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the New Aqua button inspired by iPhone 16e entirely made in Rive with Layout + Vector Feathering. — Fluent — Native 120 FPS (on my display) — Dynamic text support — 5KB — Infinite and fast zoom, without detail loss — works everywhere straight away

Dimitri Novikov 🇺🇦

31,917 subscribers

17,146 Aufrufe • vor 1 Jahr •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

11 Kommentare

Profilbild von Sanja Stepa

Sanja Stepavor 1 Jahr

@rive_app I didn’t know the world needed jiggle buttons but I’m v happy we’re here

Profilbild von Dimitri Novikov 🇺🇦

Dimitri Novikov 🇺🇦vor 1 Jahr

@rive_app Water should behave like a liquid isn’t ;)

Profilbild von WillScuderi

WillScuderivor 1 Jahr

🚀 We've just launched the ultimate travel companion for all business travellers. The espresso Display 15 with Stand+ features: ✅ Aluminium Build ✅ Works with one cable for Mac, PC, iphone (15 and later) ✅ Display above laptop ✅ 1080p/16m colours/300nits ✅ 0.2"/5mm thin

Profilbild von Duque

Duquevor 1 Jahr

@rive_app beautiful

Profilbild von sy

syvor 1 Jahr

@rive_app I have to solve this area...

Profilbild von Dimitri Novikov 🇺🇦

Dimitri Novikov 🇺🇦vor 1 Jahr

@rive_app Do you need help with it?

Profilbild von Sneg Hd

Sneg Hdvor 1 Jahr

@rive_app Steve Jobs would be proud

Profilbild von okt

oktvor 1 Jahr

@rive_app Want: Rive powered window manager. Let’s go!

Profilbild von Paul Martens

Paul Martensvor 1 Jahr

@rive_app

Profilbild von Dimitri Novikov 🇺🇦

Dimitri Novikov 🇺🇦vor 1 Jahr

@rive_app yes!

Profilbild von Joe Evil

Joe Evilvor 1 Jahr

@rive_app Can I remix? I wanna play with some sound design stuff on this

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What's next for OpenTUI? Here's a technical write-up. Over the last few months OpenTUI gained a lot of stability improvements, new unnecessary but fun features like live audio streaming, and useful features like rendering to the scrollback buffer mixed with a live TUI, called footer mode. Overall the feature set enables building large and complex applications. React and Solid make it super simple and convenient. There is still so much to do though. Three big milestones we have set out to achieve are: - Moving most of the behavioural logic currently living in TypeScript down to the native Zig core - Node compatibility - Optimizing the hell out of primitives like text rendering The render tree mechanisms are currently only usable from TypeScript. Think of the DOM, but controllable like a scene graph. Elements in the render tree are called renderables. They can expose a render method to draw themselves. All renderables are derived from a BaseRenderable. 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A signature can have at most eight arguments and everything must fit into CPU registers. x86-64 Unix systems have room for six GP (general-purpose) and eight FP (floating-point) arguments. AArch64 has room for seven GP and eight FP arguments. Anything that spills onto the stack falls back to a slower path. These are Node fast-path restrictions, not general FFI restrictions. Bun also does not support passing structs by value through its current FFI API. OpenTUI uses bun-ffi-structs to pack ABI-aligned struct data into an ArrayBuffer and passes a pointer instead. Despite the name, the package also works with Node. Pointers need some care too. Typed arrays and ArrayBuffers normally have to be resolved into BigInt addresses first. Eligible functions with exactly one pointer argument get another Fast API entrypoint that can extract the address directly from the buffer. An eligible signature is still not enough. V8 has to optimize a direct call with a fixed number of consistently typed arguments. Wrappers that collect arguments and forward them using spread or Reflect.apply can hide that call shape and keep the function on a slower path. The practical rules are: keep hot signatures within register limits, use direct fixed-arity calls with stable argument types, reuse owned buffers safely, and batch small operations. Then measure the real call site, because eligibility only makes a function fast-capable. We have to design the ABI around these constraints where it makes sense and gives the expected performance improvement. The third big area is text rendering. Today a Text renderable accepts a string, StyledText, or a tree of TextNodes. Before rendering, the TextNode tree is walked and flattened into styled chunks. Those chunks are packed in TypeScript, sent through FFI, copied into a native TextBuffer, and stored in a rope. Styles are represented separately as highlights. A TextBufferView then wraps the rope into visual lines, which are drawn into the visible buffer. This works, but updates are much more expensive than they should be. setStyledText effectively throws away and rebuilds the rope, copies and reparses all text and recreates the style highlights. Changing one TextNode also walks and flattens the complete tree before going through this path again. Text and style segments should instead live directly in the rope and support incremental replacement. Memory ownership is split between retained JavaScript buffers, the native memory registry, rope arenas, wrapping caches, styled-text storage, and highlights. Different operations preserve or reset different parts of that state. This is hard to reason about and can retain memory for much longer than expected. Text storage needs clearer ownership, with fewer lifetimes split across JavaScript and native code. The public API reflects the same split. The t template literal is convenient, but creates another intermediate chunk representation that is mutable, not cached, and not merged. Text also maintains both StyledText content and a special TextNode tree, which do not compose properly. TextNode is only a style scope, not a normal layout primitive, so Text renderables cannot naturally compose inside each other. I think this should become one Text primitive backed directly by rope segments. The template literal API might disappear or become a very thin helper around those native segments. Editing has another temporary layer in TypeScript. Extmarks currently monkey-patch editing operations, scan and adjust all marks after changes, maintain their own undo state, and recreate native highlights. They should become native marks anchored directly in the rope. A proper mark tree, similar to Neovim's marktree, could update marks together with edits, undo, and redo, and provide the foundation for highlights and concealment. Text wrapping has also become too complex. Supporting CJK, emoji, combining characters, ZWJ sequences, tabs, and different terminal width rules currently mixes byte offsets, grapheme indexes, and display-cell columns across several custom algorithms. Dirty views rewrap the complete document. Measurement and drawing can repeat some of the same work. The wrapping implementation needs an overhaul, but the exact shape is still open. The goal is to make Unicode handling easier to maintain, avoid repeated full-document work, and clearly separate byte offsets, graphemes, and terminal display cells. None of this will happen as one big rewrite. We will replace pieces when we understand the problem well enough and when the result is clearly simpler, faster, or more useful. To achieve all of this we might break public interfaces. Thanks to OpenCode and a lot of good models, migration to a new version with breaking changes mostly is not an issue anymore. What do you want to see next for OpenTUI?

