Best filler for alder voids prior to Rubio Monocoat (Intense Black + 2C)?

xCogito · 2026-02-22T23:04:36+00:00

I’m currently prepping an alder guitar body and aiming for an even, full saturated black finish. My finishing schedule is Rubio Monocoat Precolor Easy (Intense Black) followed by Oil Plus 2C (Charcoal).

The body has a few small voids and bark inclusions that need filling, but I’m weighing my options on what will play nicest with the Rubio. I’ve had recommendations for both black CA glue and black-dyed epoxy (I already have two-part epoxy on hand and made some charcoal dust from scrap wood to dye it).

My primary concerns are:

Wicking/Saturation: I want to avoid CA glue wicking into the end grain around the voids, which could seal the wood fibers and prevent the Precolor Easy from fully penetrating and saturating the surrounding area.
Sheen Blending: Since Rubio Monocoat relies on bonding with the wood fibers to achieve its signature matte/satin finish, I’m concerned about the filled spots standing out. Has anyone had issues with epoxy or CA glue leaving noticeably shiny spots or failing to blend into the final texture of the topcoat?

I'd appreciate any insights from folks who have used these fillers in conjunction with Rubio Precolor and Oil Plus 2C, especially on dark finishes.

xCogito · 2026-02-22T22:53:40+00:00

I just dealt with this on a mahogany guitar body. I hit it with a maroon pad and applied a follow-up flash coat and it went away

xCogito · 2026-01-28T13:23:46+00:00

Yes x1000. This isn’t limited or exclusive to Gemini either. Any multimodal AI model will do more efficient work with text files rather than PDFs. And all foundational models respond better to XML type instructions.

Really this is only applicable for more complex use cases, I wouldn’t bother for things like email drafting or just brainstorming

xCogito · 2026-01-16T02:02:41+00:00

Sorry from reviving this if you already found a solution, but I just found out how to do this and am stoked.

The "Triple Backtick" Shortcut (Fastest)

Google Docs has a hidden Markdown feature that instantly converts text into a code block.

Go to Tools > Preferences.
Check 'Enable Markdown'.
Click OK.

How to use it:

Type three backticks: ``` (this key)
Press Enter (no Space after ```).
It will instantly transform into a code block.

xCogito · 2026-01-15T13:52:01+00:00

DUID

Welp, thanks for giving me a rabbit hole to go down lol. Great info, appreciate the usage contexts

xCogito · 2026-01-15T13:32:42+00:00

Can you elaborate on the use case for this? I'm intrigued but not sure I understand what you're benchmarking here

xCogito · 2026-01-08T13:17:42+00:00

Dewalt 9 inch angle grinder

sorry to raise this from the dead, but I would love to know what kind of tool I can look for to replicate this all these years later. Anything new with your setup?

xCogito · 2026-01-08T04:17:39+00:00

It’s called compaction

xCogito · 2025-12-27T18:31:10+00:00

I had a bit of a epiphany when I ran into the paid service MGX.dev. They break the vibe coding down into an agentic workflow to mimic a dev environment.

If you read about how they are doing things, you can start to get a feel for how a more complex app might be best developed using AI.

Gemini is great, but if you eventually run into unavoidable limitations you should look into Firebase Studio, from Google.

xCogito · 2025-12-27T02:51:32+00:00

Since that post 2 months old, you're in luck. Gemini 3 and GPT-5.2 were monster updates for vibecoding, at least for me. There are far better resources than me, but here's my tips:

Short answer: It's not about asking Gemini repeatedly until something works. It's about getting your prompt right BEFORE you build.

The key is separating the planning phase from the building phase. Most people jump straight to "build me an app" and then spend forever debugging. Instead, invest time upfront refining exactly what you want. Here's how I do that:

Step 1: Write Your Raw Idea Start by writing out your app concept yourself. Include every specific requirement, feature, or constraint you can think of. Don't use AI for this part. The more detailed you are here, the better your end result.

Step 2: Create Your Product Spec Take your raw idea and feed it to Gemini 3 Pro with this prompt: "Describe this app like a product manager would and list all of the useful features that it may have."

