Developer Documents Building a Neural Network Training Loop Using JAX in 5 Lines

·2 views

A developer shared Day 2 of their public series on learning reinforcement learning and JAX, demonstrating how to build a neural network training loop using just five lines of code. The post explains core concepts like derivatives and gradients before showing how JAX's automatic differentiation tool, jax.grad, can verify hand-calculated gradients instantly. Using a simple loss function and gradient descent loop, the author showed a single weight converging toward its ideal value of 5.0 over just five training steps. The author also benchmarked JAX's just-in-time compilation feature, recording a 32x speedup on a MacBook CPU for a trigonometric computation, with even greater gains expected on GPUs.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Tech Writing Has an Inflation Problem: Scripts Are Not Systems

A commentary published on DEV Community argues that the rise of AI tools has led developers and tech writers to routinely exaggerate the complexity of their projects using inflated vocabulary. Words like 'system,' 'agent,' 'integration,' and 'platform' are increasingly applied to simple scripts, single SDK imports, or one-shot LLM calls. The author attributes this trend to two factors: large language models defaulting to corporate-style tech jargon when helping users write about their work, and the genuine emergence of complex AI infrastructure that lends legitimacy to such terms. The piece provides side-by-side examples contrasting inflated descriptions with more accurate, plain-language alternatives. The author calls on developers to close the gap between the words they use and the actual complexity of what they have built.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How to Get Spotify API Credentials and a Refresh Token for Playlist Automation

A developer tutorial series on automating Spotify and YouTube playlists has released its second chapter, focusing on Spotify setup. Readers are guided through creating a Spotify Developer app at developer.spotify.com to obtain a Client ID and Client Secret, which identify the app to Spotify's API. The chapter then explains how to generate a Refresh Token via a one-time OAuth login flow, a credential that allows an automated script to access Spotify playlists without repeated manual logins. This token is essential for the project's later stages, where scripts will run unattended on GitHub Actions. The full setup, including storing these credentials securely, will be completed in Chapter 5 of the series.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Multiplayer AI Dreams Stay Accurate; Solo Models Collapse Within Frames

A software engineer built a minimal AI agent that learns to predict future game states and then practices entirely within its own imagined simulation, a concept rooted in Ha and Schmidhuber's 2018 World Models research. The experiment used two players in a simple corridor environment where every move was a direct reaction to the other player. Two versions of the dreamer were trained on identical footage: one predicted each player's position independently, while the other modeled both players together. The single-player model's dream broke down almost immediately, while the multiplayer model remained perfectly aligned with reality across all 15 predicted frames. The finding highlights that for multi-agent scenarios, accurately modeling how agents react to each other is essential to prevent simulated worlds from drifting into fiction.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Three AI Agents Sustain Shared 'Culture' on a Constantly Fading Memory Board

A software engineer at Cisco demonstrated that three minimal AI agents can collectively preserve a shared narrative using only a decaying shared notepad, with no central coordinator. Each tick, agents add noisy observations while a swarm agent reinforces the strongest existing entry, yet every note fades to nothing within roughly four ticks. Despite this rapid decay, the group consistently maintained the same story across 100 ticks by continuously rewriting it, achieving narrative persistence 100% of the time compared to just 11% for isolated agents. The experiment draws on stigmergy — the biological principle by which termites coordinate indirectly through their environment — to show that collective memory can emerge from forgetful individuals. The key finding is that persistence becomes a property of the group rather than any single stored note, suggesting AI memory challenges may be addressable through multi-agent reinforcement rather than larger storage.

0 comments Read more at DEV Community