The Engineering Notebook — page 18 of 32
Notes on building with AI, agents & the modern stack.
Deep dives on AI/ML, RAG systems, agent engineering, and senior-engineer architecture decisions — a new post every week.
Developer Tools Bun vs Deno in 2026: Which Next-Gen JS Runtime Actually Wins?
Bun wins for raw speed and Node.js drop-in replacement; Deno wins for security-first architectures and standards compliance. Here's exactly when to pick each.
Cloud and DevOps Neon vs Supabase in 2026: Which Managed Postgres Platform Actually Wins?
Neon wins for serverless, scale-to-zero workloads and branching-heavy dev workflows; Supabase wins for full-stack apps needing auth, storage, and real-time out of the box. Here's the full breakdown.
AI and Machine Learning Qdrant vs Chroma 2026: Which Open-Source Vector DB Wins for RAG?
Qdrant wins for production RAG at scale; Chroma wins for local prototyping and developer speed. Here's the full breakdown to help you choose the right vector database before you're locked in.
AI and Machine Learning DSPy vs LangChain 2026: Which LLM Framework Actually Wins?
DSPy wins for teams who want the model to optimize its own prompts automatically; LangChain wins for teams who need fast, flexible prototyping with a massive ecosystem. The right choice depends entirely on whether you're tuning for performance or building for breadth.
AI and Machine Learning LangGraph vs CrewAI 2026: Which Agent Framework Actually Wins?
LangGraph wins for production systems requiring precise control flow and stateful orchestration; CrewAI wins for teams who need fast, role-based multi-agent prototypes without deep graph theory. Here's what the benchmarks and real workloads reveal.
AI and Machine Learning AutoGen vs CrewAI 2026: Which Multi-Agent Framework Actually Ships?
AutoGen wins for research-grade, dynamic multi-agent conversations and Microsoft ecosystem teams; CrewAI wins for structured, role-based pipelines that need to reach production fast. Here's the full breakdown.
Developer Tools Ollama vs Llamafile 2026: Which Local LLM Tool Actually Wins?
Ollama wins for developers who want a persistent API server with a rich model library and ecosystem integrations. Llamafile wins for anyone who needs zero-install portability — one executable, any machine, no setup.
Developer Tools LM Studio vs Jan (2026): Which Local LLM GUI Actually Wins?
LM Studio wins for polished UX and OpenAI-compatible APIs; Jan wins for open-source transparency and offline-first privacy. Here's exactly when to pick each.
AI and Machine Learning Apple Silicon vs NVIDIA GPU for Local LLMs in 2026: Which Wins?
NVIDIA wins on raw throughput and ecosystem depth for serious multi-GPU workloads; Apple Silicon wins on memory bandwidth per dollar and zero-friction local inference for solo developers. Your budget and batch size decide the rest.
AI and Machine Learning Intel Arc B580 vs RTX 4060 for AI Workloads in 2026: Which Budget GPU Actually Wins?
The RTX 4060 wins for production AI pipelines thanks to CUDA's mature ecosystem, but the Intel Arc B580 wins on raw memory bandwidth and value per dollar for local LLM inference. Your choice comes down to software stack, not just specs.
AI and Machine Learning Apple M4 vs M4 Max for Local LLMs in 2026: Which Should You Buy?
The M4 Max wins for serious local LLM work thanks to its unified memory ceiling and bandwidth advantage; the base M4 wins for portability and budget-conscious inference on smaller models. Here's exactly where the line falls.
AI and Machine Learning DeepSeek Coder vs Llama 3 for Coding in 2026: Which Wins?
DeepSeek Coder wins for pure coding tasks with superior benchmark scores and leaner hardware needs; Llama 3 wins for general-purpose projects needing broad reasoning, multilingual support, and a mature ecosystem.