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

Hiwonder SO-ARM101 doesn't need a programmer – it needs a teacher. That's you. Move the leader arm, and the follower arm picks up every detail. Learn more 👉 #huggingface# #LeRobot# #modeltraining# #ImitationLearning# #ailearning# #algorithm# #opensource#

🚀 Excited to introduce BrainPilot — a human-in-the-loop 1-1-N multi-agent framework designed to accelerate brain science research. 🧠 BrainPilot integrates: • 72 expert skills spanning 7 major neuroscience domains • A curated knowledge base of 7,200+ papers • Automatic review & verification • Trace visualization for transparent, reproducible scientific workflows Our system achieves performance comparable to state-of-the-art agentic frameworks on both Agents' Last Exam and our newly proposed BrainPilotBench. Everything is open source! We'd love for you to ⭐ star the repos, join the community, deploy BrainPilot locally, and adding new features, or proposing new benchmarks. (1/8) 🏠 Homepage: 📄 Technical Report: 🌟 BrainPilot: 📈 BrainPilotBench: #AI# #Neuroscience# #MultiAgent# #AgenticAI# #ScientificAI# #OpenSource# #BrainScience# #LLM# #ResearchAgents# #NeuroAI#

🎉 Congratulations to Daniel Povey, Xiaomi Distinguished Scientist, on being elected a 2026 ISCA Fellow. 🏆 ISCA Fellowship recognizes outstanding and sustained contributions to speech communication science and technology. Daniel's pioneering work on the open-source speech toolkit Kaldi has made foundational contributions to modern speech AI. At Xiaomi, he continues advancing the next generation of open speech technologies through projects and technologies including k2, Lhotse, Icefall, Sherpa, OmniVoice, and Zipformer. 👏 Congratulations on this well-deserved recognition! #XiaomiAI# #SpeechAI# #OpenSource# #AIResearch# #ISCAFellow#

Thinking Machines - Inkling Thinking Machines Lab, the company founded by Mira Murati, has released Inkling, its first open-weights model under the Apache 2.0 license. Inkling is a mixture-of-experts transformer with 975 billion parameters in total, yet only 41 billion of them are active for any given token. Every layer contains 256 routed experts and 2 shared experts, and a router selects just the 6 most relevant experts per token. This means that only about 4 percent of the model performs computation during inference. The backbone is a 66-layer decoder-only transformer that combines local and global attention layers and supports a context window of one million tokens, which is roughly enough to fit eight novels or an entire codebase into a single prompt. The model was pretrained on 45 trillion tokens spanning text, images, audio, and video. Instead of relying on separate vision or audio encoders, it converts images into patches and audio into discrete tokens, then projects everything into one shared hidden space. All modalities are therefore fused from the very first layer. Inkling accepts text, images, and audio as input, while its output remains text only. On public benchmarks, it performs at the level of GPT 5.6 Sol and Claude Fable 5 in reasoning and agentic coding. The weights are available on Hugging Face, and the model can be fine-tuned through the Tinker API. #MiraMurati# #thinkingmachines# #inkling# #OpenSource#

🚨 OCR İçin Artık API Parası Ödemeyin Birçok kişi hâlâ bulut tabanlı OCR servislerine sayfa başı ücret ödüyor. Yeni açık kaynak proje **Ollama-OCR** bunu tamamen değiştiriyor: • Kendi bilgisayarında çalışıyor (internet gerekmiyor) • API anahtarı veya abonelik yok • Görüntü ve PDF’lerden metin çıkarabiliyor • El yazısı, fatura, makbuz ve tabloları okuyabiliyor • Markdown, JSON ve düzenli veri çıktısı verebiliyor • Hassas belgeler cihazdan dışarı çıkmıyor (tam gizlilik) Tek komutla kurulum: `pip install ollama-ocr` GitHub: API bağımlılığından kurtulmak isteyenler için güçlü bir alternatif. #OCR# #OpenSource# #Ollama# #AI# #Privacy#

finally liquid glass web is here and it's open source. real WebGL refraction, chromatic aberration, liquid motion not usual backdrop-filter css hack. playground at code at MIT so do whatever you want with it #LiquidGlass# #OpenSource# #ReactJS# #WebGL# #UIComponents# #FrontendDevelopment# #CSS# #JavaScript# #WebDev# #UI# #UX# #React# #ComponentLibrary# #OpenSourceSoftware# #Frontend#

Let's take a look at what kind of automation is possible with an AI team. This time on the OpenSource project Kuberhealthy. First, I got an idea to add Claude and Codex skills for Kuberhealthy. I picked up my phone and informally told one of the agents in the Kuberhealthy stack that I wanted this in a single sentence. The agent made this issue: (1/...)

The AI Business model trap: LLMs want cash flow to fund the race to AGI or the next model. Enter free consumer AI - they are losing a lot of money on the breadth of models to serve consumers for free! They are caught in the post training data trap, free consumer usage feeds post training needs, it can't be right to stop serving customers for free? But they need money for the compute: The monetization challenge is being pointed to Enterprises. Phase 1 - seemed easy, value capture in coding, the most bottom up motion in enterprise - with low customization per customer. Developers continue to train coding, tasks and eventually will train flawless skills. Phase 2 is where the challenge lies, showing true enterprise value. The promise of efficiency, accuracy, elimination of resources - that requires a different approach, build depth with harnesses, context, memory, solving for edge cases with deterministic guardrails! Build skill libraries - enter FDEs. Yes,FDEs will train the enterprise Waymos of the world. The risk - high token pricing for enterprises while consumers for free! Yes for consumer distribution businesses (aka Google, Meta, Apple, etc) it makes sense to hold on the distribution with free AI. If you want to win enterprise, you should be forward pricing tokens. The cheaper the tokens for enterprises it will allow for experimentation, workflow reimagination - instead CIOs are busy restricting AI use and working on making the use more efficient! Paradox: They still haven't fully understood and embraced the value of AI in the enterprise. If I were them: 1. Cut token pricing now, else send enterprises to secure opensource and end up with friction filled routing layers. 2. Show me how enterprises can use their context, training and data as their competitive advantage. 3. Build tools for rapid edge case learning and reducing false positives. @HarryStebbings @sama @DarioAmodei @demishassabis