Lecture I — From Autocomplete to Agents

AI-Assisted Programming for PhD Researchers

Dr. Tobias Vlćek

Helmut Schmidt University — September 2026

Welcome

About This Course

  • Three days, hands-on from the first hour
  • You build a real data pipeline, then your own project
  • Accredited through a final presentation, not an exam
  • The goal: use AI to code well, not just fast

About Me

  • Field: Optimizing and simulating complex systems
  • Languages: Julia, Python, and Rust
  • Interests: Modelling, Simulations, Machine Learning
  • Teaching: OR, Algorithms, and Programming
  • More: tobiasvlcek.com

The Three Days

  • Day 1 — Foundations and the core agent loop
  • Day 2 — Discipline: verification, safety, extension
  • Day 3 — Your own project, then presentations

What You Will Deliver

A final presentation covering four things:

  • A demo of what you built
  • A reflection on your process
  • The challenges you hit
  • The insights you take away

This Website

  • Everything you need lives here — no separate handouts
  • Slides open as a deck via the RevealJS button
  • Labs are step-by-step pages you follow at your pace
  • A course chatbot sits in the sidebar for questions

Who Are You?

Quick intro round.

Tell us three things:

  • Your name
  • Your field
  • One piece of code you fight with

Why This Course

Research Runs on Code

  • Most PhDs write research software with little to no formal software training (Hannay et al. 2009)
  • Hannay’s survey of nearly 2,000 scientists showed learning was mostly informal, from peers
  • That was already hard — before AI entered the loop

What Changed

  • 2021: autocomplete suggests the next line as you type
  • 2023: chat answers questions in a side window
  • 2025+: agents read your files and run commands
  • A capability jump, and a widening discipline gap

The Promise and the Trap

  • Promise: hours saved on boilerplate, debugging, and learning
  • Trap: code you don’t understand ends up in your thesis
  • A gap widens between those who use AI well and those who lean on it as a crutch (Prather et al. 2024)

The Tool Landscape

Three Kinds of AI Coding Tools

  • Autocomplete: inline, at the keystroke level
  • Chat: you copy and paste, it answers
  • Agents: they act inside your repository

Autocomplete

  • Completes code as you type
  • Zed edit predictions are what you got with the student plan
  • Great for boilerplate
  • Useless for design decisions

Chat

  • Paste your code, get an answer back
  • No repository access — you are the clipboard
  • Still useful for concepts and quick explanations

Agents

  • Read files, edit them, run commands, iterate toward a goal
  • Live in your terminal or your editor
  • This is the course’s focus: agentic coding

The 2026 Market

  • IDE-integrated: Copilot (sign-up freeze noted), Cursor, Windsurf
  • Vendor CLIs: Claude Code, Codex, Gemini CLI
  • Open source: OpenCode, Aider, Goose

Orientation only — no detail needed today.

Our Stack, and Why

  • Zed: fast editor, free student plan
  • OpenCode: open source, works with any model
  • Mistral: free tier, EU-hosted, no training on opted-out data

Learn the workflow, not the tool. These concepts transfer to every other agent.

How Agents Work

A Language Model in a Loop

  • The model proposes an action
  • A tool runs it: read, edit, or shell command
  • The result feeds back into the model
  • Repeat until the goal is met

Nothing magical, and no memory between sessions.

Context Is Everything

  • The model only knows what is in its context window
  • That means: your prompt, files it read, command output
  • Nothing else — not your screen, not your intent
  • Garbage in, garbage out

Where It Breaks

  • Hallucination: plausible is not the same as true
  • Stale knowledge: training has a cutoff date
  • Long-context degradation: quality drops in huge sessions

The answer is verification — we build that on Day 2.

The Evidence

Faster — Sometimes, Somewhere

These are real gains — on the right kind of work.

…But Slower Where It Counts

  • Experienced devs on their own mature codebases:
  • 19% slower — while believing they were about 20% faster (Becker et al. 2025)
  • The perceived speedup and the real one point opposite ways

Does AI Assistance Hurt Learning?

  • Anthropic ran a randomized controlled trial on coding skill
  • AI group scored 50% on a mastery quiz vs 67% hand-coding (Shen and Tamkin 2026)
  • The largest gap was on debugging questions

Comprehension Debt

  • Code that works today but nobody understands tomorrow (Osmani 2026)
  • Like technical debt, but for understanding
  • The interest comes due during revisions and review
  • You pay it when a reviewer asks “why does this work?”