What's next for OpenTUI? Here's a technical write-up. Over the last few months OpenTUI gained a lot of stability improvements, new unnecessary but fun features like live audio streaming, and useful features like rendering to the scrollback buffer mixed with a live TUI, called footer mode. Overall the feature set enables building large and complex applications. React and Solid make it super simple and convenient. There is still so much to do though. Three big milestones we have set out to achieve are: - Moving most of the behavioural logic currently living in TypeScript down to the native Zig core - Node compatibility - Optimizing the hell out of primitives like text rendering The render tree mechanisms are currently only usable from TypeScript. Think of the DOM, but controllable like a scene graph. Elements in the render tree are called renderables. They can expose a render method to draw themselves. All renderables are derived from a BaseRenderable. Renderables and the render tree will become native primitives. Building blocks usable from any language bindings. Reducing the TypeScript bindings to a very thin layer, with all the behavioural logic living in the native binary. Moving this down is not just a matter of porting TypeScript classes to Zig. TypeScript currently owns the tree, dirty-state propagation, layout reads, culling, and render ordering. If it still has to walk every node and call into native code for each step, we keep most of the complexity and add FFI overhead. Whole passes and their state need to move together. We took a big step towards this recently by building yoga-layout into the native binary. It exposes part of the official yoga-layout TypeScript package via FFI. Only the API surface that is actually used by OpenTUI. Covered by the test suite of the original yoga-layout package. This already gave a median speedup of ~2.5x, and up to 30x for narrow scenarios. The yoga-layout integration is useful beyond the speedup. Built-in text and editor measurement can now happen entirely in native code during layout instead of calling back into JavaScript. I ran an experiment last month taking this even further, having GPT 5.6 port yoga-layout from C++ to Zig, which gave extremely good results. It would be a burden to maintain right now though, so that's off the table for now. I might come back to it. Simon Klee is working relentlessly on Node compatibility and already has a full Node version of OpenCode running. Node got FFI support in v26.4.0, thanks to help from the Node community, namely Matteo Collina and Paolo Insogna. Behaviour and interfaces seem similar between Node and Bun, but there are some major differences. To get the best performance out of the Node FFI implementation, its usage has to follow some rules. Node has three ways to call native functions: the generic C++/libffi path, the SharedBuffer path, and the V8 Fast API. The generic path converts every argument in Node's C++ layer and then calls the function through libffi. It is flexible, but also the slowest option for frequently called functions. The SharedBuffer path is a middle ground. JavaScript writes scalar values and BigInt pointers into a small per-function buffer, reducing some conversion work. The actual native call still goes through libffi though. Typed arrays used as pointers cannot be packed into this buffer and fall back to the generic path. The path we really want is the V8 Fast API. Node generates a small machine-code trampoline for the exact function signature, allowing optimized JavaScript to call the native function without going through the generic converter or libffi. This only applies to JavaScript-to-native calls. Callbacks from native code into JavaScript still use libffi closures. Getting onto this path is quite strict. A signature can have at most eight arguments and everything must fit into CPU registers. x86-64 Unix systems have room for six GP (general-purpose) and eight FP (floating-point) arguments. AArch64 has room for seven GP and eight FP arguments. Anything that spills onto the stack falls back to a slower path. These are Node fast-path restrictions, not general FFI restrictions. Bun also does not support passing structs by value through its current FFI API. OpenTUI uses bun-ffi-structs to pack ABI-aligned struct data into an ArrayBuffer and passes a pointer instead. Despite the name, the package also works with Node. Pointers need some care too. Typed arrays and ArrayBuffers normally have to be resolved into BigInt addresses first. Eligible functions with exactly one pointer argument get another Fast API entrypoint that can extract the address directly from the buffer. An eligible signature is still not enough. V8 has to optimize a direct call with a fixed number of consistently typed arguments. Wrappers that collect arguments and forward them using spread or Reflect.apply can hide that call shape and keep the function on a slower path. The practical rules are: keep hot signatures within register limits, use direct fixed-arity calls with stable argument types, reuse owned buffers safely, and batch small operations. Then measure the real call site, because eligibility only makes a function fast-capable. We have to design the ABI around these constraints where it makes sense and gives the expected performance improvement. The third big area is text rendering. Today a Text renderable accepts a string, StyledText, or a tree of TextNodes. Before rendering, the TextNode tree is walked and flattened into styled chunks. Those chunks are packed in TypeScript, sent through FFI, copied into a native TextBuffer, and stored in a rope. Styles are represented separately as highlights. 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The t template literal is convenient, but creates another intermediate chunk representation that is mutable, not cached, and not merged. Text also maintains both StyledText content and a special TextNode tree, which do not compose properly. TextNode is only a style scope, not a normal layout primitive, so Text renderables cannot naturally compose inside each other. I think this should become one Text primitive backed directly by rope segments. The template literal API might disappear or become a very thin helper around those native segments. Editing has another temporary layer in TypeScript. Extmarks currently monkey-patch editing operations, scan and adjust all marks after changes, maintain their own undo state, and recreate native highlights. They should become native marks anchored directly in the rope. A proper mark tree, similar to Neovim's marktree, could update marks together with edits, undo, and redo, and provide the foundation for highlights and concealment. Text wrapping has also become too complex. Supporting CJK, emoji, combining characters, ZWJ sequences, tabs, and different terminal width rules currently mixes byte offsets, grapheme indexes, and display-cell columns across several custom algorithms. Dirty views rewrap the complete document. Measurement and drawing can repeat some of the same work. The wrapping implementation needs an overhaul, but the exact shape is still open. The goal is to make Unicode handling easier to maintain, avoid repeated full-document work, and clearly separate byte offsets, graphemes, and terminal display cells. None of this will happen as one big rewrite. We will replace pieces when we understand the problem well enough and when the result is clearly simpler, faster, or more useful. To achieve all of this we might break public interfaces. Thanks to OpenCode and a lot of good models, migration to a new version with breaking changes mostly is not an issue anymore. What do you want to see next for OpenTUI?

kmdr

29,430 Aufrufe • vor 19 Tagen

QVAC SDK 0.12.0 is now live, bringing longer context, increased memory optimisation, new modalities, and broader ecosystem support directly to your device. Key Features and Updates: - TurboQuant KV-Cache Quantization: Fit much longer context in the same memory. TurboQuant, an algorithm from Google Research, compresses the KV cache by up to 5x, near-lossless. - Text-to-Video: Generate video from a text prompt, fully local, with the new wan2.1 model in the Diffusion addon - Apple Metal Performance for Flux2-klein: Diffusion on Apple Silicon now matches MLX performance, the native benchmark for Apple GPUs - Robot Control (new VLA addon): A GGML-based Vision-Language-Action addon brings fast, efficient robot control to edge devices - Coding Assistant / Harness Support: QVAC now works with OpenCode and OpenClaw as a local provider. A new @qvac/ai-sdk-provider package automates model registry and provider integration - Cross-Platform Voice: Text-to-speech and Parakeet transcription moved from ONNX to the GGML engine for better CPU and GPU support on macOS, iOS, Windows, Linux, and Android. Parakeet also adds long-term streaming diarization (tracking who spoke when on live audio) - Faster Lightweight Visual Classification: A new GGML-based Classification addon delivers millisecond-level classification, useful where a vision-language model (VLM) would be unnecessarily slow - Under the Hood: Fabric synced to llama.cpp v8828 (from v8189), plus GPU acceleration added to image-upscale models for faster results Full release notes:

QVAC SDK 0.12.0 is now live, bringing longer context, increased memory optimisation, new modalities, and broader ecosystem support directly to your device. Key Features and Updates: - TurboQuant KV-Cache Quantization: Fit much longer context in the same memory. TurboQuant, an algorithm from Google Research, compresses the KV cache by up to 5x, near-lossless. - Text-to-Video: Generate video from a text prompt, fully local, with the new wan2.1 model in the Diffusion addon - Apple Metal Performance for Flux2-klein: Diffusion on Apple Silicon now matches MLX performance, the native benchmark for Apple GPUs - Robot Control (new VLA addon): A GGML-based Vision-Language-Action addon brings fast, efficient robot control to edge devices - Coding Assistant / Harness Support: QVAC now works with OpenCode and OpenClaw as a local provider. A new @qvac/ai-sdk-provider package automates model registry and provider integration - Cross-Platform Voice: Text-to-speech and Parakeet transcription moved from ONNX to the GGML engine for better CPU and GPU support on macOS, iOS, Windows, Linux, and Android. Parakeet also adds long-term streaming diarization (tracking who spoke when on live audio) - Faster Lightweight Visual Classification: A new GGML-based Classification addon delivers millisecond-level classification, useful where a vision-language model (VLM) would be unnecessarily slow - Under the Hood: Fabric synced to llama.cpp v8828 (from v8189), plus GPU acceleration added to image-upscale models for faster results Full release notes:

