Build, tweak, repeat

Build faster with Buck2: Our open source build system

How engineers at Nextdoor use Codex to build without limits

How engineers at Nextdoor use Codex with GPT-5. 5 to investigate hard-to-reproduce issues, build across platforms, and focus on product outcomes.

New tools for building agents

Working with Codex

Learn how to set up your Codex workspace, create threads and projects, manage files, and start completing tasks with step-by-step guidance.

Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On

Enterprise Document Intelligence [Vol. 1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops).

SynAct: A Reasoning-Acting Large Language Model Agent for Adaptive Synthesis Optimization

arXiv:2608. 12751v1 Announce Type: cross Abstract: Logic synthesis transforms RTL designs into gate-level netlists, where PPA results are highly sensitive to the choice of optimization commands, making synthesis tuning both high-dimensional and expensive.

How to Effectively Deploy Code With Claude Code

Learn how to optimize your CI/CD pipeline for coding agents The post How to Effectively Deploy Code With Claude Code appeared first on Towards Data Science .

How to Maximize Codex Exec Command

Build a more powerful coding agent setup with a model ensemble The post How to Maximize Codex Exec Command appeared first on Towards Data Science .

Library-Aware Doubles and Iterative Repair for Large Language Model-Generated Unit Tests in OpenSIL Firmware

arXiv:2606. 19725v1 Announce Type: cross Abstract: Validating changes in low-level C firmware is expensive because unit tests (UTs) are fragile under strict build constraints, where missing headers, unresolved symbols, and dependency mismatches frequently prevent compilation and linking.

RAT: RunAnyThing via Fully Automated Environment Configuration

arXiv:2604. 23190v2 Announce Type: replace-cross Abstract: Automating repository-level software engineering tasks is a foundational challenge for autonomous code agents, largely due to the difficulty of configuring executable environments.

Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments

Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation. Each task must couple a realistic software state with a specification, development tools, and reliable verification.

Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable

arXiv:2607. 13285v1 Announce Type: new Abstract: The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution.