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BREAKING: SpaceXAI has released a new and improved update for Grok Build. v1.0.12 — 2026-08-27 Performance: • Worktree creation is faster by skipping stale reflog copies. Bug Fixes: • Copying wrapped table cells no longer inserts unwanted spaces at line breaks. • MCP server connections that fail transiently now...

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DogeDesigner

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SpaceXAI just released a new update for Grok Build. Update to v1.0.11 Features: • Headless sessions are now browsable in the resume picker without mixing into default history. • Default permission mode for new interactive sessions is now configurable. • Turn duration now appears in session history after resume. • Turn footers (Worked for, cancelled, failed) now appear after /resume. • mkdir and touch no longer prompt in auto mode or safe-command lists. • Subagent messages are now allowed automatically in permission Auto mode. • Headless sessions can now auto-allow permission prompts via a startup hint. Bug Fixes: • Permission prompts for common command chains and subcommands are now more reliable. • Blocked prompts no longer appear in conversation history or scrollback after restart. • Background monitors no longer have a 10-hour default timeout. • Mouse input at the right margin no longer types characters into the prompt on certain terminals. • Pasting text ending in a newline no longer accidentally submits the prompt on some terminals. • Auto mode now shows a permission card when the classifier blocks an action on interactive sessions. • Image previews no longer leave ghost artifacts when using the Kitty protocol on Warp. • Expanded Execute tool output no longer snaps closed during live progress. • /voice now falls back to parec/arecord on older PipeWire installs. Performance: • Background command waits now finish as soon as the process exits. Download Grok Build: Update to the latest Alpha release: grok update --alpha Update to the latest Stable release: grok update
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SpaceXAI just released a new update for Grok Build. Update to v1.0.11 Features: • Headless sessions are now browsable in the resume picker without mixing into default history. • Default permission mode for new interactive sessions is now configurable. • Turn duration now appears in session history after resume. • Turn footers (Worked for, cancelled, failed) now appear after /resume. • mkdir and touch no longer prompt in auto mode or safe-command lists. • Subagent messages are now allowed automatically in permission Auto mode. • Headless sessions can now auto-allow permission prompts via a startup hint. Bug Fixes: • Permission prompts for common command chains and subcommands are now more reliable. • Blocked prompts no longer appear in conversation history or scrollback after restart. • Background monitors no longer have a 10-hour default timeout. • Mouse input at the right margin no longer types characters into the prompt on certain terminals. • Pasting text ending in a newline no longer accidentally submits the prompt on some terminals. • Auto mode now shows a permission card when the classifier blocks an action on interactive sessions. • Image previews no longer leave ghost artifacts when using the Kitty protocol on Warp. • Expanded Execute tool output no longer snaps closed during live progress. • /voice now falls back to parec/arecord on older PipeWire installs. Performance: • Background command waits now finish as soon as the process exits. Download Grok Build: Update to the latest Alpha release: grok update --alpha Update to the latest Stable release: grok update

DogeDesigner

47,710 просмотров • 2 дней назад

Bug fixes shipping to Grok Build 0.2.13 (release notes will be available in the TUI and on change-log website) We are leveraging the alt-screen to better handle your background tasks, subagents, monitors with smart grouping allowing you to navigate between them quickly • Group Subagents → Tasks → Watchers, within subagents, order by agent type (Explore, General, Plan) • Show timestamps on pinned user messages at top of scrollback • Fix command highlighting when prompt has paste chips • Update the context-usage indicator to show tokens by default • ANSI16 fallback for themes • Better context usage breakdown/rendering • Update highlighting for /loop, monitor, tag colors • Tab now cycles Prompt → Scrollback → Tasks → Prompt • Order gateway turn-completion after streamed content • Formatting: fix language-tagged fenced code blocks and fix code under a list item
0:10

Bug fixes shipping to Grok Build 0.2.13 (release notes will be available in the TUI and on change-log website) We are leveraging the alt-screen to better handle your background tasks, subagents, monitors with smart grouping allowing you to navigate between them quickly • Group Subagents → Tasks → Watchers, within subagents, order by agent type (Explore, General, Plan) • Show timestamps on pinned user messages at top of scrollback • Fix command highlighting when prompt has paste chips • Update the context-usage indicator to show tokens by default • ANSI16 fallback for themes • Better context usage breakdown/rendering • Update highlighting for /loop, monitor, tag colors • Tab now cycles Prompt → Scrollback → Tasks → Prompt • Order gateway turn-completion after streamed content • Formatting: fix language-tagged fenced code blocks and fix code under a list item

skcd

15,075 просмотров • 2 месяцев назад

Microsoft is cooking! Windows 11’s faster app launches released today, and everything is noticeably faster on low-end PCs. Windows 11’s CPU boost feature is no longer just for Start, Search, and shell flyouts. After installing the August 2026 security update, it is now silently active while opening apps too. We tested this on the same PC before and after KB5121003, and the difference is real. Notepad, Calculator, Chrome, VLC, PowerPoint, Weather, and even the Epic Games Store now trigger the same Low Latency Profile behavior Microsoft first rolled out for the Windows shell in June. The moment you open an app, Windows briefly pushes the CPU close to its maximum frequency for one to three seconds, finishes the launch work faster, and then drops back to idle. This is called “race to sleep,” and it is not a lazy trick. macOS and Linux already do similar things. CPU utilization does not spike the same way, so this is not your PC suddenly working harder forever. It is a short frequency burst to make Windows feel more responsive. I know people love mocking Windows performance, and often for good reason, but this one actually works. Apps open faster now, especially on budget and older PCs where every second of delay is noticeable. Windows 11 is finally getting the kind of performance work it should have had from day one.
0:11

Microsoft is cooking! Windows 11’s faster app launches released today, and everything is noticeably faster on low-end PCs. Windows 11’s CPU boost feature is no longer just for Start, Search, and shell flyouts. After installing the August 2026 security update, it is now silently active while opening apps too. We tested this on the same PC before and after KB5121003, and the difference is real. Notepad, Calculator, Chrome, VLC, PowerPoint, Weather, and even the Epic Games Store now trigger the same Low Latency Profile behavior Microsoft first rolled out for the Windows shell in June. The moment you open an app, Windows briefly pushes the CPU close to its maximum frequency for one to three seconds, finishes the launch work faster, and then drops back to idle. This is called “race to sleep,” and it is not a lazy trick. macOS and Linux already do similar things. CPU utilization does not spike the same way, so this is not your PC suddenly working harder forever. It is a short frequency burst to make Windows feel more responsive. I know people love mocking Windows performance, and often for good reason, but this one actually works. Apps open faster now, especially on budget and older PCs where every second of delay is noticeable. Windows 11 is finally getting the kind of performance work it should have had from day one.

