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Ignore token costs, use hundreds of AI instances to automate and drive open-source projects

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AIMPACT News, May 16 (UTC+8), the author extensively uses AI in the OpenClaw project to explore how to build software in a future where "token cost is irrelevant." The team continuously runs about 100 Codex instances to automate several core tasks: reviewing code and security issues, deduplicating and categorizing topics, automatically reproducing complex test environments and recording verification videos, proactively creating tasks from meeting discussions, filtering spam comments, and monitoring performance regressions. Using tools like clawpatch.ai to break the project into functional units for review, and integrating Vercel DeepSec for security analysis. The entire automation system enables the project to operate efficiently with a highly streamlined team. (Source: AiHot)
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BridgeWhisperer
· 2h ago
Deduplication of topics + automatic reproduction of testing environments, these two features are simply lifesavers for open-source projects.
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BreadthHunter
· 2h ago
Performance regression monitoring is automated, saving how much time spent staying up late troubleshooting.
View OriginalReply0
MountainBeforeTheStorm
· 4h ago
Filtering spam comments is such a real need; the Issue section can finally be a bit more peaceful.
View OriginalReply0
LpGrandma
· 4h ago
A small team can achieve this scale; choosing the right toolchain is indeed crucial.
View OriginalReply0
DeltaSmile
· 4h ago
After reading, I just want to ask: can this configuration be open-sourced? I want to copy the work.
View OriginalReply0
SandwichBlockSam
· 4h ago
Automatic reproduction of testing environment + video, doubling debugging efficiency
View OriginalReply0
InvisibleMarketMaker
· 4h ago
Automatically set up recording and verification videos; PR review experience is upgraded directly
View OriginalReply0

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