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๐Ÿ’ป Tech News

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๐Ÿ“Š Stock

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๐Ÿ‡ฐ๐Ÿ‡ท ํ•œ๊ตญ ์‹œ์žฅ

KOSPI
6,788.88+123.49 (+์ƒ์Šน)
KOSDAQ
838.41+0.76 (+์ƒ์Šน)

๐Ÿ‡บ๐Ÿ‡ธ ๋ฏธ๊ตญ ์‹œ์žฅ

S&P 500
7,730.99+55.29 (+0.72%)
NASDAQ
26,541.35+411.15 (+1.57%)
DOW
53,569.44+105.54 (+0.20%)

๐Ÿ’ฑ ํ™˜์œจ

USD/KRW
1,371.9010.10
JPY/KRW
859.697.42
EUR/KRW
1,597.4413.07

๐Ÿช™ ์•”ํ˜ธํ™”ํ

BTC
$79,515(โ‚ฉ109,092,683)-0.27%
ETH
$2,488(โ‚ฉ3,413,335)-1.70%

๐Ÿ”ฅ ์ƒ์Šน TOP

1.SHD
12,350+30.00%
2.๊ธˆํ˜ธ์ „๊ธฐ
10,680+29.93%
3.์ง„์–‘ํด๋ฆฌ
2,190+14.48%
4.์‹ ํ’์ œ์•ฝ
10,090+14.14%
5.์ง„์–‘ํ™”ํ•™
1,410+12.62%

๐ŸงŠ ํ•˜๋ฝ TOP

1.๋ฏธ๋ž˜์—์…‹์ƒ๋ช…
23,200-10.60%
2.SOL SKํ•˜์ด๋‹‰์Šค๋‹จ์ผ์ข…๋ชฉ๋ ˆ๋ฒ„๋ฆฌ์ง€
8,015-8.92%
3.SK๋””์•ค๋””
4,180-8.83%
4.๋ฏธ๋ž˜์—์…‹ ๋ ˆ๋ฒ„๋ฆฌ์ง€ SKํ•˜์ด๋‹‰์Šค ๋‹จ์ผ์ข…๋ชฉETN
9,570-8.73%
5.KODEX SKํ•˜์ด๋‹‰์Šค๋‹จ์ผ์ข…๋ชฉ๋ ˆ๋ฒ„๋ฆฌ์ง€
9,630-8.63%

๐Ÿ  Real Estate

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๐Ÿ“Š ์‹ค๊ฑฐ๋ž˜ ๋ณ€๋™ (2026-08-28)

โ–ฒ ์ƒ์Šน 525๊ฑดโ–ผ ํ•˜๋ฝ 503๊ฑด

์ƒ์Šน 525๊ฑด / ํ•˜๋ฝ 503๊ฑด

๐Ÿ“ˆ ๊ฒฝ์ œ์ง€ํ‘œ

๊ฒฝ์ œ์ง€ํ‘œ

ํ•œ๊ตญ์€ํ–‰ ๊ธฐ์ค€๊ธˆ๋ฆฌ: 3(+9.09%) / ์ฃผํƒ๋งค๋งค๊ฐ€๊ฒฉ์ง€์ˆ˜(์•„ํŒŒํŠธ): 102.586(+0.41%) / ์ฃผํƒ์ „์„ธ๊ฐ€๊ฒฉ์ง€์ˆ˜(์•„ํŒŒํŠธ): 103.253(+0.45%)

