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Still trying to figure out the boundaries of these tools' strengths without outsourcing important thinking, problem-solving, and the collective building-up of a mental model of the systems we're developing.
The shape of LLM tools as toolsโfit for a purpose, ergonomicโis becoming clearer, and human patterns of behavior with them that do more harm than good are also becoming easier to recognize.
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.
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.
๐
let N=6,D0=[0,1,0,-1],D1=[1,0,-1,0],s=[],g={},d=x=y=xa=xb=ya=yb=0;
for(let n=0;n<N;n++){s=s.concat([1]).concat(s.map(x=>!x).reverse())}
for(let i=0;i<s.length;i++) {
x<xa && (xa=x);y<ya && (ya=y);x>xb && (xb=x);y>yb && (yb=y);
let t=s[i]?1:-1,e=(d+t+4)%4;
g[x]||(g[x]={});
g[x][y]=g[x][y]?"โผ":"โโโโ"[(t*(D0[d]+D0[e])>0?0:2)+(t*(D1[d]+D1[e])>0?0:1)];
x+=D0[e];y+=D1[e];d=e;
}
for(j=yb;j>=ya;j--) {
let u=""; for(i=xa;i<=xb;i++){u+=(g[i][j]||"ยท")} console.log(u);
}ยทโโโโยทยทยทยทยทยทยท
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โยทยทโโโโยทยทยทโโ
ยทยทยทโโผโผโยทยทยทโโ
ยทยทยทโโผโผโโโโโผโ
ยทยทยทโโผโโโผโผโโโ
ยทยทยทโโผโยทโโผโยทยท
ยทยทยทยทโโยทยทโโยทยท
