Lightweight agent to use on laptop?
Hi there. I have a Lenovo Yoga laptop (16GB LPDDR5, AMD Ryzen 7 8840HS, integrated GPU). I have downloaded Ollama and installed Qwen 2.5coder:3b llm. I have tried OpenClaw, and Qwen Code but it seems
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Hi there. I have a Lenovo Yoga laptop (16GB LPDDR5, AMD Ryzen 7 8840HS, integrated GPU). I have downloaded Ollama and installed Qwen 2.5coder:3b llm. I have tried OpenClaw, and Qwen Code but it seems
Release: llm 0.33 My highlights from this release: Upgraded to the OpenAI Python library 3.x and switched the HTTP client dependency from httpx to httpx2. #1608, #1631 I shipped a quick 0.32.1 fix for
Anna Heim / TechCrunch: London-based Inherent, founded by DeepMind alumni and with $50M in seed funding, says its new Faraday agent beats GPT-5.5 at reproducing research paper findings — Inherent, a L
As I’m navigating the best setup for using Qwen3.8 27b as both my main coder and my personal assistant, I want to hook it into Wiki & beyond but I don’t want to be reliant on an internet connection. W
Using llama.cpp I seem to be unable to get my to GPUs working tougether correclty, so I need help somehow. Setup: 96GB RAM, one Blackwell 5000 (48GB) and one 3090 (24GB). I am trying to run the UD-Q3_
New/Old benchmark that provides a lot of answers for local LLM. I present to you a new test that I developed somewhat by accident: https://huggingface.co/cHunter789/Qwen3.8-27B-i1-IQ4_KS_KT-GGUF/tree/
I've been trying to figure out what the best configuration for my local stack would be and have been primarily determining this through trial and error. I wanted to get some outside opinions and stack
One of the interesting properties we’ve observed around schema-guided, complex document extraction tasks 📑 is that coding agent harnesses are good baselines (in terms of cost/accuracy) for longer docu
Hi, I have been using opencode with openrouter for quite a while now. Having read the success stories of using Qwen3.8-27B, I thought of trying it too. But every attempt has been a failure for me so f
Own your harness. I really wish the Claude Code harness was open source. I often think about this, especially now that custom harnesses are foundational to AI-native companies. I am now building mostl
I was finally able to replicate tensor level allocation outside the Gemma family. https://huggingface.co/ByteOtter/Qwen3.5-4B-CADA-IQ2_XS After the Gemma 4 12b, e4b and gemma 3 4b results, I attempted
That is the second comparison and the last one. I will not be spamming again ;) Continuation from: https://www.reddit.com/r/LocalLLaMA/comments/1vu0u2v/qwen_38_27b_pi_agent_vs_opencode/ That is one of
Row-Bot v4.8.0 is now available. This release adds provider-aware reasoning controls, letting each chat keep a valid reasoning choice for the exact model in use. Depending on model support, you can se
Sharp v22.1 is u/peculiar-ragdoll's system prompt that makes Qwen answer way more tersely without losing correctness. NInfer is a hyper-tailored inference engine that only runs certain Qwen models on
This is the Qwen3.8-27B setup I actually use every day on one RTX 5090. I wanted to write it down with enough detail that another 5090 owner can reproduce it instead of guessing which memory knobs I u
Robbie Whelan / Wall Street Journal: Sources: Nvidia plans to use its $6B deal with Poolside to build an open-weight AI model to compete with Chinese models like DeepSeek and Kimi — A sweeping agreeme
(To make this work you need to run the ChatGPT or Claude apps on your computer & turn on browser control, which means you need to trust the models to do that. Definitely check the work carefully first
What's Changed mlx: fix mac assumptions on linux/windows by @dhiltgen in #17898 mlx update by @dhiltgen in #17886 lint fixes by @dhiltgen in #17897 app: add claude desktop app by @ParthSareen in #1789
Released today: version 1.3 of energygraph Zero build dependencies, lightweight tool for live views of the power-consumption. Version 1.3 adds support for dGPUs from nvidia, intel, amd. Depending on v
What a fascinating paper on AI agents. A lot of the issues we see with AI agents today revolve around wrong assumptions the LLMs make. This leads to problems like hallucination, cost inefficiencies, u
Where did THESE things go for ChatGPT? Claude has them. I can access older versions of my conversations easily. But with ChatGPT, they just DISAPPEARED. I can no longer regenerate answers or following
arXiv:2608.19908v1 Announce Type: cross Abstract: Probability estimation over large alphabets under log loss is a well-studied problem, with celebrated methods such as the Good-Turing estimator. We in
arXiv:2608.19879v1 Announce Type: cross Abstract: This document describes the application of a first order linearised nonlinear repeated measurements approach to the analysis of battery cell ageing pr
arXiv:2608.20181v1 Announce Type: cross Abstract: Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends strongly on
arXiv:2608.19932v1 Announce Type: new Abstract: Public cardiac cohorts annotate different subsets of the heart, so shapes from separate sources cannot be pooled without shared correspondence. Among re
arXiv:2608.19487v1 Announce Type: cross Abstract: Python is widely used in scientific research because it enables rapid development and provides rich ecosystems for data analysis, artificial intellige
arXiv:2608.19202v1 Announce Type: new Abstract: Interactive AI agents must acquire the right context as efficiently as possible. When a user omits a constraint, preference, file, or task variable, an
arXiv:2608.20318v1 Announce Type: new Abstract: Recursive self-improvement (RSI) asks whether an AI system can improve the process that produces AI systems, so that the next system inherits the improv
arXiv:2608.19299v1 Announce Type: new Abstract: Air traffic control (ATC) communication is a safety-critical dialogue that remains largely human-driven even as other parts of air traffic management ha