QVAC

9,932,369 Aufrufe • vor 2 Monaten

Next mlx-vlm release will ship with continuous batching support on the server 🚀 What's coming: → Continuous batching — new requests join the active batch immediately, no waiting. Mixed image + text batches supported → OpenAI-compatible API — field-for-field match with mlx-lm, reasoning/content split for thinking models, tag-aware streaming → Multi-turn tool calling — full tool use support across streaming and non-streaming, works with Gemma4 and other templates → Vision feature caching — cache image embeddings across turns. Gemma4: 228x speedup, Qwen3.5: 23x on cache hit All running locally on Apple Silicon. Check our this demo running 4 concurrent requests (mixed image + text) to gemma-4-26B-A4B-IT by Google Gemma in bf16 using Pi + MLX-VLM server on my M3 Ultra. One of the requests ingests a 8K resolution image!

Next mlx-vlm release will ship with continuous batching support on the server 🚀 What's coming: → Continuous batching — new requests join the active batch immediately, no waiting. Mixed image + text batches supported → OpenAI-compatible API — field-for-field match with mlx-lm, reasoning/content split for thinking models, tag-aware streaming → Multi-turn tool calling — full tool use support across streaming and non-streaming, works with Gemma4 and other templates → Vision feature caching — cache image embeddings across turns. Gemma4: 228x speedup, Qwen3.5: 23x on cache hit All running locally on Apple Silicon. Check our this demo running 4 concurrent requests (mixed image + text) to gemma-4-26B-A4B-IT by Google Gemma in bf16 using Pi + MLX-VLM server on my M3 Ultra. One of the requests ingests a 8K resolution image!

Prince Canuma

82,169 Aufrufe • vor 3 Monaten

🚨 “WOW!” Joe Rogan Was Absolutely Mind-Blown By This iPhone/iPad Addiction Hack 🔥 His guest, Chase Hughes, dropped the ultimate parental (and personal) life hack: “I did it on my 2-year-old’s iPad… and nothing is addictive anymore. She won’t sit there and stare at it for more than 3 or 4 minutes anymore.” Joe’s reaction? A shocked “Whoaa!” The trick? A simple red color tint filter in your device’s Accessibility settings. It strips away the bright, colorful, dopamine-spiking visuals that keep us (and kids) glued to screens, while also cutting blue light for better sleep. One quick change. Massive difference in screen time and focus. Try it yourself: 1Settings → Accessibility → Display & Text Size → Color Filters 2Turn on Color Filters → Color Tint 3Slide Hue all the way to red + max Intensity Works on iPhone and iPad. You can even set a triple-click shortcut to toggle it instantly.

🚨 “WOW!” Joe Rogan Was Absolutely Mind-Blown By This iPhone/iPad Addiction Hack 🔥 His guest, Chase Hughes, dropped the ultimate parental (and personal) life hack: “I did it on my 2-year-old’s iPad… and nothing is addictive anymore. She won’t sit there and stare at it for more than 3 or 4 minutes anymore.” Joe’s reaction? A shocked “Whoaa!” The trick? A simple red color tint filter in your device’s Accessibility settings. It strips away the bright, colorful, dopamine-spiking visuals that keep us (and kids) glued to screens, while also cutting blue light for better sleep. One quick change. Massive difference in screen time and focus. Try it yourself: 1Settings → Accessibility → Display & Text Size → Color Filters 2Turn on Color Filters → Color Tint 3Slide Hue all the way to red + max Intensity Works on iPhone and iPad. You can even set a triple-click shortcut to toggle it instantly.

Skint Eastwood

2,031,062 Aufrufe • vor 1 Monat

GeoLibre v1.6.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings multi-map layouts, advanced cartographic symbology and labeling, and a one-click way to install external plugins from a zip. What's new in v1.6.0 - Multi-map grid: Split the workspace into a grid of synchronized map views to compare basemaps, layers, or time steps side by side. - Advanced symbology: Style features with a rule-based renderer, proportional symbols, fill patterns, and a built-in marker library. - Label engine: Label vector features by any attribute, with full placement and styling control. - Install plugins from a zip: Add external plugins from an uploaded zip on both desktop and web. - New vector analysis: Movement, space-time, and cell-coverage tools under Processing. - Faster sample data: Ready-to-load example datasets from a dropdown in every Add Data panel. - Place search in the layer panel: Geocode and fly to a location without leaving the Layers panel. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python #Cartography

