Logging with a Lens: Using Visual AI to Document Glaze Tests and Results

We need to produce a concise Dev.to article 400-500 words. Must count words. Let's aim for about 440 words.

Structure: Title, intro (2-3 sentences), core (explain ONE key principle or framework clearly), include 1 specific tool name and its purpose (from facts), mini-scenario (2 sentences), implementation (3 high-level steps), conclusion (summarize key takeaways only). Use markdown with # title, ## subheadings, paragraphs.

We must not reveal exact prompts or detailed configurations. No placeholders.

We need to use facts: advanced search, application notes, before mixing, color description, disconnection, firing log, inconsistency, performance, subjectivity, texture, the stage, the tool, unsearchability, post-firing, pre-firing checklist.

We need to include one specific tool name: e.g., Obsidian or Notion or Google Photos. Choose one and describe its purpose.

We must keep professional tone.

Now count words. Let's draft then count.

Draft:

Title: # Logging with a Lens: Using Visual AI to Document Glaze Tests and Results

Intro: maybe 2-3 sentences.

Let's write.

We'll then count.

I'll write then count manually.

Article:

Small‑batch ceramic artists know the frustration of a glaze that looks perfect in the studio but behaves differently on the shelf. Inconsistent lighting, varied backgrounds, and scattered notes make it hard to compare results or spot trends. By pairing a consistent visual record with searchable metadata, you turn fleeting photos into a reliable knowledge base.

The Principle: Standardize the Image, Enrich the Data

The core idea is to decouple the glaze’s appearance from its context by fixing every photographic variable and linking each image to structured data. When the backdrop, lighting, and angle are identical, differences in the photo reflect real changes in the glaze—such as gloss, texture, or crystal formation—rather than environmental noise. Those visual cues become searchable attributes once you tag them with objective descriptors (gloss meter reading, texture terms, color description) and firing details.

Tool: Use Obsidian as a free digital notebook that lets you create a note for each test, embed the standardized photo, and attach properties like Test ID, Recipe ID, Gloss, Texture, and Firing Log. Obsidian’s graph view and tag‑based search then enable queries such as “show all glazes with a gloss meter reading >70 GU that are also stable on vertical surfaces.”

Mini‑scenario

You photograph a new shino glaze on a mid‑grey matte card under diffuse LED light. In Obsidian you create a note tagged #shino #cone10_reduction #matte, fill in the gloss reading of 72 GU, note the texture as “smooth,” and link it to the recipe. A week later you run the advanced search and instantly see that this shino meets your gloss‑and‑stability criteria.

Implementation Steps

1. Set up the stage – Place a non‑reflective mid‑grey backdrop, fix your camera on a tripod, and use the same lighting setup for every test. Capture the fired tile straight‑on, ensuring the entire sample fills the frame.
2. Create the test record – In Obsidian, make a new note using the Test ID format (YYMMDD-Name-Number). Attach the photo, then add properties: Recipe ID, Gloss (GU), Texture (choose from bubbled, crystalline, smooth, orange‑peel, etc.), Color Description (objective phrase), and full Firing Log (cone, atmosphere, peak temp, hold time, kiln position). Add at least five descriptive tags such as #shino, #carbon_trap, #matte, #cone10_reduction, #porcelain.
3. Link and review – Connect the note to your master recipe file (either via a link or embedded reference). Before mixing a production batch, open the note, verify the visual log and data, and add any application notes (dip vs. brush, number of coats, sieving). Use Obsidian’s search to answer questions like “Show me all glazes where the blue crystallized.”

Conclusion

Standardizing your glaze photography eliminates subjective variability, while embedding each image in a searchable digital notebook turns anecdotal observations into actionable data. The result is faster recipe refinement, fewer repeat defects, and a growing visual library you can query with precision—empowering small‑batch potters to automate glaze recipe calculation and batch consistency tracking with confidence.

Now count words.

Let's count manually. I'll copy text and count.

Title line: "Logging with a Lens: Using Visual AI to Document Glaze Tests and Results" => words: Logging(1) with2 a3 Lens:4 Using5 Visual6 AI7 to8 Document9 Glaze10 Tests11 and12 Results13. That's 13 words.

But title line counts as part of article? Usually yes. We'll include.

Now paragraphs.

I'll rewrite full article with line breaks and count.

Full article:

# Logging with a Lens: Using Visual AI to Document Glaze Tests and Results

Small‑batch ceramic artists know the frustration of a glaze that looks perfect in the studio but behaves differently on the shelf. Inconsistent lighting, varied backgrounds, and scattered notes make it hard to compare results or spot trends. By pairing a consistent visual record with searchable metadata, you turn fleeting photos into a reliable knowledge base.

