Project ideas from Hacker News discussions.

We will ban you and ridicule you in public if you waste our time on crap reports

πŸ“ Discussion Summary (Click to expand)

Based on the Hacker News discussion about cURL removing its bug bounty program, here are the four most prevalent themes:

1. The AI Slop Flood and Maintainer Burden

The core issue is the overwhelming volume of low-effort, AI-generated reports and pull requests that are flooding open-source projects. This consumes an inordinate amount of maintainer time and resources, forcing them to find ways to filter out the noise. cURL's removal of financial incentives for bug reports is presented as a direct attempt to disincentivize this behavior.

"Open source code library cURL is removing the possibility to earn money by reporting bugs, hoping that this will reduce the volume of AI slop reports." (jraph)

"Maintainers don't have infinite time." (creata)

"cURL has been flooded with AI-generated error reports. Now one of the incentives to create them will go away." (jraph)

2. Cultural and Motivational Drivers of Slop

A significant portion of the discussion speculates on the cultural and motivational origins of these submissions. Many commenters associate the behavior with Indian students and contractors seeking to pad their resumes and LinkedIn profiles, citing a cultural context where "saving face" or gaming the system is perceived differently than in Western cultures. However, others argue the primary driver is economic desperation in a highly competitive environment, not culture itself.

"I've been helping a bit with OWASP documentation lately and there's been a surge of Indian students eagerly opening nonsensical issues and PRs and all of the communication and code is clearly 100% LLMs." (nchmy)

"It’s desperational. The desperation of not having to lose any contract... For students, often there is no pathway to actually become good due to lack of resources. So, the only way is to fake it into a job and then become good." (whateverboat)

"The key point is that this usually isn’t lack of curiosity or reflection, but risk management under different norms." (nelox)

3. The Ineffectiveness and Toxicity of Shaming

There is a strong debate over the use of public shaming as a deterrent. While some argue it is a necessary and effective tool to discourage bad-faith actors, others contend it is counterproductive, creating a toxic environment that drives away good-faith contributors and is ineffective against anonymous or shameless trolls.

"Public humiliation is actually a great solution here." (mikkupikku)

"Public ridicule only creates a toxic environment where good faith actors are caught up in unnecessary drama... Shaming does not work, you look like an idiot, people will start to despise you..." (hypeatei)

"How effective is it against people who just simply does not care?" (johnisgood)

4. Systemic Failures and Unsustainable Models

The discussion frames the problem as a symptom of broader systemic issues. This includes the unsustainability of the current open-source model, where free labor is expected to support commercial use, and platform incentives (like GitHub's integration with Microsoft's AI division) that may unintentionally encourage slop. Commenters suggest that financial incentives tied to reputation (like bug bounties) are flawed in an era of low-cost, high-volume AI generation.

"The currently default model of having an open issue tracker, accepting third party pull requests, doing code reviews, providing support by email or chat, timely security patches etc, has nothing to do with open source and is not sustainable." (mixedbit)

"GitHub is under Microsoft’s AI division... Finally an explanation to why GitHub suddenly have way more bugs than usual for the last months (year even?), and seemingly whole UX flows that no longer work." (embedding-shape)

"Punishing bad behavior to disincentivize it seems more sensible." (jraph) (This reflects a shift from an incentive-based to a punitive model, highlighting the systemic failure of the former.)


πŸš€ Project Ideas

Generating project ideas…

AI-Powered Issue Triage Bot

Summary

  • [A service that pre-filters incoming GitHub issues/PRs, using an LLM to assess whether they are low-effort, AI-generated slop or genuine, well-documented contributions.]
  • [Core value proposition: Drastically reduces maintainer noise by automatically closing low-quality submissions before they consume human review time.]

Details

Key Value
Target Audience Open source maintainers of high-traffic projects.
Core Feature LLM-powered analysis of issue text, code snippets, and user history to score submission quality and flag for closure.
Tech Stack Python, OpenAI/Anthropic API, GitHub Webhooks, FastAPI.
Difficulty Medium
Monetization Revenue-ready: Freemium tier for small projects, paid tiers based on monthly issue volume.

Notes

  • [Addresses the "high asymmetry between the low effort to submit vs the high effort to review" mentioned by ezst. It automates the initial filter so maintainers don't have to.]
  • [Practical utility is high; most open source maintainers deal with noise. This tool offers a scalable solution to the "death by a thousand slops" problem.]
  • [Potential for discussion: Ethics of LLM-based rejection, false positive rates, and integration complexity.]