GeoLibre v1.6.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings multi-map layouts, advanced cartographic symbology and labeling, and a one-click way to install external plugins from a zip. What's new in v1.6.0 - Multi-map grid: Split the workspace into a grid of synchronized map views to compare basemaps, layers, or time steps side by side. - Advanced symbology: Style features with a rule-based renderer, proportional symbols, fill patterns, and a built-in marker library. - Label engine: Label vector features by any attribute, with full placement and styling control. - Install plugins from a zip: Add external plugins from an uploaded zip on both desktop and web. - New vector analysis: Movement, space-time, and cell-coverage tools under Processing. - Faster sample data: Ready-to-load example datasets from a dropdown in every Add Data panel. - Place search in the layer panel: Geocode and fly to a location without leaving the Layers panel. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python #Cartography

Qiusheng Wu

15,397 Aufrufe • vor 1 Monat

$"Please fix Markdown tables!" Okay: text-only, wrapping, columnar selection, proper border conjunctions, fill full width, in opencode beta now. "What took so long?" Here's a writeup: OpenTUI uses yoga layout and has elements called Renderables. Boxes with borders, plain text, code with tree-sitter backed highlighting and other primitives. Organised in a tree structure resembling somewhat of a DOM. Approaching a table naively, given the primitives are there, one would think to just stack box and text elements the right way in a flex-box layout to visually represent a table. Boxes support borders. Problem solved. This is what an LLM would one-shot in a working state, given OpenTUI's API surface. Ignoring the fact that just using box local borders don't handle border conjunctions properly. Benchmarking something like that quickly shows that instantiating an average table takes >70ms and incremental updates become expensive. Hugely due to yoga-layout via wasm having a painful price on yoga API calls. The whole ordeal becomes memory hungry, because a Text element handles more than just plain text. A simple 4x6 table needs a Box and Text per cell, ending up with 48 heavy nodes that yoga must lay out. "But that's just OpenTUI being slow" - you might say. Yes, but no. Yoga should be integrated in the zig native binary core of OpenTUI. It is on the roadmap to do so, which will speed up render passes by 2-5x. Yoga-layout has an open PR to support CSS Grids, which would greatly ease building something like a table. We will use that for fully laid out tables when it gets there. Below the typescript core level Renderables, there are lower level primitives like TextBuffers and TextBufferViews, bound via FFI and completely handled in Zig. I was stuck expecting a table primitive to handle a full layout like a table in the browser does. For Markdown all we need is a text-only table. So we had to come up with a better idea, something that is feasible now. A table layout is pretty straight forward. No need to have yoga deal with that. Using TextBufferViews for cells directly gives lower level control and eliminates some overhead that Boxes and Text renderables have. A simple native method to draw a grid with proper conjunctions is a nice library method. It will surely be used for other cases, so that's what we added. Using this simplified approach we were able to bring down initial instantiation to <1ms, more than 70x improvement. With a far smaller memory footprint. Given all the low level primitives are known and implementing a text-only table like this is possible, Codex was of great help to carve out the PoC, setup the benchmarks and tests. That's only a fraction of what was needed though. The table needs options to span the full available width, render different border styles, show/hide borders, padding, selection etc. So many iterations later OpenTUI now has a text-only, performant and relatively cheap TextTable that we can leverage to render Markdown tables in a streaming/incremental manner. Efficiently and properly.$

"Please fix Markdown tables!" Okay: text-only, wrapping, columnar selection, proper border conjunctions, fill full width, in opencode beta now. "What took so long?" Here's a writeup: OpenTUI uses yoga layout and has elements called Renderables. Boxes with borders, plain text, code with tree-sitter backed highlighting and other primitives. Organised in a tree structure resembling somewhat of a DOM. Approaching a table naively, given the primitives are there, one would think to just stack box and text elements the right way in a flex-box layout to visually represent a table. Boxes support borders. Problem solved. This is what an LLM would one-shot in a working state, given OpenTUI's API surface. Ignoring the fact that just using box local borders don't handle border conjunctions properly. Benchmarking something like that quickly shows that instantiating an average table takes >70ms and incremental updates become expensive. Hugely due to yoga-layout via wasm having a painful price on yoga API calls. The whole ordeal becomes memory hungry, because a Text element handles more than just plain text. A simple 4x6 table needs a Box and Text per cell, ending up with 48 heavy nodes that yoga must lay out. "But that's just OpenTUI being slow" - you might say. Yes, but no. Yoga should be integrated in the zig native binary core of OpenTUI. It is on the roadmap to do so, which will speed up render passes by 2-5x. Yoga-layout has an open PR to support CSS Grids, which would greatly ease building something like a table. We will use that for fully laid out tables when it gets there. Below the typescript core level Renderables, there are lower level primitives like TextBuffers and TextBufferViews, bound via FFI and completely handled in Zig. I was stuck expecting a table primitive to handle a full layout like a table in the browser does. For Markdown all we need is a text-only table. So we had to come up with a better idea, something that is feasible now. A table layout is pretty straight forward. No need to have yoga deal with that. Using TextBufferViews for cells directly gives lower level control and eliminates some overhead that Boxes and Text renderables have. A simple native method to draw a grid with proper conjunctions is a nice library method. It will surely be used for other cases, so that's what we added. Using this simplified approach we were able to bring down initial instantiation to <1ms, more than 70x improvement. With a far smaller memory footprint. Given all the low level primitives are known and implementing a text-only table like this is possible, Codex was of great help to carve out the PoC, setup the benchmarks and tests. That's only a fraction of what was needed though. The table needs options to span the full available width, render different border styles, show/hide borders, padding, selection etc. So many iterations later OpenTUI now has a text-only, performant and relatively cheap TextTable that we can leverage to render Markdown tables in a streaming/incremental manner. Efficiently and properly.

