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An article exploring the implementation of convolutional neural networks using the APL programming language, from 2019.
A tweet promoting TensorTonic, a platform that allows users to practice implementing nine common activation functions (Sigmoid, ReLU, Tanh, Softmax, Leaky ReLU, GELU, Swish, ELU, SELU) from scratch, including forward pass and gradient computation.
A curated collection of GNN papers, datasets, and implementation tools, hosted on GitHub.
A post aimed at agent builders, offering tips or tools to improve existing implementations.
The author describes implementing a biologically plausible neural network training algorithm proposed by Geoffrey Hinton.
A minimal, hackable CUDA implementation of a GPT-like transformer language model that processes byte sequences, with sample outputs and build instructions.
An agency founder shares lessons from 50+ AI automation implementations, highlighting that most fail due to broken underlying processes, lack of internal ownership, and over-engineering, while the most successful automations are simple, focused, and backed by a named client-side owner.
A tweet highlighting the necessity of auditing current operations before AI transformation, promoting Varick Agents' audit-first approach for higher implementation success.
Sebastian Raschka added a from-scratch implementation of DeepSeek Sparse Attention (DSA) to the LLMs-from-scratch educational repository, including motivation, overview, and a GPT-style reference implementation.
A discussion on why implementing AI agents requires significant technical and change management work, making the role of FDEs (Foundation Deployment Engineers) a lasting job category, unlike earlier cloud adoption.
A tweet asking if anyone has seen an elegant primitive for implementing stateful agents, decision traces, and context graphs.
Eric Jang announces he has been working on a from-scratch implementation of AlphaGo, the 2016 AI breakthrough that inspired him to enter deep learning.
Initial DFlash implementation by Zai_org is integrated into ZML AI, with plans to include it in zml/llmd.
Sebastian Raschka discusses the value of implementing LLM architectures from scratch in Python/PyTorch, sharing his workflow for understanding new open-weight models by dissecting configs, coding, and layer-by-layer debugging.
The author shares practical insights on building client trust in AI agent systems, emphasizing the importance of narrow scope, robust error handling, and clear communication of system status.
BNY Mellon partners with OpenAI to deploy enterprise-wide AI platform called Eliza, supporting 125+ live use cases and 20,000 employees building AI agents with integrated governance framework. The initiative demonstrates how a major financial institution balances innovation with regulatory responsibility through centralized AI deployment and education.
OpenAI shares lessons learned while implementing DQN as part of their Baselines project, covering debugging tips such as greyscale calibration issues, hyperparameter tuning, and correct interpretation of the Huber Loss in the original Nature paper.