it
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
it has 27 facts recorded in Dontopedia across 13 references, with 3 live disagreements.
Mostly:rdf:type(6), has property(2), implicature of(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (21)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
rdf:typeRdf:type(6)
- Cluster Health
ex:cluster-health - Current Logging State
ex:current-logging-state - Heavy Load Condition
ex:heavy-load-condition - Heavy System Load
ex:heavy-system-load - Too Many Processes
ex:too-many-processes - Unoptimized Processes
ex:unoptimized-processes
hasComponentHas Component(2)
- Context Components
ex:context-components - Context Weights
ex:context-weights
hasMemberHas Member(2)
- Context Components
ex:context-components - Context Components
ex:context-components
assignedToAssigned to(1)
- System State Weight
ex:system-state-weight
assumesAssumes(1)
- Turn 10097
ex:turn-10097
containsContains(1)
- Context Components
ex:context-components
containsValueContains Value(1)
- Query
ex:query
enableRestoreOfEnable Restore of(1)
- Checkpoints
ex:checkpoints
hasExampleHas Example(1)
- Step 1
ex:step-1
hasKeyHas Key(1)
- Weights
ex:weights
hasPropertyHas Property(1)
- Query
ex:query
includesIncludes(1)
- Context Components
ex:context-components
isObservableIs Observable(1)
- R Global
ex:r-global
retrievesKeyRetrieves Key(1)
- System State Weight
ex:system-state-weight
Other facts (24)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | State | [3] |
| Rdf:type | Software System | [9] |
| Rdf:type | Context Component | [10] |
| Rdf:type | Context Component | [11] |
| Rdf:type | Context Component | [12] |
| Rdf:type | Context Component | [13] |
| Has Property | Disordered | [6] |
| Has Property | Coherent | [6] |
| Implicature of | Ajaxdavis Message Restarting | [1] |
| Achieved Via | minimal tweaks | [2] |
| Operational Status | almost working as intended | [3] |
| Memory Status | Free | [4] |
| Is in Phase | Disordered Phase | [5] |
| Depth in Phase | deep | [5] |
| Relaxes to State | Stable Sub Critical State | [6] |
| Functionally Provides | Representation Diversity | [6] |
| Oscillation Status | enough | [7] |
| Visual Status | way better | [7] |
| Coupling Percentage of Critical | 13% | [8] |
| Coupling Status | Deeply Subcritical | [8] |
| Has Initial Weight | 0.2 | [10] |
| Has Weight | System State Weight | [12] |
| Is Member of | Context Components | [12] |
| Is Component of | Context Components | [13] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (13)
ctx:discord/blah/omega/part-432ctx:discord/blah/safiersemantics/37- full textsafiersemantics-37text/plain3 KB
doc:agent/safiersemantics-37/1bf7ce90-cb99-448a-95a1-806c99190cc2Show excerpt
[2026-01-29 15:04] xenonfun: Okay `master` branch is fully functional, resume even works correct https://youtu.be/uCT3By5_Fn4 next up is optimizing tool call output to be less verbose [2026-01-29 15:36] xenonfun: https://github.com/rsafie…
ctx:discord/blah/safiersemantics/77- full textsafiersemantics-77text/plain3 KB
doc:agent/safiersemantics-77/44c2a4ed-2103-4ae6-a8d3-39339a1ed0c3Show excerpt
[2026-04-29 01:32] xenonfun: last I saw was 32GB of swap and the server isn't responding but proof of concept works [2026-04-29 02:07] xenonfun: private repo runs showing in ci, tho now gotta get them working correct (files: Screenshot_2026…
ctx:discord/blah/watt-activation/12- full textwatt-activation-12text/plain3 KB
doc:agent/watt-activation-12/2b226561-3075-47ab-89b3-591d7663c93bShow excerpt