## The Principle: Standardize the Image, Enrich the Data

The core idea is to decouple the glaze’s appearance from its context by fixing every photographic variable and linking each image to structured data. When the backdrop, lighting, and angle are identical, differences in the photo reflect real changes in the glaze—such as gloss, texture, or crystal formation—rather than environmental noise. Those visual cues become searchable attributes once you tag them with objective descriptors (gloss meter reading, texture terms, color description) and firing details.

**Tool:** Use **Obsidian** as a free digital notebook that lets you create a note for each test, embed the standardized photo, and attach properties like Test ID, Recipe ID, Gloss, Texture, and Firing Log. Obsidian’s graph view and tag‑based search then enable queries such as “show all glazes with a gloss meter reading >70 GU that are also stable on vertical surfaces.”

### Mini‑scenario
You photograph a new shino glaze on a mid‑grey matte card under diffuse LED light. In Obsidian you create a note tagged `#shino #cone10_reduction #matte`, fill in the gloss reading of 72 GU, note the texture as “smooth,” and link it to the recipe. A week later you run the advanced search and instantly see that this shino meets your gloss‑and‑stability criteria.

### Implementation Steps
1. **Set up the stage** – Place a non‑reflective mid‑grey backdrop, fix your camera on a tripod, and use the same lighting setup for every test. Capture the fired tile straight‑on, ensuring the entire sample fills the frame.
2. **Create the test record** – In Obsidian, make a new note using the Test ID format (YYMMDD-Name-Number). Attach the photo, then add properties: Recipe ID, Gloss (GU), Texture (choose from bubbled, crystalline, smooth, orange‑peel, etc.), Color Description (objective phrase), and full Firing Log (cone, atmosphere, peak temp, hold time, kiln position). Add at least five descriptive tags such as `#shino`, `#carbon_trap`, `#matte`, `#cone10_reduction`, `#porcelain`.
3. **Link and review** – Connect the note to your master recipe file (either via a link or embedded reference). Before mixing a production batch, open the note, verify the visual log and data, and add any application notes (dip vs. brush, number of coats, sieving). Use Obsidian’s search to answer questions like “Show me all glazes where the blue crystallized.”

## Conclusion
Standardizing your glaze photography eliminates subjective variability, while embedding each image in a searchable digital notebook turns anecdotal observations into actionable data. The result is faster recipe refinement, fewer repeat defects, and a growing visual library you can query with precision—empowering small‑batch potters to automate glaze recipe calculation and batch consistency tracking with confidence.

Now count words.

I'll count each word.

I'll copy and count manually using systematic approach.

I'll break into lines.

Line 1: "# Logging with a Lens: Using Visual AI to Document Glaze Tests and Results"

Words: Logging(1) with2 a3 Lens:4 Using5 Visual6 AI7 to8 Document9 Glaze10 Tests11 and12 Results13. (Ignore #). So 13.

Line 2: empty.

Line 3: "Small‑batch ceramic artists know the frustration of a glaze that looks perfect in the studio but behaves differently on the shelf."

Count: Small‑batch1 ceramic2 artists3 know4 the5 frustration6 of7 a8 glaze9 that10 looks11 perfect12 in13 the14 studio15 but16 behaves17 differently18 on19 the20 shelf21. =>21.

Line 4: "Inconsistent lighting, varied backgrounds, and scattered notes make it hard to compare results or spot trends."

Count: Inconsistent1 lighting,2 varied3 backgrounds,4 and5 scattered6 notes7 make8 it9 hard10 to11 compare12 results13 or14 spot15 trends16. =>16.

Line 5: "By pairing a consistent visual record with searchable metadata, you turn fleeting photos into a reliable knowledge base."

Count: By1 pairing2 a3 consistent4 visual5 record6 with7 searchable8 metadata,9 you10 turn11 fleeting12 photos13 into14 a15 reliable16 knowledge17 base18. =>18.

Line 6: empty.

Line 7: "## The Principle: Standardize the Image, Enrich the Data"

Count: The1 Principle:2 Standardize3 the4 Image,5 Enrich6 the7 Data8. =>8.

Line 8: "The core idea is to decouple the glaze’s appearance from its context by fixing every photographic variable and linking each image to structured data."

Count: The1 core2 idea3 is4 to5 decouple6 the7 glaze’s8 appearance9 from10 its11 context12 by13 fixing14 every15 photographic16 variable17 and18 linking19 each20 image21 to22 structured23 data24. =>24.

Line 9: "When the backdrop, lighting, and angle are identical, differences in the photo reflect real changes in the glaze—such as gloss, texture, or crystal formation—rather than environmental noise."

Count: When1 the2 backdrop,3 lighting,4 and5 angle6 are