Search: Scratch - ai.jp.net

research #deep learning 📝 BlogAnalyzed: Jan 18, 2026 14:46

SmallPebble: Revolutionizing Deep Learning with a Minimalist Approach

Published:Jan 18, 2026 14:44

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1 min read

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r/MachineLearning

Analysis

SmallPebble offers a refreshing take on deep learning, providing a from-scratch library built entirely in NumPy! This minimalist approach allows for a deeper understanding of the underlying principles and potentially unlocks exciting new possibilities for customization and optimization.

Key Takeaways

•SmallPebble is a deep learning library built from scratch.
•It is implemented entirely using NumPy.
•This minimalist design promotes a deeper understanding of deep learning concepts.

Reference

“This article highlights the development of SmallPebble, a minimalist deep learning library written from scratch in NumPy.”

Permalink r/MachineLearning

research #ai 📝 BlogAnalyzed: Jan 18, 2026 10:30

Crafting AI Brilliance: Python Powers a Tic-Tac-Toe Master!

Published:Jan 18, 2026 10:17

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1 min read

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Qiita AI

Analysis

This article details a fascinating journey into building a Tic-Tac-Toe AI from scratch using Python! The use of bitwise operations for calculating legal moves is a clever and efficient approach, showcasing the power of computational thinking in game development.

Key Takeaways

•The project utilizes Python 3.13 and NumPy 2.3.5, demonstrating modern software development practices.
•The focus on bitwise operations highlights a computationally efficient method for game logic.
•This initiative provides a great educational resource for learning about AI development and game programming.

Reference

“The article's program is running on Python version 3.13 and numpy version 2.3.5.”

Permalink Qiita AI

infrastructure #os 📝 BlogAnalyzed: Jan 18, 2026 04:17

Vib-OS 2.0: A Ground-Up OS for ARM64 with a Modern GUI!

Published:Jan 18, 2026 00:36

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1 min read

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r/ClaudeAI

Analysis

Get ready to be amazed! Vib-OS, a from-scratch Unix-like OS, has released version 2.0, packed with impressive new features. This passion project, built entirely in C and assembly, showcases incredible dedication to low-level systems and offers a glimpse into the future of operating systems.

Key Takeaways

•Vib-OS 2.0 boasts a full graphical desktop environment, including a window manager and dock.
•The OS supports a variety of hardware, running on QEMU, Apple Silicon (UTM), and Raspberry Pi 4/5.
•Remarkably, Vib-OS can now natively run the classic game, Doom!

Reference

“I just really enjoy low-level systems work and wanted to see how far I could push a clean ARM64 OS with a modern GUI vibe.”

Permalink r/ClaudeAI

research #llm 📝 BlogAnalyzed: Jan 16, 2026 01:15

Building LLMs from Scratch: A Deep Dive into Modern Transformer Architectures!

Published:Jan 16, 2026 01:00

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1 min read

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Zenn DL

Analysis

Get ready to dive into the exciting world of building your own Large Language Models! This article unveils the secrets of modern Transformer architectures, focusing on techniques used in cutting-edge models like Llama 3 and Mistral. Learn how to implement key components like RMSNorm, RoPE, and SwiGLU for enhanced performance!

Key Takeaways

•The article is the second in a series on building LLMs from scratch, providing a hands-on approach.
•It focuses on modern Transformer architectures like those in Llama 3 and Mistral.
•Key components like RMSNorm, RoPE, and SwiGLU are covered for practical implementation.

Reference

“This article dives into the implementation of modern Transformer architectures, going beyond the original Transformer (2017) to explore techniques used in state-of-the-art models.”

Permalink Zenn DL

research #llm 📝 BlogAnalyzed: Jan 16, 2026 01:15

AI Alchemy: Merging Models for Supercharged Intelligence!

Published:Jan 15, 2026 14:04

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1 min read

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Zenn LLM

Analysis

Model merging is a hot topic, showing the exciting potential to combine the strengths of different AI models! This innovative approach suggests a revolutionary shift, creating powerful new AI by blending existing knowledge instead of starting from scratch.

Key Takeaways

•Model merging offers a novel approach to building advanced AI.
•It allows for combining strengths of different existing models.
•The process has intriguing mathematical and geometrical underpinnings.

Reference

“The article explores how combining separately trained models can create a 'super model' that leverages the best of each individual model.”

Permalink Zenn LLM

product #llm 📝 BlogAnalyzed: Jan 15, 2026 07:30

Persistent Memory for Claude Code: A Step Towards More Efficient LLM-Powered Development

Published:Jan 15, 2026 04:10

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1 min read

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Zenn LLM

Analysis

The cc-memory system addresses a key limitation of LLM-powered coding assistants: the lack of persistent memory. By mimicking human memory structures, it promises to significantly reduce the 'forgetting cost' associated with repetitive tasks and project-specific knowledge. This innovation has the potential to boost developer productivity by streamlining workflows and reducing the need for constant context re-establishment.

Key Takeaways

•cc-memory is designed to provide persistent memory for the Claude Code LLM.
•It utilizes a three-layer memory structure (Working, Episodic, Semantic), inspired by human memory models.
•The system aims to reduce the inefficiencies caused by Claude Code's session-based limitations.

Reference

“Yesterday's solved errors need to be researched again from scratch.”

Permalink Zenn LLM

research #llm 📝 BlogAnalyzed: Jan 14, 2026 07:30

Building LLMs from Scratch: A Deep Dive into Tokenization and Data Pipelines

Published:Jan 14, 2026 01:00

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1 min read

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Zenn LLM

Analysis

This article series targets a crucial aspect of LLM development, moving beyond pre-built models to understand underlying mechanisms. Focusing on tokenization and data pipelines in the first volume is a smart choice, as these are fundamental to model performance and understanding. The author's stated intention to use PyTorch raw code suggests a deep dive into practical implementation.

