Give Your Coding Agents a Memory You Own

Components of A Coding Agent

How coding agents use tools, memory, and repo context to make LLMs work better in practice

How Much Memory Does Your Agent Actually Need?

Tiny Agents: an MCP-powered agent in 50 lines of code

Upgrading agentic coding capabilities with the new Devstral models

Note on 24th September 2026

Simon Willison reflects on his experience with coding agents, noting that while they enable impressive feats, they also complicate software engineering. He emphasizes that fully harnessing their capabilities demands exceptional discipline and deep knowledge. The article highlights the dual nature of coding agents as both powerful tools and challenging additions to development workflows.

Make Memory work for you.

Tiny Agents in Python: a MCP-powered agent in ~70 lines of code

Personal Copilot: Train Your Own Coding Assistant

Using Local Coding Agents

Using Open-Weight Models in Local Coding Harnesses as an Alternative to Claude Code and Codex Subscriptions

Measure Before You Manage: Evaluating Agent Working Memory in Coding Agents

arXiv:2608.31057v1 Announce Type: new Abstract: Agent working memory is heterogeneous. Objects such as instructions, artifacts, tool outputs, and agent-generated state play different semantic roles a...

Computer-Using Agent

EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory

arXiv:2609.37923v1 Announce Type: new Abstract: Agents can learn from past executions, but enabling different agents to reuse and build on one another's experience remains challenging. We introduce E...