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Justinaquino — Sun, 02 Aug 2026 06:05:55 GMT

Add New Articles feed (10 most recent, pruned) + auto Newest Pages section, per the hub-and-spoke publishing protocol

@@ Line 3: / Line 3: @@
 Welcome to the '''Comfac IT Knowledge Base'''. Use the sections below to find guides, SOPs, and references organized by topic.
 </div>
 == ERPNext / Frappe ==
@@ Line 60: / Line 81: @@
 * [[Directed Execution: Token-Efficient Agent Development Process]] — Director/Executor/Checker loop and skill distillation for token-efficient agent development (v0.1, 2026-07-15)
 * [[De-Risk: Sovereign Training Data v0.1]] — Frontier-Token Quarantine and the ToS-clean path to owned model weights, prompted by the June 2026 Fable 5 export-control suspension (v0.1, 2026-07-15)
 </div>

Hugging Face Practical Guide 260802

Justinaquino — Sun, 02 Aug 2026 05:03:29 GMT

New article: Hugging Face practical guide (Spaces, local builds, Model Hub/datasets, enterprise + hf jobs) - lae/HF backlog

New page

= Hugging Face — Practical Guide =

<blockquote>'''Compiled:''' 2026-08-02 · '''Status:''' Living page — open for deeper research</blockquote>
<blockquote>'''Why it is here:''' Hugging Face is currently used at Comfac as a download endpoint for stock model files. It is four things, and the other three are the ones we under-use.</blockquote>

The goal behind this page: access to free open-source models that are '''better than the stock ones''' and more tailored to a specific job, the '''harnesses''' built around them, and the '''training resources''' that take a corpus of a few hundred owned data points and work it up toward thousands.

That last one is the unclosed loop in [[De-Risk: Sovereign Training Data v0.1]] — we have clean, owned process knowledge and no mechanism yet for turning it into '''weights we own'''.

Each section below ends with '''search keywords''' for going deeper on that one bucket.

== Source videos ==

<blockquote>'''Note:''' this wiki does not have the <code>EmbedVideo</code> extension installed (verified 2026-08-02 via <code>api.php ... siprop=extensions</code>), so videos are listed as preview cards rather than embedded players. The [https://wiki.gi7b.org/index.php/Hugging_Face_Practical_Guide_260802 gwiki copy of this page] has EmbedVideo and shows them inline. Installing the extension here is an administrator action.</blockquote>

{| class="wikitable" style="width:100%;"
! style="width:34%;" | Video !! What it covers !! Use it for
|-
| '''[https://www.youtube.com/watch?v=3kRB2TXewus What Is Hugging Face and How To Use It]'''
| The general tour — Spaces, the Model Hub, the Datasets section, and how they relate.
| Sections 1–3 below. Start here if Hugging Face has only ever been a download link.
|-
| '''[https://www.youtube.com/watch?v=wUzfRBi9wZ0 The Hugging Face Hub for Enterprise & Academia]'''
| Private workspaces, SSO, access control, separated compute/storage billing, SSH onto Spaces hardware, and remote training with <code>hf jobs</code>.
| Section 4. This is the one that matters for training on our own data.
|}

----

== 1. Exploring and using pre-built AI (Spaces) ==

'''Spaces''' are ready-to-use, browser-based applications built by the AI community — image and video generators, voice synthesis, transcription, live demos of new papers. All runnable without writing code.

If a Space is useful, click '''Duplicate Space'''. That forks it into your own account as your own copy, public or private, on hardware you select.

Two things worth internalising:

* A Space is a '''real repository''', not a demo sandbox. Duplicating gives you the source.
* Most Spaces are '''Gradio''' apps. Learn Gradio once and the whole catalogue becomes editable.

'''Search keywords:''' ''"Hugging Face Spaces tutorial"'' · ''"How to build and deploy Gradio apps on Hugging Face"'' · ''"Duplicating Hugging Face Spaces"''

== 2. Building custom apps locally ==

Because Spaces are open source, the code comes to your machine. Clone the repository, open it in an AI-assisted IDE, run it locally, and connect your own API keys instead of the demo's. From there the IDE can help you change colours, fonts, or actual behaviour.

This is the cheapest route to '''a harness we control''' — instead of writing scaffolding from scratch, start from a working app and cut it down.

Practical notes:
* Expect the first local run to fail on dependencies or a missing key. That failure, written down, is the reusable knowledge — not the happy path.
* Check the licence before adapting anything. '''Open-source and permissively-licensed are not the same thing''', and licence direction is one-way once code is borrowed.

