Why I Chose HuggingChat and Perplexity as My Primary AI Environments
Executive Summary
After experimenting with various AI tools, I’ve found that a hybrid approach using HuggingChat for experimental flexibility and Perplexity for real-time research offers the ideal balance for my work as a genealogist, historian, and author. This combination provides customizability, cost control, and research precision without the limitations of mainstream, one-size-fits-all AI solutions.
My Personal Background
Because I am a genealogist, historian, technologist, story teller and author, I want and need a better way to rapidly create thorough thoughtful articles. My writings/articles serve both as an archive as well as a memory for me and my family. If these writings and musings also assist others, I rejoice. Unlike most authors, I have no particular need for world-wide fame or riches. I simply wish to share what I learn and believe to be true.
If you examine my two primary websites (eirenicon & ManyRoads), you will become acquainted with my eclectic interests and writings. What you will find on these sites has taken me more the 25 years to generate. I don’t have another 25 years left in me and I have more to learn and share more information than I have time left to live.
Why I Need AI Tools
To accelerate my output, AI Chatbots and their use seemed to provide a great opportunity to grow and expand what I am able to accomplish. That’s how I arrived where I am in my AI journey.
The Problem with One-Size-Fits-All AI
When I ventured into today’s style of AI workflows, I relied on monolithic platforms like ChatGPT, DeepSeek, Claude, and Gemini. But over time, frustrations emerged:
- Vendor lock-in: Inability to customize responses for niche technical tasks
- Opaque costs: Unpredictable pricing tiers for API-heavy projects
- Generic outputs: Hallucinations in coding or research-specific queries
- Security concerns: Maybe they are all in my head, but I’d rather do this in a more local less Oligarchic environment
While these platforms excel at general tasks, they fell short for my specialized needs in historical research and genealogy. ChatGPT offered consistency but lacked the citation capabilities crucial for academic work. Claude provided nuanced responses but at a premium cost structure. Gemini integrated well with my existing tools but struggled with niche historical queries.
It became pretty obvious that I needed a hybrid approach—something adaptable for experiments (HuggingChat) and precise for real-time fact-checking (Perplexity). But more than anything, I need flexibility and agility. Here’s why this duo works.
HuggingChat and Perplexity: A Comparison
| Feature | HuggingChat | Perplexity | Mainstream AI (ChatGPT, Claude) |
|---|---|---|---|
| Model Access | Open-source, customizable | Proprietary but citation-focused | Proprietary, fixed parameters |
| Cost Structure | Free (self-hosted) or pay-as-you-go | Free tier with premium options | Subscription-based |
| Strengths | Customization, transparency | Research accuracy, citations | Polished UI, consistent outputs |
| Weaknesses | Technical complexity | Limited offline capabilities | Less flexible, potential vendor lock-in |
| Best For | Experimentation, custom tasks | Academic research, fact-checking | General-purpose tasks |
HuggingChat: The Open-Space Playground
Why It Shines
- Customizability: Unlike closed models, HuggingChat allows tweaking inference parameters (temperature, top-p sampling) and even fine-tuning smaller models like Llama-3 or Mistral for domain-specific tasks (e.g., code generation tailored to legacy systems).
- Open-Source Transparency: Community-driven updates mean faster patching of vulnerabilities and ethical auditing (critical for academic work).
- Cost Control: Self-hosting options via Hugging Face’s Inference Endpoints cut costs for non-urgent batch processing (e.g., analyzing large datasets overnight). By way of full transparency, I like running things in a no-cost manner. To date, that is what I have done.
Limitations
- Steep Learning Curve: Configuring models for GPU efficiency requires familiarity with transformers and PyTorch.
- Latency Tradeoffs: Larger open-source models (e.g., Falcon 180B) demand significant RAM, making real-time use impractical on consumer hardware.
Perplexity: The Precision Researcher
Why It Shines
- Speed and Accuracy: Instant citations with sources like ArXiv and PubMed streamline fact-checking for technical writing.
- Search Integration: The “Focus” feature (e.g., Academic, Reddit) provides context-aware answers, reducing manual filtering.
- Minimalist Design: No bloated features—ideal for quick queries during meetings or coding sprints.
Limitations
- Black-Box Model: Limited visibility into how results are weighted or filtered (a risk for reproducibility).
- No Offline Mode: Reliant on web connectivity, unlike locally hosted HuggingChat models.
