Jetson Orin NX Build for Hermes Agent + Benchmarking
r/LocalLLaMA top day5 days agoHardwareThe post describes turning an unused Jetson Orin NX into a compact local LLM server for Hermes Agent testing. The goals were low noise, over 10 tok/s generation, 300 tok/s prompt processing, at least 65K context, and a custom case. After testing Gemma 4, Qwen 3.6, and many quant variants, the author reports Gemma 4 26B A4B UD Q2_K_XL reaching 66K context and 10.21 tok/s near 60K context.
Five things you need to know about AI
MIT Tech Review AI5 days agoCommentaryThe article is based on a talk titled “Five things you need to know about AI,” delivered at SXSW London. The author frames it as a guide to the biggest AI themes right now, drawing partly from MIT Technology Review’s first AI10 list. From the provided excerpt, it reads as a trend-oriented editorial overview rather than a product release, paper, or technical tutorial.
NeuroBait: I fine-tuned a model to spark dopamine for ADHD brain
Hugging Face Blog5 days agoNew ToolNeuroBait is a Hugging Face community project built to help with ADHD task-initiation freeze rather than diagnosis or to-do planning. It fine-tunes google/gemma-3-12b-it with LoRA to produce short, warm, context-aware nudges. The project uses Unsloth and Modal for training, then deploys on a Hugging Face Space with Gradio, transformers, peft, and a runtime LoRA adapter.
Amap Releases ABot-Earth 0.5: Shifting from 2D Distillation to 3D Native for Consistent Scene Generation★ 70
量子位 QbitAI5 days agoReleaseAmap has released ABot-Earth 0.5, its latest spatial intelligence model. Moving beyond traditional 2D distillation methods (like Score Distillation Sampling), the model adopts a 3D native driving architecture. This breakthrough addresses multi-view inconsistency and distortion, enabling highly consistent 3D scene generation for autonomous driving simulation, smart cities, and digital twin mapping.
ByteDance Open-Sources Bernini, a Unified Framework for AI Video Editing★ 74
量子位 QbitAI5 days agoReleaseByteDance’s commercial technology team has open-sourced Bernini, a unified framework for AI video generation and editing. Its design separates semantic planning from visual rendering: an MLLM-based planner understands text, source videos, images, and video references, then a DiT-based renderer produces the final video. The released Bernini-R includes inference code and weights, while the full planner-enabled version is still being prepared.
A 4B Edge-Deployable Cognitive Model Built in China
量子位 QbitAI5 days agoReleaseQbitAI’s headline says a domestic Chinese team has built a 4B-parameter “cognitive model” suitable for edge deployment. The framing links it to a model direction previously associated with Andrej Karpathy. Since the article body was not provided, details such as the model name, architecture, benchmark results, hardware requirements, open-source status, and licensing remain unverified.
Is a New Player Joining China’s Top-Tier General AI Models?
量子位 QbitAI5 days agoCommentaryBased only on the title, the article likely examines China’s domestic general-purpose AI model landscape and asks whether a new company or model is entering the top tier. It appears to be an industry observation rather than a technical paper or tutorial. Without the full text, the specific model, company, benchmark evidence, and business context cannot be verified.
Microsoft's open source tools were hacked to steal passwords of AI developers★ 78
Hacker News (AI keywords)5 days agoIncidentMicrosoft temporarily removed several open source GitHub projects while investigating suspected malicious content. The affected repos were linked to Azure and developer workflows involving AI coding tools such as Claude Code, Gemini CLI, and VS Code. Security researchers said the malware could steal passwords and sensitive credentials when compromised tools were opened, though Microsoft has not disclosed how many users were affected.
FrontierCode: Benchmarking for Code Quality over Slop
Latent Space5 days agoBenchmarkLatent Space briefly announced FrontierCode with the line “We made a thing!” From the title, FrontierCode appears to be a benchmark for frontier coding systems that prioritizes code quality rather than sheer code generation volume. The provided excerpt does not include methodology, model results, datasets, or tooling details, so conclusions should remain cautious.
L'Affaire Siloxane
Hacker News (AI keywords)5 days agoCommentaryPinboard founder and prominent tech critic Maciej Cegłowski published a piece titled in the style of historical French scandals, suggesting a serious controversy worth scrutiny. The word 'Siloxane' — a silicon-oxygen chemical compound and basis of silicone — likely serves as a metaphor or pseudonym for a tech or AI entity. Original article content was unavailable; details must be confirmed by reading the source directly.
