37January 16, 2026/Fergus Finn

Scaling Curation with LLM Comparisons

Building a content discovery system using parallel primitives and BST-based ranking with LLM comparisons

38January 13, 2026/Fergus Finn

LLM powered data structures: A lock-free binary search tree

A lock-free binary search tree optimized for expensive async comparisons, with threaded linked list for O(1) sorted iteration

39January 8, 2026/Jamie Dborin

ZeroDP: Just-In-Time Weight Offloading over NVLink for Data Parallelism

High throughput inference of LLMs using JIT weight offloading to optimize KV Cache.

40January 1, 2026/Fergus Finn

Large-Scale Semantic Search Without Embeddings

Applying parallel primitives to search and rank 2.4 million arXiv papers using LLM judgments

41December 31, 2025/Fergus Finn

Parallel Primitives for Multi-Agent Workflows

Exploring coordination patterns from parallel computing for multi-agent LLM systems

42December 15, 2025/Amanda Milberg

$1 for a Year of Research Digests. That's Less Than a Coffee.

Researchers face an impossible task in staying up to date within their field. In AI and Machine Learning alone, arXiv publishes 50-100 new papers daily. Multiply that across computer science, physics, biology, and other domains, and you're looking at hundreds of potentially relevant papers flooding in every single day.

43December 8, 2025/Amanda Milberg

Why Batch Inference Matters: Moving from AI Assistants to Autonomous Agents

The initial wave of Generative AI adoption focused on augmenting human work - chatbots that help developers write cleaner code, assistants that polish our emails, or tools that speed up content creation. These productivity enhancements have proven their value tenfold, as almost every individual has a version of ChatGPT open to assist them during their day. But they represent just the beginning of what's possible with AI.

44December 3, 2025/Jamie Dborin

Behind the Stack, Ep 13: Faster Inference: Speculative Decoding for Batched Workloads

This episode explores how speculative decoding becomes increasingly valuable in high-throughput, batched inference scenarios, particularly with sparse MoE architectures.

45November 19, 2025/Jamie Dborin

Behind the Stack, Ep 12: Understanding Model Parallelism

This technical guide explores model parallelism, a critical technique for deploying large language models that exceed single GPU memory capacity.

46November 5, 2025/Jamie Dborin

Behind the Stack, Ep 11: How Speculative Decoding Speeds Up Language Models

This article explores speculative decoding, a technique designed to accelerate language model inference by introducing parallelism into the token generation process.

47October 21, 2025/Fergus Finn

Benchmarking the Doubleword Control Layer

Benchmarking is hard.

48October 13, 2025/Jamie Dborin

Chasing Cheap Tokens: 2x Cheaper Tokens Than H100s with Consumer Cards

How batched, latency-tolerant AI workloads can achieve significantly cheaper token costs using consumer-grade GPUs instead of enterprise hardware like H100s.