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<feed xmlns="http://www.w3.org/2005/Atom"><title>Justuju: AI news</title><subtitle>Stories from labs, community, research and press, tagged by concept.</subtitle><id>https://justuju.org/</id><link href="https://justuju.org/" /><link rel="self" href="https://justuju.org/feed.xml" /><updated>2026-10-13T21:50:42Z</updated><generator>Justuju</generator><entry><title>How to Rewrite Apache Spark UDFs for GPU Acceleration with AI Agents</title><link href="https://developer.nvidia.com/blog/rewrite-apache-spark-udfs-gpu-acceleration-ai-agents/" /><id>urn:justuju:68e3de130cf5</id><updated>2026-10-13T21:33:21Z</updated><author><name>NVIDIA Technical Blog</name></author><summary>Enterprise applications have become increasingly demanding. Agentic AI has compounded the need for vast amounts of enterprise data with frequent, complex... [The agent loop]</summary><category term="agent-loop" label="The agent loop" /></entry><entry><title>v0.40.4-rc2</title><link href="https://github.com/ollama/ollama/releases/tag/v0.40.4-rc2" /><id>urn:justuju:a17663b427bf</id><updated>2026-10-13T21:23:05Z</updated><author><name>Ollama releases</name></author><summary>launch: allow usage credits without upgrading ( #18985) [Ollama]</summary><category term="ollama" label="Ollama" /></entry><entry><title>OpenAI employee arrested for allegedly planting a bug in a conference room</title><link href="https://www.theverge.com/ai-artificial-intelligence/1010642/openai-employee-arrested-recording-device" /><id>urn:justuju:082a179fcd58</id><updated>2026-10-13T20:45:25Z</updated><author><name>The Verge AI</name></author><summary>An OpenAI engineer was arrested after the company said it discovered a "sophisticated recording device" hidden in one of its offices. [GPT (OpenAI)]</summary><category term="gpt" label="GPT (OpenAI)" /></entry><entry><title>Judge orders OpenAI to turn over hiring records in Apple trade secrets case</title><link href="https://techcrunch.com/2026/10/13/judge-orders-openai-to-turn-over-hiring-records-in-apple-trade-secrets-case/" /><id>urn:justuju:70093601d146</id><updated>2026-10-13T20:36:44Z</updated><author><name>TechCrunch AI</name></author><summary>A federal judge ruled that Apple can access OpenAI's interview materials involving former Apple employees, as well as private messages and testimony from key… [GPT (OpenAI)]</summary><category term="gpt" label="GPT (OpenAI)" /></entry><entry><title>Building agents that transact with Anchor Browser and AgentCore payments</title><link href="https://aws.amazon.com/blogs/machine-learning/building-agents-that-transact-with-anchor-browser-and-agentcore-payments/" /><id>urn:justuju:cdd5fd8d1326</id><updated>2026-10-13T20:10:33Z</updated><author><name>AWS Machine Learning Blog</name></author><summary>Learn how the AWS Generative AI Innovation Center Partner Agent Factory and Anchor Browser built AI agents that autonomously pay for x402-protected web resources and APIs. Amazon Bedrock AgentCore payments checks budgets, signs transactions, and retries requests without exposing wallet credentials to the agent runtime. [Bedrock, Vertex, Azure AI Foundry, The agent loop]</summary><category term="hyperscalers" label="Bedrock, Vertex, Azure AI Foundry" /><category term="agent-loop" label="The agent loop" /></entry><entry><title>Why AnythingLLM is moving from Ollama to Lemonade and llama.cpp</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x5720r/why_anythingllm_is_moving_from_ollama_to_lemonade/" /><id>urn:justuju:72146ea92bf4</id><updated>2026-10-13T19:59:31Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>I'm excited to see our Lemonade embedded SDK getting used out there! [Gemma, Llama, Mistral, llama.cpp, Ollama]</summary><category term="gemma" label="Gemma, Llama, Mistral" /><category term="llama-cpp" label="llama.cpp" /><category term="ollama" label="Ollama" /></entry><entry><title>Developing Gemini 4 Argon responsibly and safely</title><link href="https://deepmind.google/blog/developing-gemini-4-argon-responsibly-and-safely/" /><id>urn:justuju:25432b6b093a</id><updated>2026-10-13T19:43:07Z</updated><author><name>Google DeepMind</name></author><summary>[Gemini (Google)]</summary><category term="gemini" label="Gemini (Google)" /></entry><entry><title>Shopify opens its catalog up to more AI agents</title><link href="https://www.theverge.com/news/1010365/shopify-opens-its-catalog-up-to-more-ai-agents" /><id>urn:justuju:c839b9474fae</id><updated>2026-10-13T19:42:42Z</updated><author><name>The Verge AI</name></author><summary>More AI agents will soon be able to browse and purchase from Shopify listings, marking an expansion of the company's agentic commerce ambitions. [The agent loop]</summary><category term="agent-loop" label="The