'''Search keywords:''' ''"Clone Hugging Face Space locally"'' · ''"Build AI apps with Cursor and Hugging Face"'' · ''"Integrate LLM API keys in Hugging Face local apps"''

== 3. Finding models and datasets ==

The '''Model Hub''' is the core: over 1.5 million open-source models categorised '''by task''' — computer vision, NLP, audio, robotics. The '''Datasets''' section holds the data that trained them.

'''Navigate by task tag, not by keyword.''' Searching an acronym returns papers; filtering by task returns weights:

: <code>https://huggingface.co/models?pipeline_tag=robotics</code>
: <code>https://huggingface.co/api/models?pipeline_tag=robotics</code> ''(API form, for scripting)''

The advanced feature almost nobody touches is the '''dataset viewer''': chat with a dataset through an AI agent and '''run SQL queries in the browser''' to analyse it ''before'' downloading a single gigabyte. For evaluating training data this is the highest-value habit on the page.

'''Our standing admission rule applies here:''' a model card is a '''claim, not a measurement'''. Nothing enters the local model roster on a card alone — it enters on a real API call, the same gate used for the Synopsis model-selection slate. See [[Comfac GPU Scaling and AI Research Goals]] §Applied Model Evaluation and [[🧠 Process: Selecting and Installing the Right Ollama Model for Your Hardware]].

'''Search keywords:''' ''"How to choose and run Hugging Face models"'' · ''"Hugging Face dataset viewer SQL tutorial"'' · ''"Fine-tuning open source models Hugging Face"''

== 4. Scaling for teams and enterprise ==

For organisations and academic teams the paid tiers add private workspaces, single sign-on (SSO), and granular access control.

The structural benefit is that '''compute and storage are billed separately''' — no paying for idle hardware. Two working modes follow:

* '''SSH directly onto Spaces hardware''' and develop there, as if it were a remote box.
* '''<code>hf jobs</code>''' — fire a heavy training workload at remote hardware and collect the result.

The second is what makes a fine-tune possible without owning the GPU it would need.

'''Search keywords:''' ''"Hugging Face Enterprise setup guide"'' · ''"Running hf jobs on zero GPU Hugging Face"'' · ''"Hugging Face private workspaces and security"''

----

== Open problem — where this is going ==

Three goals, none closed. Tracked in <code>work/comfac-operations/BACKLOG-huggingface-model-sourcing-and-training.md</code>.

# '''Better than stock.''' The models we run locally today are stock bases. Tuned community models in the same size band may follow instructions better for a specific task, at no cost — but only a run against a gate written '''beforehand''' counts as evidence.
# '''Harnesses instead of rebuilds.''' Identify the Spaces that overlap tooling we would otherwise write ourselves.
# '''Training on owned data.''' Start at a few hundred data points and work toward thousands.

=== The constraint on goal 3 ===

'''Frontier-Token Quarantine is binding.''' Per [[De-Risk: Sovereign Training Data v0.1]], distilling commercial frontier-model outputs into our own weights trades a regulatory dependency for a '''contractual liability''' — in writing, with discoverable provenance logs. Any training corpus must be '''clean by construction'''. Tooling cannot launder provenance; that was named as the weakest claim in the source report.

=== Hardware reality check ===

Anything meant to run on a normal workstation has to fit the VRAM actually available, '''alongside whatever else is already resident''' — a vision model kept loaded for a document pipeline is not free. Size band and quantisation are a filter applied '''first''', not a compromise made afterwards. A related finding already measured here: 4-bit quantisation degrades digit reading, so anything touching numbers on a form runs at 8-bit.

== See also ==

* [[De-Risk: Sovereign Training Data v0.1]] — why owned weights matter and what may not go in the corpus
* [[Comfac GPU Scaling and AI Research Goals]] — GPU capacity and the applied model-evaluation method
* [[🧠 Process: Selecting and Installing the Right Ollama Model for Your Hardware]]
* [[Project OpenCoder: AI Independence Initiative]]
* [[Directed Execution: Token-Efficient Agent Development Process]]

[[Category:AI]]
[[Category:Research]]
[[Category:Open Source]]
[[Category:Comfac]]

Audio Learning

Justinaquino — Sat, 01 Aug 2026 06:15:16 GMT

Created: Audio Learning research-to-TTS tool article with link to citfj repo

New page

{{New article banner|date=2026-08-01}}
'''Audio Learning''' is the practice of converting research, articles, notes, and study material into scripts written to be '''listened to''' rather than read. It is a shared Comfac tool maintained as a private Forgejo repository and designed for staff who consume technical material hands-free — commuting, walking, between machines on the plant floor.