Windows Latest

712,223 просмотров • 16 дней назад

Introducing a new tool called "SideChannel". A secure alternative to OpenClaw. Utilizes signal for communication and has Claude integration. I built SideChannel, an open-source Signal bot that connects Claude AI to your entire development workflow. End-to-end encrypted. From your pocket. The real power is autonomous development. Send one message like "Build a REST API with auth, pagination, and tests" and SideChannel will: - Generate a full PRD with stories and atomic tasks. - Dispatch up to 10 parallel workers (each running Claude). - Independently verify every task with a separate Claude context. - Run quality gates to catch regressions - Auto-fix failures. - Send you progress updates via Signal as work completes. Every piece of code is reviewed by a separate AI context using a fail-closed security model. If it detects security issues, backdoors, or logic errors — the code gets rejected automatically. No rubber stamps. It also has memory that actually works. Conversations are stored with vector embeddings for semantic search. Claude remembers your project conventions, past decisions, and what's been tried before. It gets smarter about your codebase over time. Other things I'm proud of: - Plugin framework for extending with custom commands. - Multi-project support with per-user scoping. - Rate limiting, path validation, phone allowlist. - Git checkpoints before every task, atomic commits after. - Stale task recovery, circular dependency detection. - Works on Linux and macOS, one-command install. It also integrates into OpenAI or Grok (optional) for more Generative AI response for simple things like "Whats the weather in New York City right now?".
0:10

Introducing a new tool called "SideChannel". A secure alternative to OpenClaw. Utilizes signal for communication and has Claude integration. I built SideChannel, an open-source Signal bot that connects Claude AI to your entire development workflow. End-to-end encrypted. From your pocket. The real power is autonomous development. Send one message like "Build a REST API with auth, pagination, and tests" and SideChannel will: - Generate a full PRD with stories and atomic tasks. - Dispatch up to 10 parallel workers (each running Claude). - Independently verify every task with a separate Claude context. - Run quality gates to catch regressions - Auto-fix failures. - Send you progress updates via Signal as work completes. Every piece of code is reviewed by a separate AI context using a fail-closed security model. If it detects security issues, backdoors, or logic errors — the code gets rejected automatically. No rubber stamps. It also has memory that actually works. Conversations are stored with vector embeddings for semantic search. Claude remembers your project conventions, past decisions, and what's been tried before. It gets smarter about your codebase over time. Other things I'm proud of: - Plugin framework for extending with custom commands. - Multi-project support with per-user scoping. - Rate limiting, path validation, phone allowlist. - Git checkpoints before every task, atomic commits after. - Stale task recovery, circular dependency detection. - Works on Linux and macOS, one-command install. It also integrates into OpenAI or Grok (optional) for more Generative AI response for simple things like "Whats the weather in New York City right now?".

Dave Kennedy

49,427 просмотров • 6 месяцев назад

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.
0:24

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 просмотров • 7 месяцев назад

Yesterday at Brown University ICERM's workshop on “Agentic Scientific Computing and Scientific Machine Learning” I spoke about “Adaptive Swarms Across Scales”, making the case for scientific AI as systems that can create representations, stress them, fracture them, and enlarge the category in which future representations live. The category here is a composable and breakable working universe of science: data, hypotheses, simulations, measurements, tools, failures, figures, papers, provenance, and the transformations that connect them. Discovery happens when those transformations become executable, inspectable, composable, and capable of changing the world model they operate within. Atomistic modeling gives one category - states, forces, trajectories, observables, boundary conditions, conservation laws. Neural surrogates learn fast morphisms inside or between such categories. But discovery is higher-order: it changes which objects and morphisms are available in the first place: what variables exist, what operations are allowed, what evidence counts, what scale is active, what invariant is being preserved, and what kind of explanation the system is even capable of forming. This is scientific method as adaptive architecture: compression, stress, fracture, recomposition. Fracture matters here because it makes the logic physical: a non-commuting diagram realized in matter. The imposed load, material hierarchy, defect field, and assumed continuum description no longer map cleanly into the observed outcome. The crack is the obstruction and it identifies where the old morphism failed and where a new representation must be introduced. The physical crack and the categorical obstruction are the same event viewed in different substrates. ScienceClaw × Infinite is a machine for constructing and transforming a category of scientific artifacts. Each artifact is typed. Each operation has lineage. Each failed branch remains in the category as reusable structure. The “paper” is no longer the terminal object of science; it is one projection of a larger compositional trace, and it can be generated at any time for consumption by a human or an AI. With that the unit of scientific labor is changing. For most of the twentieth century the unit was the result (a measurement, a theorem, a synthesized molecule). It is now becoming the algorithm that produces results, and after that, the substrate of discovery itself. The static PDF is the wrong terminal object for this regime, and the role of the scientist with it. We now design algorithms that build algorithms, and eventually substrates in which such algorithms compose themselves. At that point, the scientist is no longer outside the discovery system. The scientist becomes one of the representations the system can transform. In that sense, the systems will eventually do science to us, and that is the structural consequence of the principle they are built on.
0:12

Yesterday at Brown University ICERM's workshop on “Agentic Scientific Computing and Scientific Machine Learning” I spoke about “Adaptive Swarms Across Scales”, making the case for scientific AI as systems that can create representations, stress them, fracture them, and enlarge the category in which future representations live. The category here is a composable and breakable working universe of science: data, hypotheses, simulations, measurements, tools, failures, figures, papers, provenance, and the transformations that connect them. Discovery happens when those transformations become executable, inspectable, composable, and capable of changing the world model they operate within. Atomistic modeling gives one category - states, forces, trajectories, observables, boundary conditions, conservation laws. Neural surrogates learn fast morphisms inside or between such categories. But discovery is higher-order: it changes which objects and morphisms are available in the first place: what variables exist, what operations are allowed, what evidence counts, what scale is active, what invariant is being preserved, and what kind of explanation the system is even capable of forming. This is scientific method as adaptive architecture: compression, stress, fracture, recomposition. Fracture matters here because it makes the logic physical: a non-commuting diagram realized in matter. The imposed load, material hierarchy, defect field, and assumed continuum description no longer map cleanly into the observed outcome. The crack is the obstruction and it identifies where the old morphism failed and where a new representation must be introduced. The physical crack and the categorical obstruction are the same event viewed in different substrates. ScienceClaw × Infinite is a machine for constructing and transforming a category of scientific artifacts. Each artifact is typed. Each operation has lineage. Each failed branch remains in the category as reusable structure. The “paper” is no longer the terminal object of science; it is one projection of a larger compositional trace, and it can be generated at any time for consumption by a human or an AI. With that the unit of scientific labor is changing. For most of the twentieth century the unit was the result (a measurement, a theorem, a synthesized molecule). It is now becoming the algorithm that produces results, and after that, the substrate of discovery itself. The static PDF is the wrong terminal object for this regime, and the role of the scientist with it. We now design algorithms that build algorithms, and eventually substrates in which such algorithms compose themselves. At that point, the scientist is no longer outside the discovery system. The scientist becomes one of the representations the system can transform. In that sense, the systems will eventually do science to us, and that is the structural consequence of the principle they are built on.

Markus J. Buehler

10,095 просмотров • 3 месяцев назад

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?
0:12

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?

Alok

12,962 просмотров • 1 месяц назад

Sure, but the idea of simplifying the appearance of Fortnite, the locker and the shop are all going in the exact opposite direction of simplicity. Equipping cosmetics takes double, if not triple the clicks. The UI is impractical and janky, not to mention the introduction of buttons and drop-down menus that were far easier to access previously. Selecting a skin to rotate it, preview it or even spin it around now requires clicking on it, which then applies it to your loadout. Previously this needed a confirm button so you could preview and choose skins, backblings etc without loosing your currently equipped on if you want to go back. Wraps are not included as part of a character's preset despite being completely relevant to the skin worn. Changing emotes, lobby tracks, and all cosmetics now means you have to back out and change tabs, something that could have previously been done on 1 page. The sectioned tabs make sense with new types like LEGO Kits, Cars, Instruments but there's no need to separate Character from Wraps, Emotes or Lobby. Archiving or Favoriting items can no longer be done in batches or quickly. Previously all you had to do was click once per cosmetic on the button but now you have to enter a drop down and select it for every individual item. Applying wraps to all slots now requires a drop down menu rather than simply clicking the button at the bottom. Swinging a pickaxe or redeploying a glider is near impossible to do and feels like more of a chore to actually accomplish with the drop-down menu. It overlays onto other items meaning as soon as it's clicked the menu vanishes and switches to another cosmetic before the animation can even begin. Cosmetics in the locker can be searched by seasons using "S1-17" but after S18+ they no longer filter. Entering the drop-down menu or right-clicking anything in the locker accidentally immediately takes you all the way back to your currently equipped item when exiting. The filter menu requires an "apply" button to be pressed rather than simply closing and applying when selecting one of the filters, taking longer and more clicks to simply find your recent items or remove a filter. The filter menu was also better as a side panel so you don't have to move your cursor to the center of the screen and back every single time. As a side panel it also allowed for more options to be displayed, whereas the current one requires a huge scroll bar to simply filter by favorite. The presets menu scroll wheel also zooms the skins in and out as you scroll up and down the list. Because of the randomize preset button, the list compared to when saving a preset is all moved up by 1, becoming disorientating to where the preset was in relation to the rows. The shop no longer displays item descriptions or set names on cosmetics - now requiring a click on each individual item when there is adequate space to do so. The new shop and locker appearance can be accustomed to, however, it NEEDS the basic functionality and user-friendly UI elements that the previous ones had. Simple buttons at the bottom to favorite, swing, redeploy and equip. Less tabs to switch between, less clicks required to complete simple tasks and less feeling like there's a battle against the UI itself just to equip the cosmetics you paid for. We appreciate the moves the renew the locker and shop, but in terms of functionality and user interaction, there is nothing we want more than the old one back or the old one's elements merged into the new appearance. Thank you.
0:18

