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Daily bookmarks: relevant news, facts and discoveries.

Engineering ·

Standalone C headers for 8-bit emulators

Published on July 15, 2026, the floooh/chips repository contains emulators of chips and 8-bit systems implemented as standalone C headers, providing engineers with concrete code to study emulation in C.

Engineering ·

Post reports an eight-day autoresearch experiment

Published on July 14, 2026, the post claims an autoresearch agent surpassed a manually tuned harness on benchmarks not used in training, after eight days of improvement.

Engineering ·

GPU-Tile-Sim: GPU simulation for LLM co-design

Published on July 14, 2026, this entry presents GPU-Tile-Sim (GTSim), a tile-based GPU simulation framework proposed to support hardware and software co-design for LLMs.

Engineering ·

WANDR: an environment for evaluating agentic search

On July 14, 2026, Perplexity said it was making available an evaluation and reinforcement-learning environment for agentic search, synthesized from production traces with weak human supervision.

Engineering ·

Harness and costs of corporate agents

A July 13, 2026 publication reports that changing agent orchestration reduced cost and latency while maintaining equivalent quality across 22 tasks and six models.

Engineering ·

StreamDQ brings dequantization closer to HBM memory

Published on July 13, 2026, the summary presents StreamDQ, an architecture that dequantizes LLM weights in real time on the HBM base die while preserving conventional GPU load semantics.

Engineering ·

REINFORCE: an estimator for policy gradients

Published on July 12, 2026, the first post in a series derives the REINFORCE estimator as an unbiased policy-gradient method and analyzes how variance grows with trajectory length.

Engineering ·

Prompt injection in binaries analyzed with AI

A July 12, 2026 bookmark describes Naval Postgraduate School research on prompt-injection strings in C binaries and their influence on LLM reverse-engineering agents in Ghidra.

Engineering ·

OpenResearch reports automated paper reproduction

Published on July 11, 2026, the entry describes a tool that uses agents to reproduce research results and provision cloud GPUs. The author says it reproduced their paper end to end.

Engineering ·

Colibri loads MoE experts on demand

Published on July 11, 2026, Colibri is described as a dependency-free C inference engine that loads MoE model experts from disk as needed. The post says it runs a 744-billion-parameter model on a laptop with 25 GB of RAM.

Engineering ·

A hypothesis on how RL expands SFT

Published on July 11, 2026, the post presents a hypothesis: reward-guided variation during RL may separate useful, intertwined components in SFT solutions into reusable skills and routing rules.

Engineering ·

Verifying code-agent rewards

Published on July 11, 2026, the paper examines four designs for verifying code-agent rewards and finds no single solution.

Engineering ·

Memory layout of multidimensional arrays

Eli Bendersky’s article examines the memory layout of multidimensional arrays and includes benchmark code comparing the performance of different access patterns.

Engineering ·

MiniCPM-SALA combines sparse and linear attention

Published on July 11, 2026, the report presents a 9-billion-parameter hybrid architecture for long contexts and reports good benchmark results, with scalable inference up to 1 million tokens.

Engineering ·

What happens when a CUDA kernel runs

An introduction to the CPU–GPU communication required to launch a CUDA kernel and the work involved in that operation.

Engineering ·

RLSD adjusts RLVR updates per token

A paper published on July 11, 2026 combines environmental rewards with self-distillation and reports higher average accuracy on five multimodal benchmarks.

Engineering ·

SDM proposes sparse memory for RNNs

Published on July 11, 2026, the post describes Sparse Delta Memory: an explicit memory with sparse addressing used alongside an RNN that, according to the post, expands state capacity without raising compute per token.

Engineering ·

ThetaEvolve explores test-time learning

Published on July 11, 2026, ThetaEvolve is an open-source framework for test-time learning on open-ended optimization problems. Its description outlines components, without reporting quantitative results.

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