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An Update to Our Asset Listings Announcements
TL;DR: @CoinbaseAssets is now the dedicated source for all official Coinbase asset announcements on X. @CoinbaseMarkets will remain focused on traders, including market insights, product updates and trading education, while amplifying asset moments that are relevant to its audience.
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Singapore Sees 43% Drop in Crypto Scam Losses and Partnerships Like Ours Are Helping Drive the Change
TL;DR: Singapore’s crypto scam losses fell 43% year over year, from S$114.9 million to S$65.5 million, as a result of stronger enforcement, public education, and partnerships between the Anti-Scam Centre and industry leaders like Coinbase. Through four joint operations, Coinbase’s Global Intelligence team helped identify 720+ victims and prevent more than S$18.9 million in potential losses, while a new anti-scam awareness campaign reached Singapore retail customers.
Part 3/3 Owning Intelligence Series: How Coinbase Built, Evaluated, and Post-Trained a Fraud Agent
TL;DR: Using SkyRL on Anyscale, we post-trained Qwen3.5-9B for Onramp fraud detection and served it with Ray Serve LLM and vLLM. On our benchmark, it exceeded Opus 4.5 across all four detection metrics, including 9.6% higher F1. In online inference, we reduced end to end latency by 55%.
Part 2/3 Owning Intelligence Series: How Coinbase Built, Evaluated, and Post-Trained a Fraud Agent
TL;DR: Newer LLMs are not necessarily better at domain-specific tasks. On our fraud benchmark, newer Opus, Sonnet, and GPT versions all had lower recall, F1, and dollar-weighted recall—even when precision improved. The lesson: choose models using high-quality, domain-specific benchmarks, with reproducible evaluation and ideally deterministic scoring against verified outcomes. These findings also motivated our next step: post-training open-weight models for greater control over quality, cost, and deployment.
Part 1/3 Owning Intelligence Series: How Coinbase Built, Evaluated, and Post-Trained a Fraud Agent
TL;DR: How Coinbase built, evaluated, and post-trained an Onramp fraud agent to achieve 9.6% higher performance (F1 score) and 55% lower end to end latency than frontier models.