Anyone seen benchmarks comparing Gemma 4 4-bit QAT vs. 8-bit standard quants?
r/LocalLLaMA top day5 days agoBenchmarkA r/LocalLLaMA user is looking for benchmarks comparing Gemma 4 4-bit QAT models, via Unsloth, against standard 8-bit non-QAT quantized models. They understand QAT is expected to preserve much of the BF16 baseline accuracy, but want hard numbers against traditional 8-bit PTQ. The post highlights scattered feedback but no clear head-to-head evaluation yet.
ggml-webgpu improves prefill speeds for k-quants in llama.cpp PR
r/LocalLLaMA top day5 days agoBenchmarkllama.cpp PR #24225 improves ggml-webgpu matrix multiplication performance for k-quants and refactors matmul paths for Q4/Q5/Q8 and k-quants. In pp512 tests on an M2 Pro, reported speedups range from about 1.33x to 3.78x across Q2_K, Q3_K, Q4_K, Q5_K, and Q6_K. The largest gains appear on Q3_K models, including Qwen and Gemma examples.
Packed twin inference doubles Qwen3.6-27B throughput on one MI50
r/LocalLLaMA top day5 days agoBenchmarkA LocalLLaMA user shared an early packed-twin-inference experiment for local LLM acceleration. The idea resembles speculative decoding, but uses the same quantized model side-by-side instead of a smaller draft model. On a single AMD MI50, the author reports Qwen3.6-27B improving from 19.4 to 38.1 tk/s, with Q8-or-lower quantization as the main target.
JetBrains Mellum 2: a really good and performant model
r/LocalLLaMA top day5 days agoBenchmarkA r/LocalLLaMA user shared informal impressions of JetBrains Mellum 2, focusing on local coding-style tasks and tool calls. On an AMD Radeon RX 7900 XT with llama.cpp Vulkan and 131K context, the model reportedly generated around 111 tokens/s and stayed above 100 tokens/s near full context. The author stresses this is not a scientific benchmark, but a practical workflow-oriented test.
Omi Med STT v1: Open-Weight Medical ASR Fine-Tuned from Parakeet 0.6B★ 72
r/LocalLLaMA top day5 days agoReleaseOmi Health’s founder says he fine-tuned NVIDIA Parakeet TDT 0.6B v2 for clinical speech and released Omi Med STT v1 under CC-BY-4.0. The runtime supports Mac, Windows, and Linux, auto-selecting MLX, NeMo, or GGUF/parakeet.cpp backends. In the author’s held-out medical benchmark, it reports 2.37% medical-WER and 145× realtime on local A10 compute.
A llama.cpp CLI Command Builder
r/LocalLLaMA top day6 days agoNew ToolA r/LocalLLaMA post introduces a llama.cpp CLI Command Builder with no accounts, email, pop-ups, cookies, or ads. It stores information locally in the browser and includes editable fields for flags and arguments found in the documentation. Users can build CLI or server commands, log run information, and compare which configurations work best for their hardware; only Linux is currently supported.
Pipeline parallelism in llama.cpp may be wasting your VRAM
r/LocalLLaMA top day6 days agoBenchmarkThe author compared three llama.cpp Vulkan builds: default 4 sched copies, 1 sched copy, and no pipeline parallelism. In their Qwen GGUF test, input and output throughput were nearly identical across all configurations. However, the default setting used about 1.5GB more VRAM for compute buffers and reduced usable context from roughly 113K tokens to around 88K, though parallel-request benefits were not tested.
Quick note on recent QAT issues
r/LocalLLaMA top day6 days agoCommentaryThe post argues that recent Google QAT quantization has several implementation problems, including token embeddings being quantized to q6k instead of using a pure mode. It also claims llama-quantize has a hardcoded parameter that mismatches some optimized groups, and that 32-block groups are misaligned. The author recommends Unsloth UD Q4_K_XL as a temporary option and says they are working on a patch.
llama.cpp PR adds MTP support for Gemma-4 E2B and E4B assistants
r/LocalLLaMA top day6 days agoReleaseThe Reddit post links to ggml-org/llama.cpp Pull Request #24282, which adds MTP support for Gemma-4 E2B and E4B assistants. The submitter frames it as useful for tiny Gemma models on phones, low-end machines, Raspberry Pi, or similarly constrained devices. The post does not include benchmarks, merge status, or setup instructions, so it should be treated as a development signal rather than a finished release.
Introducing FrontierCode★ 78
Hacker News (AI keywords)6 days agoBenchmarkCognition launched FrontierCode, a coding benchmark focused on mergeability rather than only functional correctness. It evaluates correctness, tests, scope discipline, style, and repository-specific quality standards. Built with open-source maintainers and extensive quality control, it shows current frontier models still struggle: Claude Opus 4.8 scores 13.4% on the hardest Diamond subset, ahead of GPT-5.5 and Gemini 3.1 Pro.
