PeaDB: Redis-Compatible Database Coded with AI Assistants in C++20
Project Overview
PeaDB is a Redis 7.2.5 drop-in replacement written in modern C++20. The developer "vibe-coded" it during Tết (Lunar New Year) with about one week of part-time work while balancing family commitments.
Key Features
- Speaks RESP2/3 protocol
- Implements approximately 147 Redis commands
- Includes persistence, replication, and cluster functionality
- Goal: behave indistinguishably from Redis while optimizing for multi-core CPUs
Development Tools and Cost
The project used a combination of AI coding assistants:
- Codex (ChatGPT Go plan) - $8/month (free via Vietnam promo)
- GitHub Copilot Pro - $10/month
- Total cost: ~1 month of Codex budget + ½ month of Copilot budget
Models used: Claude Opus 4.6, GPT-5.2, and GPT-codex-5.3. The developer noted that Codex 5.3 feels cheaper and sometimes solves problems Opus doesn't, but using all three models together works best.
Three-Model Workflow
For hard problems, the developer used this approach:
- Ask each model to write opinions/solutions into three separate markdown files
- Ask Claude to verify, merge, point out mistakes, and learn from the other two models
- Implement, test, and iterate
Benchmarks and Performance
Benchmark results show PeaDB performance is "quite close to Redis" according to the developer's comparison report. The developer specifically requests critique of their benchmark methodology, asking for feedback on workload mix, client settings, pipelining, CPU pinning, warmup, latency percentiles, and other factors to ensure honest comparisons.
Repository and Resources
The project is available on GitHub, and the developer has provided a detailed comparison report showing benchmark results against Redis.
📖 Read the full source: r/ClaudeAI
👀 See Also
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altRAG: Replace Vector DB RAG with 2KB Pointer Files for AI Coding Agents
altRAG is a Python tool that replaces vector database RAG with lightweight pointer files. It scans Markdown/YAML skill files to create a 2KB skeleton file mapping sections to exact line numbers and byte offsets, allowing AI agents to read only needed sections instead of entire files.
Benchmark Shows CLI Tool Reduces Claude Code Token Costs by 32% Through Structural Navigation
A developer built a Rust CLI tool that gives Claude Code agents structural navigation commands like 'show me a 180-token summary of this 6,000-token class.' Benchmarking on Sonnet 4.6 across 54 automated runs showed 32% lower cost per task and 67% more code edits per session.