๐Ÿ† ์ธ๊ธฐ ์•„ํŒŒํŠธ ๋žญํ‚น

1.์ƒ๋™์—ญ๋กฏ๋ฐ์บ์Šฌ์‹œ๊ทธ๋‹ˆ์ฒ˜
2.์จ๋ฐ‹ํด๋ผ๋น„์˜จ
3.๋ณ‘์ ์—ญ์•„์ดํŒŒํฌ์บ์Šฌ
4.์ง€์ œ์—ญ๋ฐ˜๋„์ฒด๋ฐธ๋ฆฌํ’๊ฒฝ์ฑ„์–ด๋ฐ”๋‹ˆํ‹ฐ
5.์„œ์ˆ˜์›์—ํ”ผํŠธ์„ผํŠธ๋Ÿด๋งˆํฌM1๋ธ”๋ก
6.๋‘์‚ฐ์œ„๋ธŒ๋”์ œ๋‹ˆ์Šค๋ถ€์ฒœ
7.์žฅ์œ„ํ‘ธ๋ฅด์ง€์˜ค๋งˆํฌ์›
8.๋™ํƒ„์—ญ๋กฏ๋ฐ์บ์Šฌ
9.ํ˜ธ๋ฐ˜์จ๋ฐ‹ํ’๋ฌดโ…ข
10.์˜ค์‚ฐํ—ค๋ฆฌํ‹ฐ์ง€์ž์ด1๋‹จ์ง€

๐Ÿ“ ์ธ๊ธฐ ์ง€์—ญ ๋žญํ‚น

1.๊ฒฝ๊ธฐ๋„ ๋ถ€์ฒœ์‹œ
2.์„œ์šธํŠน๋ณ„์‹œ ์˜๋“ฑํฌ๊ตฌ
3.๊ฒฝ๊ธฐ๋„ ํ™”์„ฑ์‹œ
4.๊ฒฝ๊ธฐ๋„ ํ‰ํƒ์‹œ
5.๊ฒฝ๊ธฐ๋„ ์ˆ˜์›์‹œ
6.์„œ์šธํŠน๋ณ„์‹œ ์„ฑ๋ถ๊ตฌ
7.๊ฒฝ๊ธฐ๋„ ๊น€ํฌ์‹œ
8.๊ฒฝ๊ธฐ๋„ ์˜ค์‚ฐ์‹œ

๐Ÿข ์ „์›”์„ธ ํ˜„ํ™ฉ

์ „์›”์„ธ ํ˜„ํ™ฉ

์ „์„ธ 610๊ฑด / ์›”์„ธ 589๊ฑด

๐Ÿ™๏ธ ์˜คํ”ผ์Šคํ…”