agent loop" /></entry><entry><title>Gemini 4 Argon is now available worldwide as Google's most capable frontier model</title><link href="https://the-decoder.com/gemini-4-argon-is-now-available-worldwide-as-googles-most-capable-frontier-model/" /><id>urn:justuju:4a4a81eb8e4e</id><updated>2026-10-13T18:00:49Z</updated><author><name>The Decoder</name></author><summary>Google is making Gemini 4 Argon available worldwide through its API, the Gemini app, and Google Antigravity. [What 'frontier' means now, Gemini (Google), Consumer AI assistants]</summary><category term="frontier-capabilities" label="What 'frontier' means now" /><category term="gemini" label="Gemini (Google)" /><category term="consumer-assistants" label="Consumer AI assistants" /></entry><entry><title>Laplace Intelligence red-teams robots and self-driving cars before they hit the real world</title><link href="https://techcrunch.com/2026/10/13/laplace-intelligence-red-teams-robots-and-self-driving-cars-before-they-hit-the-real-world/" /><id>urn:justuju:fa1a37fdc416</id><updated>2026-10-13T17:30:00Z</updated><author><name>TechCrunch AI</name></author><summary>University of Michigan spinout Laplace Intelligence uses AI agents and interactive simulation to stress-test robots and self-driving vehicles before deployment. [The agent loop]</summary><category term="agent-loop" label="The agent loop" /></entry><entry><title>Manage private pricing offers for Amazon Bedrock with managed entitlements</title><link href="https://aws.amazon.com/blogs/machine-learning/manage-private-pricing-offers-for-amazon-bedrock-with-managed-entitlements/" /><id>urn:justuju:4826ab478a6a</id><updated>2026-10-13T17:17:25Z</updated><author><name>AWS Machine Learning Blog</name></author><summary>Managing AI model access across dozens or hundreds of AWS accounts creates friction when each account must accept AWS Marketplace Private Pricing Offers individually. This post shows two ways to activate MPPO entitlements in AWS License Manager: a lightweight script for a one-time fix, and CDK automation for ongoing… [Bedrock, Vertex, Azure AI Foundry]</summary><category term="hyperscalers" label="Bedrock, Vertex, Azure AI Foundry" /></entry><entry><title>"New AI fake text generator may be too dangerous to release, say creators" (2019 Guardian headline)</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x52lsb/new_ai_fake_text_generator_may_be_too_dangerous/" /><id>urn:justuju:d1d86d368e3b</id><updated>2026-10-13T17:15:27Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>I'm sure some here read this at the time and remember, but I recently found this hilarious Guardian article from 2019 through Wikipedia: Some choice quotes: "OpenAI... says its new AI model, called GPT2 is so good and… [GPT (OpenAI)]</summary><category term="gpt" label="GPT (OpenAI)" /></entry><entry><title>LiquidAI/LFM2.5-24B-A2B</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x52kco/liquidailfm2524ba2b/" /><id>urn:justuju:b7825d705351</id><updated>2026-10-13T17:14:08Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>Gguf's: Free Demo: "LFM2.5-24B-A2B is a 24B-parameter reasoning model with only 2B active parameters, built for fast, private agents on-device. It is the largest and final model in the LFM2.5 family, which now spans two orders of… [Mixture of Experts (MoE), Reasoning / thinking models, Small &amp; on-device models]</summary><category term="moe" label="Mixture of Experts (MoE)" /><category term="reasoning-models" label="Reasoning / thinking models" /><category term="small-models" label="Small &amp; on-device models" /></entry><entry><title>Run fast, private agents on-device with LFM2.5-24B-A2B</title><link href="https://huggingface.co/blog/LiquidAI/lfm25-24b-a2b" /><id>urn:justuju:550c4c02e9ff</id><updated>2026-10-13T17:05:48Z</updated><author><name>Hugging Face Blog</name></author><summary>[Small &amp; on-device models, Mixture of Experts (MoE)]</summary><category term="small-models" label="Small &amp; on-device models" /><category term="moe" label="Mixture of Experts (MoE)" /></entry><entry><title>Tencent open-sourced Agent Memory and it runs 100% locally on SQLite</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x50rgo/tencent_opensourced_agent_memory_and_it_runs_100/" /><id>urn:justuju:12e59a3a5c8b</id><updated>2026-10-13T16:07:55Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>[Agent memory]</summary><category term="agent-memory" label="Agent memory" /></entry><entry><title>Natural language security queries at Orca Security using Amazon Bedrock</title><link href="https://aws.amazon.com/blogs/machine-learning/natural-language-security-queries-at-orca-security-using-amazon-bedrock/" /><id>urn:justuju:958759977490</id><updated>2026-10-13T16:03:31Z</updated><author><name>AWS