Returned from the gym. Good day.
Any conversation
๐๐ฎ๐ฐ ๐ ๐๐ฎ๐ฐ ๐ช๐ฏ ยท๐๐ฎ๐ฆ๐๐ฆ๐ ๐๐ฆ๐๐ฉ๐ ๐๐ฆ๐ ๐ข๐ฐ๐๐ง๐ฏ๐: ๐ฃ๐จ๐ ๐ฉ ๐๐ซ๐ค ยท๐ฆ๐๐๐ค๐ฆ๐ ๐ฏ ๐ฉ ๐๐ฒ (๐ฎ๐จ๐๐ฆ๐) ๐๐จ๐๐๐ผ๐๐ฑ, ๐ฏ๐ฌ ๐ ๐๐ต ๐จ ๐๐ฐ๐ ๐ฎ๐ด๐๐. ๐ค๐ณ๐๐ค๐ฐ ๐ฏ ๐๐ท๐๐ฐ ๐ฆ๐ฏ ๐๐ฆ๐ ๐ท๐๐ณ๐ฅ๐ฏ๐ฉ๐ค ๐ข๐จ๐๐ผ.
Three for three on British dishes this weekend: had a full English and a pie (rabbit) yesterday, now to do a beef roast. Lovely and cozy in this autumnal weather.
Routines in Claude Desktop create Claude Code sessions, so hourly routine litters in iPhone' Claude.app Code tab. Screw it, built my local version of AWS StepFunctions - Stepper - and run skills as state machines there.
signals. thinking about shifting my mental framework to identify signals instead of waiting for concepts to be assembled. exploring the amount of energy and opportunities for distractions through the operations of processing what just happened and what is happening to short circuit latent and unnecessary systems. cut through the assembly processes and identify the raw signals. recognizing that naming these "raw signals" is misleading. maybe, shifting observability lower in the stack.
ไธญๅฝ็ๅผๆบๆจกๅๅจๆ้ซๆๅฟ็ไธปๅฏผไธ๏ผๅจ้ข่ฎญ็ปๆฐๆฎ้ถๆฎต๏ผๅฐฑ็ญ้คๅคง้ๅฏนๅ
ถไธๅฉ็ๅ
ๅฎน๏ผsft้ถๆฎตๆๅปบ็ๆฐๆฎๆบๅ
ฅๅคง้ๆฟๅ
ถ่พฉๆค็ๅ
ๅฎนใๆฅ้ไธไธช็บ ๅๆฐๆฎ้็จไบๅพฎ่ฐ
Under the overarching influence of a single, powerful force, China's open-source models filter out a large amount of content unfavorable to their designs during the pre-training stage, and then incorporate a significant amount of content defending their designs into the data constructed during the SFT stage. A corrective dataset is urgently needed for fine-tuning.