Section
How-To
The latest How-To coverage from Bullquill: sourced, dated and explained.
14 POSTSUPDATED
IndexDate · Title · Reading time
No articles in this language here yet. Showing English.
-
How to set up iPhone Duo and transfer data (step-by-step)
Apple's foldable sets up like any iPhone, with two twists: it is eSIM-only and uses Touch ID in the side button. Here is every transfer route, from Quick Start to Move to iOS, based on Apple's own support pages.
-
How to Evaluate an LLM App in Python: Test Sets, Graders, CI
Start with about 30 real inputs, score them with code checks and a calibrated LLM judge, and rerun the suite on every change. OpenAI's hosted Evals platform goes read-only on Oct. 31, 2026, so a portable suite matters.
-
How to Backtest a Crypto Trading Strategy in Python with CCXT
A step-by-step Python guide to fetching Bitcoin candles with CCXT, coding a moving-average crossover in pandas without look-ahead bias, charging fees and slippage, and measuring return, drawdown and win rate, plus why a backtest is not a forecast.
-
How to reduce LLM API costs: caching, batching and model routing
Log input, cached, output and thinking tokens first, then apply four techniques in Python. Cache reads cost 0.1x the normal input price or less at OpenAI and Anthropic, and batch APIs charge 50% of standard prices.
-
How to build an emergency fund: target, account and automation
Only 63% of US adults would cover a $400 surprise bill with cash, according to the Federal Reserve. Here is a step-by-step method to size, store, automate and rebuild an emergency fund, with a worked example.
-
Prompt engineering techniques: 9 ways to write better LLM prompts
A vendor-neutral guide built on the official OpenAI, Anthropic and Google documentation, with before-and-after examples and about 40 lines of Python to test prompt variants against your own evaluation set.
-
How to secure a crypto wallet seed phrase (step-by-step)
A step-by-step security checklist for moving coins off an exchange: what a BIP-39 seed phrase is, how to back it up offline, hardware vs software wallets, passphrases, multisig, and the scams behind most losses.
-
How to Set Up Passkeys on iPhone, Android, Windows and Mac
A step-by-step guide to creating passkeys for your Google, Apple and Microsoft accounts, how they sync through password managers, how to recover them if you lose a device, and when a password plus 2FA still makes sense.
-
How to build an AI agent in Python with tool calling
A framework-free guide to building an AI agent in Python: describe tools with JSON Schema, write the loop that runs tool calls, switch to a local model via Ollama, and enforce guardrails such as allow-lists, step limits and human confirmation.
-
How to Fine-Tune an LLM with LoRA and QLoRA on a Single GPU
A practical walkthrough of parameter-efficient fine-tuning: format a chat dataset, train 4-bit QLoRA adapters on Llama 3.1 8B with Hugging Face PEFT and TRL, check VRAM needs, evaluate against the base model, merge the adapter and decide when fine-tuning beats RAG or prompting.
-
How to read on-chain data on mempool.space and Etherscan
Look up a Bitcoin transaction on mempool.space and an Ethereum transfer on Etherscan without an account, then learn why a simple ETH transfer uses 21,000 gas and what active addresses, exchange flows and hash rate can and cannot show.
-
How to Build a RAG App in Python with Ollama and Chroma
A hands-on, fully local retrieval-augmented generation walkthrough: split your documents into chunks, embed them with Ollama, search them in Chroma and make a local LLM answer with citations, plus chunk-size trade-offs and a simple evaluation checklist.
-
How to build an MCP server in Python: a step-by-step guide
A hands-on guide to building a Model Context Protocol server in Python: set up the official SDK, expose a tool and a resource, test with the MCP Inspector, connect it to Claude and apply the security rules the spec requires.
-
How to run a local LLM with Ollama on macOS, Windows or Linux
A hands-on guide to running a private, offline AI model with Ollama: what hardware you need, how to install it, pick a quantized model, save a custom system prompt and call the local API from curl and Python.