Arguing with an AI bot posting outdated Llama 3.1 takes
r/LocalLLaMA top day6 days agoCommentaryA r/LocalLLaMA post jokes about arguing with an AI bot that posted outdated commentary involving Llama 3.1. The author says such bots should enable web search instead of relying on stale knowledge. The post also mocks exaggerated model testimonial posts, using Qwen3.6 27B as a sarcastic example, making it more of a community quality complaint than technical news.
Qwen3.6-35B-A3B Tool Calling Benchmark: ByteShape vs Unsloth GGUFs
r/LocalLLaMA top day6 days agoBenchmarkThe post benchmarks eight Qwen3.6-35B-A3B GGUF quants from ByteShape and Unsloth using llama.cpp and tool-eval-bench. It compares f16, q8_0, and q4_0 KV cache quantization under short and long-context pressure, totaling 144 runs and roughly 300 GPU-hours. The author reports no clear ByteShape versus Unsloth winner, q8_0 as close to a free lunch, q4_0 as weaker, and long context as a major tool-calling degradation factor.
Was BitNet a dead end? What happened to ternary LLMs?
r/LocalLLaMA top day6 days agoCommentaryA r/LocalLLaMA user questions whether BitNet and ternary LLMs were a dead end after earlier promise around efficient low-bit models. The post notes that the largest ternary model appears to remain around 2B parameters. It asks why frontier open-weight AI labs are not visibly pursuing the approach, but provides no technical evidence or definitive answer.
Why Are Cells Small?
Hacker News (AI keywords)6 days agoTutorialThis essay explains why most cells remain small through two physical limits: surface-area-to-volume ratio and diffusion. As cells grow, volume rises faster than membrane area, making nutrient intake, waste removal, and energy support harder. Larger cells also slow molecular encounters, though examples like red blood cells, oocytes, organelles, and giant bacteria show how biology works around these constraints.
Gemini 3.5 and Antigravity come to Google NotebookLM
Ars Technica AI6 days agoReleaseGoogle is upgrading NotebookLM with Gemini 3.5 and Antigravity, pushing the product beyond source-based Q&A into more agentic research workflows. The update adds a secure cloud computer for each notebook, enabling code execution, deeper analysis, and richer file outputs. For now, availability is limited to AI Ultra and enterprise customers, with broader rollout planned later.
Apple Core AI Framework★ 76
Hacker News (AI keywords)6 days agoReleaseApple’s Core AI framework is positioned as a developer stack for deploying AI models directly inside apps on Apple silicon. The documentation describes Swift APIs, `.aimodel` assets, model specialization, caching, Xcode profiling, and debugging tools. It appears aimed at developers building low-latency, privacy-conscious on-device inference workflows, though the documentation is marked as preliminary beta information.
LocalLLaMA post tier list
r/LocalLLaMA top day6 days agoOpinionThe author proposes a tier list for r/LocalLLaMA posts in response to complaints about declining post quality. Top-tier posts include new local model releases with GGUF/MLX or benchmark data, meaningful optimizations, complete hardware performance reports, and well-analyzed research. Low-tier posts include repeated toy benchmarks, unrelated cloud AI chatter, AI-generated slop, and thinly disguised ads for Claude-wrapper startups.
For the 2nd time in weeks, Microsoft packages laced with credential stealer★ 72
Ars Technica AI6 days agoIncidentArs Technica reports a second Microsoft-package security incident in weeks, involving 73 packages laced with a credential stealer. The supplied summary says the malware runs as soon as the packages are opened by an AI agent and can self-replicate. The case highlights a growing software supply-chain risk: AI agents that inspect or operate on code may become execution triggers for malicious packages.
Ask HN: What are tools you have made for yourself since the advent of AI?
Hacker News (AI keywords)6 days agoCommentaryThis Ask HN post invites the community to share tools they have built for themselves in the AI era. No original discussion content or replies were provided, so only the topic can be assessed. The likely value is inspirational rather than definitive: it may surface personal automation ideas, workflow hacks, and AI-assisted software experiments, but no specific tools or models can be confirmed from the title alone.
When every other post is an AI benchmark, best-model question, or slop app
r/LocalLLaMA top day6 days agoCommentaryThis r/LocalLLaMA post is a meme-like complaint about the subreddit’s recent content quality. The author points to repeated AI-generated benchmark reports, recurring “best model” questions, and hastily built apps or engines presented as groundbreaking. It is not a technical release or evidence-based analysis, but it reflects frustration with noise, hype, and low-effort AI-generated discussion in local model communities.