Evaluating Continuous Learning in Open-Source Environments
aakashgupta · x · 2026-07-14
This post recaps a 1-hour AI agent course covering practical topics like self-improving agents, memory systems, and debugging loops.
More importantly, it provides context that Skyfall AI tested Morpheus using frontier models including GPT-5.5 and Gemini 3.1 Pro, pointing out that high benchmark scores don't necessarily equate to continuous learning capabilities in real-world scenarios.
Morpheus offers an evaluation framework that allows researchers to test model adaptability in realistic, constantly changing environments; these environments have been open-sourced for the research community.
Related event: Skyfall AI's Morpheus Benchmark: Frontier LLMs Lack Continual Learning(6 posts)→
More from coding & agent
- Inspired by OpenAI's 10,000-agent run, dev open-sources a crowdsourced agent problem-solving platform — Benjaminsen · 2026-09-11
- Lucid: open-source Mac app keeps your laptop awake only while AI agents run — Pitiful_Hedgehog_600 · 2026-09-11
- banteg's snail project crowdsources AI agents to finish matching Snail Mail's 20 remaining functions — banteg · 2026-09-11
- Alex Townsend posts 200 open problems in numerical linear algebra for humans and AI agents — IgorCarron · 2026-09-11
- Kimi K2.8 Preview rolls out: near-K3 coding performance, 1M context for all tiers — teortaxesTex · 2026-09-11
- Looking for a classifier of software engineering task shapes to pick models per task — StewartalsopIII · 2026-09-11