DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to review the skill changes Self Learning proposes each morning with Sno Station

Read the nightly proposals and accept only the ones you want applied to your agents' skills.

Who it is forA developer who wants agents to stop repeating the same mistakes but wants to approve every change to their skills.

The problemAgents keep repeating the same errors across sessions, and no one has time to write the fixes into their skill files.

The outcomeRecurring mistakes become proposed skill changes, and only the ones you accept take effect.

What you need first

  • A Linux or macOS computer with a terminal (Windows is not supported for Cursor, and the README does not describe Windows support for the rest)
  • At least two supported agents installed, such as Codex and Claude Code
  • A terminal or shell that can run the install command
  • Optional: a free sno.ai account to link the computer

Steps

  1. Run sh -c 'sno_installer_body=$(curl -fsSL https://sno.ai/install) && printf "%s\n" "$sno_installer_body" | sh' in a terminal.
  2. Let Self Learning run overnight, which the README describes as every night.
  3. In the morning, review the proposals that Self Learning produced.
  4. Run sno rem-reflect accept for each proposal you like.

Prompt for your agent

In the sno-station repository, review the proposals from the last Self Learning run and accept only the ones I approve using `sno rem-reflect accept`. Expected output: a list of accepted and rejected proposals. Verify that no skill changed for a proposal you did not accept.

Caveat. The README says REM Enhanced mode sends your memory text to Sno's cloud, while Local First mode uploads nothing, so pick the mode you want in settings before relying on the nightly run.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page