Merge Conflict Digest
AI
Artificial intelligence, machine learning & the algorithms shaping tomorrow
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👋 Editor's Note
Engine Parts - The Dashboard This newsletter engine is running entirely on my laptop, as I have mentioned in an earlier issue. I have created an admin dashboard with no login, that runs on localhost, as a way to curate and edit the final cut of the newsletter... at least it started out this way. Now, this dashboard allows me to do recurring ad placements, write editor notes, generate podcast scripts, and much more. It has grown from a simple scraper to a live ecosystem that's thriving, on my local machine. There’s something deeply satisfying about building the exact tool you need, and having the power to accelerate that development. It’s messy, it has no auth, and it’s perfect!
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21 min read
Insightful
The article shows teachers and students how to use Google Gemini and NotebookLM to design custom graphic novels. It outlines educational benefits, provides examples, and details a workflow: choose a topic and art style, generate a script, import it, and export a PDF.
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5 min read
Trending
DeepSeek researchers fixed training instability in large language models by applying a 1967 matrix normalization algorithm (Sinkhorn-Knopp) to constrain hyper-connection mixing matrices, reducing explosive gradient amplification and improving stability and performance with minimal overhead.
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6 min read
Light
The article traces AI assisted development from early IDE plugins embedding language models to a new agent first paradigm where the terminal acts as a conversational interface. Claude Code exemplifies this shift, handling natural language tasks, git, testing and code generation with minimal oversight.
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Products & Industry Moves
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1 min read
Light
OpenAI opened applications for the second Grove cohort, a five week program supporting founders at any stage, from concept to product. Participants receive API credit, early access to forthcoming tools, and mentorship from OpenAI staff, aiming to accelerate development of AI‑driven ventures.
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4 min read
Insightful
Andrej Karpathy’s Zero to Hero course teaches the complete neural network pipeline using language models. It starts with a code first backpropagation demo, builds a character level model, adds multilayer perceptrons, activation analysis, batch normalization, convolutional layers, and ends with a from scratch GPT implementation and tokenizer design discussion.
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29 min read
Insightful
The last installment explains how to evaluate, test, and deploy the historic London‑English language models, a 117 M‑parameter SLM and a 354 M‑parameter model. It outlines metrics such as perplexity and BLEU, provides scripts for benchmarking, and details conversion to Hugging Face format with API and CLI deployment options.
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37 min read
Developing
The article explains how to construct a compact compiler that translates high level tensor operations expressed in MLIR into executable CUDA code for Nvidia GPUs. It outlines the required MLIR dialects, the transformation pipeline, and demonstrates dynamic compilation and execution from Python.
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6 min read
Insightful
Recursive Language Models treat the prompt as an external string accessed through a Python‑style REPL, allowing a root model to orchestrate sub‑models that process and summarize massive token streams. Experiments show significant accuracy and F1 gains on long‑context benchmarks compared with standard baselines.
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10 min read
Insightful
The article defines perplexity as the exponential of the average negative log probability, describing it as a language model’s uncertainty about the next token. It explains computing perplexity with PyTorch and Hugging Face, then evaluates models on HellaSwag, showing accuracy improves from GPT‑2 to Llama 3.2‑1B.
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22 min read
Developing
The article details a full workflow for pre training a decoder only Llama model on a single GPU. It covers tokenizer creation with special tokens, preparing a PyTorch dataset, defining a compact 12 layer Llama architecture, training with AdamW, learning rate scheduling, loss handling, checkpointing and practical scaling considerations.
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2 min read
HuggingFace
HyperNova-60B, released 2 January 2026, is a 60 billion‑parameter model built on GPT‑OSS‑120B. MXFP4 quantization cuts active parameters to 4.8 billion, allowing single‑GPU inference under 40 GB while preserving reasoning.
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MiniMax
MiniMax M2.1 PRISM is an uncensored language model extending the MiniMax M2.1 base with a PRISM technique that limits unwanted refusals.
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