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Project ideas from Hacker News discussions.

Ask HN: DDD was a great debugger – what would a modern equivalent look like?

Hacker News Discussion

📝 Discussion Summary (Click to expand)

1. Causality & “time‑travel” debugging is still a hard problem
Many users lament that a live debugger shows what is happening but not why.

“What I keep struggling with is understanding how a particular state came to be … that gap between state visualization and causality feels hard to bridge” – manux81
“Sounds like you want a time travel debugger, eg. rr.” – omnicognate

2. Reproducibility limits the usefulness of powerful debuggers
Even the best tools are only helpful if the bug can be reproduced under the debugger.

“Not being able to use your most powerful tools on your hardest problems reduces their value.” – omnicognate
“The hardest bugs I’ve dealt with were almost always the least reproducible ones …” – manux81

3. Debugging is an investigative process that could benefit from AI/agent support
Users see debugging as hypothesis‑driven evidence gathering and wonder how tooling can aid that.

“Framing debugging as an investigation … feels much closer to how I experience real debugging.” – manux81
“AI will be a partner to a human developer who needs to debug … managing investigations, clues, evidence.” – mark_undoio

These three themes—causality, reproducibility, and investigative tooling—capture the core concerns voiced throughout the discussion.

🚀 Project Ideas

CausalTrace

Summary

  • A debugger extension that records a causal graph of all state changes, including concurrent events, and visualizes how a particular state arose.
  • Enables “time‑travel” inspection without full replay, focusing on the minimal history needed to explain a bug.

Details

Key Value
Target Audience Systems developers, embedded engineers, distributed‑systems teams
Core Feature Causal graph capture + interactive visualization + selective history replay
Tech Stack Rust (core engine), WebAssembly + React (UI), gRPC for host‑target communication
Difficulty High
Monetization Revenue‑ready: tiered subscription ($49/mo for small teams, $199/mo for enterprises)

Notes

  • Users lament “understanding how a particular state came to be” and “gap between state visualization and causality”.
  • “I keep struggling with is understanding how a particular state came to be — especially with concurrency or events that happened much earlier.” – ordu
  • Provides a concrete solution to the “time‑travel debugger” need while keeping data volume manageable.

LiveReplay

Summary

  • A lightweight, on‑board instruction‑trace streaming system for embedded devices that allows live, low‑latency debugging without full offline replay.
  • Uses selective capture and on‑device summarization to keep bandwidth and storage minimal.

Details

Key Value
Target Audience Embedded developers, IoT firmware teams
Core Feature Live instruction‑trace streaming + on‑device filtering + remote UI
Tech Stack C/C++ (firmware), Rust (host agent), WebSocket + Electron UI
Difficulty Medium
Monetization Hobby (open source with optional paid support)

Notes

  • “I would love to see better observability in embedded systems.” – chrsw
  • Addresses the pain of “usually so much data” and “live view is impractical” expressed by embedded users.
  • Encourages adoption of live debugging in environments that traditionally rely on offline dumps.

DebugAgent

Summary

  • AI‑powered debugging assistant that models debugging as an investigation: hypotheses, evidence, gaps, and next actions.
  • Integrates with IDEs and debuggers, automatically logs evidence, and suggests targeted tests or fuzzing.

Details

Key Value
Target Audience Developers dealing with hard‑to‑reproduce bugs, QA teams
Core Feature Investigation workflow, evidence tracking, AI‑driven next‑step suggestions
Tech Stack Python (LLM inference), LangChain, VS Code extension, SQLite for evidence store
Difficulty Medium
Monetization Revenue‑ready: freemium with paid “Enterprise” plan ($99/mo)

Notes

  • “Framing debugging as an investigation — with hypotheses, evidence and gaps — feels much closer to how I experience real debugging.” – manux81
  • Provides a structured way to avoid “overwhelming” tooling while still giving powerful AI assistance.
  • Sparks discussion on AI’s role in debugging and could become a standard part of modern IDEs.

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