested with 4000 samples. It showed an accuracy of 96%. This means it can detect AI-generated text with 96% accuracy. The study showed that SciSpace AI detector has outclassed AI detectors like GPTZero, ZeroGPT, and Grammarly. 🔴Anything you'd like to add?

Faheem Ullah

13,102 Aufrufe • vor 9 Monaten