[2026-02-27 14:42] xenonfun: the codebase already computes SVD in model.py:effective_rank (files: Screenshot_2026-02-27_at_9.41.31_AM.png) [2026-02-27 15:41] xenonfun: (files: Screenshot_2026-02-27_at_10.41.22_AM.png) [2026-02-27 15:44] xe…
ctx:discord/blah/watt-activation/219- full textwatt-activation-219text/plain3 KB
doc:agent/watt-activation-219/c4912ff6-d2ed-42a3-a8a7-43eb7014e9ecShow excerpt
[2026-03-11 04:40] xenonfun: --- Three Things the β Signal Is Revealing 1. β_gate≈0.12 constant = the gate is not working. K=0.177 << K_c=1.33 means β≈25 throughout — we're so deep in the disordered phase that β never varies. To get …
ctx:discord/blah/watt-activation/232- full textwatt-activation-232text/plain3 KB
doc:agent/watt-activation-232/3c01e7f2-4764-4305-aaa5-72adde3c0ccbShow excerpt
[2026-03-11 20:31] xenonfun: ## SpectralAttention vs. AnchorKAN (files: Screenshot_2026-03-11_at_4.31.27_PM.png) [2026-03-11 20:39] xenonfun: ``` ⏺ Here's what each panel is showing, with the interesting bits: --- Row 1 — Loss + Global…
ctx:discord/blah/watt-activation/339- full textwatt-activation-339text/plain3 KB
doc:agent/watt-activation-339/b6cfe4ca-d9ef-43d5-a9f8-4c8cabbb54c7Show excerpt
[2026-03-15 19:42] xenonfun: ``` ⏺ 1010 B/s — that's garbage text (only 50 steps of training) but the speed is the point. Compare: ┌────────────────────────────────┬───────────┐ │ Mode │ Speed │ ├───────…
ctx:discord/blah/watt-activation/344- full textwatt-activation-344text/plain3 KB
doc:agent/watt-activation-344/aa3dc64f-a03a-4cc7-a0ef-a9fda84ff8b8Show excerpt
[2026-03-15 23:37] xenonfun: ``` ⏺ K is the Lohe coupling strength — it controls how strongly groups pull toward their mean field during sync. Currently frozen at init value 1/sqrt(G*H) ≈ 0.177. The critical coupling is K_c = H/(H-1) = 1.…
ctx:claims/beam/a2411ec7-4597-46a0-8aca-e6f61a739745ctx:claims/beam/c8578409-db7a-4511-babf-7af22c569322- full textbeam-chunktext/plain1 KB
doc:beam/c8578409-db7a-4511-babf-7af22c569322Show excerpt
For each combination of weights, evaluate the performance using your test queries and measure the intent precision. ### Example Implementation Here's an example of how you might structure your experiments: ```python import itertools impo…
ctx:claims/beam/c0dac4b7-a8bf-4fc4-b8c0-172938ac7e75- full textbeam-chunktext/plain1 KB
doc:beam/c0dac4b7-a8bf-4fc4-b8c0-172938ac7e75Show excerpt
[Turn 10470] User: I'm trying to optimize the intent precision of my LLM prompts, and I've been experimenting with different context weights. Currently, I'm achieving 88% intent precision on 2,500 test queries, but I want to improve it furt…
ctx:claims/beam/a71afa78-3ac4-4931-987f-ad0a5b6a3f57- full textbeam-chunktext/plain1 KB
doc:beam/a71afa78-3ac4-4931-987f-ad0a5b6a3f57Show excerpt
Identify the different components of your context and assign initial weights. For example: - `user_history` - `current_query` - `system_state` - `external_data_sources` ### Step 2: Generate Weight Combinations Use a systematic approach t…
ctx:claims/beam/17359c4f-ce82-472f-b0cd-20671ade934f- full textbeam-chunktext/plain1 KB
doc:beam/17359c4f-ce82-472f-b0cd-20671ade934fShow excerpt
``` Replace the placeholder functions with your actual logic to evaluate the intent precision. Would you like to proceed with these steps or do you have any specific questions about any part of the process? [Turn 10474] User: Sure, let's…
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