kmdr

208,295 Aufrufe • vor 5 Monaten

Text-to-speech is moving way too fast. Just a few days ago, I tweeted about PersonaPlex-7B, NVIDIA's new open source TTS ( And today, Qwen just open-sourced Qwen3-TTS 🤯 It’s a revolutionary text-to-speech model built for control. Not just about generating speech, but about shaping how it sounds directly from language. You can guide the pace, the tone, and the expressiveness straight from text, without touching audio graphs or hand-tuning parameters. That’s the real shift! What makes Qwen3-TTS stand out is how practical it already is: → voice cloning from just a few seconds of audio → voice creation without any reference sample → support for 10 languages out of the box → end-to-end latency down to ~97ms → works in both streaming and non-streaming setups The models come in two sizes (0.6B and 1.7B), so you can trade off quality and hardware cost depending on your setup. You can work with curated voices, designed voices, or cloned ones, and it integrates cleanly with vLLM for production use. It also ships as a simple Python package you can pip install. If you’re building real-time voice systems, this removes a lot of friction! 100% free and open source. I put the repo in the 🧵↓

Text-to-speech is moving way too fast. Just a few days ago, I tweeted about PersonaPlex-7B, NVIDIA's new open source TTS ( And today, Qwen just open-sourced Qwen3-TTS 🤯 It’s a revolutionary text-to-speech model built for control. Not just about generating speech, but about shaping how it sounds directly from language. You can guide the pace, the tone, and the expressiveness straight from text, without touching audio graphs or hand-tuning parameters. That’s the real shift! What makes Qwen3-TTS stand out is how practical it already is: → voice cloning from just a few seconds of audio → voice creation without any reference sample → support for 10 languages out of the box → end-to-end latency down to ~97ms → works in both streaming and non-streaming setups The models come in two sizes (0.6B and 1.7B), so you can trade off quality and hardware cost depending on your setup. You can work with curated voices, designed voices, or cloned ones, and it integrates cleanly with vLLM for production use. It also ships as a simple Python package you can pip install. If you’re building real-time voice systems, this removes a lot of friction! 100% free and open source. I put the repo in the 🧵↓

Charly Wargnier

59,144 Aufrufe • vor 6 Monaten

today Atlas celebrates it's first anniversary (one day old) so naturally, it's time for v1.1 → the #1 request: play videos & gifs right in the grid (toggle it on in settings) → full svg + pdf support (super handy for storing logos and icons) → ⌘-scroll to zoom (great if you're on a mouse + generally app now is way more stable with a mouse) → like badges on thumbnails (hide-able in settings for a cleaner look) → space to open (macos quick look style, also works on hover) → pan the canvas like figma (space + drag, or middle mouse button) → quick-like straight from the grid with ⌥-click → reduced motion (if the bouncy animations are too much) → big performance boost for power users (tested on a 50K item library) and many more. most of these improvements were based on feature requests and feedback from the app. please keep them coming. i’ve made the mistake of not listening to users enough in the past, trying things differently this time. download → (or use atlas — check for updates from the app's menu)

today Atlas celebrates it's first anniversary (one day old) so naturally, it's time for v1.1 → the #1 request: play videos & gifs right in the grid (toggle it on in settings) → full svg + pdf support (super handy for storing logos and icons) → ⌘-scroll to zoom (great if you're on a mouse + generally app now is way more stable with a mouse) → like badges on thumbnails (hide-able in settings for a cleaner look) → space to open (macos quick look style, also works on hover) → pan the canvas like figma (space + drag, or middle mouse button) → quick-like straight from the grid with ⌥-click → reduced motion (if the bouncy animations are too much) → big performance boost for power users (tested on a 50K item library) and many more. most of these improvements were based on feature requests and feedback from the app. please keep them coming. i’ve made the mistake of not listening to users enough in the past, trying things differently this time. download → (or use atlas — check for updates from the app's menu)

