Dontopedia

Rust

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

Rust has 70 facts recorded in Dontopedia across 14 references, with 3 live disagreements.

70 facts·63 predicates·14 sources·3 in dispute

Mostly:rdf:type(5), uses cached rotor state(2), depends on cached state for o1(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (11)

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.

assessesCoverageOfAssesses Coverage of(1)

confirmedByConfirmed by(1)

existsBetweenExists Between(1)

impliesPrecisionAdvantageImplies Precision Advantage(1)

isCorrectIs Correct(1)

isFullyIntegratedIs Fully Integrated(1)

isSimilarForIs Similar for(1)

lacksContentAwarePoleTrackingLacks Content Aware Pole Tracking(1)

lacksTestsLacks Tests(1)

notConnectedToInferenceCodeNot Connected to Inference Code(1)

requiresContentAwareDecayRequires Content Aware Decay(1)

Other facts (68)

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.

68 facts
PredicateValueRef
Rdf:typeImplementation[9]
Rdf:typeSoftware Implementation[11]
Rdf:typeSoftware Implementation[12]
Rdf:typeImplementation[13]
Rdf:typeSoftware Implementation[14]
Uses Cached Rotor StateRotor State[1]
Uses Cached Rotor Statetrue[8]
Depends on Cached State for O1Rotor State[1]
Uses Pure Cpu Vec F32 Arithmetic With LoopsInference[1]
Performs Raw F32 ArithmeticInference[1]
Criticized for No Gpu YetInterlocutor[1]
Presupposes Rotor Models OptimalCached State[1]
Superior in Speedup With Sequence LengthPython Mlx[1]
Scales Better With Sequence LengthPython Mlx[1]
Known to Be Cpu Only CurrentlyXenonfun[1]
Accounts for Large Constant FactorAlgorithmic Advantage[1]
Has Zero OverheadFramework Overhead[1]
Advocated As SuperiorPython Mlx[1]
Avoids AllPython Mlx Overhead[1]
Teleologically Designed for Low OverheadRaw Arithmetic[1]
Is Nativetrue[2]
Superior PerformanceMnist Task[3]
Goes Further ThanPython Proposal[4]
Achieves High CoverageHelmholtz Breakthrough[4]
Uses Explicit TauPole Time Constants[4]
CoversHelmholtz Breakthrough[4]
Covers Percentage of90[4]
Extends BeyondCals Full Pipeline Python[4]
IntegratesGivens Coupling[4]
Is More Precise Than PythonPole Time Constants[4]
Is Superior in PrecisionPython Proposal[4]
MatchesHelmholtz Fiber Fixed Decay[4]
Superior TrainingPython Implementation[5]
OutperformsPython Implementation[5]
Trains HarderMnist Mlp Train[5]
Presupposes Porting NeededCnn Support[6]
Achieves Better Test AccPython Implementation[6]
Has Not PortedCnn Front Ends[6]
ReplicatesPython Implementation[6]
Lacks Kernel FusionTorch Compile Analogue[7]
Post Prefill ComplexityO(1)[8]
Scaling ReasonCached Rotor State[8]
Avoids Python Overheadtrue[8]
Avoids Graph Buildingtrue[8]
Avoids Giltrue[8]
Avoids Framework Dispatchtrue[8]
Performance Advantage SourceAlgorithmic Advantage[8]
Overheadzero[8]
Execution Moderaw f32 arithmetic[8]
Uses Gpufalse[8]
Cpu Implementation DetailVec<f32> arithmetic with loops[8]
:isNative Implementation[9]
Has Accuracy99.1[10]
Compares Favorably toBest Python Baseline[10]
Uses Gpu AccelerationTraining Process[10]
Performs More Effective OptimizationTraining Process[10]
Has Gelation Step12[10]
Has Evolutionary DynamicsEquivalent Dynamics[10]
Reaches Depth6[10]
Has Similar Parameter CountsPython Implementation[10]
Has Slower Wall Clock TimePython Implementation[10]
Has Test Accuracy69[10]
Overfits to1000 Sample Subset[10]
Overfits More ThanPython Implementation[10]
Trains More EffectivelyPython Implementation[10]
Has Coverage Percent90[11]
Coverage Is Approximatetrue[11]
Training Intensityharder[12]

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.

