MLOps vs LLMOps vs AgentOps in 2026: What Carries Over, What's New
MLOps vs LLMOps vs AgentOps explained for 2026: how the lifecycle shifts from trained models to prompts to tool-using agents, what carries over (versioning, registries, CI gates, monitoring), what is genuinely new (evals, traces, tool permissions), and a current tool map.
13 minOct 20, 2026What Are AI Agents? Workflows, ReAct and Real Success Rates (2026)
AI agents explained for engineers in 2026: Anthropic's workflows vs agents split, the ReAct loop, tool calling, planning, memory and multi-agent designs, measured success rates from OSWorld (66.3%) to TheAgentCompany (30%), and when not to build one.
13 minOct 20, 2026Vector Databases in 2026: Do You Need One, or Is pgvector Enough?
An engineer's guide to vector databases in 2026: pgvector 0.8.7, Pinecone, Qdrant, Milvus 3.0, Weaviate, turbopuffer, Amazon S3 Vectors and Elasticsearch compared on price and architecture, why the benchmarks mislead, and when Postgres is enough.
13 minOct 20, 2026How to Run a Local LLM With Ollama: Setup, GPU and API Guide (2026)
Step-by-step Ollama guide for 2026 (v0.40): install on macOS, Linux, Windows or Docker, confirm NVIDIA, AMD or Apple GPU use, pick a model that fits, set context length, write a Modelfile, call the OpenAI-compatible API, connect coding tools, and fix slow or out-of-memory runs.
14 minOct 20, 2026RAG vs Long Context vs Fine-Tuning in 2026: 1M Tokens Isn't Free
RAG vs long context vs fine-tuning in 2026: current 1M-token context windows and prices, what context rot, NoLiMa and RULER show about long-context accuracy, where RAG fails, the per-query cost math, and a decision framework you can apply this quarter.
13 minOct 20, 2026How to Build a RAG Pipeline With Postgres and pgvector (2026)
Step-by-step RAG pipeline on Postgres 18 and pgvector 0.8.7: install, schema, chunking, OpenAI embeddings, an HNSW index, hybrid search with full-text and reciprocal rank fusion, a Python example, retrieval evals, and fixes for slow queries and poor recall.
17 minOct 20, 2026Prompt Management in 2026: Registries, Rollbacks and Five Acquisitions
How to version, deploy and roll back LLM prompts like code in 2026: Git vs Langfuse, PromptLayer, Braintrust and MLflow registries, labels and aliases, eval gates in CI, and what the Langfuse, Promptfoo, Helicone, Galileo and Humanloop deals mean for your stack.
13 minOct 20, 2026How to Tune pgvector Performance: HNSW, ef_search and Quantization (2026)
A step-by-step pgvector tuning guide for 0.8.x: HNSW vs IVFFlat, m, ef_construction and ef_search, maintenance_work_mem for fast builds, iterative scans for filtered queries, halfvec and binary quantization, EXPLAIN, and a troubleshooting table, with real timings.
14 minOct 20, 2026MLOps Platforms Compared 2026: MLflow vs Kubeflow vs SageMaker vs Vertex
An engineer's 2026 comparison of MLflow 3.17, Kubeflow 26.03, SageMaker AI, Vertex AI (now Gemini Enterprise Agent Platform), Databricks, W&B inside CoreWeave Forge, ZenML and Metaflow, with real prices, lock-in risk and a decision table.
13 minOct 20, 2026How to Use MLflow 3: Tracking, Model Registry and GenAI Tracing (2026)
Step-by-step MLflow 3.17 guide: run a tracking server on PostgreSQL and S3, log runs and LoggedModels, register models with aliases instead of stages, trace and evaluate a GenAI app with mlflow.genai, serve the champion model, and fix the errors you will actually hit.
12 minOct 20, 2026Running LLMs Locally in 2026: Ollama vs llama.cpp vs LM Studio vs MLX
A practical 2026 guide to running LLMs locally: Ollama 0.40, llama.cpp 0.7, LM Studio 0.4, MLX and vLLM compared, which models fit in 8, 16, 32 and 64GB, how Q4_K_M quantization works, the bandwidth maths behind tokens per second, licences and privacy.
12 minOct 20, 2026How to Run LLM Evals in CI with GitHub Actions (promptfoo, 2026)
Step-by-step: gate prompt and model changes with LLM evals in GitHub Actions using promptfoo 0.124 or DeepEval 4.3, with deterministic and LLM-as-judge assertions, pass-rate thresholds, caching, cost limits, PR comments and fixes for flaky evals.
14 minOct 20, 2026Is Fine-Tuning LLMs Worth It in 2026? Prices, LoRA, RFT and OpenAI's Exit
When fine-tuning an LLM beats prompting and RAG in 2026: OpenAI is winding down its fine-tuning API, Google and AWS added RL tuning, LoRA matches full fine-tuning when configured right, and a 5,000-example run costs $5 to $300 depending on where you train it.
14 minOct 20, 2026How to Fine-Tune an LLM with LoRA Using Unsloth or TRL (2026)
Step-by-step LoRA and QLoRA fine-tuning of an open-weight LLM with Unsloth 2026.10 or Hugging Face TRL 1.16: dataset format, hyperparameters, GPU memory, training script, evaluation against the base model, GGUF and vLLM export, and fixes for OOM and loss problems.
14 minOct 20, 2026How to Deploy an ML Model on Kubernetes with KServe 0.21 (2026)
Step-by-step KServe v0.21.0 guide: install in Standard mode with Helm, cert-manager and Gateway API, deploy scikit-learn and Hugging Face/vLLM InferenceServices, add HPA or KEDA autoscaling, canary rollouts, GPUs, and fix InferenceService not ready.
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