ai-agents claude cost-optimization developer-tools

I burned all my tokens researching how to save tokens

quesma.com

July 19, 2026

10 min read

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56/100

Summary

Quesma is researching the economics of AI agents and agentic coding costs. A personal deep research setup burned through the entire limit of the Claude Max 5x plan in 30 minutes, leading to a focus on cost reduction and trust improvement using existing subscriptions.

Key Takeaways

The initial deep research setup consumed the entire token limit of the Claude Max 5x plan in 30 minutes without yielding results.
The author optimized research costs by utilizing existing subscriptions (Claude, Codex, and Antigravity) and implementing shared memory among the tools.
A model-orchestration pattern was developed to use cheaper models for specific tasks, such as Claude Sonnet 5 for searching and Claude Opus 4.8 for verification.
The research involved adjusting the model setup based on live run performance and weaknesses identified during the process.

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Coverage & discussions

Hacker News · quesma.com

I burned all my tokens researching how to save tokens ↗

Community discussions

Hacker News discussion ↗

Historical count: 5 · 5 saved in sample · Historical sample

Analysis ready

Community Sentiment

Mixed

Positives

Using a combination of cheaper models for initial exploration and funneling results through powerful models can maximize efficiency and save tokens — this approach is gaining traction.
Some commenters highlight that cloud AI can significantly outperform local models, especially when cost is considered, making it a smart choice for many developers.
There's excitement around tools like RemembrallMCP that claim to reduce token usage while improving results, indicating innovation in optimizing AI interactions.

Concerns

Many users express skepticism that the hype around cloud AI translates to real-world value, with concerns that it's often just a cycle of writing about AI without tangible products.
Commenters are frustrated with the hardware costs required for local AI models, arguing that it's financially impractical compared to using cloud solutions.
The persistent issue of hallucinations in AI outputs is a major concern, with some feeling that claims of 'no hallucinations' are unrealistic and misleading.