Sure, but the idea of simplifying the appearance of Fortnite, the locker and the shop are all going in the exact opposite direction of simplicity. Equipping cosmetics takes double, if not triple the clicks. The UI is impractical and janky, not to mention the introduction of buttons and drop-down menus that were far easier to access previously. Selecting a skin to rotate it, preview it or even spin it around now requires clicking on it, which then applies it to your loadout. Previously this needed a confirm button so you could preview and choose skins, backblings etc without loosing your currently equipped on if you want to go back. Wraps are not included as part of a character's preset despite being completely relevant to the skin worn. Changing emotes, lobby tracks, and all cosmetics now means you have to back out and change tabs, something that could have previously been done on 1 page. The sectioned tabs make sense with new types like LEGO Kits, Cars, Instruments but there's no need to separate Character from Wraps, Emotes or Lobby. Archiving or Favoriting items can no longer be done in batches or quickly. Previously all you had to do was click once per cosmetic on the button but now you have to enter a drop down and select it for every individual item. Applying wraps to all slots now requires a drop down menu rather than simply clicking the button at the bottom. Swinging a pickaxe or redeploying a glider is near impossible to do and feels like more of a chore to actually accomplish with the drop-down menu. It overlays onto other items meaning as soon as it's clicked the menu vanishes and switches to another cosmetic before the animation can even begin. Cosmetics in the locker can be searched by seasons using "S1-17" but after S18+ they no longer filter. Entering the drop-down menu or right-clicking anything in the locker accidentally immediately takes you all the way back to your currently equipped item when exiting. The filter menu requires an "apply" button to be pressed rather than simply closing and applying when selecting one of the filters, taking longer and more clicks to simply find your recent items or remove a filter. The filter menu was also better as a side panel so you don't have to move your cursor to the center of the screen and back every single time. As a side panel it also allowed for more options to be displayed, whereas the current one requires a huge scroll bar to simply filter by favorite. The presets menu scroll wheel also zooms the skins in and out as you scroll up and down the list. Because of the randomize preset button, the list compared to when saving a preset is all moved up by 1, becoming disorientating to where the preset was in relation to the rows. The shop no longer displays item descriptions or set names on cosmetics - now requiring a click on each individual item when there is adequate space to do so. The new shop and locker appearance can be accustomed to, however, it NEEDS the basic functionality and user-friendly UI elements that the previous ones had. Simple buttons at the bottom to favorite, swing, redeploy and equip. Less tabs to switch between, less clicks required to complete simple tasks and less feeling like there's a battle against the UI itself just to equip the cosmetics you paid for. We appreciate the moves the renew the locker and shop, but in terms of functionality and user interaction, there is nothing we want more than the old one back or the old one's elements merged into the new appearance. Thank you.

FNAssist

178,290 просмотров • 2 лет назад

🧐 At this point, Billboard's latest "Top 50 Songs of 2026 So Far" list feels less like an objective ranking and more like another example of the music industry and media attempting to minimize Drake's success. Let's be serious for a second. Drake released a No. 1 album in 2026, generated some of the biggest streaming numbers of the year, dominated online conversations, and continued to be one of the most commercially successful artists in music. Yet somehow Billboard couldn't find room for any of the biggest records from that project inside the Top 10. But an unreleased Jay-Z freestyle from the Roots Picnic landed at No. 7. Make it make sense. This wasn't even an official single. It wasn't commercially released. It wasn't a chart-dominating record. It wasn't a song fans were streaming for months. It was a basic freestyle performance that generated a few headlines because people were debating who Jay-Z was taking shots at. Meanwhile, Drake's first appearance on the list doesn't arrive until No. 18 with "Shabang." And what makes it even more interesting is Billboard's write up itself. Instead of simply explaining why the freestyle ranked so highly, the article almost reads like it was written specifically to elevate Jay-Z while taking subtle shots at Drake. 💬 “Jigga had everyone talking about who he dissed and why for like two weeks straight.” 💬 “Now we wait and see what Drake has to say about some of the jabs sent his way.” The obvious question is: 🤔 Since when did two weeks of social media discussion become more important than actual chart performance, streaming numbers, sales, and commercial impact? Because if cultural conversation is now the standard, Drake has been one of the most discussed artists in music all year. If commercial success is the standard, Drake should be much higher. If streaming numbers are the standard, Drake should be much higher. If chart performance is the standard, Drake should be much higher. So what exactly is the standard? That's why many fans continue to believe there is an effort within parts of the industry and media to downplay Drake's accomplishments. Every time Drake breaks records, the conversation becomes about somebody else. Every time Drake has a successful release, the focus shifts to a narrative. Every time Drake reaches another milestone, there seems to be an attempt to explain away the achievement instead of simply acknowledging it. And this ranking feels like another example. An unreleased freestyle being placed ahead of records from one of the biggest artists of the year isn't just questionable it raises legitimate questions about whether these lists are actually based on music or whether they're being influenced by industry relationships, personal preferences, and predetermined narratives. Of course, Billboard is entitled to its opinion. But when a No. 1 Drake album struggles to place songs in the Top 10 while a freestyle performance is treated like one of the defining records of the year, fans are naturally going to question the credibility of the ranking. 💭 “To some Drake fans, moments like this fuel the belief that there’s an industry network of gatekeepers whether it’s powerful media figures, executives, or influential artists who consistently seem to move in lockstep whenever Drake is the target. Seeing an unreleased Jay-Z freestyle ranked above songs from a No. 1 Drake album only adds to that perception. Some supporters even describe it as a ‘secret society’ mentality, where certain media outlets and industry power players appear quick to elevate narratives that benefit Jay-Z and others while simultaneously downplaying Drake’s accomplishments. Whether that’s actually happening or not is up for debate, but rankings like this are exactly why those conversations continue.
0:15