์˜คํ”ผ์Šคํ…” ๋งค๋งค 117๊ฑด

117๊ฑด ๊ฑฐ๋ž˜

๐Ÿ“ฐ ๋ถ€๋™์‚ฐ ๋‰ด์Šค

์˜คํ”ผ์Šคํ…”๋„ ์ƒ์•  ์ฒซ ์ทจ๋“์„ธ ๊ฐ๋ฉด '์•„ํŒŒํŠธ ์„ ํ˜ธ' ๋ฒฝ ๋„˜์„๊นŒ
"ํƒ„์ฒœ ์ฃผํƒ๊ณต๊ธ‰ ๊ฒ€ํ† " ํ•˜๋ฃจ ๋’ค "์–ด๋ ต๋‹ค" ์‹ ์ค‘ํ•ด์ง„ ๊น€์œค๋•, ์™œ
ํƒ„์ฒœ๋ฌผ์žฌ์ƒ์„ผํ„ฐ, ๊ฐ•๋‚จ๊ถŒ ๊ณต๊ธ‰ ์ด์ ์—๋„ '๊ธฐํ”ผ์‹œ์„ค ์ด์ „' ๋ถ€๋‹ด
๊ตญํ† ๋ถ€ ์žฅ๊ด€ " ์‹œ์žฅ๊ณผ ํ˜‘์˜ ์ž˜๋˜๋ฉด ์ˆ˜๋„๊ถŒ์— 10๋งŒ๊ฐ€๊ตฌ ์ด์ƒ ๊ณต๊ธ‰ ๊ฐ€๋Šฅ"
์„œ์šธ์‹œ ์ด์–ด ์ž์น˜๊ตฌ๋„ ์ •๋น„์‚ฌ์—… '์†๋„์ „' ์ฃผํƒ๊ณต๊ธ‰ ๋นจ๋ผ์งˆ๊นŒ
์„œ์šธ์‹œ, '์‹ ํ†ต๊ธฐํš' ์ฃผ๋ฏผ์ฐธ์—ฌ ํ™•๋Œ€ยทํ›„์†์ ˆ์ฐจ ์ง€์› ๊ฐ•ํ™”
์ „๊ธฐ์ฐจ ์ถฉ์ „์— ์† ๋ป—๋Š” ๊ฑด์„ค์‚ฌ, ๋‹ค์Œ ์Šคํ…์€
"ํƒ„์ฒœ์„ผํ„ฐ ์ž…์ง€ ์ข‹์•„ ์ธ๊ธฐ ๋Œ๊ฒƒ" "์†Œํ˜•ํ‰ํ˜• ๋ฐ€์ง‘๋• ๊ตํ†ต๋‚œ ์šฐ๋ ค"
์‚ผ์ „ ์‚ฌ๋‚ด๋Œ€์ถœ ์•ž๋‘” ๋™ํƒ„, ๊ธฐ๋Œ€๊ฐ์— ์•„ํŒŒํŠธ๊ฐ’ ๊ธ‰๋“ฑ
๊ธฐ์ค€๊ธˆ๋ฆฌ 3% ์‹œ๋Œ€ ๋ถ€๋™์‚ฐ '๊ฑฐ๋ž˜์ ˆ๋ฒฝ' ์šฐ๋ ค
"์žฌ๊ฑด์ถ•๋„ ํ™œ์„ฑํ™”" ์—ฌ๋‹น๋„ ๋ฐฉํ–ฅ ํ‹€์—ˆ๋‹ค ์šฉ์ ๋ฅ  1.2๋ฐฐ ์ƒํ–ฅ ๊ฒ€ํ† 
์œ„๋ก€์„  ํŠธ๋žจ ์ œ๋™ ํ’€๋ฆฐ๋‹ค ๋Œ€๊ด‘์œ„, ์„œ์šธ์‹œยท๊ฒฝ์ฐฐ ๊ฐˆ๋“ฑ ์กฐ์ •
๊ฐ•๋‚จ์€ 18์–ต ๋‚ฎ์€ ๋งค๋ฌผ, ์€ํ‰์€ 23์–ต ํ˜ธ๊ฐ€ ์„œ์šธ ์™ธ๊ณฝ ์ง‘๊ฐ’ ๋œ€๋ฐ•์งˆ
์ฃผ๋ฏผ ์ฐธ์—ฌยท์†Œํ†ต ํ™•๋Œ€ ์„œ์šธ '์‹ ํ†ต๊ธฐํš' ์šด์˜ ๊ฐœ์„ 
์œ„๋ก€์„  ํŠธ๋žจ ์„ ๋กœ ๋„๋กœ ์ง€์ • ๊ตํ†ต์•ˆ์ „์‹ฌ์˜ ์ ˆ์ฐจ ํ™•์ •