Credential Verification Platform for Bug Bounties

Summary

  • [A platform that acts as an intermediary for bug bounty submissions, requiring a small refundable deposit to submit a report.]
  • [The deposit is returned if the report is triaged as valid; otherwise, the funds are withheld to disincentivize slop and cover review costs.]

Details

Key Value
Target Audience Security researchers, bug bounty hunters, and open source projects running bounty programs.
Core Feature Escrow system for bug reports: payment processing, validation workflows, and automated refund/denial logic.
Tech Stripe Node.js, Stripe Connect, OAuth (GitHub), Redis.
Difficulty High
Monetization Revenue-ready: Percentage fee on retained deposits (from invalid reports) or flat SaaS fee for program setup.

Notes

  • [Directly addresses the discussion about paid bug reports. It validates the idea of "making people pay for security reports" (jraph) but structures it as a refundable investment rather than a punishment.]
  • [High utility for projects drowning in AI slop, as mentioned by hobs regarding opportunity cost. It creates a financial barrier to entry that AI spammers are unlikely to cross.]
  • [Potential for discussion: Accessibility (unbanked researchers), fairness to legitimate researchers from lower-income regions, and maintainer bias in validation.]

Identity-Weighted Contribution Scoring

Summary

  • [A GitHub App that analyzes a user's repository history to assign a "trust score" based on behavioral patterns (e.g., percentage of LLM-like text vs. original thought, issue closure rates).]
  • [Projects can configure thresholds to auto-hide or deprioritize issues from users with scores below a certain limit.]

Details

Key Value
Target Audience Maintainers of large open source projects seeking to filter noise.
Core Feature NLP analysis of issue/PR descriptions and code diffs to detect AI generation patterns and contribution quality.
Tech Stack Python, spaCy/Hugging Face, GitHub GraphQL API, PostgreSQL.
Difficulty High
Monetization Hobby: Open source core. Revenue-ready: Managed service with advanced analytics for enterprise teams.

Notes

  • [Addresses the "throwaway accounts" problem mentioned by ezst by looking at behavioral signals rather than just reputation.]
  • [Practical utility for identifying users who generate "LLM slop" to pad resumes (mentioned by multiple commenters).]
  • [Potential for discussion: Privacy concerns, potential for bias against non-native English speakers using AI for grammar assistance, and gamification of the scoring system.]

Community-Governed Triage Queue

Summary

  • [A decentralized moderation tool where community members earn "steward" status by consistently flagging low-quality issues correctly.]
  • [High-score stewards have the power to close issues or mark them as "invalid" before a core maintainer sees them.]

Details

Key Value
Target Audience Open source projects with active community members willing to help with maintenance.
Core Feature Gamified moderation queue: Users review random open issues, vote on quality, and build a track record of accuracy.
Tech Stack JavaScript, React, Node.js, GitHub App, Redis.
Difficulty Medium
Monetization Hobby: Free to use. Revenue-ready: Enterprise version with custom branding and reporting.

Notes

  • [Aligns with the sentiment from mikkupikku: "They'll still get bug reports and fixes from people who actually give a shit." It empowers those who care to help filter out those who don't.]
  • [Solves the "opportunity cost" issue by distributing the review load.]
  • [Potential for discussion: The risk of "mob justice" or brigading, and ensuring fair representation of the community.]

Private Fork & Patch Submission Portal

Summary

  • [A service that allows security researchers to submit patches and bounties anonymously to a secure, private fork of the repository.]
  • [Only validated, high-confidence patches are merged into the public upstream repository, shielding maintainers from direct public spam.] | Key | Value | |-----|-------| | Target Audience | High-profile open source projects (like cURL) vulnerable to public spam. | | Core Feature | Secure, anonymous drop-box for code patches and vulnerability reports with mandatory triage workflows. | | Tech Stack | Go, Git, End-to-end encryption (libsodium), Kubernetes. | | Difficulty | High | | Monetization | Revenue-ready: Contract-based security reporting infrastructure for large OSS foundations. |

Notes

  • [Addresses the core issue of the asymmetry in effort (jraph: "low effort to submit vs the high effort to review") by creating a buffer zone.]
  • [Provides a safe harbor for legitimate researchers who might be deterred by public hostility (yorwba: "if you make me pay, I don't think I will bother").]
  • [Potential for discussion: The centralization of trust, the cost of maintaining a private infrastructure, and the latency introduced by the extra layer of abstraction.]

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