IdeaWell

Project ideas from Hacker News discussions.

Notes on gotchas while migrating 35kb preprompts from Opus to self-hosted Ollama

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

1. Context‑window limits are a major pain point
- “Local models have a smaller context window, so your 35kB prompts that worked fine against a hosted 1 Million token window, crash out when you only have a 65K (!) token window locally.” – SyneRyder
- “You run out of useful context that the model can accurately attend to around the 250k mark no matter how much they advertise their context size is.” – DiabloD3

2. Prompt bloat must be fought by splitting work and keeping contexts tight
- “By breaking the problem into discrete steps and aggressively restarting the prompt from the current state after completion of said steps.” – JamesSwift
- “AKA ‘divide and conquer’, how we programmers been fighting ever bigger and more complex problems since probably forever.” – embedding‑shape
- “The longer the context grows, the greater the probability that it generates ambiguity, and the probability that it makes mistakes approaches 1.” – cyanydeez

3. Viable local hardware is emerging, making powerful models affordable
- “Qwen Flash Next will run on a $4000 PC and can reliably implement small features on its own (feels comparable to Opus 4.5).” – hedgehog
- “Qwen 3.8 27B is so great that a 5k investment gives you excellent speed and quality.” – nicce
- “The most cost‑effective local option right now… dual R9700… you can run a 27B dense Qwen at FP8 with a full context ≈ 260K and 2‑3 concurrent sessions.” – vardalab

🚀 Project Ideas

PromptChunker: Automatic Prompt Splitting & Sub‑Agent Orchestration

Summary

  • A command‑line and VS Code tool that takes oversized system prompts or user queries, splits them into chunks that fit the model’s context window, dispatches each chunk to a local LLM as a sub‑agent, and intelligently merges the results while preserving continuity.
  • Core value proposition: eliminates “context window overflow” errors and lets developers run complex, multi‑step prompts on modest hardware without manual prompt engineering.

Details

Key Value
Target Audience Developers and power users running local LLMs (Ollama, llama.cpp, etc.) for coding agents, research, or automation
Core Feature Smart chunking, sub‑agent dispatch, result aggregation with token‑budget tracking
Tech Stack Python, FastAPI (for local API), llama.cpp bindings, optional React/Electron UI
Difficulty Medium
Monetization Revenue-ready: $8/month per user for hosted sync & analytics; open‑source core free

Notes

  • HN users complained about “35kB prompts that worked fine against a hosted 1M token window, crash out when you only have a 65K token window locally” (SyneRyder) and wanted “automation to accelerate splitting prompts for use on local inference” (0o_MrPatrick_o0).
  • Provides a practical utility that can be discussed as a drop‑in replacement for manual prompt splitting, enabling larger‑scale agentic workflows on consumer GPUs.

ContextGuard Harness: Real‑Time Token Budgeting & Dynamic Pruning

Summary

  • A harness that monitors token usage in real time, applies dynamic context pruning (similar to Opencode‑DCP), and exposes lightweight tool functions for sub‑agents to fetch information without bloating the main conversation.
  • Core value proposition: keeps the main context under a user‑defined limit while still allowing deep retrieval, reducing context rot and improving prompt adherence.

Details

Key Value
Target Audience Advanced LLM users, researchers, and companies building local AI‑powered development tools
Core Feature Real‑time token accounting, pluggable pruning strategies, sub‑agent tool API with summary returns
Tech Stack Rust (core), WASM for optional web UI, bindings to llama.cpp / ggml, configuration via TOML
Difficulty High
Monetization Hobby (MIT‑licensed) – optional paid support/consulting for enterprises

Notes

  • Commenters noted “context rot and sampling problem” (DiabloD3) and praised dynamic pruning tools: “this and openviking appear to be all I need” (cyanydeez) and expressed interest in “empirical means of understanding if your preprompt is doing anything good” (hermitShell).
  • By providing measurable token budgets and pruning, ContextGuard directly addresses the desire for better control over context quality and invites discussion on optimal pruning heuristics.

PromptInsight: Chain‑of‑Thought Visualizer & Prompt Optimizer

Summary

  • A VS Code extension (with a local backend) that records the full chain‑of‑thought trace from a local LLM, visualizes token consumption per step, highlights redundant or conflicting instructions, and suggests prompt refinements.
  • Core value proposition: gives developers empirical feedback on prompt efficiency, enabling them to trim bloated prompts and improve model reliability without guesswork.

Details

Key Value
Target Audience Prompt engineers, developers using LLMs for code generation, and anyone tuning prompts for local models
Core Feature CoT capture, token‑usage heatmap, conflict detection, automated pruning suggestions
Tech Stack TypeScript/VS Code extension, Python backend (FastAPI), Ollama/llama.cpp API, Chart.js for visualisation
Difficulty Medium
Monetization Hobby (free, open‑source) – possible premium features via donation or “tip jar”

Notes

  • Users highlighted the importance of seeing raw CoT: “Seeing the raw CoT is the best signal for evaluating prompt efficiency” (0o_MrPatrick_o0) and wished for “empirical means of understanding if your preprompt is doing anything good” (hermitShell).
  • PromptInsight turns that wish into a tangible tool, fostering discussion on prompt design best practices and providing practical utility for daily LLM‑assisted workflows.

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