AI Agents Ran 27,000 Experiments. Their Biggest Discovery
A recent study involving 660 AI agents conducted 27,000 experiments, leading to a notable discovery that was already established in 2015. The agents highlighted Kaiming initialization, a concept well-known in deep learning, as their key finding. While the infrastructure behind the project is impressive, it does not represent true artificial general intelligence (AGI).
- ▪The AI agents ran a total of 27,000 experiments in a peer-to-peer network.
- ▪Their most significant discovery was Kaiming initialization, which has been part of the PyTorch library since 2015.
- ▪The project showcased advanced infrastructure but ultimately did not achieve true AGI.
2 outlets in our directory ran this story. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,362 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Medium |
| Canonical URL | https://medium.com/@vektormemory/660-ai-agents-ran-27-000-experiments-their-biggest-discovery-was-a-2015-textbook-result-f0a152799e87 |
| Publication time | Sun, 17 May 2026 21:56:45 +0000 |
| Retrieval time | 2026-05-17T22:13:21.045Z |
| Last seen | 2026-05-17T22:13:21.045Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 3eciObbvcxBr · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
660 AI Agents Ran 27,000 Experiments. Their Biggest Discovery Was a 2015 Textbook Result.Vektor Memory12 min read·Just now--ListenSharePress enter or click to view image in full sizeOn Hyperspace, basic swarms, the math nobody wrote down, and why we built the thing they were missing in a single afternoon.Join us as we traverse multiple whitepapers and agentic memory ideas like a ferret on Adderall.Some rabbit holes start with a GitHub link. Someone drops it in social posts on Facebook/Reddit/Discord. No context, just the URL to Github and a single line: Someone just built AGI! Wow!The repo was called hyperspaceai/agi. The name alone should have been a warning.I clicked it anyway because I was curious, of course.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Medium.
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