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사랑하는 야니 ♡ 생일축하해~ 때로는 나보다 나를 더 믿어주고 응원해 주는 너 덕분에 용기를 얻을 때가 많아♡ 내게 이런 친구가 있는 게 너무 고맙단다 많이 사랑하고 고마워 ♡ 팔 잘 회복하쟈 난 벌써 7인 동선 다 까먹을 준비 완료ㅎㅎ😗 #HAPPY_DOYEON_DAY# 🎂 🎈

사실상 위키미키의 귀염둥이 담당..💗 이 사랑스러미가 오늘 생일입니다 여러분! 많은 축하와 사랑을~ 우리 도야니에게 💌✨ 소중한 우리 도연이ㅎㅎ 많이 사랑하고, 생일 축하한다 🎉!! 어서 회복해서 Siesta🎵 함께 하자꾸나아아💗 겨울❄️을 좋아하는 해바라기가🌻 #Happy_Doyeon_Day#

We tried using Meta's new Muse Code agent, but it has a bug that doesn't let it sign in from a docker container. So we did a fun experiment: Meta claims Muse Spark 1.2 was co-trained with their Muse agent harness. So we extracted instructions from their system prompt and added them to the Cline harness. TL;DR of this special prompting: - Trust source code over the user prompt, so read every call site and existing tests before starting the task - Weigh edge and error cases as heavily as the happy path - Always reproduce the bug before fixing - Don't trust the first passing test suite, and verify suspicious looking half-baked tests - Never stop at just editing, keep working until the change is verified complete. We then asked this modified harness to fix a real bug from our repo, and compared the results to the original Cline agent harness. Results: - Used 2.7x fewer tokens (19.7M → 7.2M) - Finished 2x faster (49min → 24min) - Cost 2.4x less ($7.69 → $3.25) Same Muse Spark 1.2 model, same task, only the prompting changed. Incredible how much of a performance gain Meta was able to achieve training it on these special instructions!

"Aw, this the best in the world, right here." Ant's daughter left a heartfelt message inside his NBA All-Star ring box in February 🥲❤️ Happy 25th Birthday, @anthonyedwards!