The Bacon Platform — Building Edge AI for the People Who Need It Most
February 26, 2026
The Bacon Platform
Building Edge AI for the People Who Need It Most
Jaime Bacon | February 2026
github.com/jaimebaconx
1. The Origin Story
The Revelation in the Waiting Room
It started on a Monday morning in a hospital waiting room in Springfield, Missouri. My son was getting his IVIG infusion. I had my laptop, a Smash Bros controller within arm's reach, and a question nagging at me: could I actually run AI locally, offline, on my own hardware?
By noon I had installed Ollama, pulled Llama 3.2, built my first RAG pipeline in Python, fed Where There Is No Doctor into ChromaDB, and watched an offline AI answer a medical question from a local knowledge base for the first time. Zero internet. Zero cloud. Zero cost per query.
“WHY DIDN’T ANYONE TELL ME IT WAS THIS EASY”
That was the moment. Not a boardroom. Not a pitch deck. A hospital waiting room, a sick kid, and a laptop. The Bacon Platform was conceived between IVIG drip cycles and Smash Bros matches.
What I Built in 48 Hours
Monday (Hospital):
- Installed Ollama and pulled Llama 3.2
- Built first RAG pipeline in Python
- Fed Where There Is No Doctor into ChromaDB
- Bacon-Buddy answered its first medical question from a local knowledge base
- Validated edge AI thesis against MIT/Stanford funded startup Tiiny AI
- Conceptually founded the Bacon Platform
- Bacon-Buddy code pushed to GitHub
Tuesday (Day Job + Hospital Follow-Up):
- ITsec meeting — now own Jira data classification project
- Spun up Azure DevOps org under ODOC banner
- Pivoted to Campfire — old time radio AI curation platform
- Installed CUDA, PyTorch cu118, Whisper — got GPU acceleration working on 1660 Ti
The Personal Context
All of this was built while present at my son’s IVIG appointment, playing Smash Bros in a hospital, leading worship with family on Sunday, watching my oldest son come home from his first dance to tell his dad he held a girl’s hand, hanging library shelves on Friday evening, filing taxes on Saturday morning, and launching Gov’t Dev Chronicles Episode 1 on LinkedIn and X.
AI doesn’t replace builders. It removes the barrier between vision and manifestation.
2. What We’ve Built
2.1 Bacon-Buddy Medical v0.1
An offline medical AI assistant that answers medical questions grounded in trusted public domain field guides. No internet. No subscription. No cloud. Every answer cites its source.
Knowledge Base:
- Where There Is No Doctor — Hesperian Health Guides
- Where There Is No Dentist — Hesperian Health Guides
- FM 4-25.11 — US Army First Aid Field Manual (Public Domain)
- Special Forces Medical Handbook ST 31-91B (Public Domain)
- Ship’s Medicine Chest and Medical Aid at Sea — US Coast Guard (Public Domain)
- Emergency Childbirth — US Government (Public Domain)
Sample Output:
Q: How do I splint an arm?
A: To splint an arm, pad the splints where they touch bony parts... use at least four ties (two above and two below the fracture)... check distal pulses before and after applying the splint.
Source: FM 4-25.11/NTRP 4-02.1/AFMAN 44-163(I)
Tech Stack:
- Python + LangChain RAG pipeline
- Ollama + Llama 3.2 3B (local inference)
- ChromaDB vector database (persistent, local)
- HuggingFace sentence-transformers (embedding)
- PyMuPDF (PDF ingestion)
GitHub: github.com/jaimebaconx/Bacon-Buddy
2.2 The Good Book v0.1
An offline Bible study assistant that answers theological questions, finds relevant passages, and synthesizes across scripture and trusted commentaries. Built on the same RAG pipeline as Bacon-Buddy with Bible-optimized chunking.
Knowledge Base:
- King James Bible (Public Domain)
- World English Bible (Explicitly Public Domain)
- Matthew Henry’s Complete Commentary (1706, Public Domain)
- Easton’s Bible Dictionary (1893, Public Domain)
- Nave’s Topical Bible (Public Domain)
Sample Output:
Q: What does the Bible say about anxiety and fear?
A: The Bible says that a holy fear is enjoined as a preventive of carelessness in religion... This fear is not a slavish dread, but rather filial reverence (Easton’s Dictionary)... ‘The fear of the LORD is the beginning of wisdom’ (Psalm 111:10, KJV)
GitHub: github.com/jaimebaconx/good_book
3. The Vision — Connect by Disconnecting
The Bacon Platform isn’t just about offline AI. It’s about a specific worldview: technology should increase autonomy, not dependency. The through line across every product is simple — you don’t need permission, connectivity, or a corporation’s continued goodwill to use this.
“Connect by Disconnecting” — edge AI as a tool for building tighter community bonds offline, not looser ones online.
