https://axiomi.si/ Curated directory of open weight models, ML frameworks, fine tuning tools, interpretability, datasets and research platforms, with live arXiv and Hugging Face feeds. en Thu, 29 Oct 2026 01:12:19 +0000 https://axiomi.si/guides/what-is-safetensors What is safetensors? A plain guide to the Hugging Face model file format: why it is safer than pickle, how it compares to GGUF, and how to load or convert it. Explainers Wed, 28 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/what-is-safetensors https://axiomi.si/guides/how-to-use-tensorflow-in-jupyter-notebook How to use TensorFlow in Jupyter Notebook: install it in a venv, register the kernel with ipykernel, pick it in JupyterLab or VS Code, and fix common errors. How-to Wed, 28 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/how-to-use-tensorflow-in-jupyter-notebook https://axiomi.si/guides/ollama-vs-vllm Ollama vs vLLM compared: setup, model formats, speed for one user or many, hardware, Mac support and the signs it is time to move from Ollama to vLLM. Comparisons Tue, 27 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/ollama-vs-vllm https://axiomi.si/guides/how-to-run-comfyui-on-mac How to run ComfyUI on Mac: install Comfy Desktop or set it up by hand with PyTorch and Metal, where model files go, what fits in memory and how to fix errors. How-to Tue, 27 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/how-to-run-comfyui-on-mac https://axiomi.si/guides/rtx-3090-bf16-gpu-unlock RTX 3090 BF16 runs at half rate, and a new Linux mod restores it on RTX 30 cards. What it speeds up for local LLMs, what it doesn't, the risks and setup. Explainers Sun, 25 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/rtx-3090-bf16-gpu-unlock https://axiomi.si/guides/tinygrad-vs-mlx Tinygrad vs MLX compared for Apple silicon and beyond: hardware support, lazy graphs, JIT and compile, LLM tooling, install and which framework fits your project. Comparisons Sat, 24 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/tinygrad-vs-mlx https://axiomi.si/guides/run-deepseek-v4-1-flash-locally Can you run DeepSeek V4.1 Flash locally? Real RAM, VRAM and disk needs by GGUF, Strata on one 12 GB GPU, the llama.cpp PR, vLLM, Ollama and DGX Spark. How-to Sat, 24 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/run-deepseek-v4-1-flash-locally https://axiomi.si/guides/smollm-vs-llama SmolLM vs Llama compared on benchmarks, licence, context, languages, memory and local setup, so you can choose between SmolLM3 and Llama 3.2 for your project. Comparisons Fri, 23 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/smollm-vs-llama https://axiomi.si/guides/sglang-vs-vllm-on-dgx-spark SGLang vs vLLM on DGX Spark compared on setup, memory, NVFP4 models, speed and clustering, so you can pick the right server for your GB10 desktop. Comparisons Fri, 23 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/sglang-vs-vllm-on-dgx-spark https://axiomi.si/guides/run-recursive-flint-locally Recursive Flint is an Apache 2.0 coding model with 40B total and 2.5B active parameters. How to run Flint locally with Ollama, llama.cpp, rec or Unsloth. How-to Fri, 23 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/run-recursive-flint-locally https://axiomi.si/guides/smollm-vs-gemma SmolLM vs Gemma compared on size, licence, context, languages, vision and audio, local setup and fine tuning, so you can pick the right small model in 2026. Comparisons Thu, 22 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/smollm-vs-gemma https://axiomi.si/guides/moss-image-comfyui MOSS-Image is an Apache 2.0 open weight 5B text to image model built for text and layouts. How to run MOSS-Image in ComfyUI, file sizes and best settings. How-to Thu, 22 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/moss-image-comfyui https://axiomi.si/guides/best-llm-pretraining-dataset The best LLM pretraining dataset depends on your goal. FineWeb-Edu for small models, DCLM-Baseline and Nemotron-CC for scale, Common Pile for licensed text. Best of Thu, 22 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/best-llm-pretraining-dataset