Project ideas from Hacker News discussions.

Extending C with Prolog (1994)

📝 Discussion Summary (Click to expand)

1. Prolog’s enduring relevance and the nostalgia for “expert‑system” logic

“We recently installed Gateway multi‑media kits… a simple expert system could have helped to resolve those IRQ conflicts.” – vintagedave
“I remember when so‑called ‘expert systems’ written in Prolog or LISP were supposed to replace doctors.” – copx
“Prolog was doing chain‑of‑thought reasoning 30 years before LLMs made it cool.” – dirtytoken7

2. A skeptical view of modern LLMs versus logic‑based AI

“LLMs are error‑prone by design… they will hallucinate… all the money in the world is thrown at people who preach LLMs will eventually be able to do every human job.” – copx
“The second AI winter cannot come soon enough.” – copx
“LLMs don’t really do chained reasoning.” – mcswell

3. Practical integration of Prolog with contemporary systems

“I started using Prolog in my self‑written home automation system… I replaced the C++ parts with Java, but the Prolog is pretty nice the way I can now specify predicates for MQTT topic paths.” – sprior
“I often wonder what a Prolog implemented as an Objective‑C like extension to C would look like.” – HexDecOctBin
“People are using Prolog with LLMs.” – rramadass

These threads collectively highlight Prolog’s historical legacy, the critique of current AI hype, and real‑world use cases that blend logic programming with modern tooling.


🚀 Project Ideas

IRQ Conflict Resolver

Summary

  • Automates detection and resolution of IRQ conflicts during hardware installation.
  • Provides a user-friendly interface and actionable suggestions, eliminating manual troubleshooting.

Details

Key Value
Target Audience PC builders, hobbyists, IT admins
Core Feature Automatic IRQ conflict detection, conflict mapping, and resolution suggestions
Tech Stack Python, libpci, Windows registry API, cross‑platform GUI (Tkinter/Qt)
Difficulty Medium
Monetization Hobby

Notes

  • “We recently installed Gateway multi‑media kits on our PCs, but found the installation less than trivial because of conflicts in our interrupt (IRQ) channels.” – vintagedave
  • Solves the frustration of legacy hardware configuration that older expert systems could have handled.
  • Sparks discussion on modernizing legacy hardware support and integrating automated conflict resolution into OS installers.

Prolog‑LLM Hybrid Reasoning Engine

Summary

  • Combines Prolog logic constraints with LLM outputs to reduce hallucinations and enforce domain rules.
  • Enables developers to embed declarative knowledge into generative AI workflows.

Details

Key Value
Target Audience AI developers, data scientists, LLM integrators
Core Feature Prolog constraint engine that validates or guides LLM responses
Tech Stack Python, SWI‑Prolog, OpenAI API, Docker, RESTful microservice
Difficulty High
Monetization Revenue‑ready: subscription per request

Notes

  • “LLMs are error‑prone by design… they will hallucinate… we need logic programming.” – copx
  • Addresses the pain point of unreliable LLM outputs highlighted by multiple commenters.
  • Encourages debate on hybrid AI safety, the role of symbolic reasoning, and practical integration patterns.

Prolog Home Automation Platform

Summary

  • Declarative rule engine for home automation using Prolog, with native MQTT integration.
  • Deployable on k3s, enabling scalable, container‑based automation without heavy C++ code.

Details

Key Value
Target Audience Home automation enthusiasts, hobbyists, small IoT projects
Core Feature Prolog rule engine + MQTT broker, web UI for rule management
Tech Stack SWI‑Prolog, Node.js, Mosquitto, Docker, k3s
Difficulty Medium
Monetization Hobby

Notes

  • “I started using Prolog in my self written home automation system… replaced the C++ parts of my HA system with Java.” – sprior
  • Provides a clean, declarative alternative to imperative home‑automation code.
  • Promotes discussion on IoT, declarative programming, and containerized automation stacks.

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