Machine Learning Blog</name></author><summary>Orca Security built Discovery AI, a natural language query agent on Amazon Bedrock that converts plain-English questions into Sonar queries. The post shares the multi-stage architecture and the results: 90% lower query cost, 93% fewer input tokens, and 75% lower latency than the initial baseline. [Bedrock, Vertex, Azure AI Foundry]</summary><category term="hyperscalers" label="Bedrock, Vertex, Azure AI Foundry" /></entry><entry><title>Gemini 4 Argon</title><link href="https://deepmind.google/models/gemini/" /><id>urn:justuju:d1f8258ec212</id><updated>2026-10-13T16:03:16Z</updated><author><name>Hacker News (best, AI-filtered)</name></author><summary>[Gemini (Google)]</summary><category term="gemini" label="Gemini (Google)" /></entry><entry><title>Gemini 4 Argon for Robotics</title><link href="https://deepmind.google/blog/gemini-4-argon-for-robotics/" /><id>urn:justuju:8b0463614a7b</id><updated>2026-10-13T16:01:58Z</updated><author><name>Google DeepMind</name></author><summary>[Gemini (Google), World models &amp; physical AI]</summary><category term="gemini" label="Gemini (Google)" /><category term="world-models" label="World models &amp; physical AI" /></entry><entry><title>Gemini 4 Argon: Built to solve your hardest problems</title><link href="https://deepmind.google/blog/introducing-gemini-4-argon/" /><id>urn:justuju:316153953adf</id><updated>2026-10-13T16:01:52Z</updated><author><name>Google DeepMind</name></author><summary>[Gemini (Google)]</summary><category term="gemini" label="Gemini (Google)" /></entry><entry><title>Gemini 4 Argon begins rolling out in Gemini app and Antigravity, but only if you pay</title><link href="https://arstechnica.com/ai/2026/10/gemini-4-argon-begins-rolling-out-in-gemini-app-and-antigravity-but-only-if-you-pay/" /><id>urn:justuju:2bad0427cfe4</id><updated>2026-10-13T16:00:02Z</updated><author><name>Ars Technica AI</name></author><summary>Developers will get 50 percent discounted API rates through November. [Gemini (Google), Consumer AI assistants]</summary><category term="gemini" label="Gemini (Google)" /><category term="consumer-assistants" label="Consumer AI assistants" /></entry><entry><title>Gemini 4 Argon: Built to solve your hardest problems</title><link href="https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/" /><id>urn:justuju:66cb1e68848f</id><updated>2026-10-13T16:00:00Z</updated><author><name>Google AI Blog</name></author><summary>TBD [Gemini (Google)]</summary><category term="gemini" label="Gemini (Google)" /></entry><entry><title>Google finally releases Gemini 4 Argon</title><link href="https://www.theverge.com/ai-artificial-intelligence/1009862/google-releases-gemini-4-argon" /><id>urn:justuju:24ef7cf40bd2</id><updated>2026-10-13T16:00:00Z</updated><author><name>The Verge AI</name></author><summary>After months sitting on the sidelines of the frontier AI race, Google has finally launched its long-awaited Gemini 4 Argon model, claiming it can compete with… [Gemini (Google), What 'frontier' means now]</summary><category term="gemini" label="Gemini (Google)" /><category term="frontier-capabilities" label="What 'frontier' means now" /></entry><entry><title>How ShipStation Global built an AI IT help desk on Amazon Bedrock AgentCore</title><link href="https://aws.amazon.com/blogs/machine-learning/how-shipstation-global-built-an-ai-it-help-desk-on-amazon-bedrock-agentcore/" /><id>urn:justuju:576253ed8947</id><updated>2026-10-13T15:54:49Z</updated><author><name>AWS Machine Learning Blog</name></author><summary>ShipStation Global's engineering team built Deckhand, an AI IT help desk agent for Slack on Amazon Bedrock AgentCore. It answers routine IT questions from a curated knowledge base, reads error-message screenshots, and files Jira tickets. In its first three months it resolved 97.5% of channel queries with a 96%… [Bedrock, Vertex, Azure AI Foundry]</summary><category term="hyperscalers" label="Bedrock, Vertex, Azure AI Foundry" /></entry><entry><title>Build long-horizon agents with Grok 4.7 on Amazon Bedrock</title><link href="https://aws.amazon.com/blogs/machine-learning/build-long-horizon-agents-with-grok-4-7-on-amazon-bedrock/" /><id>urn:justuju:c4d6082c07a1</id><updated>2026-10-13T15:52:20Z</updated><author><name>AWS Machine Learning Blog</name></author><summary>Grok 4.7 is now available on Amazon Bedrock. In this post, we build a tool-calling agent on Grok 4.7 and evaluate it on three reasoning tasks at four reasoning effort levels, measuring correctness and token cost per completed task. We show how to tune the trade-off per request and monitor it in production. [Bedrock, Vertex, Azure AI Foundry, Grok (xAI), Reasoning / thinking models, Tool / function calling]</summary><category