alexey

13,940 Aufrufe • vor 29 Tagen

the editing part of AI UGC just got solved you can hook claude straight into after effects through higgsfield, and now i just talk to my ad project, it reads the comp, the layers, the keyframes, and it builds the thing while i keep directing it kinetic captions on the hook, a wiggle-locked zoom on the punchline, animated price tags, it writes the expression and puts it on the right property and you can do it in batches, just feed it a sheet of hooks and it swaps the text across the comp and hands you every variant with layout intact, same for markets, one ad becomes 6 languages without touching you keyboard i broke the ENTIRE ad post-production system into a workflow and giving it all to you now here's what you're getting: - exact setup to get claude running inside AE (5 min, copy paste) - caption + hook animation prompt (steal it, works on any UGC clip) - the batch variant method (1 ad, 12 hooks, one pass) - full pipeline, gen to final export rt + reply "UGC" and i'll send it so you can ship 10x more creatives this week (must follow so i can dm)

the editing part of AI UGC just got solved you can hook claude straight into after effects through higgsfield, and now i just talk to my ad project, it reads the comp, the layers, the keyframes, and it builds the thing while i keep directing it kinetic captions on the hook, a wiggle-locked zoom on the punchline, animated price tags, it writes the expression and puts it on the right property and you can do it in batches, just feed it a sheet of hooks and it swaps the text across the comp and hands you every variant with layout intact, same for markets, one ad becomes 6 languages without touching you keyboard i broke the ENTIRE ad post-production system into a workflow and giving it all to you now here's what you're getting: - exact setup to get claude running inside AE (5 min, copy paste) - caption + hook animation prompt (steal it, works on any UGC clip) - the batch variant method (1 ad, 12 hooks, one pass) - full pipeline, gen to final export rt + reply "UGC" and i'll send it so you can ship 10x more creatives this week (must follow so i can dm)

MAX

38,311 Aufrufe • vor 25 Tagen

Gemini for Android is here! This new app makes it easier to access Google's chatbot, powered by the Gemini Pro LLM, right from your Android device. You can ask Gemini a question by tapping the launcher icon or long-pressing the power button. Once invoked, the Gemini overlay lets you enter a text or text+image prompt. You can tap the camera icon to snap a photo or press the "add this screen" button to take a screenshot of the current page to include in your prompt. The Gemini app for Android is available on Google Play with support for English, but support for Korean and Japanese will be coming next week. While there won't be an app for iOS, iPhone users can access Gemini by opening the Google App and tapping the "Gemini" button up top. How can you long-press the power button to invoke Gemini if that gesture is handled by Google Assistant? The answer is that the Gemini Android app can replace Google Assistant as your default assistant if you want, meaning all the ways you'd normally invoke Google Assistant on your phone can instead invoke Gemini. Unfortunately, Gemini currently doesn't offer ALL the same functionality as Google Assistant and requires an active data connection, but more features will be added over time. There's also now a Gemini Advanced tier, which offers access to Google's most powerful Ultra 1.0 LLM. Access to Gemini Advanced requires a subscription to the new $20/month "AI Premium" Google One plan, which offers the same benefits as the 2TB plan but adds access to Gemini Advanced and soon Gemini features in Gmail, Docs, & other Workspace apps (formerly under the Duet AI umbrella). Gemini Advanced is available in English on the web.

Gemini for Android is here! This new app makes it easier to access Google's chatbot, powered by the Gemini Pro LLM, right from your Android device. You can ask Gemini a question by tapping the launcher icon or long-pressing the power button. Once invoked, the Gemini overlay lets you enter a text or text+image prompt. You can tap the camera icon to snap a photo or press the "add this screen" button to take a screenshot of the current page to include in your prompt. The Gemini app for Android is available on Google Play with support for English, but support for Korean and Japanese will be coming next week. While there won't be an app for iOS, iPhone users can access Gemini by opening the Google App and tapping the "Gemini" button up top. How can you long-press the power button to invoke Gemini if that gesture is handled by Google Assistant? The answer is that the Gemini Android app can replace Google Assistant as your default assistant if you want, meaning all the ways you'd normally invoke Google Assistant on your phone can instead invoke Gemini. Unfortunately, Gemini currently doesn't offer ALL the same functionality as Google Assistant and requires an active data connection, but more features will be added over time. There's also now a Gemini Advanced tier, which offers access to Google's most powerful Ultra 1.0 LLM. Access to Gemini Advanced requires a subscription to the new $20/month "AI Premium" Google One plan, which offers the same benefits as the 2TB plan but adds access to Gemini Advanced and soon Gemini features in Gmail, Docs, & other Workspace apps (formerly under the Duet AI umbrella). Gemini Advanced is available in English on the web.