dependsOnCachedStateForO1blah/watt-activation/part-454
ex:rotor-state
usesPureCpuVecF32ArithmeticWithLoopsblah/watt-activation/part-454
ex:inference
performsRawF32Arithmeticblah/watt-activation/part-454
ex:inference
criticizedForNoGpuYetblah/watt-activation/part-454
ex:interlocutor
presupposesRotorModelsOptimalblah/watt-activation/part-454
ex:cached-state
superiorInSpeedupWithSequenceLengthblah/watt-activation/part-454
ex:python-mlx
scalesBetterWithSequenceLengthblah/watt-activation/part-454
ex:python-mlx
knownToBeCpuOnlyCurrentlyblah/watt-activation/part-454
ex:xenonfun
accountsForLargeConstantFactorblah/watt-activation/part-454
ex:algorithmic-advantage
usesCachedRotorStateblah/watt-activation/part-454
ex:rotor-state
hasZeroOverheadblah/watt-activation/part-454
ex:framework-overhead
advocatedAsSuperiorblah/watt-activation/part-454
ex:python-mlx
avoidsAllblah/watt-activation/part-454
ex:python-mlx-overhead
teleologicallyDesignedForLowOverheadblah/watt-activation/part-454
ex:raw-arithmetic
isNativeblah/watt-activation/part-456
true
superiorPerformanceblah/watt-activation/part-468
ex:mnist-task
goesFurtherThanblah/watt-activation/part-473
ex:python-proposal
achievesHighCoverageblah/watt-activation/part-473
ex:helmholtz-breakthrough
usesExplicitTaublah/watt-activation/part-473
ex:pole-time-constants
coversblah/watt-activation/part-473
ex:helmholtz-breakthrough
coversPercentageOfblah/watt-activation/part-473
90
extendsBeyondblah/watt-activation/part-473
ex:cals-full-pipeline-python
integratesblah/watt-activation/part-473
ex:givens-coupling
isMorePreciseThanPythonblah/watt-activation/part-473
ex:pole-time-constants
isSuperiorInPrecisionblah/watt-activation/part-473
ex:python-proposal
matchesblah/watt-activation/part-473
ex:helmholtz-fiber-fixed-decay
superiorTrainingblah/watt-activation/part-477
ex:python-implementation
outperformsblah/watt-activation/part-477
ex:python-implementation
trainsHarderblah/watt-activation/part-477
ex:mnist-mlp-train
presupposesPortingNeededblah/watt-activation/part-469
ex:cnn-support
achievesBetterTestAccblah/watt-activation/part-469
ex:python-implementation
hasNotPortedblah/watt-activation/part-469
ex:cnn-front-ends
replicatesblah/watt-activation/part-469
ex:python-implementation
lacksKernelFusionblah/watt-activation/part-641
ex:torch-compile-analogue
usesCachedRotorStateblah/watt-activation/452
true
postPrefillComplexityblah/watt-activation/452
O(1)
scalingReasonblah/watt-activation/452
ex:cached-rotor-state
avoidsPythonOverheadblah/watt-activation/452
true
avoidsGraphBuildingblah/watt-activation/452
true
avoidsGilblah/watt-activation/452
true
avoidsFrameworkDispatchblah/watt-activation/452
true
performanceAdvantageSourceblah/watt-activation/452
ex:algorithmic-advantage
overheadblah/watt-activation/452
zero
executionModeblah/watt-activation/452
raw f32 arithmetic
usesGpublah/watt-activation/452
false
cpuImplementationDetailblah/watt-activation/452
Vec<f32> arithmetic with loops
typeblah/watt-activation/454
ex:Implementation
isblah/watt-activation/454
ex:native-implementation
hasAccuracyblah/watt-activation/464
99.1
comparesFavorablyToblah/watt-activation/464
ex:best-python-baseline
usesGpuAccelerationblah/watt-activation/464
ex:training-process
performsMoreEffectiveOptimizationblah/watt-activation/464
ex:training-process
hasGelationStepblah/watt-activation/464
12
hasEvolutionaryDynamicsblah/watt-activation/464
ex:equivalent-dynamics
reachesDepthblah/watt-activation/464
6
hasSimilarParameterCountsblah/watt-activation/464
ex:python-implementation
hasSlowerWallClockTimeblah/watt-activation/464
ex:python-implementation
hasTestAccuracyblah/watt-activation/464
69
overfitsToblah/watt-activation/464
ex:1000-sample-subset
overfitsMoreThanblah/watt-activation/464
ex:python-implementation
trainsMoreEffectivelyblah/watt-activation/464
ex:python-implementation
typeblah/watt-activation/471
ex:SoftwareImplementation
labelblah/watt-activation/471
Rust
hasCoveragePercentblah/watt-activation/471
90
coverageIsApproximateblah/watt-activation/471
true
trainingIntensityblah/watt-activation/475
harder
typeblah/watt-activation/475
ex:SoftwareImplementation
typeblah/watt-activation/486
ex:Implementation
labelblah/watt-activation/486
Rust implementation
typeblah/watt-activation/631
ex:SoftwareImplementation