term="hyperscalers" label="Bedrock, Vertex, Azure AI Foundry" /><category term="grok" label="Grok (xAI)" /><category term="reasoning-models" label="Reasoning / thinking models" /><category term="tool-calling" label="Tool / function calling" /></entry><entry><title>Redesigning source control at Anthropic</title><link href="https://www.anthropic.com/engineering/redesigning-source-control" /><id>urn:justuju:8c17f1f1ac30</id><updated>2026-10-13T15:49:56Z</updated><author><name>Hacker News (best, AI-filtered)</name></author><summary>[Claude (Anthropic)]</summary><category term="claude" label="Claude (Anthropic)" /></entry><entry><title>Mixture-of-Experts kernel engineering on Amazon SageMaker AI</title><link href="https://aws.amazon.com/blogs/machine-learning/mixture-of-experts-kernel-engineering-on-amazon-sagemaker-ai/" /><id>urn:justuju:ba8a6ffbbe0f</id><updated>2026-10-13T15:17:09Z</updated><author><name>AWS Machine Learning Blog</name></author><summary>AWS engineers share kernel optimizations for fused Mixture-of-Experts (MoE) models on Amazon SageMaker AI: Swap-AB for WGMMA utilization, Triton backend routing for FP8 quantization, and eliminating Triton JIT recompilation. Together they deliver up to 10% higher throughput and up to 9% faster Time to First Token on… [Mixture of Experts (MoE), FP8, FP4, NVFP4 &amp; MXFP4, Quantisation basics]</summary><category term="moe" label="Mixture of Experts (MoE)" /><category term="low-bit-formats" label="FP8, FP4, NVFP4 &amp; MXFP4" /><category term="quantization" label="Quantisation basics" /></entry><entry><title>PSA for 3090 FE owners: Running LLMs overheats your VRAM</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4z19k/psa_for_3090_fe_owners_running_llms_overheats/" /><id>urn:justuju:1ac263d2b9b0</id><updated>2026-10-13T15:02:59Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>The 3090 Founders edition has a reputation for hot VRAM. Since LLM token generation maxes out VRAM's read capacity, I checked VRAM temps with Nvidia's stock fan/power curves and discovered that VRAM… [VRAM math, Multi-RTX home rigs]</summary><category term="vram-math" label="VRAM math" /><category term="rtx-rigs" label="Multi-RTX home rigs" /></entry><entry><title>4x 6000 Pro for the company</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4yoqm/4x_6000_pro_for_the_company/" /><id>urn:justuju:4e49d23a4ed6</id><updated>2026-10-13T14:49:31Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>Here is how i serve 40 people (10 of them developers) with local AI services. n8n for automatisation whisper and Vexa AI, a bot joins Teams and makes transcripts, n8n later extracts decisions and todos OpenWebUI, ChatGPT-like webinterface for everyone, most agentic features are disabled, websearches via Tavily are… [n8n, Dify &amp; workflow tools, GPT (OpenAI)]</summary><category term="workflow-automation" label="n8n, Dify &amp; workflow tools" /><category term="gpt" label="GPT (OpenAI)" /></entry><entry><title>NVIDIA Run:ai Is Now Open Source as Part of NVIDIA DSX AI Factory Platform</title><link href="https://developer.nvidia.com/blog/nvidia-runai-is-now-open-source-as-part-of-nvidia-dsx-ai-factory-platform/" /><id>urn:justuju:01fed557964c</id><updated>2026-10-13T14:19:40Z</updated><author><name>NVIDIA Technical Blog</name></author><summary>NVIDIA is making the full NVIDIA Run:ai platform available as open source software, giving customers, developers, and ecosystem partners access to... [AI datacenters &amp; energy]</summary><category term="ai-datacenters" label="AI datacenters &amp; energy" /></entry><entry><title>Basalt: Fast Blackwell inference engine for Qwen 3.8 Flash Next (and how we got vision for free)</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4xmxb/basalt_fast_blackwell_inference_engine_for_qwen/" /><id>urn:justuju:e89033413096</id><updated>2026-10-13T14:08:21Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>Dynamic vision, in action Basalt is an inference engine for 3.8 Flash Next, forked from Strata and heavily tuned for Blackwell. It supports vision, and for this to work you need to have the image encoder either in VRAM or CPU all the time, having it on VRAM takes valuable space from experts that would otherwise stay… [NVIDIA Hopper → Blackwell → Rubin, Qwen (Alibaba), VRAM math]</summary><category term="nvidia-dc" label="NVIDIA Hopper → Blackwell → Rubin" /><category term="qwen" label="Qwen (Alibaba)" /><category term="vram-math" label="VRAM math" /></entry><entry><title>An 80B MoE (Qwen3-Next, 48 GB GGUF) answering in a Chrome tab: llama.cpp's WebGPU backend, experts paged in from disk</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4xjuh/an_80b_moe_qwen3next_48_gb_gguf_answering_in_a/" /><id>urn:justuju:cd375fccf76f</id><updated>2026-10-13T14:04:51Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>Follow-up