https://axiomi.si/guides/what-is-deepinfer DeepInfer is DeepSeek's open source engine for DeepSeek-V4.1-Flash. What it does, the hardware it needs, what is missing, and when vLLM or SGLang fits better. Explainers Wed, 21 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/what-is-deepinfer https://axiomi.si/guides/llm-distillation-vs-quantization LLM distillation vs quantization: distillation trains a smaller student model, quantization stores the same weights in fewer bits. When to use each, or both. How-to Wed, 21 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/llm-distillation-vs-quantization https://axiomi.si/guides/best-local-llm-for-image-generation The best local LLM for image generation is really an open image model. Our picks by GPU and license: FLUX 3 Klein, Z-Image Turbo, Qwen-Image 2.1 and more. How-to Wed, 21 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/best-local-llm-for-image-generation https://axiomi.si/guides/flux-3-klein-comfyui FLUX open weights explained: every Black Forest Labs model you can download, its license, file size and VRAM, plus how to run FLUX 3 Klein in ComfyUI. How-to Sat, 17 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/flux-3-klein-comfyui https://axiomi.si/guides/what-is-mechanistic-interpretability-research What is mechanistic interpretability research? A plain guide to features, circuits, superposition, sparse autoencoders, key papers, open tools and how to start. Explainers Fri, 16 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/what-is-mechanistic-interpretability-research https://axiomi.si/guides/best-ml-frameworks-python The best ML frameworks for Python in 2026, ranked by job: PyTorch, scikit-learn, JAX, Keras 3, XGBoost, LightGBM, TensorFlow, MLX and more, with versions. Best of Fri, 16 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/best-ml-frameworks-python https://axiomi.si/guides/run-muse-spark-open-locally Can you run Muse Spark Open locally? Real memory needs by quant (GGUF, MLX, NVFP4), llama.cpp and MLX commands, Mac Studio setups, licence terms and limits. How-to Fri, 16 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/run-muse-spark-open-locally https://axiomi.si/guides/run-lfm2-5-24b-locally Run Liquid AI LFM2.5-24B-A2B locally with Ollama, llama.cpp or MLX: VRAM by quant, Q4_K_M sizes, Ollama 0.17.1 caveat, sampling settings and agent tips. How-to Thu, 15 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/run-lfm2-5-24b-locally https://axiomi.si/guides/tinygrad-vs-pytorch-performance Tinygrad vs PyTorch performance: when TinyJit and BEAM can outrun eager PyTorch, when torch.compile and the ecosystem win, and how to compare them fairly. Comparisons Thu, 15 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/tinygrad-vs-pytorch-performance https://axiomi.si/guides/how-to-use-tensorflow-in-python How to use TensorFlow in Python in 2026: install it in a venv, work with tensors and gradients, train a Keras model, feed data with tf.data and save it. How-to Thu, 15 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/how-to-use-tensorflow-in-python https://axiomi.si/guides/how-to-use-tensorflow-with-gpu How to use TensorFlow with GPU in 2026: the pip [and-cuda] install on Linux and WSL2, Docker, Mac Metal, how to verify the GPU, and fixes for common errors. How-to Wed, 14 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/how-to-use-tensorflow-with-gpu https://axiomi.si/guides/fine-tune-llm-for-code-generation How to fine tune an LLM for code generation: pick a coder base model, build an instruction or FIM dataset, train a LoRA with TRL or Unsloth, test with pass@k. How-to Wed, 14 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/fine-tune-llm-for-code-generation https://axiomi.si/guides/run-deepseek-locally-ollama Run DeepSeek Locally with Ollama: pick R1 distill tags by download size, set context length, handle thinking output, and know when 671B V3 is server only. How-to Sun, 11 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/run-deepseek-locally-ollama https://axiomi.si/guides/tensorflow-vs-pytorch-vs-keras TensorFlow vs