context

A reflection on the challenges AI agents face in personalizing based on user data, emphasizing the need for consented, scoped access rather than broad memory.

This week's 7 top papers focus on core challenges of AI Agents: prompt design, reasoning cost, and context explosion, each with engineering insights.

Codex's Chronicle feature, once enabled, continuously screenshots your screen, providing massive context, making answers more personalized and easier to use over time.

Introduces BeliefTrack, a method for contextual belief management in LLMs, reducing reasoning failures by over 70%.

This paper demonstrates methods for LLMs to use shorter context windows while maintaining answer quality, reducing token usage by around 25% and over 50% in some cases.

This article explains how to use Go's net/http/httptrace package to trace HTTP request phases (DNS, connection, TLS, etc.) via context-based hooks, and demonstrates building a CLI trace tool and a RoundTripper logger.

The article describes lessons learned from building a 'harness' system to wrap coding agents with context, tools, provenance, and verification, detailing the first two of eight pillars: Context and Provenance.

The article argues that current AI agents do not truly learn but rather accumulate noise and outdated context over time, highlighting persistent problems with memory and retrieval.

This article introduces an 18-step framework to fully unleash the capabilities of Claude AI, covering context management, prompt optimization, system configuration, and advanced techniques, helping users transition from a chat interface to a continuously running intelligent system.

Visr is a beta tool that captures shell and AI agent sessions, converting ephemeral terminal interactions into reusable transcripts, runbooks, skills, and evals for better context in future runs.

A systematic study on detecting Schwartz values in political text, comparing context lengths, model sizes, and retrieval-augmented generation methods. Results show that full-document context improves supervised models but not zero-shot LLMs, while retrieved moral knowledge consistently helps via early fusion.

Andrej Karpathy points out that 90% of Claude's errors come from missing context, and shares 12 rules (such as think before writing code, simplicity first, etc.) that reduce the error rate from 41% to 3%, emphasizing that discipline and effective context management are more important than frameworks.

Andrej Karpathy states that 90% of Claude's mistakes stem from missing context, not model weakness, and provides a set of 12 rules that reduced error rates from 41% to 3% in experiments.

A personal reflection on the transformative potential of AI agents with persistent memory, arguing that context and workflow organization will become more important than the models themselves.

HydraPlus is an open-source memory and context layer for AI agents that uses a live knowledge graph, combining graph traversal, semantic search, and BM25 to provide persistent, secure, and self-managing context across multiple agents.

Comie.dev provides a production context for AI applications, integrating logs, databases, and error tracking.

Contextberg turns your work into AI agent memory, served over the Model Context Protocol (MCP).