to last week's post about streaming MoE experts from disk in a browser tab. That ran on hand-written engines for two models. This week the same disk tier sits under llama.cpp's own WebGPU backend, so it works for the MoE models llama.cpp already runs, with no per-model engine. The video is one real recording… [llama.cpp, Qwen (Alibaba), Gemma, Llama, Mistral, Mixture of Experts (MoE), GGUF &amp; k-quants]</summary><category term="llama-cpp" label="llama.cpp" /><category term="qwen" label="Qwen (Alibaba)" /><category term="gemma" label="Gemma, Llama, Mistral" /><category term="moe" label="Mixture of Experts (MoE)" /><category term="gguf" label="GGUF &amp; k-quants" /></entry><entry><title>SpaceXSI bundles X Premium, Grok, and Cursor into one subscription</title><link href="https://techcrunch.com/2026/10/13/spacexsi-bundles-x-premium-grok-and-cursor-into-one-subscription/" /><id>urn:justuju:48866c22a637</id><updated>2026-10-13T13:50:19Z</updated><author><name>TechCrunch AI</name></author><summary>The new X Subscription combines X's paid features with AI tools like Grok and Cursor, offering plans ranging from $8 to $200 per month. [Grok (xAI), AI IDEs (Cursor, Windsurf, Copilot)]</summary><category term="grok" label="Grok (xAI)" /><category term="ai-ides" label="AI IDEs (Cursor, Windsurf, Copilot)" /></entry><entry><title>Built an open-source Cotypist/Granola hybrid: autocomplete on Gemma 4 E2B (~50 ms/suggestion on M4 Max) using your own meeting notes as context</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4x5km/built_an_opensource_cotypistgranola_hybrid/" /><id>urn:justuju:7b4ab1a30d91</id><updated>2026-10-13T13:49:31Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>I built a desktop app harness that keeps local meeting notes and can use them to autocomplete my typing as well as complement my dictation with additional context via reading my screen or using the previous meetings notes. It's been a bit of a struggle to get it to work well and also to not be too intrusive and laggy… [Apple Silicon Macs, Gemma, Llama, Mistral]</summary><category term="apple-silicon" label="Apple Silicon Macs" /><category term="gemma" label="Gemma, Llama, Mistral" /></entry><entry><title>Is everyone in here buying up smart TVs now?</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4wcnn/is_everyone_in_here_buying_up_smart_tvs_now/" /><id>urn:justuju:8fb9aa71a73e</id><updated>2026-10-13T13:16:08Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>Anthropic says we can run Gemma4 26b a4b with only 14 smart tvs. [Claude (Anthropic)]</summary><category term="claude" label="Claude (Anthropic)" /></entry><entry><title>Amex introduces a framework to prevent AI agents from making unauthorized purchases</title><link href="https://techcrunch.com/2026/10/13/amex-introduces-a-framework-to-prevent-ai-agents-from-making-unauthorized-purchases/" /><id>urn:justuju:bc6b0eca0236</id><updated>2026-10-13T13:00:00Z</updated><author><name>TechCrunch AI</name></author><summary>American Express is introducing a new framework designed to ensure AI agents only make purchases their users have authorized, potentially preventing costly… [The agent loop]</summary><category term="agent-loop" label="The agent loop" /></entry><entry><title>Higgsfield takes AI video from 1 minute to over 10</title><link href="https://openai.com/index/higgsfield-longer-videos-voice" /><id>urn:justuju:ffc748cb8d88</id><updated>2026-10-13T12:00:00Z</updated><author><name>OpenAI News</name></author><summary>Higgsfield brings creators longer videos with GPT-6 Astra, precise visuals with GPT-Image-2.5, and a path to voice filmmaking with GPT-Live. [GPT (OpenAI)]</summary><category term="gpt" label="GPT (OpenAI)" /></entry><entry><title>Horsey challenge | Strata Fork | Qwen 3.8 FN NVFP4</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4uipn/horsey_challenge_strata_fork_qwen_38_fn_nvfp4/" /><id>urn:justuju:b7472565c79d</id><updated>2026-10-13T11:50:07Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>So I wanted to try the horsey challenge myself on the latest engine i been testing which is a strata fork by sergqwer. I seen a post earlier and seen i believe it was a iq3 i think and it was pretty dope so i thought i try out the NVFP4. I wasn't expecting to share but i… [FP8, FP4, NVFP4 &amp; MXFP4, Qwen (Alibaba)]</summary><category term="low-bit-formats" label="FP8, FP4, NVFP4 &amp; MXFP4" /><category term="qwen" label="Qwen (Alibaba)" /></entry><entry><title>Update: Converting dense models into Mixture-of-Experts</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4uint/update_converting_dense_models_into/" /><id>urn:justuju:d64746323721</id><updated>2026-10-13T11:50:03Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>This