PyTorch vs Keras in 2026: Keras 3 multibackend, which is easier to learn, open weight LLM ecosystem fit, and who should pick each stack. Comparisons Sun, 11 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/tensorflow-vs-pytorch-vs-keras https://axiomi.si/guides/run-qwen-3-6-locally Run Qwen 3.6 locally with Ollama, llama.cpp or LM Studio: pick 27B or 35B-A3B by VRAM, thinking mode, vision tips, MTP speedups and sampling settings. How-to Sun, 11 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/run-qwen-3-6-locally https://axiomi.si/guides/run-gemma-4-locally Run Gemma 4 locally with Ollama, llama.cpp or LM Studio: pick E2B, E4B, 12B, 26B-A4B or 31B by memory, thinking mode, vision tips and sampling settings. How-to Sat, 10 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/run-gemma-4-locally https://axiomi.si/guides/how-to-use-local-llm-in-vscode Wire a local LLM into VS Code with Ollama: official Chat extension, Continue.dev for tab complete, model picks by VRAM, and privacy tips for offline coding. How-to Sat, 10 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/how-to-use-local-llm-in-vscode https://axiomi.si/guides/how-to-quantize-llm-models Practical how to quantize LLM models: llama.cpp convert then Q4_K_M, Hugging Face bitsandbytes 4 bit loads, Ollama GGUF import, and when to use each path. How-to Sat, 10 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/how-to-quantize-llm-models https://axiomi.si/guides/open-source-llm-models-to-run-locally Open source LLM models to run locally: match Qwen3, Gemma 4, gpt-oss and DeepSeek R1 distill tags to your VRAM, with Ollama setup and licence caveats. Best of Fri, 09 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/open-source-llm-models-to-run-locally https://axiomi.si/guides/run-bonsai-2-27b-locally Run Prism ML's ternary Bonsai 2 27B locally: why stock llama.cpp, Ollama and LM Studio can't load it, PQ2_0 vs PTQ1_0, memory needs, setup steps and settings. How-to Thu, 08 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/run-bonsai-2-27b-locally https://axiomi.si/guides/embeddinggemma-2-local-guide How to run Google's EmbeddingGemma 2 locally: setup with Sentence Transformers, task prefixes, Matryoshka truncation, GGUF and MLX builds, and memory needs. How-to Wed, 07 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/embeddinggemma-2-local-guide https://axiomi.si/guides/run-qwen-3-8-locally Run Qwen 3.8 27B locally with Ollama, llama.cpp, LM Studio or vLLM: which quant fits 8, 16 or 24 GB, setup commands, thinking controls and context memory. How-to Tue, 06 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/run-qwen-3-8-locally https://axiomi.si/guides/vllm-vs-llama-cpp SGLang vs vLLM vs llama.cpp compared: what SGLang is, how RadixAttention works, Docker setup, independent benchmarks, hardware and when to pick each. Comparisons Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/vllm-vs-llama-cpp https://axiomi.si/guides/ollama-vs-llama-cpp-vs-lm-studio Ollama vs llama.cpp vs LM Studio compared for 2026: what each one is, ports, licences, Mac and GPU support, the context length trap, and which to pick. Comparisons Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/ollama-vs-llama-cpp-vs-lm-studio https://axiomi.si/guides/train-small-llm-from-scratch Train a small LLM from scratch with nanochat, and learn when NVIDIA Megatron-LM fits: model sizes, budgets, datasets, compute math and multi GPU parallelism. How-to Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/train-small-llm-from-scratch https://axiomi.si/guides/transformerlens-vs-nnsight Neuronpedia explained (SAE features, Jacobian Lens, circuit tracer, API) and TransformerLens vs nnsight compared: TransformerLens 4, nnsight 0.8 and NDIF. Comparisons Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/transformerlens-vs-nnsight https://axiomi.si/guides/pytorch-vs-jax PyTorch vs JAX in 2026: how eager PyTorch and functional JAX differ, hardware support, the LLM ecosystem, TPUs and TorchTPU, and which framework fits your work. Comparisons Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/pytorch-vs-jax