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World Congress 2026 Europe • Jul 10, 2026 • Session details

It's Dangerous to Code Alone! Take This: Developer's AI Survival Guide

Salih Gueler

Are you spending more time fixing AI hallucinations than writing logic? Master strict, boundary-driven prompting to conquer technical debt and stay firmly in the driver's seat.

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#1 about 2 min

The rapid evolution from autocomplete to AI agents

Artificial intelligence in coding software has transitioned from simple autocompletion scripts to fully autonomous agentic frameworks.

#2 about 2 min

The hidden technical debt of live AI coding

Unstructured AI generation creates unmaintainable software projects as development iterations compound without proper architectural context.

#3 about 2 min

Defining explicit boundaries for autonomous AI agents

Establishing explicit markdown protocols constrains AI choices and prevents the inclusion of unnecessary frontend frameworks.

#4 about 4 min

The trap of zero-shot prompts in app creation

Vague zero-shot creation prompts lead AI agents to guess project conventions and default to unwanted languages.

#5 about 3 min

Setting negative boundaries and using an imperative tone

Employing imperative tones and explicit negative constraints restricts AI autonomy and prevents predictable tool mistakes.

#6 about 3 min

Optimizing context windows with organized router patterns

Organizing project context efficiently separates broad repository knowledge from direct and actionable agent instructions.

#7 about 2 min

Leveraging markdown formatting for better AI comprehension

Applying bold text styling and hierarchical headings signals critical importance levels to the agent's parsing logic.

#8 about 2 min

Preventing context rot in long AI conversations

Managing early chat instructions combats the model's tendency to forget initial constraints during long development sessions.

#9 about 2 min

Adapting the software development life cycle for AI

Traditional analysis, design, and deployment phases can be effectively aligned with human-guided AI agent execution.

#10 about 2 min

Writing robust specifications for reliable AI generation

Establishing strict project goals, technology stacks, and acceptance criteria safely anchors the AI application build process.

#11 about 4 min

Injecting behavioral skills and strict technology constraints

Injecting modular behavioral definitions and strict framework choices prevents AI agents from hallucinating project features.

#12 about 4 min

Generating architecture documents and organized task lists

Raw system specifications easily translate into structured design documents and actionable subtasks before the compilation phase.

#13 about 6 min

Debugging autonomous builds through supervised AI approvals

Feeding terminal execution errors back into the agent context allows the model to propose software fixes for manual approval.

Matching moments

3:45 min

Balancing AI tool mandates with developer trust and productivity

Chris Heilmann +2 · LIVE

5:16 min

Motivations for adopting AI to enhance developer productivity

1:55 min

Shifting developer workloads and realistic AI productivity gains

Chris Heilmann +2 · LIVE

1:46 min

Introduction to AI code generation and developer habits

Prashanth Chandrasekar Prashanth Chandrasekar · WWC 2023

2:34 min

Balancing developer autonomy with the adoption of coding agents

Clemens Wasner Clemens Wasner +4 · WWC Europe 2026

49 sec

Navigating technical debt generation in the era of AI

Adam Tornhill · Coffee With Developers

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