is an update to my previous post which can be found here: Since my last post I have continued working on the conversion process and also converting new models. My latest model is the Qwen2.5-1.5B-MoE-A0.9B. I have also released GGUF versions for both the 0.5B and… [Mixture of Experts (MoE), llama.cpp, GGUF &amp; k-quants]</summary><category term="moe" label="Mixture of Experts (MoE)" /><category term="llama-cpp" label="llama.cpp" /><category term="gguf" label="GGUF &amp; k-quants" /></entry><entry><title>We turned EmbeddingGemma 2 into a speech-to-text model</title><link href="https://eg2.haku.sh/" /><id>urn:justuju:0f8702d20d78</id><updated>2026-10-13T11:27:14Z</updated><author><name>Hacker News (best, AI-filtered)</name></author><summary>[Vision, speech &amp; generative media]</summary><category term="multimodal" label="Vision, speech &amp; generative media" /></entry><entry><title>Got 2x DGX Sparks. Excited and confused!</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4ttde/got_2x_dgx_sparks_excited_and_confused/" /><id>urn:justuju:7517cf52afea</id><updated>2026-10-13T11:11:44Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>Got this couple. It was difficult purchase in this economy but paid only roughly $6100 equivalent for each in my country. I justified the purchase for learning (so I try to convince myself) and coding workflow improvements. I started looking into recipes for GLM 5.3 flash but surprisingly there is very little chatter… [NVIDIA DGX Spark]</summary><category term="dgx-spark" label="NVIDIA DGX Spark" /></entry><entry><title>Anthropic says it cut Claude reasoning token theft by up to 99% in fight against industrial-scale distillation</title><link href="https://the-decoder.com/anthropic-says-it-cut-claude-reasoning-token-theft-by-up-to-99-in-fight-against-industrial-scale-distillation/" /><id>urn:justuju:625fccaa6d73</id><updated>2026-10-13T11:06:46Z</updated><author><name>The Decoder</name></author><summary>Anthropic says it has cut the extraction of Claude's internal reasoning tokens by 90 to 99 percent. [Claude (Anthropic), Distillation, pruning &amp; synthetic data]</summary><category term="claude" label="Claude (Anthropic)" /><category term="distillation" label="Distillation, pruning &amp; synthetic data" /></entry><entry><title>Anthropic study warns that small AI models could soon replicate across everyday devices</title><link href="https://the-decoder.com/anthropic-study-warns-that-small-ai-models-could-soon-replicate-across-everyday-devices/" /><id>urn:justuju:459735cd30fd</id><updated>2026-10-13T10:33:28Z</updated><author><name>The Decoder</name></author><summary>Small AI models may be just months away from spreading on their own across poorly secured consumer devices like smart TVs and robot vacuums, according to a new… [Claude (Anthropic)]</summary><category term="claude" label="Claude (Anthropic)" /></entry><entry><title>Open models have real benefits. Do they outweigh the risks?</title><link href="https://www.technologyreview.com/2026/10/13/1146366/open-models-have-real-benefits-do-they-outweigh-the-risks/" /><id>urn:justuju:c48f77cfce80</id><updated>2026-10-13T10:00:00Z</updated><author><name>MIT Technology Review AI</name></author><summary>MIT Technology Review Explains: Let our writers untangle the complex, messy world of science and technology to help you understand what’s coming next. [Open weights vs open source]</summary><category term="open-weights" label="Open weights vs open source" /></entry><entry><title>Based on advice from LocalLLama implemented a n+2 lookahead prediction for ssd streaming Qwen Flash Next - and it worked!</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4sit6/based_on_advice_from_localllama_implemented_a_n2/" /><id>urn:justuju:d5672e760479</id><updated>2026-10-13T09:57:21Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>My project is optimising qwen flash next on Mac Mini m5 64gb through SSD streaming. I recently asked LocalLLama for guidance on how to optimise token output. Today a popular method to do this is a lookahead guesser which fetches experts one layer ahead of the current layer. One popular suggestion was building a… [Qwen (Alibaba)]</summary><category term="qwen" label="Qwen (Alibaba)" /></entry><entry><title>[AINews] Meta Muse Wonder &amp; Wonder Full: MSL’s Claude 5.5-tier Frontier Model</title><link href="https://www.latent.space/p/ainews-meta-muse-wonder-and-wonder" /><id>urn:justuju:ec132ecc411f</id><updated>2026-10-13T09:37:04Z</updated><author><name>Latent Space</name></author><summary>MSL’s emergence as a frontier lab is finally complete. [What 'frontier' means now, Claude (Anthropic)]</summary><category term="frontier-capabilities" label="What 'frontier' means