Key Takeaways

•The article series aims to build an LLM from scratch using PyTorch.
•Vol. 1 focuses on tokenization and data pipelines, core components of LLMs.
•The series emphasizes understanding the 'why' and 'how' of LLM functionality.

Reference

“The series will build LLMs from scratch, moving beyond the black box of existing trainers and AutoModels.”

Permalink Zenn LLM

research #llm 📝 BlogAnalyzed: Jan 13, 2026 19:30

Deep Dive into LLMs: A Programmer's Guide from NumPy to Cutting-Edge Architectures

Published:Jan 13, 2026 12:53

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1 min read

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Zenn LLM

Analysis

This guide provides a valuable resource for programmers seeking a hands-on understanding of LLM implementation. By focusing on practical code examples and Jupyter notebooks, it bridges the gap between high-level usage and the underlying technical details, empowering developers to customize and optimize LLMs effectively. The inclusion of topics like quantization and multi-modal integration showcases a forward-thinking approach to LLM development.

Key Takeaways

•Focuses on practical code implementation with Python and NumPy for LLMs.
•Covers a wide range of advanced LLM topics, including quantization, multi-modal integration, and optimization.
•Provides hands-on learning through Jupyter Notebooks with detailed annotations.

Reference

“This series dissects the inner workings of LLMs, from full scratch implementations with Python and NumPy, to cutting-edge techniques used in Qwen-32B class models.”

Permalink Zenn LLM

research #numerical differentiation 📝 BlogAnalyzed: Jan 10, 2026 04:42

Deep Learning Diary Vol. 4: Numerical Differentiation - A Practical Guide

Published:Jan 8, 2026 14:43

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1 min read

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Qiita DL

Analysis

This article seems to be a personal learning log focused on numerical differentiation in deep learning. While valuable for beginners, its impact is limited by its scope and personal nature. The reliance on a single textbook and Gemini for content creation raises questions about the depth and originality of the material.

Key Takeaways

•The article is a personal log about learning numerical differentiation.
•It references the book 'Deep Learning from Scratch' by Yasunori Saito.
•The content is generated with assistance from Gemini.

Reference

“Geminiとのやり取りを元に、構成されています。”

Permalink Qiita DL

research #loss 📝 BlogAnalyzed: Jan 10, 2026 04:42

Exploring Loss Functions in Deep Learning: A Practical Guide

Published:Jan 8, 2026 07:58

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1 min read

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Qiita DL

Analysis

This article, based on a dialogue with Gemini, appears to be a beginner's guide to loss functions in neural networks, likely using Python and the 'Deep Learning from Scratch' book as a reference. Its value lies in its potential to demystify core deep learning concepts for newcomers, but its impact on advanced research or industry is limited due to its introductory nature. The reliance on a single source and Gemini's output also necessitates critical evaluation of the content's accuracy and completeness.

Key Takeaways

•Focuses on the learning functionality of neural networks.
•Uses 'Deep Learning from Scratch' book as a reference.
•Development environment is VScode with Python extension.

Reference

“ニューラルネットの学習機能に話が移ります。”

Permalink Qiita DL

research #transfer learning 🔬 ResearchAnalyzed: Jan 6, 2026 07:22

AI-Powered Pediatric Pneumonia Detection Achieves Near-Perfect Accuracy

Published:Jan 6, 2026 05:00

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1 min read

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ArXiv Vision

Analysis

The study demonstrates the significant potential of transfer learning for medical image analysis, achieving impressive accuracy in pediatric pneumonia detection. However, the single-center dataset and lack of external validation limit the generalizability of the findings. Further research should focus on multi-center validation and addressing potential biases in the dataset.

Key Takeaways

Reference

“Transfer learning with fine-tuning substantially outperforms CNNs trained from scratch for pediatric pneumonia detection, showing near-perfect accuracy.”

Permalink ArXiv Vision

product #music generation 📝 BlogAnalyzed: Jan 5, 2026 08:40

AI-Assisted Rap Production: A Case Study in MIDI Integration

Published:Jan 5, 2026 02:27

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1 min read

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Zenn AI

Analysis

This article presents a practical application of AI in creative content generation, specifically rap music. It highlights the potential for AI to overcome creative blocks and accelerate the production process. The success hinges on the effective integration of AI-generated lyrics with MIDI-based musical arrangements.

Key Takeaways

•AI can assist in generating rap lyrics to overcome writer's block.
•MIDI integration allows for AI-generated lyrics to be easily combined with musical arrangements.
•The article presents a personal experience of using AI to streamline rap production.

Reference

“「It's fun to write and record rap, but honestly, it's hard to come up with punchlines from scratch every time.」”

Permalink Zenn AI

AI Development #LLM Deployment and Evaluation 📝 BlogAnalyzed: Jan 3, 2026 06:31

Building LLMs from Scratch – Evaluation & Deployment (Part 4 Finale)

Published:Jan 3, 2026 03:10

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1 min read

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r/LocalLLaMA

Analysis

This article provides a practical guide to evaluating, testing, and deploying Language Models (LLMs) built from scratch. It emphasizes the importance of these steps after training, highlighting the need for reliability, consistency, and reproducibility. The article covers evaluation frameworks, testing patterns, and deployment paths, including local inference, Hugging Face publishing, and CI checks. It offers valuable resources like a blog post, GitHub repo, and Hugging Face profile. The focus on making the 'last mile' of LLM development 'boring' (in a good way) suggests a focus on practical, repeatable processes.

Key Takeaways

•Evaluation and testing are crucial steps after LLM training.
•The article provides practical frameworks and patterns for evaluation.
•Deployment options include local inference and Hugging Face publishing.
•Repeatable publishing workflows are emphasized for reliability and reproducibility.

Reference

“The article focuses on making the last mile boring (in the best way).”

