Priorstack: practical guides for LLM agents, inference and training
Troubleshooting guides and research explainers for developers building with large language models. Each article starts from a real error or question and links the official documentation, papers and GitHub issues it is based on.
Start with a topic
- LLM Agent Guides: LangChain, LangGraph & AI Tooling
- LLM Inference & Serving Guides: vLLM, Ollama & Hugging Face
- LLM Training & Architecture Guides: Fine-Tuning, RL & New Models
Latest articles
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Fixing Silent PyTorch DataLoader Crashes in Docker Containers
Fix silent PyTorch DataLoader crashes in Docker containers caused by shared memory exhaustion (/dev/shm). Learn the root cause and how to resolve SIGBUS errors.
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Test-Time Compute Scaling with Process Reward Models: Mechanisms, Strategies, and Trade-offs
Learn how Test-Time Compute Scaling and Process Reward Models (PRMs) optimize AI reasoning trajectories via step-level feedback and generative verification.
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Kolmogorov-Arnold Networks: Rethinking Neural Architecture via Learnable Spline Activations
Explore Kolmogorov-Arnold Networks (KANs), a novel neural architecture replacing MLPs with learnable B-spline activations on edges for better interpretability.
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Debugging Silent Context Truncation in Ollama’s OpenAI-Compatible API
Learn how to detect and fix silent context truncation in Ollama’s OpenAI-compatible API using debug logs, custom Modelfiles, and environment variables.
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vLLM CUDA Out of Memory: Fixing Startup, Runtime, and Persistent OOM Loops
vLLM throwing CUDA out of memory? Tell the startup KV-cache ValueError apart from a runtime torch.OutOfMemoryError and the persistent 500 loop, and fix each.
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Fixing LangGraph GraphRecursionError and LangChain Agent Infinite Loops
GraphRecursionError: recursion limit reached in LangGraph? Find the looping node, when to raise recursion_limit, and how to stop agent infinite loops.
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Demystifying Mixture of Experts Routing: Core Mechanisms, Optimization, and Practical Challenges
Explore Mixture of Experts (MoE) routing, comparing Top-k and Soft Routing, analyzing loss surfaces, and addressing real-world deployment challenges.