llms.blog
AI

OpenAI's Computer History turns your Mac activity into a searchable ChatGPT memory

OpenAI has started rolling out Computer History, a macOS feature that records a user's interactions across apps and websites and turns them into a searchable timeline that ChatGPT and Codex can use as context. Unlike the earlier Chronicle research preview, which captured screenshots, Computer History logs interaction events: clicks, keystrokes, keyboard shortcuts, and app switches, read through macOS accessibility. OpenAI says it does not capture screenshots, screen recordings, microphone input

axc
axc
2 min
OpenAI's Computer History turns your Mac activity into a searchable ChatGPT memory

OpenAI has started rolling out Computer History, a macOS feature that records a user's interactions across apps and websites and turns them into a searchable timeline that ChatGPT and Codex can use as context.

Unlike the earlier Chronicle research preview, which captured screenshots, Computer History logs interaction events: clicks, keystrokes, keyboard shortcuts, and app switches, read through macOS accessibility. OpenAI says it does not capture screenshots, screen recordings, microphone input, or system audio, and it never includes private browsing.

openai-computer-history-tracks-clicks-keystrokes

The recorded events are periodically summarized into text and stored locally as Markdown memory files. The timeline view groups those summaries by day and time and shows which apps were involved.

When ChatGPT notices a repeated workflow, it can suggest a Skill or automation, and Codex can build a reusable template from the recorded steps. OpenAI cites finding recently edited documents and generating standup summaries as examples.

Access is gated. In Business and Enterprise workspaces an admin must enable the feature, then each user must consent individually, and the Memories feature has to be turned on. Users can set include and exclude lists and pause or resume recording from the menu bar. It is not yet available in the European Economic Area, Switzerland, or the United Kingdom.

The privacy limits are real. Temporary event files sit inside the ChatGPT app group and are deleted after 48 hours, but the memory files remain on disk as plaintext Markdown and are not encrypted, so any program running under the same macOS user could read them. OpenAI processes the temporary files on its servers but says it does not retain them unless required by law, and does not use them for training, though later chats that include those memories as context may still become training data depending on a user's settings.

OpenAI's own documentation flags a heightened prompt injection risk, because Computer History can pull untrusted web content into a model's context.

Sources

The Decoder: https://the-decoder.com/openais-computer-history-turns-your-clicks-and-keystrokes-into-a-searchable-chatgpt-memory-timeline/

OpenAI docs: https://learn.chatgpt.com/docs/integrations/computer-history

AILLMOpenAIPrivacyMacOS
ShareX (Twitter)LinkedIn

Written by

axc

More to read

  • Rank Collapse in Deep Transformers: Why Pure Attention Degenerates Doubly Exponentially and How Skip Connections Preserve Capacity

    When the Transformer architecture was introduced in 2017 with the seminal paper "Attention Is All You Need", the central thesis was that recurrence and convolution could be completely discarded in favor of stacked self-attention mechanisms. However, theoretical analysis has shown that the title's premise is mathematically incomplete. Stacking pure self-attention layers in isolation does not produce an expressive deep model: it triggers a catastrophic failure mode known as rank collapse. In a fo

    1 min

  • Item Response Theory Audit of 192 LLMs Exposes Safety Benchmark Redundancies, Over-Refusal Distortions, and Sandbagging

    A psychometric evaluation of 192 frontier and open-weight language models across eight major safety benchmarks has revealed structural flaws in current safety testing methodologies. The research, conducted by Joshua Fonseca Rivera, Neil Shah, David Demitri Africa, and Konstantinos Voudouris with support from the UK AI Security Institute and the UK Department for Science, Innovation, and Technology (DSIT), applies Item Response Theory (IRT) to analyze 5,255 evaluation items. The findings demonst

    1 min

  • Binary Quantization and Matryoshka Embeddings in Production: Two-Stage Rescoring, Hamming Distance Acceleration, and Vector Storage Economics

    Production vector search workloads face a severe memory scaling bottleneck. In retrieval-augmented generation (RAG) and semantic search architectures, indexing tens of millions of high-dimensional embeddings in raw 32-bit floating-point (FP32) format exhausts memory capacity and drives up infrastructure costs. Indexing 100 million 1536-dimensional vectors requires 614.4 GB of RAM purely for vector payloads, before accounting for graph edges and memory fragmentation in Approximate Nearest Neighbo

    1 min