🧐 At this point, Billboard's latest "Top 50 Songs of 2026 So Far" list feels less like an objective ranking and more like another example of the music industry and media attempting to minimize Drake's success. Let's be serious for a second. Drake released a No. 1 album in 2026, generated some of the biggest streaming numbers of the year, dominated online conversations, and continued to be one of the most commercially successful artists in music. Yet somehow Billboard couldn't find room for any of the biggest records from that project inside the Top 10. But an unreleased Jay-Z freestyle from the Roots Picnic landed at No. 7. Make it make sense. This wasn't even an official single. It wasn't commercially released. It wasn't a chart-dominating record. It wasn't a song fans were streaming for months. It was a basic freestyle performance that generated a few headlines because people were debating who Jay-Z was taking shots at. Meanwhile, Drake's first appearance on the list doesn't arrive until No. 18 with "Shabang." And what makes it even more interesting is Billboard's write up itself. Instead of simply explaining why the freestyle ranked so highly, the article almost reads like it was written specifically to elevate Jay-Z while taking subtle shots at Drake. 💬 “Jigga had everyone talking about who he dissed and why for like two weeks straight.” 💬 “Now we wait and see what Drake has to say about some of the jabs sent his way.” The obvious question is: 🤔 Since when did two weeks of social media discussion become more important than actual chart performance, streaming numbers, sales, and commercial impact? Because if cultural conversation is now the standard, Drake has been one of the most discussed artists in music all year. If commercial success is the standard, Drake should be much higher. If streaming numbers are the standard, Drake should be much higher. If chart performance is the standard, Drake should be much higher. So what exactly is the standard? That's why many fans continue to believe there is an effort within parts of the industry and media to downplay Drake's accomplishments. Every time Drake breaks records, the conversation becomes about somebody else. Every time Drake has a successful release, the focus shifts to a narrative. Every time Drake reaches another milestone, there seems to be an attempt to explain away the achievement instead of simply acknowledging it. And this ranking feels like another example. An unreleased freestyle being placed ahead of records from one of the biggest artists of the year isn't just questionable it raises legitimate questions about whether these lists are actually based on music or whether they're being influenced by industry relationships, personal preferences, and predetermined narratives. Of course, Billboard is entitled to its opinion. But when a No. 1 Drake album struggles to place songs in the Top 10 while a freestyle performance is treated like one of the defining records of the year, fans are naturally going to question the credibility of the ranking. 💭 “To some Drake fans, moments like this fuel the belief that there’s an industry network of gatekeepers whether it’s powerful media figures, executives, or influential artists who consistently seem to move in lockstep whenever Drake is the target. Seeing an unreleased Jay-Z freestyle ranked above songs from a No. 1 Drake album only adds to that perception. Some supporters even describe it as a ‘secret society’ mentality, where certain media outlets and industry power players appear quick to elevate narratives that benefit Jay-Z and others while simultaneously downplaying Drake’s accomplishments. Whether that’s actually happening or not is up for debate, but rankings like this are exactly why those conversations continue.

Cousin Tino ™️

78,331 просмотров • 2 месяцев назад

AI just hit a wall that no amount of money can move. The planet itself. There is not enough power, water, or land on Earth to build the data centers the AI race now demands. So the most valuable bet in artificial intelligence is no longer a chip company or a model. It is a rocket company. The plan is to leave. In January, SpaceX filed with the FCC to launch up to 1 million solar-powered data center satellites into orbit. In February it bought xAI, the maker of Grok, folding an entire frontier AI lab into a rocket company in the largest corporate merger ever recorded. On June 8 it unveiled the AI1, a compute satellite with a 70-meter wingspan, wider than a Boeing 747, powered by the sun, cooled by the vacuum of space, and wired to the ground through Starlink. Four days later it went public in the largest IPO in history, near 1.77 trillion dollars, touched 2.1 trillion on its first day, raised close to 86 billion, and made one man the first trillionaire alive. Now read the direction of that merger, because it is the whole story. A rocket company bought the AI lab. Not the reverse. For three years everyone assumed the constraint on AI was chips, or data, or talent. It is none of them anymore. It is energy and heat and dirt. The head of Anthropic said his company grew faster than the exponential, 80 times in a single year, and that is exactly why it ran out of compute. The answer was not to build more data centers in Virginia. It was to leave the atmosphere, where the sun never sets and a solar panel does five times the work. The moat in artificial intelligence is no longer the model. It is the launch. And the first rent is already being paid. A rival lab, Anthropic, is reported to be sending roughly 1.25 billion dollars a month to Musk for compute. Google near 920 million. If intelligence moves to orbit, the company that owns the only affordable road there becomes the landlord of the next layer of the internet, the way one bookstore became the landlord of the cloud. The merger is the proof of concept. The IPO is the war chest. Those monthly checks are the lease. Here is the part the price tag does not want you to read. Close to a trillion dollars of that valuation rests on orbital data centers that do not yet exist, and on a chip factory, Terafab, that SpaceX's own public filing calls a general framework with no binding deal, one that may not achieve commercial viability. Musk said it on camera. This is not a promise. The largest IPO ever written is priced on a future the filing itself cannot verify. The other side is just as real. Compute in orbit costs about four times what it costs on the ground today, and the curve may not cross for fifteen years. The machines that print the chips are backordered for years. Shedding heat in a vacuum at this scale has never been done. Musk's timelines have a long history of meaning later. And Bezos is racing the same orbit with a constellation of 51,600 satellites of his own. But strip it all away and the trade underneath is one sentence. Earth has run out of room for intelligence, and whoever owns the road off the planet owns whatever gets built next. Call it the most expensive science fiction ever sold, or the first time the map of the internet pointed up.
0:10

AI just hit a wall that no amount of money can move. The planet itself. There is not enough power, water, or land on Earth to build the data centers the AI race now demands. So the most valuable bet in artificial intelligence is no longer a chip company or a model. It is a rocket company. The plan is to leave. In January, SpaceX filed with the FCC to launch up to 1 million solar-powered data center satellites into orbit. In February it bought xAI, the maker of Grok, folding an entire frontier AI lab into a rocket company in the largest corporate merger ever recorded. On June 8 it unveiled the AI1, a compute satellite with a 70-meter wingspan, wider than a Boeing 747, powered by the sun, cooled by the vacuum of space, and wired to the ground through Starlink. Four days later it went public in the largest IPO in history, near 1.77 trillion dollars, touched 2.1 trillion on its first day, raised close to 86 billion, and made one man the first trillionaire alive. Now read the direction of that merger, because it is the whole story. A rocket company bought the AI lab. Not the reverse. For three years everyone assumed the constraint on AI was chips, or data, or talent. It is none of them anymore. It is energy and heat and dirt. The head of Anthropic said his company grew faster than the exponential, 80 times in a single year, and that is exactly why it ran out of compute. The answer was not to build more data centers in Virginia. It was to leave the atmosphere, where the sun never sets and a solar panel does five times the work. The moat in artificial intelligence is no longer the model. It is the launch. And the first rent is already being paid. A rival lab, Anthropic, is reported to be sending roughly 1.25 billion dollars a month to Musk for compute. Google near 920 million. If intelligence moves to orbit, the company that owns the only affordable road there becomes the landlord of the next layer of the internet, the way one bookstore became the landlord of the cloud. The merger is the proof of concept. The IPO is the war chest. Those monthly checks are the lease. Here is the part the price tag does not want you to read. Close to a trillion dollars of that valuation rests on orbital data centers that do not yet exist, and on a chip factory, Terafab, that SpaceX's own public filing calls a general framework with no binding deal, one that may not achieve commercial viability. Musk said it on camera. This is not a promise. The largest IPO ever written is priced on a future the filing itself cannot verify. The other side is just as real. Compute in orbit costs about four times what it costs on the ground today, and the curve may not cross for fifteen years. The machines that print the chips are backordered for years. Shedding heat in a vacuum at this scale has never been done. Musk's timelines have a long history of meaning later. And Bezos is racing the same orbit with a constellation of 51,600 satellites of his own. But strip it all away and the trade underneath is one sentence. Earth has run out of room for intelligence, and whoever owns the road off the planet owns whatever gets built next. Call it the most expensive science fiction ever sold, or the first time the map of the internet pointed up.

Shanaka Anslem Perera ⚡

54,458 просмотров • 2 месяцев назад

What Trump’s actions show is that globalization is over. Nations must form empires again and secure their strategic space and resources. The world has woken up and is preparing for this new age. If you do not possess a resource at home, or lack direct access to it and the military capability to defend that access and the transport routes then you are dependent on the goodwill of others. The so-called “free and open market” no longer exists. The global Walmart cop has retired. Free, secure, reliable, and cheap global transport may soon be a thing of the past. The world is wide awake and adapting. Europe, meanwhile, reacts with one-sided moral outrage. Geopolitical actions are still interpreted through the outdated ideology of the unipolar moment, judged morally by whether one likes or dislikes the regime being changed. Europe needs to wake up from its liberal, post-political slumber. We are no longer an isolated island between two blocs. We are no longer an economic eunuch living as an adorable pet in the golden cage of a Pax Americana. But the current elites refuse to wake up. They want to become the world police themsevles now - a globocop without a gun. When they speak belligerently about “defending Europe,” they do not mean defending European peoples, national identities, or national interests. They mean defending liberal universalism. They are fanatics of a cargo cult. They are incapable of responding to modern challenges or operating in a de-globalized world. They hate Trump and Putin primarily but because they think they shattered their beautiful dream of globalization out of bad will. They were just reading the signs earlier and rugpulled the unipolar world with imperial politics. Now our elites want to make it the White European man’s burden to rebuild a utopia that never existed in the first place. To get us in line they copy paste a few Vril-Edits, beat their chests and clamor about the "evil" rest of the world. I stand by my words. The real enemy is within. The greatest threat to national survival and European sovereignty is our current rotten, imbecile, unattractiv and stupid elite. Every day they are in power longer, they're losing more European soil, resources, power, lives, and ethnocultural heritage. They must go. Only then can we consolidate, build a Fortress Europe, begin remigration, and return as a sovereign player in the great game. PS: This edits are our culture, not your costume.
0:16

What Trump’s actions show is that globalization is over. Nations must form empires again and secure their strategic space and resources. The world has woken up and is preparing for this new age. If you do not possess a resource at home, or lack direct access to it and the military capability to defend that access and the transport routes then you are dependent on the goodwill of others. The so-called “free and open market” no longer exists. The global Walmart cop has retired. Free, secure, reliable, and cheap global transport may soon be a thing of the past. The world is wide awake and adapting. Europe, meanwhile, reacts with one-sided moral outrage. Geopolitical actions are still interpreted through the outdated ideology of the unipolar moment, judged morally by whether one likes or dislikes the regime being changed. Europe needs to wake up from its liberal, post-political slumber. We are no longer an isolated island between two blocs. We are no longer an economic eunuch living as an adorable pet in the golden cage of a Pax Americana. But the current elites refuse to wake up. They want to become the world police themsevles now - a globocop without a gun. When they speak belligerently about “defending Europe,” they do not mean defending European peoples, national identities, or national interests. They mean defending liberal universalism. They are fanatics of a cargo cult. They are incapable of responding to modern challenges or operating in a de-globalized world. They hate Trump and Putin primarily but because they think they shattered their beautiful dream of globalization out of bad will. They were just reading the signs earlier and rugpulled the unipolar world with imperial politics. Now our elites want to make it the White European man’s burden to rebuild a utopia that never existed in the first place. To get us in line they copy paste a few Vril-Edits, beat their chests and clamor about the "evil" rest of the world. I stand by my words. The real enemy is within. The greatest threat to national survival and European sovereignty is our current rotten, imbecile, unattractiv and stupid elite. Every day they are in power longer, they're losing more European soil, resources, power, lives, and ethnocultural heritage. They must go. Only then can we consolidate, build a Fortress Europe, begin remigration, and return as a sovereign player in the great game. PS: This edits are our culture, not your costume.

Martin Sellner

14,856 просмотров • 7 месяцев назад

❗️🇮🇷🇺🇸Iran has become emboldened after Trump’s concession. Mohsen Rezaei, Senior Military Advisor to Iran’s Supreme Leader Ayatollah Mojtaba Khamenei: “Today our fighters hold weapons in their hands, while our mediators are trying to secure the rights of the Iranian people. The war is ongoing on both dimensions. After this war, America will no longer be what it once was. We have laid the foundation for the destruction of the United States in the Middle East.” 🇮🇱🚨 An official from Benjamin Netanyahu’s administration told Israel’s Channel 14: “Dialogue with Trump is continuing non-stop. However, in yesterday’s conversation, he clearly stated for the first time that he prefers the path of an agreement… We were not surprised, but we are truly disappointed. The real result is that Iran has, in fact, received a continuation of the ceasefire without reaching a peace agreement. There are currently no real concessions, only readiness for dialogue. The economic discussions and those around the Strait of Hormuz naturally played a decisive role. However, fortunately, there is no full lifting of sanctions, and forces will remain in the region, which means military action has not been taken off the table. The fact that Lebanon is part of the agreement is terrible. We had plans for a much stronger strike on Hezbollah, and now some of them will remain on the shelf. Nevertheless, we have retained the freedom to act in case of threats.” 🇮🇷 Iran’s Khatam al-Anbiya General Staff told the Tasnim news agency: “We are in a state of war. All armed forces are in full readiness to confront any enemy at any level.” The command also called on all parties to remain prepared in the economic and social spheres, stating: “What existed before the war and what will exist after it are truly two different worlds.” Video is generated by grok AI
0:10

❗️🇮🇷🇺🇸Iran has become emboldened after Trump’s concession. Mohsen Rezaei, Senior Military Advisor to Iran’s Supreme Leader Ayatollah Mojtaba Khamenei: “Today our fighters hold weapons in their hands, while our mediators are trying to secure the rights of the Iranian people. The war is ongoing on both dimensions. After this war, America will no longer be what it once was. We have laid the foundation for the destruction of the United States in the Middle East.” 🇮🇱🚨 An official from Benjamin Netanyahu’s administration told Israel’s Channel 14: “Dialogue with Trump is continuing non-stop. However, in yesterday’s conversation, he clearly stated for the first time that he prefers the path of an agreement… We were not surprised, but we are truly disappointed. The real result is that Iran has, in fact, received a continuation of the ceasefire without reaching a peace agreement. There are currently no real concessions, only readiness for dialogue. The economic discussions and those around the Strait of Hormuz naturally played a decisive role. However, fortunately, there is no full lifting of sanctions, and forces will remain in the region, which means military action has not been taken off the table. The fact that Lebanon is part of the agreement is terrible. We had plans for a much stronger strike on Hezbollah, and now some of them will remain on the shelf. Nevertheless, we have retained the freedom to act in case of threats.” 🇮🇷 Iran’s Khatam al-Anbiya General Staff told the Tasnim news agency: “We are in a state of war. All armed forces are in full readiness to confront any enemy at any level.” The command also called on all parties to remain prepared in the economic and social spheres, stating: “What existed before the war and what will exist after it are truly two different worlds.” Video is generated by grok AI

NSTRIKE

11,490 просмотров • 3 месяцев назад

🚨BREAKING: just dropped their Shopify integration yesterday. Now you can build a complete Shopify store by talking to AI. This changes a lot of things for ecom. WHAT THIS MEANS: Lovable AI can now: • Build complete online stores from text prompts • Set up checkout and shopping cart automatically • Add products with AI-generated descriptions • Deploy live stores in minutes, not weeks They proved it by building their own merch store: lovable[.]dev/merch THE OLD ECOM SETUP: • Hire Shopify developer ($3K-$10K) • Wait 2-4 weeks for completion • Go through endless revision cycles • Pay for theme customizations • Debug technical issues • Launch after months of delays THE NEW REALITY: "Build me an online store for selling fitness equipment" → AI creates complete store in 10 minutes → Add products with descriptions → Click publish → Start selling immediately WHAT THIS MEANS FOR ECOM OWNERS: The Technical Barrier Just Disappeared: • No coding knowledge required • No designer needed for basic stores • No developer for functionality setup • No technical troubleshooting Speed Becomes the New Standard: • Test product ideas in hours, not months • Launch seasonal stores instantly • Pivot business models without rebuilding • A/B test different store concepts rapidly The Cost Structure Changes: • $29/month Shopify + AI tool vs. $10K+ development • Instant iterations vs. expensive revisions • Self-service setup vs. agency dependencies • Focus budget on marketing, not development THE REALITY: While you're waiting 6 weeks for your developer to finish your store… Your competitor just described their business idea to AI and launched 3 different store variations to test the market. THE OPPORTUNITY FOR BRANDS: • Test 10 product ideas instead of 1 • Launch seasonal campaigns instantly • Create niche stores for different audiences • Focus on products and marketing, not tech THE WINDOW IS CLOSING: Right now, most ecom owners don't know this exists. In 6 months, everyone will expect instant store creation. In 12 months, waiting weeks for a basic store will look amateur. As for me, I’ll say basic Shopify development just became commoditized. P.S. Thanks to Lovable, everyone will have a store soon. Your real edge isn't in JUST building it. It's in the operations, automations, and strategy that make it profitable.
0:13