References (14)

14 references
  1. [1]Part 45414 facts
    ctx:discord/blah/watt-activation/part-454
  2. [2]Part 4561 fact
    ctx:discord/blah/watt-activation/part-456
  3. [3]Part 4681 fact
    ctx:discord/blah/watt-activation/part-468
  4. [4]Part 47310 facts
    ctx:discord/blah/watt-activation/part-473
  5. [5]Part 4773 facts
    ctx:discord/blah/watt-activation/part-477
  6. [6]Part 4694 facts
    ctx:discord/blah/watt-activation/part-469
  7. [7]Part 6411 fact
    ctx:discord/blah/watt-activation/part-641
  8. [8]45212 facts
    ctx:discord/blah/watt-activation/452
    • full textwatt-activation-452
      text/plain3 KBdoc:agent/watt-activation-452/ff1dd4f5-3233-4ae2-8f83-249a90fd3e1d
      Show excerpt
      [2026-03-21 05:42] xenonfun: ⏺ The Rust timings include process startup + weight loading (~230ms), so let me subtract that overhead for a fair comparison: ``` ┌────────┬──────────────────┬──────────────────────────┬─────────┐ │ Tokens │
  9. [9]4542 facts
    ctx:discord/blah/watt-activation/454
    • full textwatt-activation-454
      text/plain3 KBdoc:agent/watt-activation-454/4f6603bc-7db5-4694-932b-2c38bbe4bc5b
      Show excerpt
      [2026-03-21 06:17] xenonfun: Back to Rust ``` 1 - [project_vision.md](project_vision.md) — HarmonicRust replaces Python HarmonicMLX + Phase Hub with Rust 2 - [user_profile.md](user_profile.md) — User builds novel manifold-based ML architect
  10. [10]46413 facts
    ctx:discord/blah/watt-activation/464
    • full textwatt-activation-464
      text/plain3 KBdoc:agent/watt-activation-464/599938d0-3182-4b42-bb04-4488236f82bc
      Show excerpt
      [2026-03-21 18:08] xenonfun: ``` Key observations: - Rust achieves significantly higher accuracy (99.1% vs 89.5% best) — the GPU-accelerated training does more effective optimization per epoch - Gelation detected at the same step (12)
  11. [11]4714 facts
    ctx:discord/blah/watt-activation/471
    • full textwatt-activation-471
      text/plain3 KBdoc:agent/watt-activation-471/2422db4e-e816-4cee-91c6-8067b50fc309
      Show excerpt
      [2026-03-21 19:31] xenonfun: ``` ⏺ Pushed. Here's the summary of both research results: Helmholtz Breakthrough Audit Rust covers ~90% of the breakthrough. Key findings: ┌───────────────────────────────┬────────────────────────────────
  12. [12]4752 facts
    ctx:discord/blah/watt-activation/475
    • full textwatt-activation-475
      text/plain2 KBdoc:agent/watt-activation-475/e1172583-2318-4515-b9d4-b2208603f689
      Show excerpt
      [2026-03-21 20:42] xenonfun: ``` ⏺ Clean tree, all pushed. Here's the full session summary: Session Summary: 15 commits, ~20K new lines FedSym Evolutionary Framework (complete): - 15 modules: config, activation, unit, training, fusi
  13. [13]4862 facts
    ctx:discord/blah/watt-activation/486
    • full textwatt-activation-486
      text/plain3 KBdoc:agent/watt-activation-486/c8568fef-e9f2-4d48-9840-89f375514ea3
      Show excerpt
      [2026-03-22 03:19] xenonfun: ``` ⏺ The IVF was useless before because ivf_dirty got set on every insert (128/step), and only cleared every 500 steps on rebuild. So 99.7% of lookups fell back to linear scan. Now: - New entries get assigne
  14. [14]6311 fact
    ctx:discord/blah/watt-activation/631

See also

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