3.1 Target Markets
- Prepper and Survival Communities — offline knowledge for grid-down scenarios
- Faith Communities — missionaries, rural churches, home Bible study groups
- Rural and Agricultural Communities — farmers, homesteaders, small ranchers
- Homeschool Families — offline AI tutors grounded in parent-chosen materials
- Small Businesses — tired of paying $500/month in AI subscriptions
- Regulated Industries — healthcare, legal, government — data that can’t go to the cloud
4. Technical Architecture
4.1 The Core Stack
- Python — core pipeline language
- Ollama — local LLM serving, GPU accelerated
- Llama 3.2 3B — base model, fast on consumer hardware
- LangChain — RAG pipeline orchestration
- ChromaDB — local vector database, persistent
- HuggingFace sentence-transformers — all-MiniLM-L6-v2 embedding model
- PyMuPDF — PDF and TXT document ingestion
- NVIDIA GTX 1660 Ti — 6GB VRAM, CUDA cu118, current dev hardware
4.2 The Module Architecture
Every Bacon Platform module follows the same pattern. This is intentional — the pipeline is proven, the swap is just the knowledge base and prompt.
- /model — pointer to Ollama model
- /knowledge — source documents (PDFs and TXTs)
- /data — pre-built ChromaDB vector index
- /src — pipeline code
- config — model selection, paths, prompt templates
4.3 Key Technical Decisions
- RAG over fine-tuning (for now) — Gets to 80-85% quality without the compute cost. Fine-tuning comes after revenue funds better hardware.
- HuggingFace embeddings over Ollama embeddings — Frees VRAM for inference. all-MiniLM-L6-v2 is fast and accurate.
- Smaller chunk sizes for domain-specific content — 400-500 tokens vs 800 default. Keeps procedures intact, preserves verse boundaries.
- Strict RAG prompt engineering — 'Answer ONLY from context' eliminates hallucination. Source citations are mandatory.
- Public domain content only — Not a legal constraint, a product feature. Auditable sources build trust with our specific market.
5. Business Model & Positioning
5.1 The Core Value Proposition
Pay once. Yours forever. No internet. No subscription. No company reading your data.
This isn’t competing with ChatGPT on capability. It’s competing on trust and ownership. That’s a different and arguably stronger value proposition for specific markets.
5.2 Revenue Streams
- Consumer modules — $15-29 one-time purchase per module on Gumroad
- Bundle pricing — $49 for full suite when 3+ modules exist
- Annual update modules — $9/year optional, refreshed content. Recurring revenue without subscription psychology.
- Enterprise custom builds — $2,000-10,000 engagements for regulated industries
- LoRA fine-tuning as a service — $500-2,000 per engagement as skills develop
5.3 The Enterprise Opportunity
Most enterprise AI implementations fail. The reasons are predictable: too general, hallucination risk in regulated environments, data privacy concerns, compliance nightmares, change management failures. The failure rate is over 80% within the first six months.
The pitch to enterprises that got burned by cloud AI:
“I can’t offer you GPT-infinity. But you’ve been down that road and it didn’t work. What I can offer is a custom, small AI tool for a specific task in your organization that actually works, meets your compliance requirements, and your data never leaves your premises.”
Target verticals:
- Healthcare — HIPAA compliance, data never leaves the building
- Legal — attorney-client privilege, document analysis
- Government — ITAR, FISMA, classification requirements
- Finance — SEC, FINRA compliance, proprietary data
5.4 The Competitive Moat
The public domain content constraint is not a weakness — it’s the moat. Every answer has a verifiable source. Every source is documented. Every document is auditable. Large models are getting sued for training data. Bacon Platform answers come with a bibliography. For our specific markets, that’s gold.
6. What’s Next
6.1 Immediate (This Week)
- Gumroad listings for Bacon-Buddy Medical and The Good Book
- Reddit posts: r/preppers, r/homestead, r/TrueChristian, r/LocalLLaMA
- Gov’t Dev Chronicles Episode 2 — document the build publicly
6.2 Short Term (Next 30 Days)
- Streamlit UI for both products — makes them accessible to non-technical users
- Improved chunking — eliminate topic bleed-through in medical answers
- Simple installer — one script setup for non-technical users
- Bacon Studio v0.1 — local Roblox dev assistant for building with sons
6.3 Medium Term (90 Days)
- First enterprise engagement — outreach through socials.
- LoRA fine-tuning exploration — domain-specific model adaptation
- Bacon Agent framework — overnight autonomous task completion
- Hardware upgrade — revenue funds better GPU
6.4 The Positioning Goal
Not ‘get a job at Anthropic.’ Get known as the person who spots gaps that aren’t on anyone’s radar and builds proof before anyone else realizes there’s a market. Build in public. Document everything. Ship real products. Let opportunities find the work.
“I find the people that Big Tech forgot and build AI that actually works for them.”
Built in a hospital waiting room. For the people who need it most.
github.com/jaimebaconx | February 2026