🚨BREAKING: just dropped their Shopify integration yesterday. Now you can build a complete Shopify store by talking to AI. This changes a lot of things for ecom. WHAT THIS MEANS: Lovable AI can now: • Build complete online stores from text prompts • Set up checkout and shopping cart automatically • Add products with AI-generated descriptions • Deploy live stores in minutes, not weeks They proved it by building their own merch store: lovable[.]dev/merch THE OLD ECOM SETUP: • Hire Shopify developer ($3K-$10K) • Wait 2-4 weeks for completion • Go through endless revision cycles • Pay for theme customizations • Debug technical issues • Launch after months of delays THE NEW REALITY: "Build me an online store for selling fitness equipment" → AI creates complete store in 10 minutes → Add products with descriptions → Click publish → Start selling immediately WHAT THIS MEANS FOR ECOM OWNERS: The Technical Barrier Just Disappeared: • No coding knowledge required • No designer needed for basic stores • No developer for functionality setup • No technical troubleshooting Speed Becomes the New Standard: • Test product ideas in hours, not months • Launch seasonal stores instantly • Pivot business models without rebuilding • A/B test different store concepts rapidly The Cost Structure Changes: • $29/month Shopify + AI tool vs. $10K+ development • Instant iterations vs. expensive revisions • Self-service setup vs. agency dependencies • Focus budget on marketing, not development THE REALITY: While you're waiting 6 weeks for your developer to finish your store… Your competitor just described their business idea to AI and launched 3 different store variations to test the market. THE OPPORTUNITY FOR BRANDS: • Test 10 product ideas instead of 1 • Launch seasonal campaigns instantly • Create niche stores for different audiences • Focus on products and marketing, not tech THE WINDOW IS CLOSING: Right now, most ecom owners don't know this exists. In 6 months, everyone will expect instant store creation. In 12 months, waiting weeks for a basic store will look amateur. As for me, I’ll say basic Shopify development just became commoditized. P.S. Thanks to Lovable, everyone will have a store soon. Your real edge isn't in JUST building it. It's in the operations, automations, and strategy that make it profitable.

Lian Lim | Dashboard & AI Automation Expert

14,316 просмотров • 10 месяцев назад

We all remember. We all remember when blockchain was pitched as the next big thing. And today, we feel like we’ve been waiting and waiting. Until recently, Blockchain was too expensive, slow under load, and hard to integrate for most businesses. So enterprises ignored it. It didn’t solve their business problems. That’s changed. Why blockchain, why now? Businesses don’t care about the tech, they care about cost and performance. They’d ask a simple question “Does it save or make me more money?” For a long time, blockchain didn’t clearly do this. That’s no longer true. Blockchain is proving real business cases, especially on Avalanche. On Avalanche, transactions cost fractions of a cent. settle in about a second. And instead of forcing everything onto one shared chain, businesses can launch their own Avalanche L1s with their own rules. To understand this let’s identify the problem and then provide the solution in a way that's easy to understand. Where Businesses Lose Money Most large industries lose money due to operational inefficiencies. Data lives in different systems. Teams spend hours reconciling records that should already match. Intermediaries sit in the middle, taking fees to coordinate all of it. Individually, each step looks small. Together, they create real cost: > Labor spent on manual processes > Capital locked up during settlement delays > Fees paid to intermediaries > Risk introduced by time gaps and mismatched data This is where businesses actually lose money. Not in big, obvious ways. In constant, compounding friction. Take Private Credit, for Example Private credit is loans held outside of traditional banks. It’s a multi-trillion dollar market, and much of it still runs on spreadsheets and weekly reconciliation processes. Loan data is tracked across systems. Teams manually process requests. Funds move on traditional rails, often on delayed cycles. It doesn’t have to be this way Entire teams exist just to keep systems in sync. Now move that system onto Avalanche. Loan data updates in real time. Transactions settle in about a second. Every participant sees the same state instantly. Reconciliation isn’t a separate step because the system itself is the source of truth. The impact is straightforward. > Reduced manual work > Shortened settlement cycles > Fewer layers of coordination between parties Avalanche is Infrastructure for Real Businesses Avalanche is designed to match how businesses actually operate. Instead of sharing a single chain, they can launch their own Avalanche L1s with custom rules, built-in compliance, and predictable performance. They control the system. Avalanche’s Moment For the longest time, blockchain naysayers said this could all be done better with spreadsheets or existing systems. They were right. That’s what the technology allowed. Now it’s changed. Avalanche can replace many of those systems with real-time settlement, shared data, and automated execution. For the first time, the economics work. Built for business. 🔺
0:10

We all remember. We all remember when blockchain was pitched as the next big thing. And today, we feel like we’ve been waiting and waiting. Until recently, Blockchain was too expensive, slow under load, and hard to integrate for most businesses. So enterprises ignored it. It didn’t solve their business problems. That’s changed. Why blockchain, why now? Businesses don’t care about the tech, they care about cost and performance. They’d ask a simple question “Does it save or make me more money?” For a long time, blockchain didn’t clearly do this. That’s no longer true. Blockchain is proving real business cases, especially on Avalanche. On Avalanche, transactions cost fractions of a cent. settle in about a second. And instead of forcing everything onto one shared chain, businesses can launch their own Avalanche L1s with their own rules. To understand this let’s identify the problem and then provide the solution in a way that's easy to understand. Where Businesses Lose Money Most large industries lose money due to operational inefficiencies. Data lives in different systems. Teams spend hours reconciling records that should already match. Intermediaries sit in the middle, taking fees to coordinate all of it. Individually, each step looks small. Together, they create real cost: > Labor spent on manual processes > Capital locked up during settlement delays > Fees paid to intermediaries > Risk introduced by time gaps and mismatched data This is where businesses actually lose money. Not in big, obvious ways. In constant, compounding friction. Take Private Credit, for Example Private credit is loans held outside of traditional banks. It’s a multi-trillion dollar market, and much of it still runs on spreadsheets and weekly reconciliation processes. Loan data is tracked across systems. Teams manually process requests. Funds move on traditional rails, often on delayed cycles. It doesn’t have to be this way Entire teams exist just to keep systems in sync. Now move that system onto Avalanche. Loan data updates in real time. Transactions settle in about a second. Every participant sees the same state instantly. Reconciliation isn’t a separate step because the system itself is the source of truth. The impact is straightforward. > Reduced manual work > Shortened settlement cycles > Fewer layers of coordination between parties Avalanche is Infrastructure for Real Businesses Avalanche is designed to match how businesses actually operate. Instead of sharing a single chain, they can launch their own Avalanche L1s with custom rules, built-in compliance, and predictable performance. They control the system. Avalanche’s Moment For the longest time, blockchain naysayers said this could all be done better with spreadsheets or existing systems. They were right. That’s what the technology allowed. Now it’s changed. Avalanche can replace many of those systems with real-time settlement, shared data, and automated execution. For the first time, the economics work. Built for business. 🔺

Avalanche🔺

13,104 просмотров • 5 месяцев назад

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.
0:16

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.