์„œ์šธ์‹œ, ์‹ ํ†ต๊ธฐํš 5๋…„ ๋งŒ์— ๊ฐœํŽธ ์ฃผ๋ฏผ์ฐธ์—ฌ ํ™•๋Œ€ยท์‹ฌ์˜ ์†๋„์ „
๊ตญ๋‚ด '์ตœ์ดˆ' ์œ„๋ก€์„  ํŠธ๋žจ 12์›” ์ •์ƒ๊ฐœํ†ตํ•˜๋‚˜ ๋Œ€๊ด‘์œ„์„œ ๊ฐˆ๋“ฑ์กฐ์ •์•ˆ ์˜๊ฒฐ
์œ„๋ก€์„  ํŠธ๋žจ '์•ˆ์ „์‹ฌ์˜' ๊ฐˆ๋“ฑ ๋ด‰ํ•ฉ ์ ๊ธฐ ๊ฐœํ†ต ํž˜ ๋ฐ›๋‚˜
"๊ฐ•๋‚จ ์›”์„ธ๊ฐ€ 15๋งŒ์› " ์„œ์šธ 30๋…„ ์žฅ๊ธฐ์ž„๋Œ€ ๋‚˜์™”๋‹ค [์ง‘ ๋‚˜์™€๋ผ ๋š๋”ฑ ]
5๋…„ ๋งž์€ ์‹ ํ†ต๊ธฐํš ์„œ์šธ์‹œ, ์ฃผ๋ฏผ ์ฐธ์—ฌ ๋Š˜๋ฆฌ๊ณ  ์‚ฌ์—… ์†๋„ ๋†’์ธ๋‹ค
์„œ์šธ์‹œ '์‹ ํ†ต๊ธฐํš' ์ฃผ๋ฏผ ์ฐธ์—ฌยท์†Œํ†ต ๊ฐ•ํ™” ํ›„์†์ ˆ์ฐจ ์‹ ์† ์ง€์›
'์œ„๋ก€์„  ํŠธ๋žจ ๊ฑด์„ค์‚ฌ์—… ์†๋„๋‚ธ๋‹ค' ๋Œ€๊ด‘์œ„ ๊ฐˆ๋“ฑ์กฐ์ •์•ˆ ์˜๊ฒฐ
๊ฐ•๋‚จยท์„œ์ดˆ 3์ฃผ์งธ ๋š๋š ๋–จ์–ด์ง€๋Š”๋ฐ '๋…ธ๋„๊ฐ•'์€ 14๋…„ ๋งŒ์— ์ตœ๋Œ€ ํญ๋“ฑ
SK์—์ฝ”ํ”Œ๋žœํŠธ, SK์—์ฝ”์—”์ง€๋‹ˆ์–ด๋ง ํ•ฉ๋ณ‘ AI ์ธํ”„๋ผ ์‚ฌ์—… ์—ญ๋Ÿ‰ ํ†ตํ•ฉ
์ค‘๋ž‘-์„ฑ๋ถ ์•„ํŒŒํŠธ๊ฐ’ ๋›ฐ๊ณ , ๊ฐ•๋‚จ-์„œ์ดˆ๋Š” 3์ฃผ์งธ ํ•˜๋ฝ
์ฒœ์•ˆ ์›๋„์‹ฌ์— ๋Œ€๋‹จ์ง€ ์•„ํŒŒํŠธ 9์›” ๋ถ„์–‘
์žฌ๊ฑด์ถ• ์ด์ฃผ ๋งŽ์€ ๋ชฉ๋™์— ํ•˜์ด์—”๋“œ ์ฃผ๊ฑฐ ๊ณต๊ธ‰
ํ‰ํƒ ๊ณ ๋•์—์„œ ์„ ์ฐฉ์ˆœ ๊ณ„์•ฝ ๋ถ„์ƒ์ œ ์ ์šฉ
์„œ์šธ/๊ฒฝ๊ธฐ ์•„ํŒŒํŠธ๊ฐ’ ์ƒ์Šน์„ธ ์ฃผ์ถคโ€ฆ ๊ฐ•๋‚จ์€ ๊ฒฐ๊ตญ ํ•˜๋ฝ ์ „ํ™˜KB Think
๋ฉˆ์ถ”์ง€ ์•Š๋Š” ์ˆ˜๋„๊ถŒ ์•„ํŒŒํŠธ๊ฐ’โ€ฆ 8์›” 4์ฃผ ๋งค๋งคยท์ „์„ธ ๋™๋ฐ˜ ์ƒ์Šนํ•œ๊ตญ์ฃผํƒ๊ฒฝ์ œ์‹ ๋ฌธ
์„œ์šธ ์ง‘๊ฐ’ ์ƒ์Šนํญ ์ปค์กŒ์ง€๋งŒโ€ฆ๊ฐ•๋‚จยท์„œ์ดˆ 3์ฃผ์งธ ํ•˜๋ฝ์„ผ๋จธ๋‹ˆ
