Every guide is built from real community discussions: actual benchmarks, tested solutions, and gotchas reported by practitioners. All claims include numbered source references linking to the original threads. Not generic advice an LLM could produce on demand.
| Guide | Data Points | Source |
|---|---|---|
| 48GB VRAM LLM Playbook | 38 data points from 72 user reports | r/LocalLLaMA (124 upvotes) |
Model selection, quantization configs, VRAM calculator with formulas, multi-GPU PCIe bandwidth analysis, and real performance data. All claims source-linked with numbered references.
| Guide | Data Points | Source |
|---|---|---|
| AI for Infrastructure as Code | 16 data points from 30 reports | r/devops (45 upvotes) |
| DevOps to Platform Engineering | 30 data points from 49 reports | r/devops (151 upvotes) |
How teams actually use LLMs for Terraform/CloudFormation, and career paths for the DevOps -> Platform Eng transition. Includes skills readiness matrix.
| Guide | Data Points | Source |
|---|---|---|
| Go Modular Monolith Architecture | 7 data points from 17 reports | r/golang (34 upvotes) |
Real patterns and pitfalls for structuring large Go codebases without drowning in complexity.
| Guide | Data Points | Source |
|---|---|---|
| Entry-Level Dev Survival Guide | 56 data points from 97 reports | r/webdev (355 upvotes) |
What actually works for landing dev jobs in 2025-2026. Hiring data, portfolio strategies, and career paths aggregated from 200+ comments.
- Demand scanning: Reddit and StackOverflow are monitored for high-engagement questions (20+ comments, specific technical topics)
- Community data harvesting: Thread comments are scraped and classified into benchmarks, solutions, gotchas, and configurations
- Enriched generation: An LLM generates the guide structure, but real community data points are woven throughout
- Quality check: Community citations are verified against source threads
The result: guides that contain information you can't get by just asking ChatGPT, because the value comes from aggregated real-world experience.
- Found an error? Open an issue or PR on the specific guide repo
- Have a topic suggestion? Open an issue here
- Want to add your own data? PRs with real benchmarks/experiences are always welcome
All guides are MIT licensed.