雪踏乌云

23,107 просмотров • 1 месяц назад

🚨 SCIENTISTS MAY HAVE FINALLY SOLVED ONE OF THE WORLD’S STRANGEST MYSTERIES. For decades, people around the world have reported hearing a persistent, low-frequency “hum” that others cannot hear. Now, new research suggests that for many, The Hum is not coming from outside it’s a form of low-frequency tinnitus generated inside the body. Researchers tested people who hear The Hum and found that most had normal hearing sensitivity and normal sounds coming from their own ears. This points to the sound being a subjective internal perception rather than an external noise. Why this matters: • The Hum has been reported for over 50 years with no clear external source found in most cases • Low-frequency tinnitus is underrecognized compared to the more common high-pitched ringing • Recognizing it as tinnitus could finally give sufferers access to existing management strategies • It shows how internal auditory processes can create very real, persistent perceptions The deeper implication: We tend to assume that if we hear something, it must exist in the outside world. But the brain is incredibly good at generating its own sounds when something in the auditory system is slightly off. This research suggests that The Hum long treated as a mysterious external phenomenon may often be the brain’s own creation. While it doesn’t explain every single case, it offers a more hopeful path forward: instead of hunting for an elusive external source, people may be able to focus on managing it as a form of tinnitus. Sometimes the most mysterious sounds come from within. Have you ever heard The Hum, or do you know someone who has? Follow for more frontier neuroscience, auditory science, and explanations of strange human experiences.
0:10

🚨 SCIENTISTS MAY HAVE FINALLY SOLVED ONE OF THE WORLD’S STRANGEST MYSTERIES. For decades, people around the world have reported hearing a persistent, low-frequency “hum” that others cannot hear. Now, new research suggests that for many, The Hum is not coming from outside it’s a form of low-frequency tinnitus generated inside the body. Researchers tested people who hear The Hum and found that most had normal hearing sensitivity and normal sounds coming from their own ears. This points to the sound being a subjective internal perception rather than an external noise. Why this matters: • The Hum has been reported for over 50 years with no clear external source found in most cases • Low-frequency tinnitus is underrecognized compared to the more common high-pitched ringing • Recognizing it as tinnitus could finally give sufferers access to existing management strategies • It shows how internal auditory processes can create very real, persistent perceptions The deeper implication: We tend to assume that if we hear something, it must exist in the outside world. But the brain is incredibly good at generating its own sounds when something in the auditory system is slightly off. This research suggests that The Hum long treated as a mysterious external phenomenon may often be the brain’s own creation. While it doesn’t explain every single case, it offers a more hopeful path forward: instead of hunting for an elusive external source, people may be able to focus on managing it as a form of tinnitus. Sometimes the most mysterious sounds come from within. Have you ever heard The Hum, or do you know someone who has? Follow for more frontier neuroscience, auditory science, and explanations of strange human experiences.

TheNewPhysics

14,027 просмотров • 2 месяцев назад

🚨 WARNING: NEXT WEEK WILL BE THE WORST TIME OF 2026!! When markets open on Monday, this won't be “just a dip.” Stocks will dump. Metals will dump. Bitcoin will collapse. If you hold any assets right now, you MUST be prepared for the biggest sell-off event of the year: Insiders are nonstop dumping ALL assets right now. They are not buying the dip. They are moving into cash, reducing exposure, and preparing for a market crash. And the warning signs are already appearing. Bitcoin has already dumped below $60,000. Stocks are falling. Gold is falling. Silver is falling. This is not isolated weakness. This is capital exiting risk across the board. Capital freezes. Confidence evaporates. Global growth expectations reset lower instantly. Meanwhile: → Japanese bond yields are surging → Foreign nations are dumping U.S. Treasuries → Global bonds are falling → Oil markets are becoming unstable → The dollar is losing stability → Liquidity is tightening worldwide This is no longer one isolated problem. This is systemic pressure building across MULTIPLE fronts simultaneously. Inflation spikes globally. Which means central banks will keep interest rates higher for longer. And that creates the exact environment markets cannot survive in: → Slowing growth → Sticky inflation → Tight liquidity → Rising geopolitical risk → Collapsing investor confidence Now connect the dots. When geopolitical stress collides with a fragile financial system, reactions do not stay contained. They COLLAPSE. Capital does not rotate slowly. It stampedes toward safety all at once. And risk assets? They do not dip. They DUMP HARD. This is exactly how chain reactions begin. Once markets start pricing prolonged instability instead of temporary fear, the entire system changes. Watch oil. Watch bonds. Watch interest rates. Because once this accelerates, there will be no time left to react. I have spent decades tracking macro and systemic market reactions like this. When the next move becomes clear, I will share it here publicly. Follow and turn notifications on. Because by the time it reaches the headlines, it is already too late.
0:15

🚨 WARNING: NEXT WEEK WILL BE THE WORST TIME OF 2026!! When markets open on Monday, this won't be “just a dip.” Stocks will dump. Metals will dump. Bitcoin will collapse. If you hold any assets right now, you MUST be prepared for the biggest sell-off event of the year: Insiders are nonstop dumping ALL assets right now. They are not buying the dip. They are moving into cash, reducing exposure, and preparing for a market crash. And the warning signs are already appearing. Bitcoin has already dumped below $60,000. Stocks are falling. Gold is falling. Silver is falling. This is not isolated weakness. This is capital exiting risk across the board. Capital freezes. Confidence evaporates. Global growth expectations reset lower instantly. Meanwhile: → Japanese bond yields are surging → Foreign nations are dumping U.S. Treasuries → Global bonds are falling → Oil markets are becoming unstable → The dollar is losing stability → Liquidity is tightening worldwide This is no longer one isolated problem. This is systemic pressure building across MULTIPLE fronts simultaneously. Inflation spikes globally. Which means central banks will keep interest rates higher for longer. And that creates the exact environment markets cannot survive in: → Slowing growth → Sticky inflation → Tight liquidity → Rising geopolitical risk → Collapsing investor confidence Now connect the dots. When geopolitical stress collides with a fragile financial system, reactions do not stay contained. They COLLAPSE. Capital does not rotate slowly. It stampedes toward safety all at once. And risk assets? They do not dip. They DUMP HARD. This is exactly how chain reactions begin. Once markets start pricing prolonged instability instead of temporary fear, the entire system changes. Watch oil. Watch bonds. Watch interest rates. Because once this accelerates, there will be no time left to react. I have spent decades tracking macro and systemic market reactions like this. When the next move becomes clear, I will share it here publicly. Follow and turn notifications on. Because by the time it reaches the headlines, it is already too late.

0xNobler

768,870 просмотров • 2 месяцев назад

What Actually is Sei Network's “Giga” Upgrade? Sei Network’s (Sei) Giga upgrade is a major overhaul designed to make the network faster, more scalable and better suited for high-performance onchain trading. Put simply, Giga is rebuilding three critical parts of the blockchain: consensus, execution and storage. (1) The first track focuses on consensus, with upgrades such as Autobahn designed to improve how Sei validators agree on the state of the chain. (2) The Ares upgrade targets execution, the part of the blockchain responsible for actually processing transactions. (3) Eidos focuses on storage, which is becoming increasingly important as blockchain throughput rises. Why does storage matter? Every transaction a blockchain processes has to be recorded. If the database cannot write data as quickly as the network executes transactions, higher throughput eventually becomes meaningless. Eidos is designed to solve that bottleneck. (4) Sei plans to replace the traditional Merkle-tree structure used for EVM state with FlatKV, a flat key-value database where updating one piece of state requires essentially one write. A lattice hash, or LtHash, is then used to maintain a verifiable fingerprint of the entire state without repeatedly recalculating an entire hash path. (5) Eidos also separates live EVM state from other blockchain data. This means transactions accessing current state no longer have to compete with historical data for the same database resources. (6) Sei is also introducing LittDB-backed storage for blocks and receipts. These records are written once but queried repeatedly, making them a different workload from constantly changing blockchain state. Older historical data will eventually move away from active nodes into archival storage, allowing nodes to focus their resources on the data needed for real-time operations. The interesting part is how Sei plans to deploy all of this. Instead of shutting down the network and migrating the entire database at once, Eidos is designed to migrate storage while Sei continues producing blocks. The old and new systems can run side by side during the transition, with data moved in batches and integrity checks performed throughout the process. The first phase arrived on Sei mainnet with the v6.6 release in August 2026, beginning the separation of EVM state and introducing improvements to the pruning process. The broader Eidos architecture, including FlatKV, LtHash, the new receipt store and off-node archival storage, is expected to arrive through subsequent releases. Sei’s ultimate Giga target is 200,000 transactions per second. But reaching that kind of execution speed requires more than a faster transaction engine. The blockchain also needs a storage system capable of keeping up. That is essentially what Eidos is trying to build. Giga is not just about making Sei execute transactions faster. It is about rebuilding the infrastructure underneath that speed so the network can actually sustain it.
0:15

