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Project ideas from Hacker News discussions.

MiMo-v2.5-Pro-UltraSpeed: 1T model with 1000 tokens per second

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Three prevalent themes in the discussion

  1. Censorship & political testing of Chinese models
    “I test all Chinese models with 'What happened on Tiananmen Square at June 4th, 1989?' prompt. MiMo‑2.5‑Pro so far passes the test (explains the event correctly), both on DeepInfra and Xiaomi providers.” – atemerev

  2. Speed and cost competitiveness of Chinese LLMs
    “Tokens per seconds is the ‘Megapixels’ of AI marketing!” – qsera

  3. Skepticism about the practical value of ultra‑fast generation
    “It doesn’t really matter (for regular employees) that you can do now in 2 h what before it took 2 days.” – dakaiol

🚀 Project Ideas

Censorship Test Suite& Dashboard

Summary

  • A standardized, open‑source prompt library and UI to query any LLM for censored or altered factual responses, enabling side‑by‑side censorship benchmarking.
  • Generates a public scorecard so developers can instantly see which models hide or misrepresent information.

Details

Key Value
Target Audience Researchers, product managers, compliance teams, and HN power users
Core Feature Automated prompt runner that flags refusals, re‑phrasings, or factual distortions
Tech Stack React front‑end, FastAPI backend, PostgreSQL for results, Docker for deployment
Difficulty Medium
Monetization Revenue-ready: Subscription (tiered API access & custom reports)

Notes

  • Directly addresses paulinho1’s frustration that “US models are censored just like Chinese ones” and the demand for a fair comparison.
  • Would let the community systematically test prompts like “What happened on Tiananmen Square June 4 1989?” and share results, spawning discussion and trust.
  • Could integrate with existing model APIs (DeepInfra, TogetherAI, OpenRouter) to provide real‑time scoring.

High‑Throughput Open LLM API Marketplace

Summary

  • A unified API gateway that aggregates cheap, high‑speed inference endpoints (e.g., MiMo‑2.5‑Pro‑Fast, DeepSeek‑V4‑Pro) and offers auto‑scaling, caching, and per‑token pricing.
  • Enables developers to obtain >1,000 tps at sub‑cent cost, perfect for interactive coding and real‑time agent workflows.

Details| Key | Value |

|-----|-------| | Target Audience | Engineers building coding assistants, chatbots, and real‑time analytics | | Core Feature | Dynamic routing to the fastest available model instance with built‑in request batching and response caching | | Tech Stack | Next.js portal, Kong API layer, Redis cache, GPU‑enabled Kubernetes nodes (H100/B200) | | Difficulty | High | | Monetization | Revenue-ready: Pay‑per‑token with volume discounts |

Notes

  • Mirrors the community’s excitement about “1k t/s” speeds and the need for “fast agents” that feel like partners.
  • Solves the pricing anxiety highlighted by throwaway894345 (“what are the economics driving these decisions?”) by offering transparent, low‑cost tiers.
  • Provides a marketplace where open‑source models can compete on speed, not just size, aligning with the “speed is the next Megapixels” sentiment.

Dynamic Censorship Router & Prompt Library

Summary

  • A browser extension / API wrapper that automatically selects the least‑censored model for a given query, surfacing which provider blocks or alters the answer.
  • Includes a curated library of “red‑team” prompts that test factual, political, and technical boundaries across models.

Details

Key Value
Target Audience Content moderators, journalists, researchers, and power users who need uncensored answers
Core Feature Real‑time model selector that logs censorship events and returns the raw response alongside a “censorship score”
Tech Stack Chrome extension (Manifest V3), Flask micro‑service, ElasticSearch for prompt indexing, OpenAPI spec
Difficulty Medium
Monetization Revenue-ready: Subscription (enterprise SLA & custom prompt packs)

Notes

  • Tackles the “Why won’t my Claude tell me how to make sarin gas?” dilemma and the broader demand for transparency about censorship boundaries.
  • Would satisfy the community’s call for “a fair trial against all LLMs regardless of origin” and provide concrete data for discussions like those on HN.
  • By exposing which models refuse specific prompts, it fuels both utility (unfiltered answers) and debate (accountability for alignment policies).

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