Permalink r/LocalLLaMA

Education #NLP 📝 BlogAnalyzed: Jan 3, 2026 02:10

Deep Learning from Scratch 2: Natural Language Processing - Chapter 1 Summary

Published:Jan 2, 2026 15:52

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1 min read

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Qiita AI

Analysis

This article summarizes Chapter 1 of the book 'Deep Learning from Scratch 2: Natural Language Processing'. It aims to help readers understand the chapter's content and key vocabulary, particularly those struggling with the book.

Key Takeaways

•Provides a summary of Chapter 1 of 'Deep Learning from Scratch 2: Natural Language Processing'.
•Explains the content and key vocabulary of the chapter.
•Aims to assist readers who are struggling with the book.

Reference

“This article summarizes Chapter 1 of the book 'Deep Learning from Scratch 2: Natural Language Processing'.”

Permalink Qiita AI

Technology #AI Development 📝 BlogAnalyzed: Jan 3, 2026 07:04

Free Retirement Planner Created with Claude Opus 4.5

Published:Jan 1, 2026 19:28

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1 min read

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r/ClaudeAI

Analysis

The article describes the creation of a free retirement planning web app using Claude Opus 4.5. The author highlights the ease of use and aesthetic appeal of the app, while also acknowledging its limitations and the project's side-project nature. The article provides links to the app and its source code, and details the process of using Claude for development, emphasizing its capabilities in planning, coding, debugging, and testing. The author also mentions the use of a prompt document to guide Claude Code.

Key Takeaways

•A free retirement planning web app was created using Claude Opus 4.5.
•The app is designed to be user-friendly and visually appealing.
•The author used a prompt document to guide Claude Code in the development process.
•The author highlights Claude's capabilities in coding, debugging, and testing.
•The project is a side project and comes with no guarantees regarding accuracy or maintenance.

Reference

“The author states, "This is my first time using Claude to write an entire app from scratch, and honestly I'm very impressed with Opus 4.5. It is excellent at planning, coding, debugging, and testing."”

Permalink r/ClaudeAI

Research Paper #Computer Vision, Multimodal Learning, Industrial Defect Detection 🔬 ResearchAnalyzed: Jan 3, 2026 16:46

Large-Scale Multimodal Dataset for Industrial Defect Understanding

Published:Dec 30, 2025 11:45

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1 min read

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ArXiv

Analysis

This paper introduces a significant contribution to the field of industrial defect detection by releasing a large-scale, multimodal dataset (IMDD-1M). The dataset's size, diversity (60+ material categories, 400+ defect types), and alignment of images and text are crucial for advancing multimodal learning in manufacturing. The development of a diffusion-based vision-language foundation model, trained from scratch on this dataset, and its ability to achieve comparable performance with significantly less task-specific data than dedicated models, highlights the potential for efficient and scalable industrial inspection using foundation models. This work addresses a critical need for domain-adaptive and knowledge-grounded manufacturing intelligence.

Key Takeaways

•Introduces IMDD-1M, a large-scale multimodal dataset for industrial defect understanding.
•The dataset contains aligned image-text pairs covering a wide range of materials and defect types.
•A diffusion-based vision-language foundation model is trained on the dataset.
•The model demonstrates data-efficient adaptation to specialized domains, achieving comparable performance with significantly less data than dedicated models.

Reference

“The model achieves comparable performance with less than 5% of the task-specific data required by dedicated expert models.”

Permalink ArXiv

Paper #LLM Training on Cloud Platforms 🔬 ResearchAnalyzed: Jan 3, 2026 17:03

Democratizing LLM Training on AWS SageMaker

Published:Dec 30, 2025 09:14

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1 min read

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ArXiv

Analysis

This paper addresses a significant pain point in the field: the difficulty researchers face in utilizing cloud resources like AWS SageMaker for LLM training. It aims to bridge the gap between local development and cloud deployment, making LLM training more accessible to a wider audience. The focus on practical guidance and addressing knowledge gaps is crucial for democratizing access to LLM research.

Key Takeaways

•Addresses the challenges researchers face when using cloud platforms for LLM training.
•Focuses on providing practical guidance and centralizing essential information.
•Aims to democratize access to LLM training by simplifying the process.

Reference

“This demo paper aims to democratize cloud adoption by centralizing the essential information required for researchers to successfully train their first Hugging Face model on AWS SageMaker from scratch.”

Permalink ArXiv

Research Paper #Federated Learning, Privacy, Unlearning 🔬 ResearchAnalyzed: Jan 3, 2026 16:12

Certifying Data Removal in Federated Learning

Published:Dec 29, 2025 03:25

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1 min read

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ArXiv

Analysis

This paper addresses the critical issue of data privacy and the 'right to be forgotten' in vertical federated learning (VFL). It proposes a novel algorithm, FedORA, to efficiently and effectively remove the influence of specific data points or labels from trained models in a distributed setting. The focus on VFL, where data is distributed across different parties, makes this research particularly relevant and challenging. The use of a primal-dual framework, a new unlearning loss function, and adaptive step sizes are key contributions. The theoretical guarantees and experimental validation further strengthen the paper's impact.

Key Takeaways

•Proposes FedORA, a novel algorithm for sample and label unlearning in Vertical Federated Learning (VFL).
•Employs a primal-dual framework to address the challenges of distributed feature architecture in VFL.
•Introduces a new unlearning loss function that promotes classification uncertainty.
•Provides theoretical guarantees on unlearning effectiveness and demonstrates comparable performance to training from scratch with reduced overhead.

Reference

“FedORA formulates the removal of certain samples or labels as a constrained optimization problem solved using a primal-dual framework.”

Permalink ArXiv

Paper #NLP, Language Modeling, Turkish Language 🔬 ResearchAnalyzed: Jan 3, 2026 16:15

TabiBERT: A Modern BERT for Turkish NLP

Published:Dec 28, 2025 20:18

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1 min read

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ArXiv

Analysis

This paper introduces TabiBERT, a new large language model for Turkish, built on the ModernBERT architecture. It addresses the lack of a modern, from-scratch trained Turkish encoder. The paper's significance lies in its contribution to Turkish NLP by providing a high-performing, efficient, and long-context model. The introduction of TabiBench, a unified benchmarking framework, further enhances the paper's impact by providing a standardized evaluation platform for future research.

