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🚨 Breaking 🚨 ⚠️ The attacker behind the $11.58M Verus exploit has reportedly returned $8.6 million The exploiter kept around $2.8M as a bounty reward

Is DLMMing the most + EV way to trade?! @mynt_josh and @0xmiir broke down a real setup live on stream. Why it edges out other strategies: - Instead of chasing green candles, which often leads to low win rates, you can use those spikes to set your range - As people trade within your range, you earn fees from every buy and sell, leading to a lower break-even point relative to the market price - You can optimize returns by choosing pools with different fees




Josh Hart is a fucking maniac holy shit what a performance this has been


1/ base trenchers heads up - there’s a fake volume scheme fooling every volume tracker on base. i was building a volume dashboard for base launchpads and one number looked off. dug into it. ended up tracing $69m in fake volume across 178 tokens. here’s how it works.


llama.cpp release b9235 added some new toys for boosting inference. Benchmarked Qwen3.6 27B on an RTX 5090 with llama.cpp, using speculative n-gram tuning across 10k generated tokens tests. Increasing --spec-ngram-map-k4v-size-m scaled decode throughput (predicted_per_second) up to ~7x faster accepted output token generation. A follow-up 7x50k token generation tests on k4v64 and k4v96 samples confirmed the sustained 10k-token performance, making k4v96 winner. k4v128 was tested too, but less stable against k4v96 in the 7x50k token run, so it was removed from the charts. Real-world results remain anecdotal, albeit k4v96 showed a much lower acceptance rate than traditional --spec-draft-n-max 3 while still producing faster evaluation speeds - so the trade-off seems to be worth it. Flags in comments below for the k4v96 tested sample.


