now" /><category term="claude" label="Claude (Anthropic)" /></entry><entry><title>Sipeed announces MaixStation, comparable to Strix Halo and DGX Spark</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4r1o5/sipeed_announces_maixstation_comparable_to_strix/" /><id>urn:justuju:19274c938d07</id><updated>2026-10-13T08:21:05Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>[NVIDIA DGX Spark, AMD Strix Halo (Ryzen AI Max)]</summary><category term="dgx-spark" label="NVIDIA DGX Spark" /><category term="strix-halo" label="AMD Strix Halo (Ryzen AI Max)" /></entry><entry><title>v2.0.28</title><link href="https://github.com/anomalyco/opencode/releases/tag/v2.0.28" /><id>urn:justuju:e5a7015739f1</id><updated>2026-10-13T08:17:38Z</updated><author><name>OpenCode releases</name></author><summary>release: v2.0.28 [OpenCode]</summary><category term="opencode" label="OpenCode" /></entry><entry><title>v0.40.4</title><link href="https://github.com/ollama/ollama/releases/tag/v0.40.4-rc1" /><id>urn:justuju:324d401eae58</id><updated>2026-10-13T05:26:17Z</updated><author><name>Ollama releases</name></author><summary>What's Changed llama: update llama.cpp to b11521 for decision models and EmbeddingGemma 2 by @jmorganca in #18917 server: reuse verified manifests and loaded models in inference routes by @dhiltgen in #18947 mlx: compact restored recurrent state after speculative rollback by @dhiltgen in #18955 launch: include… [Ollama, Gemma, Llama, Mistral, llama.cpp]</summary><category term="ollama" label="Ollama" /><category term="gemma" label="Gemma, Llama, Mistral" /><category term="llama-cpp" label="llama.cpp" /></entry><entry><title>Hope the software support is good, but -&gt; Intel Crescent Island Squeezes 1.3 PFLOPs Of FP8 Into Just 350W As D270 Targets The Competition With Value-Oriented LPDDR5X Up To 480 GB</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4n0bq/hope_the_software_support_is_good_but_intel/" /><id>urn:justuju:c4b53f7c6d1f</id><updated>2026-10-13T04:13:29Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>[FP8, FP4, NVFP4 &amp; MXFP4]</summary><category term="low-bit-formats" label="FP8, FP4, NVFP4 &amp; MXFP4" /></entry><entry><title>NVIDIA’s 9-Year-Old Tesla V100 Still Packs Some Inference Muscle; 4 GPUs On An NVLink Board Can Outperform M5 Ultra, DGX Spark In Various AI Benchmarks By Up To 230%</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4mtzx/nvidias_9yearold_tesla_v100_still_packs_some/" /><id>urn:justuju:64c0e2c4c487</id><updated>2026-10-13T04:03:55Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>Didn't realize the V100 were still so fast, anyone have first hand experience on these? [Multi-GPU parallelism, NVIDIA DGX Spark, Benchmarks that matter]</summary><category term="multi-gpu" label="Multi-GPU parallelism" /><category term="dgx-spark" label="NVIDIA DGX Spark" /><category term="benchmarks" label="Benchmarks that matter" /></entry><entry><title>How far are we from a local model being able to optimize it's own runtime stack?</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4j70c/how_far_are_we_from_a_local_model_being_able_to/" /><id>urn:justuju:aa4b8849242e</id><updated>2026-10-13T01:01:00Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>A lot of projects have come up recently consisting of specific hardware config specialized forks of existing conferencing engines, but they all seem to be made using Claude or OpenAI's biggest frontier models. So given that open source is about 4-6 months behind, can we reasonably expect that local runnable models… [What 'frontier' means now, Claude (Anthropic)]</summary><category term="frontier-capabilities" label="What 'frontier' means now" /><category term="claude" label="Claude (Anthropic)" /></entry><entry><title>Anthropic is doing free publicity for GLM again</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4ii8e/anthropic_is_doing_free_publicity_for_glm_again/" /><id>urn:justuju:c6054984e007</id><updated>2026-10-13T00:27:09Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>[Claude (Anthropic)]</summary><category term="claude" label="Claude (Anthropic)" /></entry><entry><title>Qwen 3.8 Flash Next Q3 S - Benchmark creation</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4h1c2/qwen_38_flash_next_q3_s_benchmark_creation/" /><id>urn:justuju:d87f52db72f5</id><updated>2026-10-12T23:18:32Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>After running a benchmark test on Exo in Opencode and being amazed by the result, I thought I'd try other models on Opencode. Many were surprisingly terrible. Especially some mid level models. Earlier posts: [Qwen (Alibaba), Benchmarks that matter, OpenCode]</summary><category term="qwen" label="Qwen (Alibaba)" /><category term="benchmarks" label="Benchmarks that