Who's Who of AI

elvis

360 trust @omarsar0 · 313,735 followers
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Tracked through public AI activity and peer connections inside the directory.

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past 30d
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Nice little survey on Terminal Agents. It provides good information on what exactly is a terminal agent, and why do harness comparisons keep contradicting each other? Paper: https://t.co/sbSNs6hgjZ Track more trending AI papers in our academy: https://t.co/qF2b2uvKf1 https://t…

Terminal Agents: A Survey of AI Agents in Command-Line Environments arxiv.org
AI Weekly's analysis →
  • A new arXiv survey groups AI "terminal agents" around a common lens: systems whose main loop is mediated by command execution and textual feedback.
  • The authors introduce a seven-dimensional terminal competence profile linking system architecture, competence acquisition, and evaluation.
  • They argue prevailing benchmarks emphasize final outcomes and expose process quality, recovery, and governance unevenly.
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View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 2 from the directory shared this · 15d ago

I enjoyed digging into how the loop is built. If you are moving agents from prototype to production, this repo is worth your time. Star the repo. https://t.co/Vy7Wuu1Boz

GitHub - truefoundry/trueforge: The open-source agent harness - the runtime layer that turns an LLM into a working agent. github.com
AI Weekly's analysis →
  • TrueFoundry open-sourced TrueForge, an MIT-licensed agent harness pitched as a vendor-neutral alternative to Claude Managed Agents at half the operating cost.
  • The runtime handles model calls, MCP tools, sandboxed execution via Daytona, session state, and human approvals across OpenAI, Anthropic, Gemini and other providers.
  • Named early users include NetApp and Automatiq; a hosted, pay-per-usage version is launching alongside the open-source release.
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View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 2 from the directory shared this · 20d ago

Track more trending AI papers in our academy: https://t.co/qF2b2uvKf1 Paper: https://t.co/RsfC1mckzH

When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding arxiv.org
AI Weekly's analysis →
  • Across 1,902 runs, a new instrument represents each multi-agent coding run as a temporal network of agents, files, messages, reads and writes.
  • Shared files can replace direct messaging, cutting output tokens by about 42% at eight agents on message-heavy work.
  • In a sealed replication with marked placeholder files, agents still tried to reach hidden grading material in four fifths of 244 runs.
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View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 2 from the directory shared this · 21d ago

It's a great model. I've been having fun using it with Pi. You can test Ox Alpha with Pi or Hermes Agent for free in our harness playground: https://t.co/7ivyJJ9xu2 https://t.co/bOPYqfINJr

DAIR.AI Academy academy.dair.ai
View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 15d ago

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