arXiv:2608.20038v1 Announce Type: cross Abstract: Artificial intelligence can support cultural heritage and digital humanities through large-scale retrieval and analysis of digitized collections. Howe
arXiv:2608.19807v1 Announce Type: new Abstract: Vision-language models (VLMs) can estimate physical quantities such as duration, speed, and acceleration from visual observations, but existing benchmar
Claude: Anthropic says Mythos 5 is now in public beta in Claude Security for Enterprise users, and it is working with providers to embed Mythos 5 in defensive tools — No items found. — Reading time —
arXiv:2608.20122v1 Announce Type: new Abstract: Large multimodal models (LMMs) have demonstrated strong OCR recognition capabilities, yet remain vulnerable to adversarial visual text that is readable
OpenAI announced on August 21, 2026 that it will reduce API and credit pricing for GPT‑5.6 Sol by more than 20% for the next three months. The price cut is part of the company’s broader effort to push
arXiv:2608.20172v1 Announce Type: new Abstract: Attention directly derives normalized information flow from pairwise scores. We introduce Relation, an alternative token-mixing primitive that first org
arXiv:2608.19201v1 Announce Type: cross Abstract: Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are of
Revert 'tensor-split meta backend fixes (#26502)' (#27433) This reverts commit d59d455. Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/42012577 macOS/iOS:
metal : dequantize quantized KV to F16 before flash attention (#27390) metal: dequantize q8_0 KV to f16 before flash attention Add a preprocessing pass for GGML_OP_FLASH_ATTN_EXT on the Metal backend:
common : gracefully fallback on unsupported regex patterns in JSON schema (#26939) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/42018860 macOS/iOS: macOS
CUDA: adding switch points per HW and quant type to tune the mvq->MMQ decode crossover (#26079) CUDA: runtime GGML_CUDA_MMVQ_MAX to tune the mvq->MMQ decode crossover Add a runtime override of the mul
server: (router) lazy-load startup_models after main setup (#27424) server: (router) lazy-load startup_models after main setup only allow is_first_load to populate it nits nits 2 Website: https://llam
CI: Use LLVM's OpenMP over MSVC_DEBUG_non_redist on Windows (#26678) CI: Use LLVM's OpenMP over MSFT_DEBUG_non_redist on Windows Currently, we ship the non-redist debug version of microsoft's libomp.
metal : dequant kv cache only for large batches (#27438) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/42031082 macOS/iOS: macOS Apple Silicon (arm64) mac
vulkan: FA MMQ should use fp32 for Q quantization calculations (#27413) Codex found that qd could be a denorm and 1/qd would overflow. Website: https://llama.app Attestations: https://github.com/ggml-
mtmd: add --mmproj-device argument (#23255) feat: add --mmproj-device arg & backwards compatible MTMD_BACKEND_DEVICE env var feat: load mmproj device backend immediately, add -mmdev shortflag fix: its
metal : clamp K extent in tensor API mat-mat kernel for K not a multiple of 32 (#27450) The Tensor API mat-mat path of kernel_mul_mm (GGML_METAL_HAS_TENSOR) fed a static K=32 tile to the matmul2d op o
TP: enable tensor split for LFM2/LFM2MOE (#26993) Assisted-by: deepseek-v4-flash Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/42096995 macOS/iOS: macOS A
llama.cpp : bump version to 0.2.0 (#27498) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/42207505 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silic
ci : run ccache-clear as the last step of release jobs (#27503) ci : run ccache-clear as the last step of release jobs Assisted-by: pi:llama.cpp/Qwen3.8-27B update disabled job too to force rebase Co-
model: use ggml_rope_set_offset() (#27382) model: use ggml_rope_set_offset() partially apply to deepseek2 Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/42
Banger paper from Google DeepMind. It's on the very hot topic of model routing. Routers assume the value estimate is free. Working out which specialist handles a query best costs money too, and it is
arXiv:2608.19385v1 Announce Type: new Abstract: Historical Arabic manuscript transcription is not only a recognition problem. A usable scholarly system must cope with shifting hands and layouts, prese
arXiv:2608.20107v1 Announce Type: new Abstract: Recent advances in generative video models have significantly improved visual realism in video object removal, yet evaluation protocols still focus on m
Brace yourselves. We just entered a new era of frontier multimodal models. DeepSeek-V4-Flash-Vision-Exp advances multimodal agent performance. Also, pay attention to Ox Alpha 1M token contexts with te
I recently bought a 32GB V100 off eBay and thought I was all set with rudimentary tests showing zero issues. But then I started seeing weird VRAM-related errors in llama.cpp. I sic'ed Claude on it, to
arXiv:2608.19677v1 Announce Type: cross Abstract: Prefix caching avoids prefill only when a repeated request returns to a server that still holds the prefix KV. Cache-blind balancing disperses that re
arXiv:2606.23487v2 Announce Type: replace Abstract: Medical vision-language models (VLMs) such as BiomedCLIP generalize broadly, but adapting them to a clinical service is as much a safety problem as
arXiv:2608.20305v1 Announce Type: new Abstract: Myocardial scar segmentation from single-stack late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging has been a longstanding and clinica
arXiv:2608.19652v1 Announce Type: new Abstract: As LLM-based agents are deployed for longer and higher-stakes tasks, their memory systems continue to have crucial gaps. While existing memory benchmark
arXiv:2512.07847v2 Announce Type: replace Abstract: Benchmarking has been the cornerstone of progress in computer vision, natural language processing, and the broader deep learning domain, driving alg