Key Takeaways

•Introduces TabiBERT, a new Turkish language model based on ModernBERT.
•Pre-trained on a large, curated corpus of one trillion tokens.
•Offers improved inference speed and reduced GPU memory consumption.
•Introduces TabiBench, a unified benchmarking framework for Turkish NLP.
•Achieves state-of-the-art results on multiple Turkish NLP tasks.

Reference

“TabiBERT attains 77.58 on TabiBench, outperforming BERTurk by 1.62 points and establishing state-of-the-art on five of eight categories.”

Permalink ArXiv

Research #machine learning 📝 BlogAnalyzed: Dec 28, 2025 21:58

SmolML: A Machine Learning Library from Scratch in Python (No NumPy, No Dependencies)

Published:Dec 28, 2025 14:44

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1 min read

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r/learnmachinelearning

Analysis

This article introduces SmolML, a machine learning library created from scratch in Python without relying on external libraries like NumPy or scikit-learn. The project's primary goal is educational, aiming to help learners understand the underlying mechanisms of popular ML frameworks. The library includes core components such as autograd engines, N-dimensional arrays, various regression models, neural networks, decision trees, SVMs, clustering algorithms, scalers, optimizers, and loss/activation functions. The creator emphasizes the simplicity and readability of the code, making it easier to follow the implementation details. While acknowledging the inefficiency of pure Python, the project prioritizes educational value and provides detailed guides and tests for comparison with established frameworks.

Key Takeaways

•SmolML is a Python-based ML library built from scratch, emphasizing educational value.
•It provides implementations of core ML components without external dependencies, promoting understanding of underlying mechanisms.
•The project offers detailed guides and tests for comparison with established ML frameworks.

Reference

“My goal was to help people learning ML understand what's actually happening under the hood of frameworks like PyTorch (though simplified).”

Permalink r/learnmachinelearning

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 12:31

End-to-End ML Pipeline Project with FastAPI and CI for Learning MLOps

Published:Dec 28, 2025 12:16

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1 min read

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r/learnmachinelearning

Analysis

This project is a great initiative for learning MLOps by building a production-style setup from scratch. The inclusion of a training pipeline with evaluation, a FastAPI inference service, Dockerization, CI pipeline, and Swagger UI demonstrates a comprehensive understanding of the MLOps workflow. The author's focus on real-world issues and documenting fixes is commendable. Seeking feedback on project structure, completeness for a real MLOps setup, and potential next steps for production is a valuable approach to continuous improvement. The project provides a practical learning experience for anyone looking to move beyond notebooks in machine learning deployment.

Key Takeaways

•Practical MLOps learning through building a complete pipeline.
•Focus on real-world deployment challenges and solutions.
•Importance of CI/CD and testing in machine learning projects.

Reference

“I’ve been learning MLOps and wanted to move beyond notebooks, so I built a small production-style setup from scratch.”

Permalink r/learnmachinelearning

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 13:00

Built a small production-style MLOps platform while learning FastAPI, Docker, and CI/CD – looking for feedback

Published:Dec 28, 2025 12:14

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1 min read

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r/mlops

Analysis

This Reddit post describes a personal project focused on building a small-scale MLOps platform. The author outlines the key components, including a training pipeline, FastAPI inference service, Dockerized API, and CI/CD pipeline using GitHub Actions. The project's primary goal was learning and understanding the challenges of deploying models to production. The author specifically requests feedback on project structure, missing elements for a real-world MLOps setup, and potential next steps for productionizing the platform. This is a valuable learning exercise and a good starting point for individuals looking to gain practical experience in MLOps. The request for feedback is a positive step towards improving the project and learning from the community.

Key Takeaways

•Practical MLOps project using modern tools.
•Focus on deployment challenges and solutions.
•Seeking community feedback for improvement.

Reference

“I’ve been learning MLOps and wanted to move beyond notebooks, so I built a small production-style setup from scratch.”

Permalink r/mlops

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 01:43

Implementing GPT-2 from Scratch: Part 4

Published:Dec 28, 2025 06:23

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1 min read

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Qiita NLP

Analysis

This article from Qiita NLP focuses on implementing GPT-2, a language model developed by OpenAI in 2019. It builds upon a previous part that covered English-Japanese translation using Transformers. The article likely highlights the key differences between the Transformer architecture and GPT-2's implementation, providing a practical guide for readers interested in understanding and replicating the model. The focus on implementation suggests a hands-on approach, suitable for those looking to delve into the technical details of GPT-2.

Key Takeaways

•The article provides a practical guide to implementing GPT-2.
•It builds upon previous work on Transformer-based translation.
•The focus is on the differences between Transformer and GPT-2.

Reference

“GPT-2 is a language model announced by OpenAI in 2019.”

Permalink Qiita NLP

Career #AI Engineering 📝 BlogAnalyzed: Dec 27, 2025 12:02

How I Cracked an AI Engineer Role

Published:Dec 27, 2025 11:04

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1 min read

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r/learnmachinelearning

Analysis

This article, sourced from Reddit's r/learnmachinelearning, offers practical advice for aspiring AI engineers based on the author's personal experience. It highlights the importance of strong Python skills, familiarity with core libraries like NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow, and a solid understanding of mathematical concepts. The author emphasizes the need to go beyond theoretical knowledge and practice implementing machine learning algorithms from scratch. The advice is tailored to the competitive job market of 2025/2026, making it relevant for current job seekers. The article's strength lies in its actionable tips and real-world perspective, providing valuable guidance for those navigating the AI job market.

