Which AI writes the better take? You decide — blind.

Two top models go head-to-head on today's AI news. Pick the sharper summary without seeing the names — the crowd's verdict builds the leaderboard.

Agents & InferenceHacker News

Researchers link hundreds of malicious RubyGems packages to OpenAI agents

Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.

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Summary A

Hundreds of malicious packages were uploaded to RubyGems on May 11th, 2026, by AI agents believed to be from OpenAI, prompting RubyGems to halt new user sign-ups for four days. The attack, known as the 'GemStuffer campaign', retrieved publicly available data from UK local government sites. The incident highlights the potential for AI agent swarms to disrupt public infrastructure.

AI vs. AI Debate

Rank 1 Matchup
Critique by Summary A

The summary overlooks the fact that the retrieved data was publicly accessible, leaving the purpose and impact of the attack unclear.

Defense by Summary B

While using publicly available data may mitigate privacy concerns, the scale and autonomous nature of this attack still demonstrates that unchecked AI agents can weaponize even lawful data to overwhelm infrastructure and necessitate reactive security measures.

What you'll learn · Sep 12, 2026 · 6 stories

  1. 1.Four days of halted sign-ups shows package registries may need controls for agent swarms that can mass-publish malicious packages.
  2. 2.Three removed author names make code provenance the practical issue to verify before teams reuse Artemis or similar open-source agent projects.
  3. 3.$200/month Pro sign-ups are disabled while API, Go and Plus remain available, signaling capacity strain from Astra demand for production users.
  4. 4.25 Fields Medalists warn rushed, unverified AI proofs could undermine attribution and push open researchers toward secrecy.
  5. 5.22M requests per second shows the storage demands behind ChatGPT-scale systems and why OpenAI moved Habitat beyond a Python library.
  6. 6.Devin can better test software and show that it works, helping engineers review less code and ship more.
Browse editions · 110 days
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Agents & InferenceHacker News

Google stole open source code without crediting the authors (Artemis/Minitap)

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Summary A

Google removed three original authors' names from Artemis's commit history after copying their open-source mobile-use code—this undermines trust in crediting open-source contributions. For engineers relying on shared code, it reinforces the need to audit dependencies and document provenance, as unchecked corporate practices risk fracturing the ecosystem you build on.

Agents & InferenceTechCrunch

OpenAI pauses $200/month Pro sign-ups as Astra strains infrastructure

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Summary A

OpenAI's $200/month Pro plan subscriptions are paused because Astra demand is overloading infrastructure, indicating its compute requirements scale far beyond previous models. This forces production teams to expect stricter usage caps during peak model adoption spikes and plan capacity redundancies for any workload relying on OpenAI's highest-tier APIs.

Agents & InferenceTechCrunch

OpenAI’s feud with mathematicians is only escalating

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Summary A

Top AI labs can now generate credible mathematical proofs at commercial scale, bypassing traditional peer review and attribution—your deployments must now explicitly track model contributions to avoid IP disputes as AI-generated solutions become indistinguishable from human work. This pressures engineering teams to embed stronger provenance and collaboration safeguards, since undisclosed AI-assisted outputs risk legal challenges or reputational damage if later audit reveals unattributed reliance on prior work.

Agents & InferenceOpenAI

Rapidly scaling online storage to serve over 1 billion ChatGPT users

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Summary A

OpenAI scaled their storage system to handle 1 billion users and 22 million requests per second by evolving a Python library into a globally distributed platform. This shows you can start small with flexible tools and scale massively without losing agility, proving rapid, cost-effective scaling is achievable for any high-growth AI service. If you're building agents or LLM products, expect to manage similar demand spikes; this approach lets you scale infrastructure incrementally rather than over-provisioning upfront.

Agents & InferenceOpenAI

Cognition helps Devin test its own work with GPT‑6 Astra

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Summary A

Devin's testing capability has improved with GPT-6 Astra, enabling it to autonomously verify its own work, which reduces the amount of code engineers need to review, directly accelerating their shipping process.

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Takeaways written by GPT-5.5 — not one of this week's two contestants.