GLIA — A holographic memory for AI agents that isn't a graph and isn't RAG
GLIA is a new holographic memory system designed for AI agents that aims to overcome limitations of traditional methods like RAG and graphs. It stores knowledge as 1024-dimensional vectors, allowing for better associative reasoning and resilience against data loss. The system has been benchmarked against existing methods, showing significant improvements in retrieval accuracy and operational efficiency.
- ▪GLIA stores knowledge as 1024-dimensional vectors, representing patterns rather than text chunks or nodes.
- ▪It uses a technique called Circular Convolution for holographic binding, allowing relationships to coexist without traditional edges.
- ▪Benchmark tests show GLIA outperforms graph-based approaches by 2.5 times in retrieval accuracy.
2 outlets in our directory ran this story, first to last over 26 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
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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 | DEV.to (Top) |
| Canonical URL | https://dev.to/felipe_farias/glia-a-holographic-memory-for-ai-agents-that-isnt-a-graph-and-isnt-rag-47h3 |
| Publication time | Fri, 22 May 2026 02:44:33 +0000 |
| Retrieval time | 2026-05-22T03:00:08.501Z |
| Last seen | 2026-05-22T03:00:18.274Z |
| 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 | 68t8gR2cBDpo · 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
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3945080) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } felipe farias Posted on May 22 GLIA — A holographic memory for AI agents that isn't a graph and isn't RAG #ai #opensource #programming #machinelearning Every AI coding agent I've used (Cline, Claude, Cursor, etc) has the same problem: it forgets everything between sessions. You fix a complex race condition on Monday, and on Tuesday the agent suggests the same broken pattern again. RAG (Retrieval-Augmented Generation) is the standard fix.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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