== What it is ==

Written prose and spoken prose are different mediums. A reader can re-read a sentence, skim a heading, and hold structure visually. A listener gets one linear pass with no backtracking. Audio Learning provides the discipline to re-compose research for the ear.

The repository contains a '''skill for AI agents''' ('''research-to-tts''') that converts knowledge into one of two house formats:

; Lecture format (Great Courses style)
: For technical study — networking, protocols, CISM/CISSP, security architecture, governance frameworks, systems engineering, ISO standards. The listener walks away understanding a mechanism they did not understand before. Useful for Comfac training material: Network+ basics, pfSense operations, BIR compliance, ERPNext workflows.

; Analyst format (thesis-and-evidence style)
: For contested material — energy markets, housing policy, supply-chain economics, regulatory analysis, industry studies. The listener walks away able to make or defend a decision. Useful for bid analysis, feasibility studies, vendor evaluations, and competitive intelligence.

== What it contains ==

* Concrete prose mechanics — how to speak numbers, acronyms, symbols, citations, code, and homographs so a text-to-speech engine renders them correctly
* Format templates — cold-open structures, spine metaphors, prerequisite ordering, decision frames, thesis stacking, approach ladders, and tripwire indicators
* Visual description protocols — a two-pass method for turning charts, diagrams, tables, and maps into something a listener can build a mental model of
* A pre-delivery audit — ten checks every script must pass before it is spoken

== Where it lives ==

The source repository is private on the Comfac Forgejo:

[https://git.comfac-it.net/cgg/audio-learning cgg/audio-learning]

== How to use it ==

An AI agent loads the skill, reads the reference files, picks a format, plans an outline from the source material, drafts a script with spoken prose only (no markdown, no symbols, no parentheticals), and audits the result before delivering it. The output is a plain-text script with a non-spoken production header and a clean spoken body — ready to be fed into any text-to-speech engine.

== Related ==

* [[GPU Scaling and AI Research|GPU Scaling and AI Research Goals]] — Comfac's model-evaluation infrastructure
* [[Open Source Software Analysis]] — tool evaluation methodology
* [[Resume / CV Renamer Pipeline]] — OCR-to-searchable pipeline (same ingestion-to-structured-output pattern)

[[Category:Tools]]
[[Category:AI Agents]]
[[Category:Training]]

Main Page

Justinaquino — Fri, 31 Jul 2026 14:25:42 GMT

Tools & Productivity: add Resume / CV Renamer Pipeline

← Older revision		Revision as of 14:25, 31 July 2026
Line 104:		Line 104:
	<div style="column-count:2; column-gap:2em;">		<div style="column-count:2; column-gap:2em;">
	* [[Excel Description Filler Tool 📝]]		* [[Excel Description Filler Tool 📝]]
			* [[Resume CV Renamer Pipeline\|Resume / CV Renamer Pipeline]] — CV intake → OCR / vision-OCR → AI field extraction → date-stamped rename + VCF export
	* [https://github.com/Comfac-Global-Group/comfac-studies Comfac Studies - Spaced Repetiton Active Recall]		* [https://github.com/Comfac-Global-Group/comfac-studies Comfac Studies - Spaced Repetiton Active Recall]
	* [[LibreOffice / Nextcloud Office – "Due Tasks" Conditional Formatting]]		* [[LibreOffice / Nextcloud Office – "Due Tasks" Conditional Formatting]]

Resume CV Renamer Pipeline

Justinaquino — Fri, 31 Jul 2026 14:25:40 GMT

Add Resume / CV Renamer Pipeline tool article (intake, OCR/vision-OCR, AI extraction, rename, VCF export)

New page

= Resume / CV Renamer Pipeline =

<div style="background:#f8f9fa; border:1px solid #ddd; border-radius:4px; padding:10px 16px; margin-bottom:16px;">
Automated resume ingestion, OCR, AI extraction, file renaming, and VCF contact export for recruitment workflows.
</div>

'''Location:''' <code>/path/to/resume-renamer/</code> (configure to your local HR/resume folder)

'''Upstream docs:''' [[Frappe HRMS - Ch01 Recruitment]]

----

== What It Does ==

# Drop a resume file (PDF, DOCX, JPG, PNG) into the folder.
# The scanner detects the new file and extracts text with <code>pdftotext</code>.
# Image files or image-based PDFs are converted with <code>img2pdf</code> and OCR'd with <code>ocrmypdf</code> if available.
# If OCR fails or is unavailable, the scanner falls back to '''vision OCR''' using a local VLM (e.g. <code>glm-ocr:bf16</code>).
# A local LLM extracts structured candidate data.
# The file is renamed to: <code>YYMMDD Full Name Degree.pdf</code>
# A <code>YYMMDD-HHMMSS_Bulk_Import.vcf</code> file is generated for contact import.

