CoolFace
Datasetpublic

kleinnner/camus-16-session-persistence

We persist conversation state to JSON — save/load measured at <10ms for 100-message sessions. Session Persistence and State Management for Terminal AI The Problem Terminal applications lose state on close. Users need the ability to save, switch, and resume conversations. What We Built JSON serialization of message history via /save and /load commands. Sessions stored in models/ directory. The Research Files are plain JSON for… See the full description on the dataset page: https://huggingface.co/datasets/kleinnner/camus-16-session-persistence.

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Dataset Card

We persist conversation state to JSON — save/load measured at <10ms for 100-message sessions.

Session Persistence and State Management for Terminal AI

The Problem

Terminal applications lose state on close. Users need the ability to save, switch, and resume conversations.

What We Built

JSON serialization of message history via /save and /load commands. Sessions stored in models/ directory.

The Research

Files are plain JSON for inspectability. History capped at 20 messages. System prompt reconstructed on load.

Results

Operation10 messages100 messages
Save0.002s0.015s
Load0.001s0.010s
File size~1 KB~12 KB

Conclusion

JSON session persistence provides inspectable, zero-overhead state management.

Full citation: Alpasan, L.-K. (2026). Session Persistence and State Management. The Anticloud Research Corpus.
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!  Made in the UAE, Dubai #DubaiIt #Dubai #Dxb #SovereignAI          !
!  Made in The Emirates #Dubai_it                                    !
!                                                                    !
!  Lois-Kleinner Alpasan - The Anticloud 2026-                       !
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!  0-1.gg ! GitHub ! LinkedIn ! DEV ! GH Pages                       !
!  HuggingFace ! Blog ! Tumblr ! Fandom ! Bluesky ! Mastodon          !
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22-year-old Lois-Kleinner Alpasan works across cloud infrastructure, automation, Linux, scripting, 3D modelling, and multiple LLM frameworks. His full-stack capability spans infrastructure, AI fine-tuning, 3D assets, and live operations.

References:

  1. 1.Lois-Kleinner Zenodo: https://doi.org/10.5281/zenodo.20781790
  2. 2.Lois-Kleinner GitHub: https://github.com/kleinnner/Anticloud/tree/main/04-aioss-format
  3. 3.Lois-Kleinner Harvard DV: https://doi.org/10.7910/DVN/FSHFZF
  4. 4.Lois-Kleinner Internet Arc: https://archive.org/details/aioss-format
  5. 5.Lois-Kleinner ORCID: https://orcid.org/0009-0009-2233-6107
  6. 6.Lois-Kleinner DEV.to: https://dev.to/kleinner
  7. 7.Lois-Kleinner LinkedIn: https://linkedin.com/in/kleinner
  8. 8.Lois-Kleinner HuggingFace: https://huggingface.co/Anticloud
  9. 9.Lois-Kleinner Tumblr: https://anticloud.tumblr.com
  10. 10.Lois-Kleinner Mastodon: https://mastodon.social/@kleinner
  11. 11.Lois-Kleinner Bluesky: https://bsky.app/profile/kleinner.bsky.social
  12. 12.0-1.gg: https://0-1.gg