IdeaWell

Project ideas from Hacker News discussions.

Show HN: Lossless-memory – a personal AI memory that never summarizes

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

1. AI should take initiative and act autonomously after a delay
Many commenters argued that current models only react to prompts and lack the ability to “wait then do something” on their own.
- “The missing thing is lack of the bot's initiative. It is always the human who takes initiative and writes something, then the bot makes a (perhaps) elaborate response and shuts up.” — Me
- “Claude can definitely wait 50 seconds and then do a second thing … It can either run a bash sleep 50 or use the ScheduleWakeup tool.” — StilesCrisis
- “I'm working on exactly that here!” — jv22222 (linking to a prototype)

2. Temporal awareness / scheduling is a core building block
The discussion repeatedly highlighted the need for AI to understand and use time—whether via simple delays, cron‑like jobs, or versioned chronology.
- “I can't resist the opportunity to paste some of the text I wrote … on an idea I had about adding temporal awareness to Claude.” — drsopp
- “temporal/versioned chronology is absolutely useful.” — alansaber
- “I have a Hermes agent running on VM that does this. I told it to keep an eye on prices. It created a cron job that triggers the scrapes and messages me …” — cassianoleal

3. Existing tools and prototypes already demonstrate practical implementations
Several users pointed to real‑world agents, scripts, or projects that embody proactive, time‑aware behavior, showing the concept is feasible today.
- “Oh cool. I'm working on exactly that here! https://innerloop.works/breadcrumb” — jv22222
- “Have you thought about using llm-wiki? It's pretty powerful and works well.” — rbansal2
- “I've been looking for a new cooker. I have a Hermes agent … It created a cron job …” — cassianoleal

🚀 Project Ideas

Generating project ideas…

Proactive AI Agent Scheduler

Summary

  • Enables users to instruct an AI to perform delayed or conditional actions (e.g., "wait 50 seconds then write a recipe") by adding a proactive messaging mode that initiates messages based on context and timers.
  • Core value proposition: gives LLMs initiative and temporal awareness, turning passive chat into an active assistant that can remind, monitor, and act without constant user prompting.

Details

Key Value
Target Audience Developers and power users building AI‑driven workflows, personal assistants, or monitoring bots
Core Feature Configurable delay/condition engine that triggers LLM generations automatically after a set time or when a watched condition changes
Tech Stack Python (FastAPI) for backend, Redis for task scheduling, OpenAI/Anthropic API for LLM calls, optional websocket UI
Difficulty Medium
Monetization Revenue-ready: Subscription tier ($9/mo) for higher rate limits and priority scheduling

Notes

  • HN users expressed frustration that "the bot will not be able to do this... It has no concept of time. No ability to take initiative" (drsopp) and asked for a "Proactive Messaging" mode.
  • Provides a concrete way to implement the suggested timer‑based initiative, sparking discussion on AI agency and enabling practical utilities like price watches or reminders.

Temporal Prompting Library

Summary

  • A lightweight SDK that continuously injects an updating time string into the LLM prompt, allowing the model to perceive elapsed time and self‑trigger when a threshold is reached.
  • Core value proposition: adds temporal awareness to any existing LLM with minimal code change, enabling self‑initiated delayed actions without external schedulers.

Details

Key Value
Target Audience AI researchers, hobbyist developers, and product builders experimenting with LLM agents
Core Feature Middleware that prepends a dynamic timestamp (e.g., "seconds since last response: 53") to each prompt and can be programmed to emit an initiative token when the value exceeds a set limit
Tech Stack TypeScript/NPM package, works with any HTTP‑based LLM API; optional deno/bun builds
Difficulty Low
Monetization Hobby

Notes

  • Commenters like altruios suggested a "KISS way to do this with our current models: use a time string that updates every second... instruct/train it to take initiative then."
  • Enables quick experimentation and discussion on whether simple temporal prompting can yield emergent initiative, lowering barrier to test the concept.

Background Monitoring Service (Price Watcher AI)

Summary

  • A hosted service where users define what to monitor (e.g., product price, stock ticker, news keyword) and receive proactive AI‑generated notifications when conditions are met, using LLMs to craft the messages.
  • Core value proposition: removes the need for users to set up cron jobs or scripts; the AI handles both monitoring and natural‑language alert generation, delivering timely, contextual updates.

Details

Key Value
Target Audience Shoppers, investors, researchers, and anyone needing automated web monitoring with human‑readable alerts
Core Feature User‑defined watch rules (URL, selector, threshold) → scheduled serverless checks → LLM‑generated alert sent via email, SMS, or webhook
Tech Stack AWS Lambda (or Cloudflare Workers) for scheduling, DynamoDB for rule storage, Anthropic/OpenAI API for alert generation, React dashboard
Difficulty Medium
Monetization Revenue-ready: Pay‑per‑check ($0.001 per check) + optional premium plan ($5/mo) for higher frequency and SMS

Notes

  • HN user cassianoleal shared that they already run a Hermes agent on a VM that scrapes prices and messages them, showing demand for such a tool.
  • Offers a practical, discussion‑worthy product that ties together monitoring, LLMs, and proactive messaging—directly addressing the initiative/temporal gap highlighted in the thread.

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