Initial commit: coding-with-ai

A practical guide to working effectively with AI coding assistants
(chat interfaces, in-editor extensions, agentic tools) for engineers
and scientists solving problems with code rather than building
production software.

Seven sections:

- 01-three-modes: web chat vs in-editor vs agentic, with heuristics
  for choosing and a framing of chat as natural-language programming.
- 02-errors-and-logs: the canonical copy-paste case; framing the
  paste for useful answers.
- 03-in-editor-workflow: autocomplete, inline edit, side panel,
  quick actions; habits that survive tool changes.
- 04-conversations: multi-turn discussions, context-window
  awareness, opening well, prompt iteration, when to start fresh.
- 05-agentic-workflow: variations on the basic loop (sub-agents,
  plan mode, async, MCP, sandboxing); briefing, supervision,
  damage control, cost and energy.
- 06-verifying-and-citing: hallucinations and silent errors;
  privacy framed against the cloud-services baseline; proportional
  disclosure norms.
- 07-local-models: local models as a cross-cutting alternative
  across all three modes; hardware tiers, tool support,
  capability gap.

Tool-agnostic where possible; current tool examples are
illustrative and expected to date.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

README.md

@@ -0,0 +1,40 @@
+# Coding with AI
+
+A practical guide to working effectively with AI coding assistants — chat interfaces (ChatGPT, Claude, Gemini, Microsoft Copilot), in-editor extensions, and agentic tools. Our focus is on *workflow* and *judgment*: when to reach for which mode, what to paste, how to prompt, how to verify, and what to cite.
+
+AI tools change quickly, but the patterns change slowly. This guide aims at the patterns and uses current tools as examples.
+
+**A note on scope.** This guide is about *coding* — writing, editing, refactoring, and debugging software. Students and engineers also use AI tools heavily for *learning* tasks: explaining concepts, summarizing literature, generating practice problems, study quizzes, mnemonics, working through homework, finding the right vocabulary for a half-remembered idea. The three-mode framework here applies broadly, but the tools, examples, and tradeoffs for learning use cases are different enough to deserve their own guide.
+
+## Sections
+
+| # | Topic | Description |
+|---|-------|-------------|
+| [01](01-three-modes/) | **Three modes** | Web chat, in-editor, and agentic. When to use each one and the heuristics for choosing. |
+| [02](02-errors-and-logs/) | **Errors and logs** | The canonical copy-paste case. How to frame what you paste so the assistant can actually help. |
+| [03](03-in-editor-workflow/) | **In-editor workflow** | Autocomplete, inline edit, "explain this," refactor. Patterns that make the editor extension worth its slot. |
+| [04](04-conversations/) | **Conversations** | Multi-turn design discussions, managing context, when to start a fresh chat. |
+| [05](05-agentic-workflow/) | **Agentic workflow** | What agentic tools (Claude Code, Cursor agent, Microsoft Copilot agent mode) actually do, and how to supervise them. |
+| [06](06-verifying-and-citing/) | **Verifying and citing** | Reviewing AI output for hallucinations and silent errors. Privacy and IP of what you paste. Attribution in academic and professional work. |
+| [07](07-local-models/) | **Using local models** | Local models as a cross-cutting alternative — privacy, cost, offline operation. Which tools support local in each of the three modes, and where the capability gap to cloud still matters. |
+
+## Who this is for
+
+Students and practicing engineers who are already using AI assistants but want to use them more deliberately — including those whose default workflow is "ask ChatGPT, copy the answer back." There is nothing wrong with copy-paste, but our goal is to know *when* it is the right tool and when to use something else.
+
+## Prerequisites
+
+- A working development setup (editor, terminal, version control). See [computing-setup](https://lem.che.udel.edu/git/furst/computing-setup) and [cli-walkthrough](https://lem.che.udel.edu/git/furst/cli-walkthrough) for the underlying skills.
+- Access to at least one AI tool. The examples use Claude and ChatGPT in chat form, and GitHub Copilot / Claude / Codeium / Microsoft Copilot interchangeably as editor extensions. University-provided access (e.g., Microsoft Copilot or Gemini through institutional agreements) works equally well for nearly everything covered here.
+
+## A note on tools and dates
+
+Tool capabilities, pricing, and policies change frequently. Where this guide names a specific feature ("Cursor's agent mode," "Claude Code"), the description reflects what those tools did as of the first half of 2026. The underlying patterns, inlcuding copy-paste versus in-editor versus agentic AI, are durable. Remember to treat any tool-specific advice as illustrative.
+
+## License
+
+MIT
+
+## Author
+
+Eric M. Furst, University of Delaware