What Actually is Sei Network's “Giga” Upgrade? Sei Network’s (Sei) Giga upgrade is a major overhaul designed to make the network faster, more scalable and better suited for high-performance onchain trading. Put simply, Giga is rebuilding three critical parts of the blockchain: consensus, execution and storage. (1) The first track focuses on consensus, with upgrades such as Autobahn designed to improve how Sei validators agree on the state of the chain. (2) The Ares upgrade targets execution, the part of the blockchain responsible for actually processing transactions. (3) Eidos focuses on storage, which is becoming increasingly important as blockchain throughput rises. Why does storage matter? Every transaction a blockchain processes has to be recorded. If the database cannot write data as quickly as the network executes transactions, higher throughput eventually becomes meaningless. Eidos is designed to solve that bottleneck. (4) Sei plans to replace the traditional Merkle-tree structure used for EVM state with FlatKV, a flat key-value database where updating one piece of state requires essentially one write. A lattice hash, or LtHash, is then used to maintain a verifiable fingerprint of the entire state without repeatedly recalculating an entire hash path. (5) Eidos also separates live EVM state from other blockchain data. This means transactions accessing current state no longer have to compete with historical data for the same database resources. (6) Sei is also introducing LittDB-backed storage for blocks and receipts. These records are written once but queried repeatedly, making them a different workload from constantly changing blockchain state. Older historical data will eventually move away from active nodes into archival storage, allowing nodes to focus their resources on the data needed for real-time operations. The interesting part is how Sei plans to deploy all of this. Instead of shutting down the network and migrating the entire database at once, Eidos is designed to migrate storage while Sei continues producing blocks. The old and new systems can run side by side during the transition, with data moved in batches and integrity checks performed throughout the process. The first phase arrived on Sei mainnet with the v6.6 release in August 2026, beginning the separation of EVM state and introducing improvements to the pruning process. The broader Eidos architecture, including FlatKV, LtHash, the new receipt store and off-node archival storage, is expected to arrive through subsequent releases. Sei’s ultimate Giga target is 200,000 transactions per second. But reaching that kind of execution speed requires more than a faster transaction engine. The blockchain also needs a storage system capable of keeping up. That is essentially what Eidos is trying to build. Giga is not just about making Sei execute transactions faster. It is about rebuilding the infrastructure underneath that speed so the network can actually sustain it.

BSCN

27,310 просмотров • 9 дней назад

Almost everyone I talk to genuinely do not understand how ordinary families are making this work anymore, especially those raising children and the uncomfortable answer is that many of them are not. People are maintaining the appearance of financial stability by carrying credit card balances, stretching vehicle loans across 6 or 7 years, dividing everyday purchases into installments and quietly falling behind. The numbers reveal what the headline economy often conceals. Childcare Center based childcare now averages roughly $1,372 per month for one child and approximately $2,333 per month for two children, or about $28,000 per year. Infant care can cost between $1,560 and $1,800 per month for each child. Vehicle Payments The average new car payment is approximately $770 per month, while the average used car payment is around $531 and the average lease payment is about $619. Nearly 20% of new car buyers now have monthly payments exceeding $1,000. The Basic Family Math One average new car payment combined with childcare for two children already consumes roughly $3,100 per month, or more than $37,000 annually, before paying for housing, groceries, insurance, utilities, healthcare, clothing or taxes. Credit Cards Roughly 13.1% of credit card balances are at least 90 days delinquent, approaching the approximately 13.7% peak recorded during the Great Financial Crisis. Auto Loans Serious auto loan delinquencies are approximately 5.6% overall, while subprime delinquencies of 60 days or more have reached roughly 6.8%, the highest level in 32 years. The stress remains concentrated among weaker borrowers, but that is often where broader economic deterioration begins. Buy Now, Pay Later Usage What began as a small checkout convenience has developed into a major parallel credit system. The largest providers originated about 180 million pay in four loans worth more than $24 billion in 2021, nearly 10x the number issued in 2019. Estimated United States transaction value later climbed toward $70 billion, while adult usage increased from roughly 10% in 2021 to 16% by 2025. Buy Now, Pay Later Stress One survey found that 47% of users made at least one late payment, compared with 41% the previous year and 34% two years earlier. Most late payments may eventually be resolved, but the trend still reveals widespread short term cash flow pressure. Millions of working families are no longer comfortably funding their lives from current income. They are financing childcare, transportation and ordinary consumption through increasingly fragile layers of debt. From the outside, the economy still appears functional. Underneath it, a growing share of families are not getting ahead or even holding steady. They are simply postponing the moment when the arithmetic finally catches up with them.
0:14

Almost everyone I talk to genuinely do not understand how ordinary families are making this work anymore, especially those raising children and the uncomfortable answer is that many of them are not. People are maintaining the appearance of financial stability by carrying credit card balances, stretching vehicle loans across 6 or 7 years, dividing everyday purchases into installments and quietly falling behind. The numbers reveal what the headline economy often conceals. Childcare Center based childcare now averages roughly $1,372 per month for one child and approximately $2,333 per month for two children, or about $28,000 per year. Infant care can cost between $1,560 and $1,800 per month for each child. Vehicle Payments The average new car payment is approximately $770 per month, while the average used car payment is around $531 and the average lease payment is about $619. Nearly 20% of new car buyers now have monthly payments exceeding $1,000. The Basic Family Math One average new car payment combined with childcare for two children already consumes roughly $3,100 per month, or more than $37,000 annually, before paying for housing, groceries, insurance, utilities, healthcare, clothing or taxes. Credit Cards Roughly 13.1% of credit card balances are at least 90 days delinquent, approaching the approximately 13.7% peak recorded during the Great Financial Crisis. Auto Loans Serious auto loan delinquencies are approximately 5.6% overall, while subprime delinquencies of 60 days or more have reached roughly 6.8%, the highest level in 32 years. The stress remains concentrated among weaker borrowers, but that is often where broader economic deterioration begins. Buy Now, Pay Later Usage What began as a small checkout convenience has developed into a major parallel credit system. The largest providers originated about 180 million pay in four loans worth more than $24 billion in 2021, nearly 10x the number issued in 2019. Estimated United States transaction value later climbed toward $70 billion, while adult usage increased from roughly 10% in 2021 to 16% by 2025. Buy Now, Pay Later Stress One survey found that 47% of users made at least one late payment, compared with 41% the previous year and 34% two years earlier. Most late payments may eventually be resolved, but the trend still reveals widespread short term cash flow pressure. Millions of working families are no longer comfortably funding their lives from current income. They are financing childcare, transportation and ordinary consumption through increasingly fragile layers of debt. From the outside, the economy still appears functional. Underneath it, a growing share of families are not getting ahead or even holding steady. They are simply postponing the moment when the arithmetic finally catches up with them.

EndGame Macro

56,779 просмотров • 1 месяц назад