My Workflow in Action
Task Distribution
| Task | Tool | Reason |
|---|---|---|
| Exploratory R&D (Website articles, Linux scripts, Genealogical Research, Home-school course materials) | HuggingChat | Prototype with models like StarCoder for niche scripting needs |
| Peer-Reviewed Writing (Article and Research Material expansion and vetting) | Perplexity | Trace sources instantly for academic rigor |
| Debugging, Idea development/expansion | Both | Cross-validate code suggestions against official docs |
Real-World Example: Researching My Family’s Eastern European Migration
When researching my ancestors’ migration patterns from Eastern Europe in the early 1900s, I used this workflow:
- Initial exploration with HuggingChat: Generated hypotheses about migration routes based on historical events and demographic patterns
- Fact verification with Perplexity: Checked claims against academic sources, census records, and published migration studies
- Content development with HuggingChat: Drafted narrative sections explaining the historical context
- Final validation with Perplexity: Verified specific dates, locations, and historical details before publication
This approach allowed me to complete in weeks what would have previously taken months of research.
My Computing Environment
In keeping with my system’s minimalist, dwm, hardened archlinux environment, I have elected to use both environments via browser (Firefox-Hardened). Downloading, maintaining and housing numerous AI models is over-kill for my application. I primarily use AI to assist me in article writing and historical research (genealogy).
For those interested in my computing setup and security approaches, I’ve documented my practices in several articles:
- A comparison of Mullvad VPN vs. ProtonVPN
- Comparison of Firefox (Hardened) – Mullvad – Tor browsers
- Google Logins & Privacy
- Secure Application & Use of KeepassXC
- Security Hardening: A Journey with Eyes Open
Technical Setup Options
Note: While I personally use browser-based interfaces, the following information may be helpful for readers interested in self-hosting options.
Hardware Requirements:
- CPU: AMD Ryzen 9 7900X | GPU: NVIDIA RTX 4090 (24GB VRAM)
- RAM: 64GB DDR5 | Storage: 2TB NVMe SSD
Software Configuration:
- HuggingChat: Running text-generation-inference Docker containers for Llama-3-70B
- Perplexity: Browser-based (Chrome), with custom Tampermonkey scripts to auto-export citations
Network Setup:
- Local Kubernetes cluster for load-balancing multiple HuggingChat instances
- Tailscale VPN for secure mobile access to self-hosted models
Key Considerations for Adopters
1. Address Bias Proactively
- HuggingChat: Use datasets library to audit training data (e.g., The Pile)
- Perplexity: Cross-reference cited sources manually for sensitive topics
2. Scalability Workarounds
- Cold Storage: Archive less-used HuggingChat models on IPFS
- Hybrid Queries: Offload Perplexity API calls via AWS Lambda during peak loads
3. Security Practices
- HuggingChat: Isolate models in Firecracker VMs with AppArmor profiles
- Perplexity: Use API token rotation and local proxies to mask metadata
The Verdict: Measurable Improvements
While giants like OpenAI dominate headlines, HuggingChat and Perplexity offer a strategic middle ground that has transformed my productivity:
- Research Efficiency: What previously took 8-10 hours of manual source checking now takes 2-3 hours with Perplexity’s citation capabilities
- Content Production: I’ve increased my article output by approximately 40% while maintaining or improving quality
- Technical Flexibility: For specialized genealogical research questions, HuggingChat’s customizability has provided insights that general-purpose AI often misses
- Cost Savings: Using primarily free tiers and browser-based access has kept my monthly AI expenses under $10
Recommendations for Different User Types:
- Academic Researchers: Prioritize Perplexity for its citation capabilities, supplemented by HuggingChat for hypothesis generation
- Content Creators: Balance both tools equally, using Perplexity to verify facts and HuggingChat for creative development
- Technical Users: Leverage HuggingChat’s customization while using Perplexity as a quick reference tool
- Casual Users: Start with browser-based versions of both before considering any self-hosting options
Future Plans:
- Experimenting with HuggingChat <-> Perplexity LoRA merges for domain-specific accuracy
- Contributing benchmarks to the Open LLM Leaderboard
- Developing custom prompts specifically optimized for genealogical research
- Honestly: I still need to use some of the big players to get my articles into final form.
References
- HuggingChat Documentation | GitHub
- Perplexity API Docs
- Biderman, S., et al. (2023). “Datasheet for the Pile”. ArXiv
- Workshop on Challenges in Deploying LLMs (NeurIPS 2024) – Proceedings
This content is free to use, adapt, and share.
Knowledge & Information should be open— please, spread them far and wide.
Remember, like with all of my work, I am able to provide the following assurance(s):
- It is almost certainly going to work until it breaks; although I have to admit it may never work and that would be sad.
- When/if it does break, you may keep all of the pieces.
- If you find my materials helpful, both you & I will be happy, at least for a little while.
- My advice is worth every penny you paid for it!
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