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https://preview.redd.it/3p60zyf8afch1.png?width=1840&format=png&auto=webp&s=10dcc90945f0db03352239579fca2132d0c90dfa https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase tl;dr pi-coding-agent (bash for everything/minimum tools) is up to 2x cheaper a
Here's what I'm thinking about: A USB thumb drive that you can plug into any PC or laptop, and immediately get a usable knowledge base powered by an LLM, without requiring an Internet connection. I believe the technology for this should be ready. Rough architecture: llama.cpp binaries for CPU-only
This PR is developed by AINekko and by members of AIFoundry (AINekko's OSS community) and adds the ET backend that supports the ET-SOC-1 processor. ET-SOC-1 was originally created by Esperanto Technologies which AINekko later open sourced under Apache 2.0 and would like to upstream the llama.c
I've been using Task Warrior (https://taskwarrior.org) - no affiliation, which works pretty well but feel like there could be better ways of keeping things organised within a project despite hundreds of different cli and app chats and sessions. I tried obsidian, it's very context heavy and is good
Author here, sharing something I built. If you run agents on a local model, feeding a whole page as raw HTML burns your context fast. barebrowse turns a URL into a pruned ARIA snapshot — the semantic tree with nav/ads/boilerplate stripped — so each page is a fraction of the tokens. Useful when the c
Welp, the age of cheap tokens coming to an end. Many here use this service to access GLM 5.2. Looks like only sustainable way moving forward to local LLM. submitted by /u/BoogerheadCult [link] [comments]
TL;DR: Quantization has a marked impact on agentic performance but little effect on knowledge. I manage a small HPC cluster at a university, and we have recently begun running common benchmarks to help our users understand the effects of quantization. We have just completed the runs on the Qwe
No real details or timescales yet, but this article has confirmation from Alexandr Wang that Meta are working on an open source variant of Muse Spark. One to keep an eye on. https://www.cnbc.com/2026/07/09/meta-jumps-into-ai-coding-market-to-chase-anthropic-and-openai.html submitted by /u/rmhu
Wow... https://github.com/kacper-daftcode/vLLM-Moet Using this customized vllm provided as a docker, I'm able to run DS V4 Flash on a single RTX 6000 Pro (apparently it also works on a single 5090 - check his readme, but I haven't tried). Apparently this also works with GLM 5.2 (though you need at l
Hello, Locallama! "long-time" member here, from the days when the max context window doubled from 2K to a whooping 4K! Now I feel like I am living in a dream world, doing things I could not have imagined back then. This is Belochka, or Bella, running on a Mac and projected to an ESP 32 w-10 b
I have to admit, a lot of people we're 100% correct to make the suggestion to try this model. I am sorry I ever doubted. The 27B passed every agentic task on a neutral system prompt in 6-9 tool calls. The 75B needed a hand-tuned profile to pass at all and used 2x the turns. For agents, fewer
I've been running some systematic tests on a few models comparing FP16 vs various GGUF quant levels, and instead of looking at one aggregate benchmark score, I broke it down by capability: math (GSM8K), code (HumanEval), reasoning (ARC-Challenge), and knowledge recall (MMLU-Pro). The results are way
When I recently built my inference server, I expected to deploy DeepSeek v4 flash, but that doesn't look like it's going to be fast for a long time, if ever. There is a massive gap, as we all know, in competent models between 30b and 400b. I was very surprised to find that this is the best mod
TLDR: 75B-total / 9B-active MoE is the perfect shape for multi-24GB rigs, and almost nobody ships it. Qwen 27B is a great model and punches way above its weight-class, it is a frequent fallback for me. Nemotron-3-Puzzle-75B-A9B, NVFP4, vLLM 0.22.1 (the new Marlin fallbacks run FP4 on Ampere),
