BUILT ON VERIFIED DATA,
NOT GUESSWORK. HERE'S EXACTLY HOW.

FixMyPrint's fix-intelligence engine is built on posts extracted from r/FixMyPrint and related 3D printing communities on Reddit. Each post was processed to extract:

• •The printer model, filament type, and slicer software
• •The failure being described
• •Every fix suggested in the comment thread
• •Whether the original poster came back to confirm a fix worked

We classify each fix into one of three confirmation buckets:

Success rates are calculated from Bucket A and B only. Bucket C fixes are only shown as a last resort when no higher-confidence data exists for that issue type.

When you describe a print failure, FixMyPrint:

1. 1Classifies your description into one of 16 canonical issue types using GPT-4o
2. 2Queries our fix database for your specific issue
3. 3Applies a three-tier lookup (below)

• •First: fixes confirmed for your exact printer model
• •Second: fixes confirmed for your filament type across all printers
• •Third: globally confirmed fixes for your issue type

Fixes are ranked by confirmed count and success rate within each tier. A fix with 10 OP confirmations ranks above one with 2, even if the lower-ranked fix has a higher raw success rate from a smaller sample.

Minimum thresholds apply- a fix needs at least 1 OP confirmation and a 20% success rate to appear in the primary results. Below that threshold, fixes are labeled as community suggestions rather than confirmed recommendations.

The Settings Generator is entirely rule-based. No AI, no machine learning, no token sampling.

We tested AI-generated settings early in development and rejected the approach for three reasons:

1. 1Hallucination risk- language models generate plausible-sounding values that may be outside safe operating ranges for specific filaments or printers
2. 2Slicer naming inconsistency- every slicer uses different field names for the same concept. AI has no reliable mapping mechanism
3. 3Inconsistency across sessions- the same question produces different answers from session to session. For a tool users rely on to fix expensive print failures, inconsistency destroys trust

The engine instead uses a deterministic resolution chain:

Every output value is auditable- you can trace it back through the chain to the rule that produced it. Safe parameter bounds are enforced at the final step before output, sourced from manufacturer specifications and filament datasheets.

FixMyPrint's filament database aggregates data from 10 sources:

• •Ultimaker/Cura fdm_materials (CC0 licensed)
• •PrusaSlicer filament profiles (Apache 2.0)
• •OrcaSlicer filament profiles
• •Bambu Studio filament profiles
• •SpoolmanDB community filament database
• •Open Filament Database (OpenFilamentCollective)
• •Marlin firmware printer configurations (hardware limits)
• •Klipper printer configurations (hardware limits)
• •3DFilamentProfiles.com community data
• •Hand-curated manufacturer specifications

Where sources disagree, we apply conflict resolution rules: community-verified data takes precedence over manufacturer claims when the discrepancy exceeds 15°C. Conservative ranges are applied when no community data exists to resolve a conflict.

1,317 brand+material combinations are currently in the database. Coverage is highest for PLA (194 brands), PETG (164 brands), and ABS (131 brands).

FixMyPrint uses AI in two places:

Fix My Print- natural language classification

GPT-4o reads your description and classifies it into one of 16 canonical issue types. This is the one task where natural language understanding genuinely outperforms rules- humans describe the same problem dozens of different ways, and a rules-based classifier would miss too many valid descriptions.

The AI classification feeds into the deterministic fix-intelligence engine. AI identifies the issue. The engine finds the fixes. The fixes come from community data, not AI generation.

Photo Diagnosis- visual failure identification (Pro)

GPT-4o Vision analyzes your photo against specific visual signatures for each failure type. The visual identification feeds into the same fix-intelligence engine. AI identifies the failure mode. The engine returns the fixes.

In both cases, AI is the front door. The fix recommendations themselves come from community-confirmed data.

• •FixMyPrint works best for the 16 most common FDM failure types. Obscure or hardware-specific failures may not have enough community data to produce confident recommendations.
• •Fix success rates reflect community data, not controlled experiments. Real-world results depend on factors we can't account for- room temperature, filament brand variation, hardware wear, and user skill.
• •The dataset is weighted toward popular printers and filaments. A Bambu Lab P1S running Bambu PLA has far more community data than a Vivedino Raptor running specialty nylon.
• •Community data has recency bias. Fixes that worked 3 years ago may be less relevant for newer printer models or updated slicer versions.

If you're a researcher, journalist, or developer who wants to dig deeper- we're happy to share more detail about our methodology, data collection process, or engine design.