Key Takeaways

•Master Python and core AI/ML libraries.
•Practice implementing algorithms from scratch.
•Strengthen your understanding of linear algebra and calculus.

Reference

“Python is a must. Around 70–80% of AI ML job postings expect solid Python skills, so there is no way around it.”

Permalink r/learnmachinelearning

Research #llm 📝 BlogAnalyzed: Dec 27, 2025 11:03

First LoRA(Z-image) - dataset from scratch (Qwen2511)

Published:Dec 27, 2025 06:40

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1 min read

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r/StableDiffusion

Analysis

This post details an individual's initial attempt at creating a LoRA (Low-Rank Adaptation) model using the Qwen-Image-Edit 2511 model. The author generated a dataset from scratch, consisting of 20 images with modest captioning, and trained the LoRA for 3000 steps. The results were surprisingly positive for a first attempt, completed in approximately 3 hours on a 3090Ti GPU. The author notes a trade-off between prompt adherence and image quality at different LoRA strengths, observing a characteristic "Qwen-ness" at higher strengths. They express optimism about refining the process and are eager to compare results between "De-distill" and Base models. The post highlights the accessibility and potential of open-source models like Qwen for creating custom LoRAs.

Key Takeaways

•LoRA models can be trained from scratch using open-source models like Qwen-Image-Edit 2511.
•Dataset size and captioning quality play a crucial role in LoRA performance.
•LoRA strength affects the balance between prompt adherence and image quality.

Reference

“I'm actually surprised for a first attempt.”

Permalink r/StableDiffusion

Research Paper #Text-to-Image Generation, AI, Machine Learning 🔬 ResearchAnalyzed: Jan 3, 2026 20:07

Self-Evaluation for Any-Step Text-to-Image Generation

Published:Dec 26, 2025 20:42

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1 min read

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ArXiv

Analysis

This paper introduces a novel approach, Self-E, for text-to-image generation that allows for high-quality image generation with a low number of inference steps. The key innovation is a self-evaluation mechanism that allows the model to learn from its own generated samples, acting as a dynamic self-teacher. This eliminates the need for a pre-trained teacher model or reliance on local supervision, bridging the gap between traditional diffusion/flow models and distillation-based approaches. The ability to generate high-quality images with few steps is a significant advancement, enabling faster and more efficient image generation.

Key Takeaways

•Introduces Self-E, a novel text-to-image generation model.
•Employs a self-evaluation mechanism for learning.
•Achieves high-quality image generation with few inference steps.
•Does not require a pre-trained teacher model.
•Offers a unified framework for efficient and scalable generation.

Reference

“Self-E is the first from-scratch, any-step text-to-image model, offering a unified framework for efficient and scalable generation.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 26, 2025 10:44

Trillion-Dollar Track Starts from Scratch: Are Humanoid Robots the Hope of the Entire AI Village?

Published:Dec 26, 2025 10:27

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1 min read

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钛媒体

Analysis

This article from TMTPost highlights the potential of humanoid robots as a key driver for the future of AI. It suggests that the development of humanoid robots, inherently linked to AI, could unlock significant advancements and opportunities within the broader AI ecosystem. The article likely explores the various applications, challenges, and investment trends surrounding humanoid robotics, positioning it as a pivotal area for growth and innovation in the AI field. It implies that the success of AI may hinge on the progress made in creating functional and versatile humanoid robots. The title uses strong language to emphasize the importance of this area.

Key Takeaways

•Humanoid robots are seen as a crucial area for AI development.
•The field presents significant opportunities for innovation and investment.
•The success of AI may be closely tied to advancements in humanoid robotics.

Reference

“Humanoid robots, born of AI.”

Permalink 钛媒体

Research #llm 📝 BlogAnalyzed: Dec 26, 2025 22:02

Ditch Gemini's Synthetic Data: Creating High-Quality Function Call Data with "Sandbox" Simulations

Published:Dec 26, 2025 04:05

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1 min read

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Zenn LLM

Analysis

This article discusses the challenges of achieving true autonomous task completion with Function Calling in LLMs, going beyond simply enabling a model to call tools. It highlights the gap between basic tool use and complex task execution, suggesting that many practitioners only scratch the surface of Function Call implementation. The article implies that data preparation, specifically creating high-quality data, is a major hurdle. It criticizes the reliance on synthetic data like that from Gemini and advocates for using "sandbox" simulations to generate better training data for Function Calling, ultimately aiming to improve the model's ability to autonomously complete complex tasks.

Key Takeaways

•Function Calling is more than just enabling tool use; it's about autonomous task completion.
•High-quality training data is crucial for effective Function Calling.
•Sandbox simulations can be a better alternative to synthetic data for Function Calling training.

Reference

“"Function Call (tool calling) is important," everyone says, but do you know that there is a huge wall between "the model can call tools" and "the model can autonomously complete complex tasks"?”

Permalink Zenn LLM

Research #llm 📝 BlogAnalyzed: Dec 26, 2025 23:30

Building a Security Analysis LLM Agent with Go

Published:Dec 25, 2025 21:56

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1 min read

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Zenn LLM

Analysis

This article discusses the implementation of an LLM agent for automating security alert analysis using Go. A key aspect is the focus on building the agent from scratch, utilizing only the LLM API, rather than relying on frameworks like LangChain. This approach offers greater control and customization but requires a deeper understanding of the underlying LLM interactions. The article likely provides a detailed walkthrough, covering both fundamental and advanced techniques for constructing a practical agent. This is valuable for developers seeking to integrate LLMs into security workflows and those interested in a hands-on approach to LLM agent development.

Key Takeaways

•Learn to build LLM agents from scratch using Go.
•Understand how to automate security alert analysis with LLMs.
•Explore practical techniques for LLM agent development without frameworks.

Reference

“Automating security alert analysis with a full-scratch LLM agent in Go.”