The single source of truth is the SQLite database <code>resumes.db</code>.

----

== Supported Inputs ==

{| class="wikitable"
! Format !! Handling
|-
| PDF with text || Direct extraction
|-
| Image-based PDF || <code>ocrmypdf</code> → vision OCR fallback
|-
| JPG / PNG || <code>img2pdf</code> → vision OCR fallback
|-
| DOCX || Detected; convert to PDF manually if needed
|}

----

== Quick Start ==

=== Run once manually ===

<syntaxhighlight lang="bash">
cd /path/to/resume-renamer
source ./venv/bin/activate
python cv_auto_scanner.py --once
</syntaxhighlight>

=== Dry-run preview ===

<syntaxhighlight lang="bash">
python cv_auto_scanner.py --once --dry-run
</syntaxhighlight>

=== Daemon mode ===

<syntaxhighlight lang="bash">
python cv_auto_scanner.py --daemon --interval 900 # 15 minutes
</syntaxhighlight>

=== Force a full re-scan ===

<syntaxhighlight lang="bash">
rm -f .scanner_state.json
python cv_auto_scanner.py --once
</syntaxhighlight>

----

== Common Issues ==

=== Degree shows "General" or is empty ===

The scanner now explicitly accepts diplomas and certificates in the <code>degree</code> field. If an older file still shows "General", reset it and reprocess:

<syntaxhighlight lang="bash">
source ./venv/bin/activate
python3 -c "
import resume_db
resume_db.init_db()
resume_db.upsert_candidate('ORIGINAL_FILENAME.pdf', status='pending', retry_count=0, last_error=None)
"
rm -f .scanner_state.json
python cv_auto_scanner.py --once
</syntaxhighlight>

=== "Text too short / unreadable" ===

The scanner automatically tries OCR and vision OCR. If it still fails:

<syntaxhighlight lang="bash">
source ./venv/bin/activate
python /path/to/rpdf-skills/vision_ocr.py FAILED_FILE.pdf --model glm-ocr:bf16 --format text -o extracted.txt
</syntaxhighlight>

Then rebuild a searchable PDF and re-run the scanner.

=== venv broken after Python upgrade ===

<syntaxhighlight lang="bash">
sudo apt install python3.14-venv python3-pip
cd /path/to/resume-renamer
rm -rf venv
python3.14 -m venv venv
source ./venv/bin/activate
pip install --upgrade pip
pip install img2pdf requests pdfplumber psycopg2-binary fpdf2
</syntaxhighlight>

----

== VCF Import ==

New <code>*_Bulk_Import.vcf</code> files are generated automatically. Import them into your contacts app:

* '''Google Contacts:''' contacts.google.com → Import → Select VCF
* '''Android:''' Copy VCF to phone → Contacts → Settings → Import
* '''Thunderbird:''' <code>thunderbird -compose "to=hr@example.com,subject=Contact Card,attachment=/path/to/YYMMDD-HHMMSS_Bulk_Import.vcf"</code>

The VCF uses a non-standard layout for multi-part names:

{| class="wikitable"
! vCard Field !! Value
|-
| <code>N</code> (Last Name) || Role context, e.g. <code>Electrical Applicant 260616</code>
|-
| <code>N</code> (First Name) || Candidate's full name
|-
| <code>FN</code> || <code>{Full Name} - {Context}</code>
|}

To specify the context for a batch:

<syntaxhighlight lang="bash">
python cv_auto_scanner.py --vcf-context "Electrical Applicant 260616"
</syntaxhighlight>

----

== Related Pages ==

* [[Frappe HRMS]]
* [[Frappe HRMS - Ch01 Recruitment]]
* [[Steps to Repair and OCR a Scanned or Corrupted PDF in Ubuntu]]
* [[🧠 Process: Selecting and Installing the Right Ollama Model for Your Hardware]]

[[Category:HR]]
[[Category:Recruitment]]
[[Category:Automation]]
[[Category:Comfac]]
[[Category:IT Operations]]

Comfac GPU Scaling and AI Research Goals

Justinaquino — Fri, 31 Jul 2026 13:02:32 GMT

Add Applied Model Evaluation - Synopsis mail-agent model-selection test (2026-07-31)

@@ Line 34: / Line 34: @@
 * Study and participate in open-source projects that allow community-based compute contributions (similar to Folding@home).
 * Learn and experiment with decentralized compute-sharing models that enable contributors to sell '''tokens or compute time''' securely and transparently.
 ----