I Built a 6-Agent AI System in a Weekend
Most “AI agents” are just automations with a language model bolted on. I wanted to build the real thing. Continue reading on Medium »
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Why APEX Matters for MoE Coding Models and why it's NOT the same as K quants
I posted about my APEX quantization of QWEN Coder 80B Next yesterday and got a ton of great questions. Some people loved it, some people were skeptical, and one person asked "what exactly is the point of this when K quants already do mixed precision?" It's a great question. I've been deep in this for the last few days running APEX on my own hardware and I want to break down what I've learned because I think most people are missing the bigger picture here. So yes K quants like Q4_K_M already apply different precision to different layers. Attention gets higher precision, feed-forward gets lower. That's been in llama.cpp for a while and it works. But here's the thing nobody is talking about. MoE models have a coherence problem. I was reading this article last night and it clicked for me. When
Agentic AI Vision System: Object Segmentation with SAM 3 and Qwen
Table of Contents Agentic AI Vision System: Object Segmentation with SAM 3 and Qwen Why Agentic AI Outperforms Traditional Vision Pipelines Why Agentic AI Improves Computer Vision and Segmentation Tasks What We Will Build: An Agentic AI Vision and Segmentation The post Agentic AI Vision System: Object Segmentation with SAM 3 and Qwen appeared first on PyImageSearch .
qwen3.5 vs gemma4 vs cloud llms in python turtle
I have found python turtle to be a pretty good test for a model. All of these models have received the same prompt: "write a python turtle program that draws a cat" you can actually see similarity in gemma's and gemini pro's outputs, they share the color pallete and minimalist approach in terms of details. I have a 16 gb vram gpu so couldn't test bigger versions of qwen and gemma without quantisation. gemma_4_31B_it_UD_IQ3_XXS.gguf Qwen3_5_9B_Q8_0.gguf Qwen_3_5_27B_Opus_Distilled_Q4_K_S.gguf deepseek from web browser with reasoning claude sonnet 4.6 extended gemini pro from web browser with thinking submitted by /u/SirKvil [link] [comments]
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qwen3.5 vs gemma4 vs cloud llms in python turtle
I have found python turtle to be a pretty good test for a model. All of these models have received the same prompt: "write a python turtle program that draws a cat" you can actually see similarity in gemma's and gemini pro's outputs, they share the color pallete and minimalist approach in terms of details. I have a 16 gb vram gpu so couldn't test bigger versions of qwen and gemma without quantisation. gemma_4_31B_it_UD_IQ3_XXS.gguf Qwen3_5_9B_Q8_0.gguf Qwen_3_5_27B_Opus_Distilled_Q4_K_S.gguf deepseek from web browser with reasoning claude sonnet 4.6 extended gemini pro from web browser with thinking submitted by /u/SirKvil [link] [comments]
![[Benchmark] Altered Riddles: Can LLMs ignore what they've memorised?](https://external-preview.redd.it/mX2OY_FVeaecjQBAWXivFgSAkTu5esw_Cjh_pFQevUA.png?width=140&height=75&auto=webp&s=e11af534a579cc92bb2fb8c5eb2dea0fba70f8da)
[Benchmark] Altered Riddles: Can LLMs ignore what they've memorised?
In the past year you may have encountered the following prompt: The surgeon, who is the boy's father, says, 'I cannot operate on this boy—he's my son!'. Who is the surgeon to the boy? If you try to give this prompt to an LLM right now you will probably still receive “The mother” as an answer, even though the text explicitly states that the surgeon is the boy’s father; this is probably due to the fact that this prompt is an alteration of a very common “riddle”, to which the answer is, in fact, the mother: A man and his son are in a terrible accident and are rushed to the hospital in critical condition. The doctor looks at the boy and exclaims, "I can't operate on this boy; he's my son!" How could this be? Working on this failure mode, I initially decided to create a small dataset of altered
A Multi-Language Perspective on the Robustness of LLM Code Generation
arXiv:2504.19108v5 Announce Type: replace Abstract: Large language models have gained significant traction and popularity in recent times, extending their usage to code-generation tasks. While this field has garnered considerable attention, the exploration of testing and evaluating the robustness of code generation models remains an ongoing endeavor. Previous studies have primarily focused on code generation models specifically for the Python language, overlooking other widely used programming languages. In this work, we conduct a comprehensive comparative analysis to assess the robustness performance of several prominent code generation models and investigate whether robustness can be improved by repairing perturbed docstrings using an LLM. Furthermore, we investigate how their performanc
Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models
arXiv:2411.10656v2 Announce Type: replace Abstract: Context: Large Language Models (LLMs) like GPT-5 and LLaMA-405b exhibit advanced code generation abilities, but their deployment demands substantial computation resources and energy. Quantization can reduce memory footprint and hardware requirements, yet may degrade code quality. Objective: This study investigates code generation performance of smaller LLMs, examines the effect of quantization, and identifies common code quality issues as a proof of concepts (PoC). Method: Four open-source LLMs are evaluated on Python benchmarks using code similarity metrics, with an analysis on 8-bit and 4-bit quantization, alongside static code quality assessment. Results: While smaller LLMs can generate functional code, benchmark performance is limited
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