Maintainer here. OpenMed is an Apache-2.0 toolkit for clinical NLP with one hard rule: patient data never leaves your hardware. No cloud calls, no API keys, works in airplane mode. What shipped in 1.8 this week: OpenMedKit for Android (Kotlin, ONNX Runtime Mobile + ML Kit OCR): read a document, str
https://preview.redd.it/ah7aif6od7ch1.png?width=3813&format=png&auto=webp&s=32bdca0eb64d36e1a1097473ed0689b2947610bb I made this simple 3D Geometry Wars-style game using my coding agent, Jarvis Code, with GLM 5.2. You can play it here: https://jarvis-llm-codec.github.io/jarvis-code/geometry-wa
submitted by /u/beneath_steel_sky [link] [comments]
I don't know where this is headed, but I don't like it. https://futurism.com/artificial-intelligence/open-source-ai-model-scary-mythos GLM-5.2 can be downloaded by anybody, can be run on virtually any hardware, and unlike Mythos or Fable, there’s no vendor playing the middle man between the AI mode
Hey Everyone, here is an update on MTPLX! One month after releasing MTPLX V1 which brought a swift based app and upgraded CLI for coding use I am happy to announce MTPLX V2. The biggest change is Turbo Mode: using custom verify-specialized quantized-matmul kernels plus a compiled verify step w
Open source AI is a shared asset for all humanity. Chinese open source models such as DeepSeek and Qwen have significantly lowered the barriers and costs of AI adoption. China is committed to further promoting open source AI for industry, academia and research institutions, encouraging innovat
TL;DR: Full GLM-5.2 (753B MoE) quantized to Int4-Int8Mix + NVFP4 4-bit KV cache, TP=4 across 4× DGX Spark (GB10) at 100K context, run on Terminal-Bench 2.1 with the same agent scaffold (Terminus-2) as the official numbers. Result: 63/89 = 70.8% vs the official full-precision 81.0%. Caveat up front:
So I decided to learn how to fine-tune LLMs. Read a few guides from Unsloth, poked around, then stumbled on Unsloth Studio and wanted to test it out. The dataset I started from a set of relatively unrelated QA pairs — Natural Questions — and stripped the answers. Then I had DeepSeek v4 Pro (th
GLM-5.2-504B-Code-GGUF is an imatrix calibrated quant of the most liked REAP, which is 0xSero/GLM-5.2-REAP-504B-GGUF. I also uploaded the imatrix file and the modified llama-quant.cpp so you can compile llama.cpp and create your own quant. I wonder how it compares to smaller LLMs like Qwen 3.6 27b a
Recently bought into the local LLM hype by buying a 32gb vram gpu and holy shit, gemma 4 31b at 5bits blows the standard ChatGPT model out of the fucking water. I just can't unsee the quality difference now that I've experienced it. Does Openai just cut costs by serving their customers this slop sin
Hey all, I've been working with DSV4 on my Frankenstein rig (5-GPU box: 2x3090 + 5060 Ti + 2x4060 Ti, 96 GiB VRAM, 125 GiB DDR4) doing agentic coding with pi and ran into some serious issues. After much work I submitted an issue and have a fork with fixes. I would love for maintainers and DSV4 users
My goal is to improve as a developer, thus I needed to know if local llms can answer technical questions accurately The conclusion is that without rag they don't do too well, but with rag they are very good. Thinking didn't really help, and took so long I only got the scores for e2b and e4b, t
submitted by /u/paf1138 [link] [comments]
https://www.theinformation.com/briefings/exclusive-chinas-minimax-plans-launch-2-7-trillion-parameter-model According to The Information, MiniMax plans to launch a new-generation large language model with 2.7 trillion parameters. Sources revealed that the internal codename for this new model is M3 P
I noticed while using both, mimo was often better, after benchmarking mimo v2.5 via open code endpoint in diff harness like codex, oh my pi, hermes. i found that mimo is indeed better in coding tasks. and over all, hermes scored 55% with mimo v2.5 via terminal bench v2.0 others did under 50% too wit
Just finished reading the paper: LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load I am starting to benchmark LLMs on edge devices, particularly phones thus been reading a lot on the what has been done and what is currently being done and wa