matter" /><category term="opencode" label="OpenCode" /></entry><entry><title>Intel "Crescent Island" GPU Gets D-Series Name, Supports Up to 480 GB of LPDDR5X</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4g90u/intel_crescent_island_gpu_gets_dseries_name/" /><id>urn:justuju:653273f51bcd</id><updated>2026-10-12T22:42:31Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>The newly named D-Series GPU has up to 480GB of LPDDR5X memory, 1.5TB/s of memory bandwidth Intel rates it for up to 1.3 PetaFLOPS of FP8 performance, with support for formats ranging from FP4 to FP64. It features a total of 32 Xe3P cores, 256 XMX engines, 32 MB of unified L2 cache. No cost estimates currently, I hope… [FP8, FP4, NVFP4 &amp; MXFP4, Memory bandwidth is king]</summary><category term="low-bit-formats" label="FP8, FP4, NVFP4 &amp; MXFP4" /><category term="bandwidth" label="Memory bandwidth is king" /></entry><entry><title>v0.32.0rc0: [HiSparse] Report host pool in KV cache capacity logs (#60285)</title><link href="https://github.com/vllm-project/vllm/releases/tag/v0.32.0rc0" /><id>urn:justuju:aafb1b36fddb</id><updated>2026-10-12T20:49:49Z</updated><author><name>vLLM releases</name></author><summary>Signed-off-by: Matthew Bonanni mbonanni@redhat.com Co-authored-by: Claude Opus 5.5 noreply@anthropic.com [vLLM &amp; SGLang, KV cache &amp; prompt caching, Claude (Anthropic)]</summary><category term="vllm" label="vLLM &amp; SGLang" /><category term="kv-cache" label="KV cache &amp; prompt caching" /><category term="claude" label="Claude (Anthropic)" /></entry><entry><title>Running unsloth/Qwen3.8-27B-UD-IQ4_XS 262k q4_0 KV + vision on 16GB VRAM @ 80t/s and 1500pp</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4cmfc/running_unslothqwen3827budiq4_xs_262k_q4_0_kv/" /><id>urn:justuju:811c660e2380</id><updated>2026-10-12T20:14:03Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>I get a lot of hate whenever I tell people I run a Q4 quant of Qwen3.8 27B with 262k KV 80t/s with vision, all while running 2 screens on windows on the same card. People seem to think I am lying or making it up. I am really just trying to help people. I am saddened whenever I see fellow 16GB VRAMers stuck on 60k… [VRAM math, Qwen (Alibaba), GGUF &amp; k-quants]</summary><category term="vram-math" label="VRAM math" /><category term="qwen" label="Qwen (Alibaba)" /><category term="gguf" label="GGUF &amp; k-quants" /></entry><entry><title>Once again anthropic doing free marketing for open weight models</title><link href="https://www.reddit.com/r/LocalLLaMA/comments/1x4cm73/once_again_anthropic_doing_free_marketing_for/" /><id>urn:justuju:e02c3eb04ac6</id><updated>2026-10-12T20:13:48Z</updated><author><name>r/LocalLLaMA (top today)</name></author><summary>[Claude (Anthropic)]</summary><category term="claude" label="Claude (Anthropic)" /></entry><entry><title>Intel’s Crescent Island AI Server GPU Gets a Name and Specs: Meet the Intel GPU D-Series</title><link href="https://www.servethehome.com/intels-crescent-island-ai-server-gpu-gets-a-name-and-specs-meet-the-intel-gpu-d-series/" /><id>urn:justuju:865d5757827e</id><updated>2026-10-12T19:25:28Z</updated><author><name>ServeTheHome</name></author><summary>Intel today has unveiled its official performance specifications and branding for its upcoming "Crescent Island" data center AI inference cards. [AI datacenters &amp; energy]</summary><category term="ai-datacenters" label="AI datacenters &amp; energy" /></entry><entry><title>vscode-v1.0.0: fix(vscode): launch the V2 CLI without --port (#49084)</title><link href="https://github.com/anomalyco/opencode/releases/tag/vscode-v1.0.0" /><id>urn:justuju:e738abb5d3be</id><updated>2026-10-12T18:58:37Z</updated><author><name>OpenCode releases</name></author><summary>Co-authored-by: uyencss uyencss@users.noreply.github.com Co-authored-by: Aiden Cline aidenpcline@gmail.com [OpenCode, Cline, Aider, Roo &amp; co]</summary><category term="opencode" label="OpenCode" /><category term="oss-coding-agents" label="Cline, Aider, Roo &amp; co" /></entry><entry><title>Meta's new frontier model Muse Wonder Full breaks into the AI top tier</title><link href="https://the-decoder.com/metas-new-frontier-model-muse-wonder-full-breaks-into-the-ai-top-tier/" /><id>urn:justuju:2da9ef0d9c24</id><updated>2026-10-12T18:48:29Z</updated><author><name>The Decoder</name></author><summary>Meta's new Muse Wonder models rank in the top three on six out of ten benchmarks, with the biggest gains in agentic tasks. [What 'frontier' means now, The agent loop, Benchmarks that matter]</summary><category term="frontier-capabilities" label="What 'frontier' means now" /><category term="agent-loop" label="The agent loop" /><category term="benchmarks" label="Benchmarks that matter" /></entry></feed>