Dontopedia

Fix request

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

Fix request has 4 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

4 facts·2 predicates·2 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (4)

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.

answersQuestionAnswers Question(1)

asksQuestionAsks Question(1)

followsUserRequestFollows User Request(1)

occursMeanwhileOccurs Meanwhile(1)

Other facts (3)

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.

3 facts
PredicateValueRef
Rdf:typeQuestion[1]
Rdf:typeRequest[2]
Made byUser[2]

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.

typeblah/watt-activation/295
ex:Question
labelblah/watt-activation/295
Fix request
typebeam/205d6773-fca4-4f2e-bf84-1c2f39cbc257
ex:Request
madeBybeam/205d6773-fca4-4f2e-bf84-1c2f39cbc257
ex:user

References (2)

2 references
  1. [1]2952 facts
    ctx:discord/blah/watt-activation/295
    • full textwatt-activation-295
      text/plain3 KBdoc:agent/watt-activation-295/3934680b-d58b-4c73-8470-2c337c1a045e
      Show excerpt
      [2026-03-14 04:39] xenonfun: ```❯ ⏺ Now I have the full picture. Here's my MLX performance review: Spherical VQ — MLX Performance Review Good patterns: 1. _l2_normalize uses + eps inside sqrt (line 38) — matches lohe_normalize sema
  2. ctx:claims/beam/205d6773-fca4-4f2e-bf84-1c2f39cbc257
    • full textbeam-chunk
      text/plain1 KBdoc:beam/205d6773-fca4-4f2e-bf84-1c2f39cbc257
      Show excerpt
      - **Rule Prioritization**: Prioritize rules based on their effectiveness and frequency of application. - **Machine Learning Integration**: Consider integrating machine learning models to predict the best rule to apply in ambiguous cases. -

See also

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