ML Journey

A complete guide to integrating Ollama into React and Next.js applications: solving the CORS problem with OLLAMA_ORIGINS or a server-side proxy, a full streaming chat component that calls Ollama directly from the browser with real-time token display, a Next.js App Router API route that proxies Ollama streams to the client, and the AI SDK useChat hook approach that replaces manual streaming code with a clean abstraction — including the route handler using createOpenAI pointed at the local Ollama endpoint.

A practical guide to optimizers for LLM training: how AdamW works and why decoupled weight decay matters, the memory cost problem at 7B to 70B scale, Adafactor factored second moments for pretraining, 8-bit Adam as a drop-in memory reduction, Lion sign-based updates and its hyperparameter tradeoffs, and a decision framework for matching optimizer to training scale and budget.

A complete guide to building a Slack bot powered by a local Ollama LLM: creating the Slack app with correct OAuth scopes and event subscriptions, setting up the Bolt for Node.js framework with Socket Mode for zero-tunnel deployment, responding to @mentions and direct messages, maintaining per-user conversation history across exchanges, a /summarise slash command that fetches and summarises recent channel messages, running the bot in development and as a systemd service, and the trade-offs between Socket Mode and HTTP mode for production.

A practical comparison of activation functions for ML engineers: ReLU and the dying neuron problem, GELU and why transformers switched from ReLU, SiLU and Swish for efficiency, SwiGLU and GeGLU gated variants used in Llama and PaLM, the 2/3 expansion factor explained, and a decision guide for choosing activations in CNNs versus transformer architectures.

A complete guide to running Ollama as a persistent Linux systemd service: checking if the installer already created a service, writing a service unit file from scratch with a dedicated ollama user, adding NVIDIA GPU support with group permissions and environment variables, all key environment variables including OLLAMA_HOST, OLLAMA_MODELS, OLLAMA_KEEP_ALIVE, and OLLAMA_NUM_PARALLEL, managing the service with systemctl, viewing logs with journalctl, a separate oneshot service to pull models at boot, storing models on a separate data drive, and troubleshooting common startup failures.

A practical guide to weight initialization for ML engineers: why poor initialization causes vanishing and exploding gradients, Xavier initialization for tanh and linear activations, Kaiming initialization for ReLU networks, GPT-2 style scaled residual initialization for LLMs, embedding initialization, and a concrete checklist for initializing custom architectures correctly.

A complete guide to using Ollama from Deno and Bun: importing the ollama npm package in Deno with the npm: specifier and –allow-net, streaming responses, using the OpenAI SDK in Deno, a deno.json configuration with import maps and task shortcuts, installing and using the ollama package in Bun, writing fast CLI scripts that compile to standalone executables with bun build –compile, testing Ollama integrations with bun:test, and when to choose Node.js vs Deno vs Bun for local LLM projects.

A practical comparison of normalization layers for ML engineers: what batch norm, layer norm, group norm, and RMSNorm each compute and why it matters, batch norm train/eval discrepancy and the hidden bugs it causes, why layer norm is the transformer default, RMSNorm as used in Llama and Mistral, group norm for small-batch detection tasks, and a decision guide for choosing the right normalization for your architecture.

A complete migration guide from OpenAI to Ollama: the two-line Python and JavaScript SDK change, streaming that works identically, batch embeddings with nomic-embed-text, an environment variable pattern for switching between cloud and local without code changes, LangChain migration via both the OpenAI-compatible endpoint and the native ChatOllama integration, LlamaIndex migration with OpenAILike, and a clear breakdown of what the compatibility layer supports versus where gaps exist including function calling, vision, and logprobs.

A practical guide to reward model training for ML engineers: preference data structure and quality requirements, Bradley-Terry pairwise loss and training objective, LoRA-based reward model fine-tuning, pairwise accuracy as a training metric, reward hacking failure modes and mitigations, best-of-N sampling as a simpler alternative to PPO, and when to use rejection sampling fine-tuning versus full RLHF.