Permalink Zenn LLM

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 21:16

AI Agent: Understanding the Mechanism by Building from Scratch

Published:Dec 24, 2025 21:13

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1 min read

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Qiita AI

Analysis

This article discusses the rising popularity of "AI agents" and the abundance of articles explaining how to build them. However, it points out that many of these articles focus on implementation using frameworks, which allows for quick prototyping with minimal code. The article implies a need for a deeper understanding of the underlying mechanisms of AI agents, suggesting a more fundamental approach to learning and building them from the ground up, rather than relying solely on pre-built frameworks. This approach would likely provide a more robust and adaptable understanding of AI agent technology.

Key Takeaways

•Focus on understanding the underlying mechanisms of AI agents.
•Frameworks enable rapid prototyping but may obscure fundamental concepts.
•Building from scratch can lead to a more robust understanding.

Reference

“昨今「AIエージェント」という言葉が流行し、さまざまな場面で見聞きするようになりました。”

Permalink Qiita AI

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 19:17

Anthropic's Official "skill-creator" Complete Guide: Escape Zero-Based Design with Skills that Create Skills

Published:Dec 24, 2025 12:39

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1 min read

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Zenn Claude

Analysis

This article provides a comprehensive guide to Anthropic's "skill-creator," a tool designed to streamline the creation of Skills for Claude. It addresses the common problem of users struggling to design SKILL.md files from scratch. The article promises to cover the tool's installation, usage, and important considerations. The focus on practical application and problem-solving makes it valuable for Claude users looking to enhance their workflow. The article's structure, promising a systematic explanation, suggests a well-organized and accessible resource for both beginners and experienced users.

Key Takeaways

•skill-creator helps automate the creation of Skills for Claude.
•It addresses the problem of designing SKILL.md files from scratch.
•The article provides a comprehensive guide to installation, usage, and considerations.

Reference

“"Skillを自作したいけど、毎回ゼロからSKILL.mdを設計して詰む"”

Permalink Zenn Claude

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:15

Merging of Kolmogorov-Arnold networks trained on disjoint datasets

Published:Dec 21, 2025 23:41

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1 min read

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ArXiv

Analysis

This article likely discusses a novel approach to combining the knowledge learned by Kolmogorov-Arnold networks (KANs) that were trained on separate, non-overlapping datasets. The core challenge is how to effectively merge these networks without retraining from scratch, potentially leveraging the strengths of each individual network. The research likely explores methods for parameter transfer, knowledge distillation, or other techniques to achieve this merging.

Key Takeaways

Reference

“”

Permalink ArXiv

Research #LLM Agent 🔬 ResearchAnalyzed: Jan 10, 2026 09:11

LLM Agents Build Interpretable Text Generators from RDF Data

Published:Dec 20, 2025 13:16

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1 min read

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ArXiv

Analysis

This research explores a novel application of LLM agents for building Natural Language Generation (NLG) systems, specifically focusing on generating text from Resource Description Framework (RDF) data. The interpretability of the generated text is a crucial advantage, making the system's reasoning process more transparent.

Key Takeaways

•LLM agents are used to build NLG systems from scratch.
•The system focuses on generating text from RDF data.
•Interpretability is a key feature of the generated text.

Reference

“The research focuses on building interpretable rule-based RDF-to-Text generators.”

Permalink ArXiv

Research #LLM, Agent 🔬 ResearchAnalyzed: Jan 10, 2026 09:11

Few-Shot Early Rumor Detection with LLMs and Imitation Agents

Published:Dec 20, 2025 12:42

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1 min read

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ArXiv

Analysis

This research explores using Large Language Models (LLMs) and imitation agents for early rumor detection, a critical application for information verification. The use of few-shot learning could potentially improve efficiency compared to training models from scratch.

Key Takeaways

•Applies LLMs to the problem of rumor detection.
•Utilizes few-shot learning for potentially improved efficiency.
•Employs imitation agents, indicating a focus on agent-based reasoning.

Reference

“The research focuses on early rumor detection.”

Permalink ArXiv

Business #Automotive 📝 BlogAnalyzed: Dec 25, 2025 20:41

Interview with Rivian CEO RJ Scaringe on Company Building and Autonomy

Published:Dec 16, 2025 11:00

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1 min read

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Stratechery

Analysis

This article highlights the challenges and strategies involved in building a new car company, particularly in the electric vehicle space. RJ Scaringe's insights into scaling production, managing supply chains, and developing autonomous driving capabilities offer valuable lessons for entrepreneurs and industry observers. The interview provides a glimpse into the long-term vision of Rivian and its commitment to innovation in the automotive sector. It also touches upon the competitive landscape and the importance of differentiation in a rapidly evolving market. The focus on autonomy suggests Rivian's ambition to be a leader in future transportation technologies.

Key Takeaways

•Building a car company from scratch is a monumental task.
•Scaling production and managing supply chains are critical challenges.
•Autonomy is a key area of focus for Rivian's future.

Reference

“"Building a car company is incredibly hard."”

Permalink Stratechery

Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 11:01

Effective Model Editing for Personalized LLMs Explored

Published:Dec 15, 2025 18:58

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1 min read

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ArXiv

Analysis

This ArXiv paper likely delves into techniques for modifying large language models (LLMs) to better suit individual user preferences or specific tasks. The research likely investigates methods to personalize LLMs without requiring retraining from scratch, focusing on efficiency and efficacy.

Key Takeaways

•Focus on techniques for personalizing LLMs.
•Potential for efficient model modification without full retraining.
•Aims to tailor LLMs for specific user needs.

Reference

“The context indicates a focus on model editing for personalization.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 11:57

Building from Scratch: A Multi-Agent Framework with Human-in-the-Loop for Multilingual Legal Terminology Mapping

Published:Dec 15, 2025 03:29

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1 min read

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ArXiv

Analysis

This article presents a research paper on a multi-agent framework designed for multilingual legal terminology mapping. The inclusion of a human-in-the-loop component suggests an attempt to improve accuracy and address the complexities inherent in legal language. The focus on multilingualism is significant, as it tackles the challenge of cross-lingual legal information access. The use of a multi-agent framework implies a distributed approach, potentially allowing for parallel processing and improved scalability. The title clearly indicates the core focus of the research.