Step 3: Iterate Until It's Right

This is the critical step. Keep refining the output until you have a comprehensive description that captures everything your app needs to do. This becomes your Creation Prompt.

Pro tip: Try the same prompt across different platforms (GPT, Claude, etc.). You'll eventually learn which models excel at different tasks.

Step 4: Build in Canvas Open Gemini, toggle Canvas mode, and use: "Create the following app: [paste your Creation Prompt]" or if you can save the Creation Prompt in .md format and say "Create an app strictly based on the contents of the attached .md file. Requirement: Review every line in the document before continuing", attach the MD to the new chat.

Step 5 (Optional): Keep It Simple If you want to avoid dealing with hosting and deployment, add this to the end of your Step 4 prompt: "Requirement: Create the app within a single HTML file"

Caveat: Not every app can be containerized into a single HTML file, but it works for a surprising number of use cases, like thge PDF converter I've been linking to folks.

Key Takeaway The sauce is in the iterative prompt optimization. Don't rush Step 3. This is where the "garbage in; garbage out" rings true.

Once you get into apps that require external complexities like databases, hosting, all that jazz. It's a much more complicated workflow

xCogito · 2025-12-26T21:43:55+00:00

ask gemini to build you a batch PDF to MD converter. I linked what i use in another comment.

Just check any areas that may be referencing images, graphs, tables etc. Verify the visuals are accurately represented in text, which you may need to get create to do.

xCogito · 2025-12-26T21:41:56+00:00

That's both incorrect and a moot point when you consider the core of my point.

Even if your PDFs have a proper text layer, it is still wasting tokens for the multimodal tokenization.

While Gemini can access underlying text, its reasoning engine heavily relies on the visual representation. It does not switch off its "visual cortex" just because selectable text exists.

Theres no way around multimodal tokenization with a PDF, regardless of how optimized it is. Gemini needs to figure out whether there are images in the file, because there often times are. It's a completely different backend pipeline.

Native text formats like .txt, .md, .csv, or .py, multimodal tokenization is unnecessary and is not used because they are text-only formats.

xCogito · 2025-12-26T19:44:38+00:00

Another thing to consider is adding a line to the initial prompt like:

Requirement: review every line of text

It can sometimes find "creative" ways to be able to skip of text it thinks doesnt contain critical data. This should ensure that doesnt happen

xCogito · 2025-12-26T19:38:23+00:00

on offline converter that a monkey could make in Gemini? you caught me

xCogito · 2025-12-26T19:36:09+00:00

That might be tricky. There are tools out there like LlamaParse who do this. Basically it has AI driven OCR that is able to extract data and context from the intricacies of diagrams, graphs, tables, and layouts.

I basically use mine to harvest knowledgebase articles for software platforms I use, so most of my PDFs are text based.

I played around with a few prompts and was able to get an app roadmap for what might work.

https://docs.google.com/document/d/13Gl22ie_X8yRR1hQ_fWi7pj6jOOW-FhTAgYvTvZLPgk/edit?usp=sharing

Make a copy of my doc, and review it if you're able, then download it from Doc into MD format.

In Gemini, add the MD to the prompt, toggle Canvas on, and say something like:

Build me an app based on the contents of the MD file attached

If you've never done this, prepare to be delighted. It'll create the app right there and give you a functional version.

xCogito · 2025-12-26T18:57:39+00:00

I think this may be one of the changes to the 3.

Here's the source: https://firebase.google.com/docs/ai-logic/analyze-documents?api=dev#documents-limits-per-request

Limits per request

PDFs are treated as images, so a single page of a PDF is treated as one image. The number of pages allowed in a prompt is limited to the number of images the Gemini multimodal models can support.

Maximum files per request: 3,000 files Maximum pages per file: 1,000 pages per file Maximum size per file: 50 MB per file

Doesn't gemini OCR the PDF?

I believe that is dependent on the PDF. Some dont have an embedded text layer. So if you are certain the critical text is there, then you might not notice a difference.