Mishaal Rahman

38,428 Aufrufe • vor 2 Jahren

GeoLibre v1.2.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 35+ pull requests of new capabilities. A few highlights: - Run SQL right in the browser. The SQL Workspace pairs DuckDB Spatial with a new in-browser PostGIS engine (PGlite), so you can query layers, local files, and remote URLs without a server. - A smarter attribute table. Add fields, run a field calculator, and explore your data with a built-in Charts panel (histogram, scatter, bar, line, and box plots). - More ways to add data. OpenStreetMap PBF extracts, Cloud-Optimized NetCDF/HDF via kerchunk, georeferenced video overlays, authenticated 3D Tiles, and a Layer builder for custom overlays. - Better visualization. Heatmap rendering, point clustering, and H3 hexagonal grids for spatial binning. - New analysis and routing. A Directions plugin, plus Spatial Join, Select by Value, and Select by Location vector tools. - Print and share. A print layout composer that exports your map to PNG or PDF. - Work faster. A command palette (Ctrl/Cmd + K), global keyboard shortcuts, and undo/redo for layer and style operations. - Built for everyone. New internationalization framework, an accessibility pass with automated axe checks, an installable offline-capable PWA web build, React error boundaries, and Playwright end-to-end tests. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #DuckDB #GeoLibre

GeoLibre v1.2.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 35+ pull requests of new capabilities. A few highlights: - Run SQL right in the browser. The SQL Workspace pairs DuckDB Spatial with a new in-browser PostGIS engine (PGlite), so you can query layers, local files, and remote URLs without a server. - A smarter attribute table. Add fields, run a field calculator, and explore your data with a built-in Charts panel (histogram, scatter, bar, line, and box plots). - More ways to add data. OpenStreetMap PBF extracts, Cloud-Optimized NetCDF/HDF via kerchunk, georeferenced video overlays, authenticated 3D Tiles, and a Layer builder for custom overlays. - Better visualization. Heatmap rendering, point clustering, and H3 hexagonal grids for spatial binning. - New analysis and routing. A Directions plugin, plus Spatial Join, Select by Value, and Select by Location vector tools. - Print and share. A print layout composer that exports your map to PNG or PDF. - Work faster. A command palette (Ctrl/Cmd + K), global keyboard shortcuts, and undo/redo for layer and style operations. - Built for everyone. New internationalization framework, an accessibility pass with automated axe checks, an installable offline-capable PWA web build, React error boundaries, and Playwright end-to-end tests. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #DuckDB #GeoLibre

Qiusheng Wu

39,959 Aufrufe • vor 1 Monat

Web scraping will never be the same. (100% open-source visual search at scale) PixelRAG is a retrieval system that skips HTML parsing completely. Instead of scraping a page into text and embedding chunks, it screenshots the page and retrieves the image. A vision-language model reads the answer straight off the pixels. Why that matters: parsing is where web RAG quietly loses information. - A single HTML-to-text parser can drop 40%+ of a page. - Tables, charts, and layout get flattened or thrown out. - Swapping parsers alone can move accuracy ~10 points on the same docs. PixelRAG indexes the page a person actually sees. The team built a visual index of all of Wikipedia, 30M+ screenshots, and it still beats the strongest text RAG baseline by 18.1% on text-only QA. The repo also ships a Claude Code plugin that gives Claude eyes. It lets Claude screenshot any URL and read the rendered page instead of scraping the DOM. So you can hand it a live page, an arXiv paper, or your local site and ask what it actually looks like. One setup script. No MCP server, no backend. How the pipeline works: - Renders each document (web, PDF, image) to image tiles. - Embeds them with Qwen3-VL-Embedding, LoRA fine-tuned on screenshots. - Builds a FAISS index and serves a search API. A stronger reader model lifts accuracy with no re-indexing, since the index is just pixels. Everything is open-source under Apache-2.0. GitHub repo: Talking about RAG, I recently wrote an article on a new approach that makes retrieval much more efficient by cutting corpus size by 40x, reducing tokens per query by 3x, and improving vector search relevance by 2.3x. The article is quoted below.

Web scraping will never be the same. (100% open-source visual search at scale) PixelRAG is a retrieval system that skips HTML parsing completely. Instead of scraping a page into text and embedding chunks, it screenshots the page and retrieves the image. A vision-language model reads the answer straight off the pixels. Why that matters: parsing is where web RAG quietly loses information. - A single HTML-to-text parser can drop 40%+ of a page. - Tables, charts, and layout get flattened or thrown out. - Swapping parsers alone can move accuracy ~10 points on the same docs. PixelRAG indexes the page a person actually sees. The team built a visual index of all of Wikipedia, 30M+ screenshots, and it still beats the strongest text RAG baseline by 18.1% on text-only QA. The repo also ships a Claude Code plugin that gives Claude eyes. It lets Claude screenshot any URL and read the rendered page instead of scraping the DOM. So you can hand it a live page, an arXiv paper, or your local site and ask what it actually looks like. One setup script. No MCP server, no backend. How the pipeline works: - Renders each document (web, PDF, image) to image tiles. - Embeds them with Qwen3-VL-Embedding, LoRA fine-tuned on screenshots. - Builds a FAISS index and serves a search API. A stronger reader model lifts accuracy with no re-indexing, since the index is just pixels. Everything is open-source under Apache-2.0. GitHub repo: Talking about RAG, I recently wrote an article on a new approach that makes retrieval much more efficient by cutting corpus size by 40x, reducing tokens per query by 3x, and improving vector search relevance by 2.3x. The article is quoted below.

Akshay 🚀

942,504 Aufrufe • vor 1 Monat