I Built a Claude Code Agent That Reviews PRs (Here's the Prompt)
Working agent that posts inline review comments and blocks on critical issues. The full system prompt, the GitHub Actions wiring, the failure modes I hit and what I changed.
Practical, project-based AI build tutorials. Real cost numbers, working code, honest tool comparisons. Written by people shipping agents to paying users — not pitching the future of AI.
Ten focused tracks. Each one is a working area we ship in — not a content category we made up to win SEO.
Building, debugging and shipping AI agents that actually work.
/llm-apps/End-to-end LLM application engineering: streaming, latency, cost, edge cases.
/rag/Retrieval pipelines that don't hallucinate. Chunking, retrieval, rerank, eval.
/vector-databases/Qdrant, pgvector, Weaviate, Pinecone — head-to-head with real workloads.
/fine-tuning/LoRA, QLoRA, full SFT. When fine-tuning beats prompting and when it doesn't.
/prompt-engineering/System prompts that survive edge cases. Patterns that scaled in production.
/tool-comparisons/Honest, hands-on comparisons of dev tools, model APIs and inference services.
/model-picking/Picking the right model for the task. Coding, reasoning, voice, vision.
/cost-optimization/Cutting your inference bill 5-10x. Real numbers from real apps.
/production-ops/Monitoring, evals, rate-limit handling, retries — running LLMs at scale.
Real projects we shipped, with the prompts, code and cost numbers. No vibes.
Working agent that posts inline review comments and blocks on critical issues. The full system prompt, the GitHub Actions wiring, the failure modes I hit and what I changed.
Concrete config to run a small open-weight model on rental GPU starting around $20/month. Throughput, latency, cold-start, gotchas.
Five concrete tactics that drove hallucination rate down: chunk dedup, retrieval rerank, refusal prompts, citation forcing, eval-gated deploy. With code.
Dataset, training loop, eval. RTX 3090 vs A100 cost trade. Push to HuggingFace, deploy to vLLM. Numbers, not theory.
I've shipped agents to paying users for 18 months. Here's the honest list of what breaks, why, and the patterns that finally held up.
A flowchart, not a feature comparison. Four questions that pick the right vector store every time.
OrionAI Build is for solo founders, indie hackers and technical PMs shipping AI products today. Every guide answers a specific question: which model, what does it cost, how do I wire it up. No agentic-AGI think pieces. No "in today's rapidly evolving AI landscape" intros. If we say a build took 4 hours, that's the actual wall-clock time. If we cite a price, it's linked to the pricing page.