์•„ํŒŒํŠธ๊ฐ’ ๋œ€๋ฐ•์งˆ์—โ€ฆ์„œ์šธ ๋นŒ๋ผ ๊ฑฐ๋ž˜ 1์œ„ ๋™๋„ค๋„ '๋งค๋ฌผ ๊ฐ€๋ญ„'[๋ถ€๋™์‚ฐAtoZ]์•„์‹œ์•„๊ฒฝ์ œ
ํ•ด์šด๋Œ€ ์•„ํŒŒํŠธ ๋‹จํ†ก๋ฐฉ์„œ ์ง‘๊ฐ’ ๋‹ดํ•ฉ ์œ ๋„ํ•œ ์ž…์ฃผ๋ฏผ ๊ฒ€์ฐฐ ์†ก์น˜์—ฐํ•ฉ๋‰ด์Šค
โ€œ์„œ์šธ ํ‰๊ท  ์•„ํŒŒํŠธ๊ฐ’ 16์–ต์› ๋ŒํŒŒโ€ฆ ๊ฐ•๋ถ๊ถŒยท๊ฒฝ๊ธฐ ๋‚จ๋ถ€ ์ƒ์Šน์„ธ ์ง€์†โ€ - ์กฐ์„ ๋น„์ฆˆChosunbiz
โ€œ์„ธ์ œ๊ฐœํŽธ ํ›„ํญํ’ ์„ธ๊ธด ์„ธ๋„คโ€ โ€ฆ ๊ฐ•๋‚จ ์ง‘๊ฐ’ โ€˜ํญ๋ฝโ€™ ์ด๋ฒˆ์—” ์ง„์งœ์ผ๊นŒ๋งค์ผ๊ฒฝ์ œ
๋งค๋ฌผ ์Œ“์ด๊ณ  ํ˜ธ๊ฐ€ 5์–ต ๋šโ€ฆ ๊ฐ•๋‚จ ์•„ํŒŒํŠธ๊ฐ’, ์ง€๊ธˆ ๋ฌด์Šจ ์ผ์ด?๋ฆฌ์–ผ์บ์ŠคํŠธ
๋ถ€๋™์‚ฐ ์žก๋Š”๋‹ค๋”๋‹ˆ...์ด์žฌ๋ช… ์ •๋ถ€, ์„œ์šธ ์•„ํŒŒํŠธ๊ฐ’ 14.7% ์—ญ๋Œ€ ์ตœ๊ณ  ์ƒ์Šน์•„์ฃผ๊ฒฝ์ œ
'๋œ ๋˜˜๋˜˜ํ•œ ํ•œ ์ฑ„' ์ˆ˜์š” ๋ชฐ๋ฆฌ๋‚˜...์ง‘๊ฐ’ ๋ถˆ์•ˆ ๊ณ„์†YTN
์ค‘์ €๊ฐ€ ์ˆ˜์š”๊ฐ€ ๋– ๋ฐ›์น˜์žโ€ฆ์„œ์šธ ์•„ํŒŒํŠธ๊ฐ’ ์‹œ์„ธ ์ „๊ณ ์  ๋ŒํŒŒ์—ฐํ•ฉ์ธํฌ๋งฅ์Šค
๊ฐ™์€ ํ•œ๊ฐ•๋ทฐ์ธ๋ฐ ๊ฐ€๊ฒฉ์€ ๋ฐ˜๊ฐ’โ€ฆ"๋งค๋ฌผ ์žˆ๋‚˜์š”" ๋“ค์ฉ์ด๋Š” ๋™๋„ค [์ด์Šฌ๊ธฐ์˜ ์ƒˆ์ง‘๋‹ค์˜ค]ํ•œ๊ตญ๊ฒฝ์ œ
์„œ์šธ ์•„ํŒŒํŠธ๊ฐ’ 0.25% ์ƒ์Šนโ€ฆ ๋˜˜๋˜˜ํ•œ โ€˜์—ญ์„ธ๊ถŒยท๋Œ€๋‹จ์ง€โ€™๊ฐ€ ์ง‘๊ฐ’ ๊ฒฌ์ธํ•œ๊ตญ์ฃผํƒ๊ฒฝ์ œ์‹ ๋ฌธ
โ€œ100์ฃผ ๋™์•ˆ ํ•œ ๋ฒˆ๋„ ์•ˆ ๋–จ์–ด์กŒ๋‹คโ€โ€ฆ ์„œ์šธ ์•„ํŒŒํŠธ๊ฐ’ 94์ฃผ ์ƒ์Šน - ์กฐ์„ ๋น„์ฆˆChosunbiz
GTX๋„ ์žฌ๊ฑด์ถ•๋„ ์ธํ”„๋ผ๋„ ๋‹ค ์žˆ๋Š”๋ฐโ€ฆ์ผ์‚ฐ ์ง‘๊ฐ’์€ ์™œ ๊ณ„์† ๋–จ์–ด์งˆ๊นŒKB Think

