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Research#llm📝 BlogAnalyzed: Dec 28, 2025 15:31

User Seeks to Increase Gemini 3 Pro Quota Due to Token Exhaustion

Published:Dec 28, 2025 15:10
1 min read
r/Bard

Analysis

This Reddit post highlights a common issue faced by users of large language models (LLMs) like Gemini 3 Pro: quota limitations. The user, a paid tier 1 subscriber, is experiencing rapid token exhaustion while working on a project, suggesting that the current quota is insufficient for their needs. The post raises the question of how users can increase their quotas, which is a crucial aspect of LLM accessibility and usability. The response to this query would be valuable to other users facing similar limitations. It also points to the need for providers to offer flexible quota options or tools to help users optimize their token usage.
Reference

“Gemini 3 Pro Preview exhausts very fast when I'm working on my project, probably because the token inputs. I want to increase my quotas. How can I do it?”

Permalinkr/Bard
Research#llm🏛️ OfficialAnalyzed: Dec 27, 2025 19:00

LLM Vulnerability: Exploiting Em Dash Generation Loop

Published:Dec 27, 2025 18:46
1 min read
r/OpenAI

Analysis

This post on Reddit's OpenAI forum highlights a potential vulnerability in a Large Language Model (LLM). The user discovered that by crafting specific prompts with intentional misspellings, they could force the LLM into an infinite loop of generating em dashes. This suggests a weakness in the model's ability to handle ambiguous or intentionally flawed instructions, leading to resource exhaustion or unexpected behavior. The user's prompts demonstrate a method for exploiting this weakness, raising concerns about the robustness and security of LLMs against adversarial inputs. Further investigation is needed to understand the root cause and implement appropriate safeguards.
Reference

“"It kept generating em dashes in loop until i pressed the stop button"”

Permalinkr/OpenAI
Research#LLM👥 CommunityAnalyzed: Jan 10, 2026 15:33

Data Scarcity: Examining the Limits of LLM Scaling and Human-Generated Content

Published:Jun 18, 2024 02:04
1 min read
Hacker News

Analysis

The article's core argument, as implied by the title, centers on the potential exhaustion of high-quality, human-generated data for training large language models. It is a critical examination of the sustainability of current LLM scaling practices.
Reference

“The central issue is the potential depletion of the human-generated data used to train LLMs.”

PermalinkHacker News