Footer

Your go-to destination for discovering trending open-source projects and uncovering the insights that matter

Legal

  • Terms and conditions
  • Privacy policy

More

  • Pricing
  • Advertise
  • onedump
  • gosnakego
  • Contact us

Social

  • X (Twitter)
  • GitHub
Trendshift owl
What they build speaks louder than words
© 2026 Trendshift, created by Julian Li with ❤️
SignalAdvertise
Trendshift
SignalAdvertise
Sign in
Trendshift owl

Discover trends that matter

SignalAdvertise
DailyWeeklyMonthlyYearly
Live mentionsTopics
RepositoriesDevelopers
InsightsStats

Bifrost
Bifrost
Busbar
Busbar

Always-On-Intelligence/HALO

HALO (Harness for Always-On Loop Oversight), from the survey Always-On Personal Intelligence: an evaluation harness and MCP plugin for always-on personal AI assistants and LLM agents. It checks whether Sensing, Memory, and Action are connected and whether self-evolving agents learn from outcomes, via paper and code audits and ablations.

Visit GitHub
Python
2
0
MIT License
website
FeaturedAdvertise to 180K+ monthly visitors
Bifrost

Fastest Enterprise AI Gateway for Routing, Governing, and Securing all your AI Traffic

Busbar

Control what AI can do before it acts

Momentum

How fast Always-On-Intelligence/HALO is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Data is not available yet
The stars, forks and merged pull requests this repository gains each day and each month will appear here once available

Social mentions

Recent discussions about this repository across the web
  1. Nov 92 posts
    • 14/15 Solving these problems would make assistants safer and more controllable, so people can manage them less and rely on them more. Personal intelligence could then fade into everyday life, as Mark…

      Read full on X, posted by @choiszt

      @choiszt · x.com

    • 9/15 We built HALO, a harness that checks what systems report and do. Read mode: three LLM reviewers read each work's paper and code and vote on its channels. Run mode: rerun released code with…

      Read full on X, posted by @choiszt

      @choiszt · x.com