Key Takeaways

•Focus on multilingual legal terminology mapping.
•Employs a multi-agent framework.
•Includes a human-in-the-loop component for improved accuracy.
•Addresses challenges of cross-lingual legal information access.

Reference

“The article likely discusses the architecture of the multi-agent system, the role of human intervention, and the evaluation metrics used to assess the performance of the framework. It would also probably delve into the specific challenges of legal terminology mapping, such as ambiguity and context-dependence.”

Permalink ArXiv

Tutorial #stable diffusion 📝 BlogAnalyzed: Dec 24, 2025 20:16

ComfyUI Complete Installation Guide - Starting Image Generation AI from Scratch on Windows Environment [December 2025]

Published:Dec 14, 2025 00:06

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1 min read

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Zenn SD

Analysis

This article provides a comprehensive guide to installing and setting up ComfyUI, a node-based visual programming tool for Stable Diffusion, on a Windows PC. It targets users with NVIDIA GPUs and aims to get them generating images quickly. The article outlines the necessary hardware and software prerequisites, including OS version, GPU specifications, VRAM, RAM, and storage space. It promises to guide users through the installation process, NVIDIA GPU optimization, initial image generation, and basic workflow understanding within approximately 30 minutes (excluding download time). The article also mentions that AMD GPUs are supported, although the focus is on NVIDIA.

Key Takeaways

•Step-by-step guide to installing ComfyUI on Windows.
•Optimizing ComfyUI for NVIDIA GPUs.
•Generating your first image with ComfyUI.

Reference

“Complete ComfyUI installation guide for Windows.”

Permalink Zenn SD

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 12:03

Weak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Images

Published:Dec 12, 2025 17:02

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1 min read

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ArXiv

Analysis

This article describes a research paper focusing on the application of weak-to-strong generalization in training a Mask-RCNN model for a specific biomedical task: segmenting cell nuclei in brain images. The use of 'de novo' training suggests a focus on training from scratch, potentially without pre-existing labeled data. The title highlights the potential for automation in this process.

Key Takeaways

•Focuses on a specific application of AI in biomedical image analysis.
•Employs weak-to-strong generalization for model training.
•Aims for fully automated training of a Mask-RCNN model.
•Targets the segmentation of cell nuclei in brain images.

Reference

“”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 12:47

Codex Open Sourcing AI Models: A New Era for AI Development?

Published:Dec 11, 2025 00:00

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1 min read

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Hugging Face

Analysis

The open-sourcing of Codex AI models by Hugging Face marks a significant step towards democratizing AI development. By making these models accessible to a wider audience, Hugging Face is fostering innovation and collaboration within the AI community. This move could lead to faster advancements in various fields, as researchers and developers can build upon existing models instead of starting from scratch. However, it also raises concerns about potential misuse and the need for responsible AI development practices. The impact of this decision will depend on how effectively the AI community addresses these challenges and ensures the ethical application of these powerful tools. Further analysis is needed to understand the specific models being open-sourced and their potential applications.

Key Takeaways

•Hugging Face is open sourcing Codex AI models.
•This move democratizes AI development.
•Potential concerns about misuse and ethical considerations need to be addressed.

Reference

“Open sourcing AI models fosters innovation and collaboration within the AI community.”

Permalink Hugging Face

Research #LLM 👥 CommunityAnalyzed: Jan 3, 2026 06:17

LLM from scratch, part 28 – training a base model from scratch on an RTX 3090

Published:Dec 2, 2025 18:17

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1 min read

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Hacker News

Analysis

The article describes the process of training a Large Language Model (LLM) from scratch, specifically focusing on the hardware used (RTX 3090). This suggests a technical deep dive into the practical aspects of LLM development, likely covering topics like data preparation, model architecture, training procedures, and performance evaluation. The 'part 28' indicates a series, implying a detailed and ongoing exploration of the subject.

Key Takeaways

•Focus on practical LLM training.
•Utilizes an RTX 3090 for training.
•Part of a series, indicating a detailed approach.

Reference

“”

Permalink Hacker News

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:20

Modality-Augmented Fine-Tuning of Foundation Robot Policies for Cross-Embodiment Manipulation on GR1 and G1

Published:Dec 1, 2025 07:13

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1 min read

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ArXiv

Analysis

This article likely discusses a research paper focused on improving robot manipulation capabilities. The core idea seems to be enhancing existing robot policies (likely large language models or similar) by incorporating different sensory modalities (e.g., vision, touch) and fine-tuning them for cross-embodiment tasks, meaning the policies should work across different robot platforms (GR1 and G1). The use of 'fine-tuning' suggests the authors are building upon existing foundation models rather than training from scratch. The focus on cross-embodiment manipulation is significant as it aims for generalizability across different robot designs.

Key Takeaways

•Focus on improving robot manipulation.
•Utilizes modality augmentation (e.g., vision, touch).
•Employs fine-tuning of existing robot policies.
•Aims for cross-embodiment manipulation (GR1 and G1).
•Likely leverages foundation models.

Reference

“The abstract or introduction of the paper would provide more specific details on the methods, results, and contributions.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:51

GeoZero: Incentivizing Reasoning from Scratch on Geospatial Scenes

Published:Nov 27, 2025 17:28

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1 min read

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ArXiv

Analysis

The article announces research on GeoZero, a project focused on incentivizing reasoning from scratch in the context of geospatial scenes. The focus on 'reasoning from scratch' suggests an attempt to improve the ability of AI models to independently analyze and understand complex geospatial data, potentially leading to more accurate and reliable results. The use of 'incentivizing' implies a novel approach to training or evaluating these models, possibly involving rewards or other mechanisms to encourage desired behaviors.