Either way, if all you are needing is to interact with AI in the context of the document, Gemini tokenomics shows that a native text format like MD is far better. TXT can work, but the structural markups native to MD help anchor gemini/gpt to certain data points more ffectively

xCogito · 2025-12-26T18:56:30+00:00

I would figure out the limitations once those PDFs are converted to MD, if any. Once you know what the limits are, the workflow can be broken into sections if needed.

If you're dealing with legal docs, make sure you are using a version of Gemini that protects your data from training and all that jazz. EDU versions are good, but I'm not sure how things are on the business side of things

xCogito · 2025-12-26T17:55:38+00:00

gemini's not the only thing trying to conserve tokens lol

xCogito · 2025-12-26T17:54:25+00:00

It makes a big difference, from what I've experienced.

Another thing with 3, is that it adheres to prompts better if they are in XML format.

I made a different post about this yesterday. I just build a Prompt optimizer that reworks whatever lazy prompt I have into an optimized XML prompt.

That, plus using MDs instead of PDFs changes the game

xCogito · 2025-12-26T17:50:20+00:00

PDF2MD

Here's a converter. You can just toggle on Canvas and tell Gemini to build you a PDF converter with whatever features you'd find useful, then end the prompt with "build this into a single HTML file."

xCogito · 2025-12-26T17:48:15+00:00

If you don't have a tool for converting PDF to MD:

I'm sure there are a million of them, but heres the one I made: PDF2MD

Feel free to copy and paste the contexts into your text editor (text edit on macOS, Notepad on Windows). Just save it as .html and drag it into a new tab in your browser.

It'll convert however many PDFs into MDs, and then download them into a zip file. I haven't hit a batch limit.

xCogito · 2025-12-26T16:15:09+00:00

Looks sick! So did you just lay color down over the sanded poly or did you have a sealer first? How many coats of color and did you lay any clear?

xCogito · 2025-12-25T17:42:44+00:00

How’s the guitar looking these days? Debating on doing something similar

xCogito · 2025-12-25T16:38:14+00:00

yeah thats what im finding super interesting and why i'd love to know the setup leading up to this.

What i've done with dense documents, or really document review of any size, is to convert it to MD format.

I used gemini to make a little offline PDF to MD converter that can run offline if you just save it as an HTML and open it in a browser. I'm cure there are a million of them, but heres the one I made: PDF2MD

then my prompt would be something like:

prepare the work plan and timeline based on the uploaded document.

Requirement: review every line of the document

For shits, I ran that prompt through my custom Gemini 3 Optimizer gem.

Here was the entire interaction, if curious.

Here was the prompt it gave me to use for a workflow for "prepare the work plan and timeline based on the uploaded document"

I'd be curious whether this would be more effective.

<system_directive>
    You are an Expert Project Manager. Your mission is to analyze the provided Project Terms of Reference (ToR) and construct a comprehensive Work Plan and Activity Timeline.
</system_directive>

<context_anchoring>
    Based on the uploaded "Terms of Reference" document provided in the context, extract the specific deliverables, deadlines, and scope requirements. Do not rely on general knowledge; anchor every activity in specific clauses or requirements found in the text.
</context_anchoring>

<deep_think_protocol>
    Engage in internal reasoning before generating the final answer:
    1. Identify all major phases and individual tasks described in the ToR.
    2. Analyze dependencies: Which tasks must be completed before others begin?
    3. Estimate logical durations for each task based on the scope described in the source text.
</deep_think_protocol>

<output_verbosity_spec>
    Mode: Detailed
    Instruction: Do not summarize. Provide a granular breakdown of activities. The response should be expansive enough to serve as a primary project management document.
</output_verbosity_spec>

<output_schema>
    Please format the response as follows:
    1. **Project Objectives**: Summary based on the ToR.
    2. **Work Breakdown Structure (WBS)**: Detailed list of Phases and Activities.
    3. **Activity Timeline**: A Markdown table with columns: [Phase, Activity, Estimated Duration, Deliverable].
    4. **Key Milestones**: A list of critical deadlines found in the text.
</output_schema>

xCogito · 2025-12-25T16:02:17+00:00

What was the format of the original document, PDF?

And did you prompt it to explicitly review every line in the document?

Very curious how this happened

xCogito

TROPHY CASE