Crystals

You mention React performance tips in a ChatGPT session. A week later, you discuss the same topic in Cursor. A month after that, you save an article about it. Each memory is useful on its own, but none gives you the full picture.

Crystals are what happens when the system notices this convergence and synthesizes a single, comprehensive reference from the pieces.

What a Crystal is

A Crystal is a memory, but a special kind. It is synthesized from three or more source memories that independently touch the same topic. The system reads the sources, identifies what each one contributes, and writes a unified summary that stands on its own.

The result is a reference article you can read without hunting down the original conversations. It answers the question "what do I actually know about X?" with a single, coherent document instead of scattered fragments.

Crystals are not auto-generated summaries. They require convergence from multiple independent sources, and the synthesis is evaluated for quality before anything is created.

How Crystals form

Crystal formation follows a pipeline:

1. EVOLVES detection finds that a new memory relates to existing ones. The system creates relationship edges (replaces, enriches, confirms, or challenges).

2. Cluster evaluation fires after new edges are created. It checks whether any cluster of related memories has reached the convergence threshold: three or more source memories on the same topic, with enough distinct information to warrant synthesis.

3. Synthesis happens if the cluster qualifies. The system reads all source memories, identifies what each one contributes, and produces a unified Crystal. The Crystal links back to every source through CRYSTALLIZED_FROM edges, so you can always trace where the knowledge came from.

This pipeline runs as part of background intelligence. A dedicated weekly crystallization review also scans for clusters that event-driven detection might have missed.

Why three sources

Two sources is a correlation. Three is a pattern. The minimum of three independent sources is a quality gate that prevents weak connections from being elevated to reference status.

The sources do not need to come from the same platform. A Crystal built from a ChatGPT conversation, a Cursor session, and a manually saved note is often more valuable than one built from three messages in the same thread, because the cross-platform convergence is stronger evidence that the knowledge matters.

Reviewing Crystals

Every Crystal starts as unreviewed. You can confirm it, dismiss it, or edit it.

Confirm means you have read the synthesis and it reflects your actual understanding. Confirmed Crystals receive a search ranking boost, so they surface more readily when you search. An amber banner on unreviewed Crystals shows Confirm and Dismiss buttons; once confirmed, a green check replaces it.

Dismiss means the synthesis missed the mark. Dismissed Crystals receive a heavy ranking penalty and are visually dimmed in your library. They are not deleted, so you can revisit the decision later.

Edit a Crystal's title or content and it is automatically confirmed. If the system got the gist right but the wording wrong, just fix it.

Crystals you have not reviewed yet receive no ranking boost. They need to earn trust before the system treats them as authoritative.

Speaker attribution

When a Crystal draws from conversations between you and an AI assistant, the synthesis distinguishes who said what. Your own statements ("I will go with Postgres") are recorded as decisions. AI recommendations ("Based on your latency requirements, consider Redis") are recorded as suggestions with their reasoning.

This prevents the common problem where AI-recommended options are presented as if you chose them. Exploring an idea in conversation is not the same as committing to it.

How Crystals affect search

Confirmed Crystals receive a ranking boost in search results. The reasoning is straightforward: a confirmed Crystal represents corroborated, user-verified knowledge, which is more likely to be what you are looking for than any single raw memory.

The boost is meaningful but not overwhelming. A Crystal that is only loosely relevant to your query will not outrank a raw memory that is a perfect semantic match. Semantic relevance is still the dominant ranking signal. Unreviewed Crystals get no boost; dismissed Crystals are penalized.

Crystals also contribute to the confidence score of their source memories. If a memory has been used as a source for one or more Crystals, that counts as evidence of its value.

What you see

Crystals appear in your feed timeline with their source count and the platforms they drew from. You can confirm, dismiss, or edit a Crystal directly from the feed without opening a separate view. Hover over a Crystal card and click the pencil icon to edit its title and content in place, then press Cmd+Enter to save. You can also open a Crystal to read the full synthesis and drill into individual sources for original context.

In the graph view, Crystals appear as distinct nodes with edges pointing back to their sources. Double-clicking a Crystal expands these edges so you can see the full provenance.

Updates, not auto-mutation

Knowledge evolves. The sources that formed a Crystal might get updated, contradicted, or replaced by newer understanding. When this happens, the system flags the Crystal as stale and proposes a re-evaluation rather than silently rewriting it.

Crystals you have already confirmed are prioritized for re-evaluation. Crystals you dismissed are left alone. The system proposes; you decide.

Next steps

• Knowledge evolution explains the EVOLVES edges that feed into Crystal formation
• Background intelligence covers when crystallization runs and how it is scheduled
• Search architecture explains how Crystals affect ranking