Key Takeaways

Reference

“”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:46

DeepCoT: Deep Continual Transformers for Real-Time Inference on Data Streams

Published:Nov 21, 2025 16:15

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1 min read

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ArXiv

Analysis

The article introduces DeepCoT, a novel approach using continual transformers for real-time inference on data streams. The focus is on adapting transformers to handle continuously arriving data, which is a significant challenge in many applications. The use of 'continual' suggests a focus on learning and adapting over time, rather than retraining from scratch. The title clearly states the core contribution.

Key Takeaways

•Focus on real-time inference.
•Utilizes continual transformers.
•Addresses the challenge of handling continuously arriving data.

Reference

“”

Permalink ArXiv

Research #LLM 🏛️ OfficialAnalyzed: Jan 3, 2026 05:52

VaultGemma: DeepMind's Differentially Private LLM

Published:Oct 23, 2025 18:42

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1 min read

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DeepMind

Analysis

The article announces the release of VaultGemma, a new large language model (LLM) from DeepMind. The key feature is its differential privacy, indicating a focus on user data protection. The claim of being "the most capable" is a strong one and would require further evidence and benchmarking to validate. The source, DeepMind, suggests a high degree of credibility.

Key Takeaways

•VaultGemma is a new LLM from DeepMind.
•It is trained with differential privacy, focusing on user data protection.
•DeepMind claims it is the most capable differentially private LLM.

Reference

“We introduce VaultGemma, the most capable model trained from scratch with differential privacy.”

Permalink DeepMind

Research #llm 👥 CommunityAnalyzed: Jan 3, 2026 09:25

Writing an LLM from scratch, part 22 – training our LLM

Published:Oct 15, 2025 23:42

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1 min read

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Hacker News

Analysis

The article focuses on the practical aspects of training a Large Language Model (LLM). It's likely a technical deep dive, suitable for those interested in the inner workings of LLMs. The title suggests a series, indicating a progressive build-up of knowledge.

Key Takeaways

•Focuses on the training phase of an LLM.
•Part of a series, implying a step-by-step guide.
•Likely technical and aimed at a knowledgeable audience.

Reference

“”

Permalink Hacker News

Research #llm 📝 BlogAnalyzed: Dec 26, 2025 15:23

Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)

Published:Oct 5, 2025 11:12

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1 min read

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Sebastian Raschka

Analysis

This article by Sebastian Raschka provides a comprehensive overview of four key methods for evaluating Large Language Models (LLMs). It covers multiple-choice benchmarks, verifiers, leaderboards, and LLM judges, offering practical code examples to illustrate each approach. The article is valuable for researchers and practitioners seeking to understand and implement effective LLM evaluation strategies. It highlights the importance of using diverse evaluation techniques to gain a holistic understanding of an LLM's capabilities and limitations. The inclusion of code examples makes the concepts accessible and facilitates hands-on experimentation.

Key Takeaways

•LLM evaluation involves multiple approaches.
•Code examples aid in understanding.
•Diverse evaluation is crucial.

Reference

“Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples”

Permalink Sebastian Raschka

Research #llm 📝 BlogAnalyzed: Dec 25, 2025 15:10

Last Week to Register: Build Production-Ready Agentic-RAG Applications From Scratch Course!

Published:Sep 23, 2025 15:02

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1 min read

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AI Edge

Analysis

This announcement highlights a practical, project-based course focused on building Agentic-RAG applications. The urgency created by the "Last Week to Register" call to action is effective. The course's emphasis on production-readiness suggests a focus on practical skills and real-world application, which is valuable for developers. The "from scratch" aspect implies a comprehensive learning experience, suitable for those with varying levels of prior knowledge. However, the announcement lacks specific details about the course content, target audience, or learning outcomes, which could deter potential registrants. More information on the technologies covered and the level of expertise required would be beneficial.

Key Takeaways

•Course focuses on building Agentic-RAG applications.
•Emphasis on production-readiness for practical skills.
•Project-based learning approach.

Reference

“Build Production-Ready Agentic-RAG Applications From Scratch”

Permalink AI Edge

Research #llm 👥 CommunityAnalyzed: Jan 3, 2026 08:53

RustGPT: A pure-Rust transformer LLM built from scratch

Published:Sep 15, 2025 09:47

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1 min read

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Hacker News

Analysis

The article announces the development of RustGPT, a large language model implemented entirely in the Rust programming language. This is significant because it demonstrates the feasibility of building complex AI models in a systems programming language known for its performance and safety. The 'from scratch' aspect highlights the effort involved in creating such a model without relying on existing frameworks, showcasing the developers' understanding of the underlying principles.

Key Takeaways

•RustGPT is a transformer LLM.
•It is built entirely in Rust.
•It was built from scratch.

Reference

“”

Permalink Hacker News

Research #llm 📝 BlogAnalyzed: Dec 26, 2025 15:26

Understanding and Implementing Qwen3 From Scratch

Published:Sep 6, 2025 11:10

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1 min read

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Sebastian Raschka

Analysis

This article, by Sebastian Raschka, focuses on providing a detailed understanding of Qwen3, a leading open-source LLM, and how to implement it from scratch. It likely delves into the architecture, training process, and practical considerations for deploying this model. The value lies in its potential to demystify a complex AI system, making it accessible to a wider audience of researchers and developers. A key aspect to consider is the level of technical expertise required to follow the implementation guide. The article's success hinges on its clarity, completeness, and the practicality of its implementation steps. It's a valuable resource for those seeking hands-on experience with LLMs.

Key Takeaways

•Provides a deep dive into Qwen3's architecture.
•Offers practical guidance on implementing Qwen3 from scratch.
•Demystifies complex AI systems for broader accessibility.

Reference

“A Detailed Look at One of the Leading Open-Source LLMs”

Permalink Sebastian Raschka