epochs
epochs on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on epochs in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around epochs, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
A Classification model trained entirely on a scientific calculator [P]
This remarkable project demonstrates the surprising potential of constrained AI. A classification model, meticulously trained solely on a Casio FX-82CE X scientific calculator—a non-programmable device—achieved a 67.04% validation accuracy on a binary MNIST dataset. The architecture, utilizing a simple 3x3 pixel input and a single output neuron, initially struggled with "zero" predictions, but reached an impressive 98.96% accuracy after 1000 epochs. For those interested in exploring the nuances of model optimization, our guide, "How to Fine-Tune an LLM: An End-to-End Guide," offers a
Imagenet-1k Classifier trained entirely on an Android [P]
Introducing a surprisingly capable Imagenet-1k classifier, trained entirely on an Android device using a compact MLP architecture with approximately 500K parameters. Despite utilizing a downscaled 32x32 dataset and training for just 5 epochs, the model achieves a Top-1 accuracy of 4.59% and a Top-5 accuracy of 12.68%. This project, executed within Termux on a Dimensity 9300+ CPU, demonstrates the potential for accessible AI development, training in roughly 30 minutes. As noted in a related discussion, "Non-Physical Intelligence Has A Ceiling," even efficient models require a
Training a harness for model-agnostic and task-environment-agnostic capability improvements with PyTorch-like framework [P]
Introducing Harness Training, a novel framework designed to unlock general capability improvements across diverse language models. Developed with a PyTorch-like structure, this project reframes "Agent-driven Self-improving Harness" to a more streamlined approach: training a harness once against a specific task environment, then leveraging it to evaluate *any* model on *any* new environment. Initial results, detailed in a recent blog post (https://www.henrypan.com/blog/2026-07-18-harness-training), demonstrate significant gains on benchmarks like Terminal Bench 2.0, as explored in a related article discussing agent evaluation challenges.
The qlora 2e-4 default is wrong under 10k samples and nobody talks about it [D]
Fine-tuning QLoRA models on smaller datasets—less than 10,000 samples—often leads to unexpected results. The pervasive default learning rate of 2e-4, widely promoted across tutorials and documentation, can actually trigger overfitting. Extensive experimentation reveals that a starting learning rate of 1e-4 or lower, combined with increased epochs, consistently yields significantly improved evaluation metrics. This adjustment, easily implemented, can save practitioners considerable time and frustration, as detailed in a recent discussion about ECCV expenses.