https://axiomi.si/guides/speculative-decoding-vs-mtp Speculative decoding vs MTP explained: how draft models, EAGLE-3, MTP heads and DFlash speed up LLM output, and how to enable each in llama.cpp, vLLM or SGLang. Explainers Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/speculative-decoding-vs-mtp https://axiomi.si/guides/open-weight-vs-open-source Open weight vs open source AI explained: what the OSI definition requires, which popular models are which, and a licence table checked against model cards. Explainers Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/open-weight-vs-open-source https://axiomi.si/guides/mlx-vs-gguf-on-mac MLX vs GGUF on a Mac: what each format is, how Ollama and LM Studio use them, speed tradeoffs, memory limits, fine tuning with mlx-lm, and which to download. Comparisons Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/mlx-vs-gguf-on-mac https://axiomi.si/guides/lora-vs-qlora QLoRA vs LoRA explained, plus DoRA and full fine tuning: how each works, real VRAM figures, quality tradeoffs, recommended settings, and when to choose which. Explainers Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/lora-vs-qlora https://axiomi.si/guides/kv-cache-quantization KV cache quantization explained: how q8_0, q4_0 and FP8 caches cut memory, the settings for llama.cpp, Ollama, LM Studio, vLLM and MLX, and when quality drops. Explainers Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/kv-cache-quantization https://axiomi.si/guides/run-gpt-oss-locally Run gpt-oss locally with Ollama, LM Studio, llama.cpp or vLLM: memory needs for 20b and 120b, setup commands, reasoning effort, harmony format and fine tuning. How-to Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/run-gpt-oss-locally https://axiomi.si/guides/fine-tune-llm-locally Fine tune an LLM locally, step by step: when it pays off, picking a base model, formatting data, Unsloth vs Axolotl vs TRL vs mlx-lm, training and GGUF export. How-to Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/fine-tune-llm-locally https://axiomi.si/guides/how-much-vram-for-llm How much VRAM do you need for an LLM? A simple formula for weights plus KV cache, worked examples from real model configs, and what fits in 8, 16, 24 or 32 GB. Explainers Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/how-much-vram-for-llm https://axiomi.si/guides/grpo-vs-ppo GRPO vs PPO explained simply: how each trains LLMs with rewards, why GRPO drops the critic, what DAPO and Dr. GRPO changed, and when to pick GRPO, PPO or DPO. Explainers Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/grpo-vs-ppo https://axiomi.si/guides/gguf-quantization-explained GGUF quantization explained in plain English: what Q4_K_M, Q8_0 and IQ quants mean, real bits per weight numbers, GGUF vs safetensors, and which file to pick. Explainers Mon, 05 Oct 2026 12:00:00 +0000 https://axiomi.si/guides/gguf-quantization-explained https://axiomi.si/reports/2026-44 ComfyUI added 4,542 GitHub stars between Oct 18 and Oct 26, the biggest gain among the 52 open source tools tracked on Axiomi. Weekly report Mon, 26 Oct 2026 12:00:00 +0000 https://axiomi.si/reports/2026-44 https://axiomi.si/reports/2026-43 Ollama added 1,105 GitHub stars between Oct 13 and Oct 21, the biggest gain among the 50 open source tools tracked on Axiomi. Weekly report Wed, 21 Oct 2026 12:00:00 +0000 https://axiomi.si/reports/2026-43 https://axiomi.si/reports/2026-42 PyTorch added 935 GitHub stars between Oct 5 and Oct 12, the biggest gain among the 45 open source tools tracked on Axiomi. Weekly report Mon, 12 Oct 2026 12:00:00 +0000 https://axiomi.si/reports/2026-42 https://axiomi.si/reports/2026-41 llama.cpp added 167 GitHub stars between Oct 5 and Oct 7, the biggest gain among the 44 open source tools tracked on Axiomi. Weekly report Thu, 08 Oct 2026 12:00:00 +0000 https://axiomi.si/reports/2026-41 https://research.google/blog/the-sleep-framework-a-new-way-for-llms-to-retain-knowledge-in-continual-learning/ [Hacker News] 33 points · 3 comments Hacker News Wed, 28 Oct 2026 12:24:25 +0000 axiomi.si:hn-50272479 https://www.brangus.io/survival-llm [Hacker News] 516 points · 194 comments Hacker News Tue, 27 Oct 2026 19:55:06 +0000 axiomi.si:hn-50264853 https://arxiv.org/abs/2610.37649v1 [arXiv] Scientific discovery often begins with an unexpected observation that prompts a new research question. In a world with altered physical laws, can large language models (LLMs) notice unexpected behavior, such as heavier objects falling more slowly, and discover the law through exp… arXiv Tue, 27 Oct 2026 17:57:00 +0000 axiomi.si:arxiv-2610.37649v1 https://arxiv.org/abs/2610.37648v1 [arXiv] We explore how to build performant large language model (LLM) tool use agents that complete complex, multistep tasks. Specifically, we study how data composition in synthetically generated tool use environments affects the training of such agents. To do so, we propose the SynthA… arXiv Tue, 27 Oct 2026 17:56:46 +0000 axiomi.si:arxiv-2610.37648v1 https://arxiv.org/abs/2610.37647v1 [arXiv] Weight decay and explicit weight norm constraints improve optimizer performance in language model pretraining, yet a unified understanding of why such control helps and how much is needed remains incomplete. We analyze norm control in Muon over the entire training trajectory usin… arXiv Tue, 27 Oct 2026 17:56:40 +0000 axiomi.si:arxiv-2610.37647v1 https://arxiv.org/abs/2610.37624v1 [arXiv] When an LLM group's discussion is compressed into a short memory for a final decision maker, what should the memory keep? Two natural heuristics keep the rarest claims or the claims relevant to the decision criteria. On hidden profile tasks both look attractive, because how widel… arXiv Tue, 27 Oct 2026 17:49:39 +0000 axiomi.si:arxiv-2610.37624v1 https://arxiv.org/abs/2610.37570v1 [arXiv] We present LCM-Promo, an LLM based uplift model for promotion allocation. LCM-Promo is a task specific Large Commerce Model (LCM): a finetuned LLM trained on transactional commerce data to predict user intent and promotion response. It learns monotonic promotion response curves f… arXiv Tue, 27 Oct 2026 17:15:15 +0000 axiomi.si:arxiv-2610.37570v1 https://arxiv.org/abs/2610.37524v1 [arXiv] Self attention mechanisms have established the Transformer as the dominant backbone across natural language processing, source code intelligence, and automated theorem proving. However, all to all pairwise token comparison enforces a quadratic $\mathcal{O}(N^2)$ compute and memor… arXiv Tue, 27 Oct 2026 16:45:29 +0000 axiomi.si:arxiv-2610.37524v1 https://arxiv.org/abs/2610.37517v1 [arXiv] Activation steering offers an effective and efficient way to modify an LLM's behavior at inference time. However, it remains unclear how to apply steering such that the signal is consistently strong and the steering strength has a universally interpretable meaning. We address thi… arXiv Tue, 27 Oct 2026 16:39:53 +0000 axiomi.si:arxiv-2610.37517v1 https://arxiv.org/abs/2610.37484v1 [arXiv] Large language models (LLMs) are increasingly used to make and execute decisions that affect living systems. Yet, to our knowledge, ecological reasoning is not a focus of pre- or post training. This increases the risk that under operational pressure, they may make decisions to ir… arXiv Tue, 27 Oct 2026 16:11:04 +0000 axiomi.si:arxiv-2610.37484v1 https://arxiv.org/abs/2610.37465v1 [arXiv] Understanding how language models were pretrained requires information about their data, processing procedures, and computational resources. However, it remains unclear whether successive releases preserve or expand earlier disclosure. We conduct a document based audit of 41 mode… arXiv Tue, 27 Oct 2026 16:00:49 +0000 axiomi.si:arxiv-2610.37465v1 https://arxiv.org/abs/2610.37451v1 [arXiv] Deep Reinforcement Learning remains difficult to interpret and align due to the black box nature of neural networks. While (neuro-)symbolic approaches improve transparency by incorporating symbolic reasoning, applying them directly to low level actions results in overly complex, … arXiv Tue, 27 Oct 2026 15:55:05 +0000 axiomi.si:arxiv-2610.37451v1 https://arxiv.org/abs/2610.37430v1 [arXiv] For reliable deployment, multimodal large language