It Depends How You Use It

  • Same study: conceptual questions and explain-back preserved learning; passive “just fix it” did not
  • At scale: unguarded AI cut exam scores, a guardrailed tutor did not (high-school maths RCT) (Bastani et al. 2025)

This course is those guardrails.

AI Code Has More Bugs Than You Think

Day 2 is built around catching exactly this.

Our Working Rules

Rule 1 — Explain Before You Accept

  • The era’s shift is from writing code to reading and evaluating it (Denny et al. 2024)
  • You must be able to explain every change the agent made
  • Labs have “Explain it” boxes for exactly this (Smith and Zilles 2024)

Rule 2 — Verify, Then Trust

  • Never believe the word “done”
  • Run it
  • Test it
  • Read the diff

Rule 3 — You Own Every Line

  • “The AI wrote it” is not a defense — not in your thesis, your paper, or this course
  • Task stewardship: delegate the labor, keep the accountability (Lee et al. 2025)

Rule 4 — Small Steps, Frequent Commits

  • Small, verified increments beat one giant diff
  • Every step stays reviewable and reversible
  • Git makes this cheap — that’s our next session

Continue Your Journey

Next Up

“AI Vs Human Code Gen Report: AI Code Creates 1.7x More Issues.” n.d. In CodeRabbit. Accessed July 9, 2026. https://coderabbit.ai/blog/state-of-ai-vs-human-code-generation-report.
Bastani, Hamsa, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, and Rei Mariman. 2025. “Generative AI Without Guardrails Can Harm Learning: Evidence from High School Mathematics.” Proceedings of the National Academy of Sciences 122 (26): e2422633122. https://doi.org/10.1073/pnas.2422633122.
Becker, Joel, Nate Rush, Elizabeth Barnes, and David Rein. 2025. Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. arXiv. https://doi.org/10.48550/ARXIV.2507.09089.
Cui, Kevin Zheyuan, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng, and Tobias Salz. 2026. “The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers.” Management Science, February, mnsc.2025.00535. https://doi.org/10.1287/mnsc.2025.00535.
Denny, Paul, James Prather, Brett A. Becker, et al. 2024. “Computing Education in the Era of Generative AI.” Communications of the ACM 67 (2): 56–67. https://doi.org/10.1145/3624720.
Hannay, Jo Erskine, Carolyn MacLeod, Janice Singer, Hans Petter Langtangen, Dietmar Pfahl, and Greg Wilson. 2009. “How Do Scientists Develop and Use Scientific Software?” 2009 ICSE Workshop on Software Engineering for Computational Science and Engineering, May, 1–8. https://doi.org/10.1109/SECSE.2009.5069155.
Lee, Hao-Ping (Hank), Advait Sarkar, Lev Tankelevitch, et al. 2025. “The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers.” Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (Yokohama Japan), April, 1–22. https://doi.org/10.1145/3706598.3713778.
Osmani, Addy. 2026. “Comprehension Debt: The Hidden Cost of AI-Generated Code.” In Addyosmani.com. https://addyosmani.com/blog/comprehension-debt/.
Peng, Sida, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer. 2023. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. arXiv. https://doi.org/10.48550/ARXIV.2302.06590.
Prather, James, Brent N Reeves, Juho Leinonen, et al. 2024. “The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers.” Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1 (Melbourne VIC Australia), August, 469–86. https://doi.org/10.1145/3632620.3671116.
Shen, Judy Hanwen, and Alex Tamkin. 2026. How AI Impacts Skill Formation. arXiv. https://doi.org/10.48550/ARXIV.2601.20245.
Smith, David H., and Craig Zilles. 2024. “Code Generation Based Grading: Evaluating an Auto-Grading Mechanism for "Explain-in-Plain-English" Questions.” Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1 (Milan Italy), July, 171–77. https://doi.org/10.1145/3649217.3653582.
Veracode. 2025. 2025 GenAI Code Security Report. Veracode. https://www.veracode.com/wp-content/uploads/2025_GenAI_Code_Security_Report_Final.pdf.