AI & Technology Updates
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NousCoder-14B-GGUF Boosts Coding Accuracy
NousCoder-14B-GGUF demonstrates significant improvements in coding problem-solving accuracy, achieving a Pass@1 accuracy of 67.87% on LiveCodeBench v6, which marks a 7.08% increase from the baseline accuracy of Qwen3-14B. This advancement was accomplished by training on 24,000 verifiable coding problems using 48 B200s over four days. Such enhancements in AI coding proficiency can lead to more efficient and reliable automated coding solutions, benefiting developers and software industries. This matters because it showcases the potential for AI to significantly improve coding accuracy and efficiency, impacting software development processes positively.
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Orchestrating LLMs Locally with n8n and SSH
Using n8n to orchestrate DeepSeek/Llama3 agents via SSH offers a cost-effective alternative to OpenAI nodes for tasks requiring heavy context. By utilizing the n8n SSH Node to connect to a local Ollama instance, it avoids the REST API and leverages an interactive CLI for stateful sessions using a Session ID. This setup allows for persistent context and error handling within the same SSH session, enabling efficient orchestration of local LLMs without complex frameworks. This matters because it provides a more affordable and streamlined approach to managing local machine learning models for repetitive tasks.
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Generating Indian Names with Neural Networks
An experiment was conducted to generate Indian names using a Vanilla Neural Network implemented in Rust. The dataset consisted of approximately 500 Indian names, which were preprocessed into 5-gram vector representations. With 758,000 parameters and a training time of around 15 minutes, the model quickly learned the patterns of Indian names and produced plausible outputs such as Yaman, Samanya, and Narayani. This matters because it demonstrates the potential of neural networks to learn and replicate complex linguistic patterns efficiently.
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Rising GPU, SSD, and RAM Prices: What to Expect
Prices for GPUs, SSDs, and RAM are expected to rise significantly soon, with AMD and NVIDIA planning monthly price increases. NAND flash contract prices have already increased by 20% in November, with further hikes expected, leading to more expensive SSDs. DRAM prices are also set to skyrocket due to limited production capacity and high demand from datacenters and OEMs, with conventional DRAM and server DRAM prices projected to rise by over 55% and 60% respectively in early 2026. These price hikes will impact NVIDIA’s RTX 50 series and AMD’s Radeon RX 9000 lineup, with NVIDIA’s GeForce RTX 5090 potentially reaching $5,000. This matters because it will affect consumers and industries relying on these components, potentially leading to higher costs and delays in technology access.
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Qwen3-30B-VL’s Care Bears Insight
The Qwen3-30B-VL model, when tested, surprisingly demonstrated knowledge about Care Bears, despite expectations to the contrary. This AI model, run on LM Studio, was given an image to analyze, and its ability to recognize and provide information about the Care Bears was notable. The performance of Qwen3-30B-VL highlights the advancements in AI's capability to understand and process visual inputs with contextually relevant knowledge. This matters because it showcases the potential for AI to enhance applications in fields requiring visual recognition and context understanding.