Project ideas from Hacker News discussions.

I want to wash my car. The car wash is 50 meters away. Should I walk or drive?

📝 Discussion Summary (Click to expand)

Key Themes from the Discussion

# Theme Representative Quotes
1 LLMs lack a true world model and rely on surface‑level pattern matching “Large Language Models have no actual idea of how the world works? News at 11.” – fmbb
“Current models seem to be fine answering that question. … It proves that this is not intelligence. This is autocomplete on steroids.” – Jean‑Papoulos
2 Context is everything – models often mis‑interpret or ignore missing details “It proves LLMs always need context. They have no idea where your car is.” – cynicalsecurity
“The question is so nonsensical… the model assumes the car is already at the car wash.” – kqr
3 Different models and settings give wildly inconsistent results “Both Gemini 3 and Opus 4.6 get this right. GPT 5.2, even with all of the pro thinking/research flags turned on, cranked away for 4 minutes and still told me to walk.” – CamperBob2
“Opus 4.6 (not Extended Thinking): Drive. You’ll need the car at the car wash.” – crimsonnoodle58
4 Prompt engineering / clarifying questions are essential “If you ask it to ask clarifying questions before answering, it helps.” – troyvit
“The model should ask: ‘What do you mean? You need to drive your car to the wash.’” – Jacques2Marais
5 Anthropomorphism fuels misunderstanding of AI capabilities “It proves LLMs are not brains, they don’t think.” – cynicalsecurity
“Humans make very similar errors… the model is just pattern matching.” – hugh‑avherald
6 Implications for deployment, safety, and alignment “This is a great opportunity for a controlled study! … I can give feedback on the draft publication.” – bayindirh
“If we can’t ask clarifying questions, we risk deploying agents that behave unintuitively.” – S3verin

These six themes capture the bulk of the conversation: the limits of current LLMs, the critical role of context, the variability across models, the need for better prompting, the danger of anthropomorphizing, and the broader concerns about safe, responsible AI deployment.


🚀 Project Ideas

ClarifyBot

Summary

  • A browser extension that intercepts user prompts to LLMs and automatically generates clarifying questions before forwarding the query.
  • Reduces frustration from ambiguous or trick questions and improves answer relevance.

Details

Key Value
Target Audience Everyday LLM users, developers, customer support agents
Core Feature Context‑aware prompt analysis + clarifying question generation
Tech Stack JavaScript/TypeScript, Chrome/Firefox APIs, OpenAI API for question generation
Difficulty Medium
Monetization Hobby

Notes

  • Users often complain that LLMs answer without asking for clarification (“walk or drive?”). ClarifyBot would surface a follow‑up question like “Where is your car located?” before the LLM responds.
  • The extension can be used in chat interfaces (ChatGPT, Claude, Gemini) and in code editors (VS Code) to improve developer productivity.

EverydayWorld KG

Summary

  • A lightweight knowledge‑graph API that exposes everyday world facts (e.g., car wash logistics, walking distances, vehicle constraints) for LLMs to query.
  • Bridges the gap between language models and real‑world reasoning.

Details

Key Value
Target Audience LLM developers, AI researchers, chatbot integrators
Core Feature RESTful API returning structured facts and inference rules
Tech Stack Python, Neo4j, FastAPI, Docker
Difficulty High
Monetization Revenue‑ready: subscription (tiered by query volume)

Notes

  • The “walk or drive” issue stems from missing world knowledge. By providing a graph of “car → needs to be present at wash” and “walking ≠ moving vehicle”, LLMs can reason correctly.
  • The API can be integrated into prompt‑engineering pipelines or used as a fallback knowledge source.

LLM Test Suite

Summary

  • A web platform that runs a curated set of trick and edge‑case questions against multiple LLMs, logs results, and visualizes performance.
  • Helps users benchmark models and identify weaknesses.

Details

Key Value
Target Audience AI enthusiasts, researchers, product managers
Core Feature Automated test runner, result dashboard, comparison charts
Tech Stack React, Node.js, OpenAI/Anthropic APIs, PostgreSQL
Difficulty Medium
Monetization Hobby

Notes

  • The discussion shows inconsistent LLM behavior (“walk” vs “drive”). The suite would expose such inconsistencies and allow users to track improvements over time.
  • Users can contribute new test cases, fostering a community‑driven benchmark.

Prompt Optimizer

Summary

  • A web tool that takes an ambiguous user prompt, analyzes it for missing context, and rewrites it into a clearer, less ambiguous version.
  • Reduces the need for users to manually craft perfect prompts.

Details

Key Value
Target Audience Non‑technical LLM users, content creators
Core Feature NLP analysis, suggestion engine, real‑time preview
Tech Stack Python, spaCy, Flask, Vue.js
Difficulty Medium
Monetization Hobby

Notes

  • Many users ask “Should I walk or drive?” without specifying car location. The optimizer would suggest adding “My car is at home” or “I want to wash my car”.
  • The tool can be integrated into chat interfaces or used as a standalone prompt‑writing aid.

Agent Builder

Summary

  • A framework for building LLM‑powered agents that can ask clarifying questions, gather context, and then produce final answers.
  • Enables developers to create more robust conversational agents.

Details

Key Value
Target Audience AI developers, chatbot creators
Core Feature Agent skeleton, clarifying‑question module, state management
Tech Stack Python, LangChain, FastAPI, Docker
Difficulty High
Monetization Revenue‑ready: freemium (open source core, paid extensions)

Notes

  • The discussion highlights that LLMs often fail to ask follow‑up questions. Agent Builder provides a plug‑in that automatically triggers a clarifying dialogue before the main answer.
  • Supports integration with existing LLM APIs and can be deployed on-premises or in the cloud.

Synthetic Data Generator

Summary

  • A SaaS that automatically generates synthetic training data for LLMs focused on trick questions and ambiguous scenarios.
  • Helps model developers improve robustness without costly data collection.

Details

Key Value
Target Audience LLM trainers, AI companies
Core Feature Prompt‑to‑data pipeline, scenario templates, quality scoring
Tech Stack Python, PyTorch, HuggingFace, Kubernetes
Difficulty High
Monetization Revenue‑ready: SaaS (per‑dataset or subscription)

Notes

  • The “walk or drive” failure shows a gap in training data. This generator can produce thousands of similar ambiguous prompts with correct answers for fine‑tuning.
  • Users can customize scenario complexity (e.g., add weather, vehicle type) to target specific use cases.

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