models (MLLMs) should recognize when an image provides insufficient evidence to answer a question and withhold unsupported answers. Yet refusal alone is not necessarily evidence of such recognition: models may refuse because of u… arXiv Tue, 27 Oct 2026 15:42:20 +0000 axiomi.si:arxiv-2610.37430v1 https://arxiv.org/abs/2610.37336v1 [arXiv] Large language models (LLMs) require substantial memory, motivating low bit weight quantization for efficient deployment. Existing vector quantization (VQ) methods often improve accuracy by increasing the effective coding dimension, which can complicate independent lookup and loc… arXiv Tue, 27 Oct 2026 14:43:00 +0000 axiomi.si:arxiv-2610.37336v1 https://arxiv.org/abs/2610.37319v1 [arXiv] Analog device sizing must satisfy all specifications across process, voltage, and temperature (PVT) corners to ensure tape out success, but simulating every candidate at all corners dominates the sizing time and slows design iteration. Conventional surrogate based optimizers sear… arXiv Tue, 27 Oct 2026 14:33:02 +0000 axiomi.si:arxiv-2610.37319v1 https://arxiv.org/abs/2610.37276v1 [arXiv] Large language model (LLM)-driven agent scaffold evolution improves agent workflows through iterative candidate generation and evaluation. Evaluation both guides subsequent search and determines the final scaffold, but evaluating every candidate on the full task set incurs substa… arXiv Tue, 27 Oct 2026 14:06:26 +0000 axiomi.si:arxiv-2610.37276v1 https://arxiv.org/abs/2610.37266v1 [arXiv] Proprietary language models and their reasoning traces are vulnerable to API based distillation, where an adversary trains a student model on the teacher model's outputs. To obtain evidence of distillation, model providers can watermark the teacher's outputs, aiming to leave a ra… arXiv Tue, 27 Oct 2026 13:59:34 +0000 axiomi.si:arxiv-2610.37266v1 https://arxiv.org/abs/2610.37245v1 [arXiv] Safety alignment research for large language models (LLMs) remains concentrated on high resource languages, leaving open how existing alignment methods perform in low resource, culturally distinct settings. We study this question for Hausa, a language spoken by more than 200 mill… arXiv Tue, 27 Oct 2026 13:46:44 +0000 axiomi.si:arxiv-2610.37245v1 https://huggingface.co/papers/2610.37381 [HF Daily Papers] 139 upvotes HF Daily Papers Mon, 26 Oct 2026 20:00:00 +0000 axiomi.si:hfp-2610.37381 https://huggingface.co/papers/2610.36729 [HF Daily Papers] 105 upvotes HF Daily Papers Mon, 26 Oct 2026 20:00:00 +0000 axiomi.si:hfp-2610.36729 https://huggingface.co/papers/2610.37662 [HF Daily Papers] 89 upvotes HF Daily Papers Mon, 26 Oct 2026 20:00:00 +0000 axiomi.si:hfp-2610.37662 https://huggingface.co/papers/2610.37666 [HF Daily Papers] 48 upvotes HF Daily Papers Mon, 26 Oct 2026 20:00:00 +0000 axiomi.si:hfp-2610.37666 https://huggingface.co/papers/2610.36456 [HF Daily Papers] 30 upvotes HF Daily Papers Mon, 26 Oct 2026 20:00:00 +0000 axiomi.si:hfp-2610.36456 https://cohere.com/blog/concerning-behaviour-popular-open-weight-ai-model [Hacker News] 329 points · 130 comments Hacker News Mon, 26 Oct 2026 16:35:44 +0000 axiomi.si:hn-50246658 https://www.theregister.com/systems/2026/10/22/ibm-ceo-says-open-weight-models-are-now-irresistibly-good-and-cheap-on-prem/5304640 [Hacker News] 425 points · 337 comments Hacker News Mon, 26 Oct 2026 01:47:46 +0000 axiomi.si:hn-50238088 https://minimaxir.com/2026/10/cheap-llms/ [Hacker News] 441 points · 436 comments Hacker News Sun, 25 Oct 2026 21:11:31 +0000 axiomi.si:hn-50236102 https://huggingface.co/papers/2610.35013 [HF Daily Papers] 373 upvotes HF Daily Papers Sun, 25 Oct 2026 20:00:00 +0000 axiomi.si:hfp-2610.35013 https://huggingface.co/papers/2610.36077 [HF Daily Papers] 312 upvotes HF Daily Papers Sun, 25 Oct 2026 20:00:00 +0000 axiomi.si:hfp-2610.36077 https://huggingface.co/papers/2610.36068 [HF Daily Papers] 51 upvotes HF Daily Papers Sun, 25 Oct 2026 20:00:00 +0000 axiomi.si:hfp-2610.36068 https://lynchlab.net/llm-skin-cancer [Hacker News] 669 points · 274 comments Hacker News Sun, 25 Oct 2026 17:15:38 +0000 axiomi.si:hn-50233779