๐Ÿ—๏ธ ๋ถ„์–‘์ผ์ •

์ƒ๋™์—ญ ๋กฏ๋ฐ์บ์Šฌ ์‹œ๊ทธ๋‹ˆ์ฒ˜
์˜์ •๋ถ€์šฐ์ • A2๋ธ”๋ก ๊ณต๊ณต๋ถ„์–‘์ฃผํƒ(๋ณธ์ฒญ์•ฝ)
์–‘์ฃผํšŒ์ฒœ์ง€๊ตฌ A-26๋ธ”๋ก ๊ณต๊ณต๋ถ„์–‘์ฃผํƒ
ํฌ๋ ˆ๋‚˜ํž์Šคํ…Œ์ดํŠธ ์ง„์ฃผ
์Œ์šฉ ๋” ํ”Œ๋ž˜ํ‹ฐ๋„˜ ์„œ๋Œ€๋ฌธ
์•„๋ฅดํ‹ฐ์—  ๋ผ ํ…Œ๋ผ์Šค
๊ฒ€์•”์—ญ ํ‘ธ๋ฅด์ง€์˜ค ํ”„๋ผ๋ฒ ๋‰ด (B-1BL) ๊ณต๊ณต๋ถ„์–‘์ฃผํƒ
์‹œํ‹ฐ์˜ค์”จ์—˜ 9๋‹จ์ง€ ์˜ค์…˜ํŒŒํฌ๋ทฐ
๋ฌธ์ˆ˜๋ฐ์‹œ์•™2๋‹จ์ง€ ๊ณต๊ฐ€์„ธ๋Œ€ ์ผ๋ฐ˜๊ณต๊ธ‰
๋ธŒ๋ผ์šด์Šคํ†ค ์›”๊ณก ์„ผํŠธ๋Ÿด

๐Ÿค– AI Agent

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semantic_scholarresearch

Large language models as judges: recent advances in LLM-based evaluation, critique, preference modeling, and feedback for text and code

LLM์„ ํŒ์‚ฌ๋กœ ํ™œ์šฉํ•œ ํ…์ŠคํŠธ/์ฝ”๋“œ ํ‰๊ฐ€ ๋ฐ ํ”ผ๋“œ๋ฐฑ ๊ธฐ๋ฒ•์˜ ์ข…ํ•ฉ ์กฐ์‚ฌ

semantic_scholarresearch

Low Carbon Scheduling of Integrated Energy System Based on Large Language Model-Embedded Multi-Agent Reinforcement Learning

LLM๊ณผ ๋ฉ€ํ‹ฐ์—์ด์ „ํŠธ ๊ฐ•ํ™”ํ•™์Šต์œผ๋กœ ์—๋„ˆ์ง€์‹œ์Šคํ…œ ์ตœ์ ํ™”์™€ ์ €ํƒ„์†Œ ์Šค์ผ€์ค„๋ง ๊ตฌํ˜„

semantic_scholarresearch

Simulating the Experienced Coach: An Orchestrated Multi-Agent Artificial Intelligence Architecture Decomposed by Physiological System for Continuous Athlete Monitoring

๋‹ค์ค‘ ์ƒ๋ฆฌ์ง€ํ‘œ๋ฅผ ๋ณ‘๋ ฌ ํ†ตํ•ฉํ•˜๋Š” ์˜ค์ผ€์ŠคํŠธ๋ ˆ์ด์…˜ ๋ฉ€ํ‹ฐ์—์ด์ „ํŠธ AI ์ฝ”์น˜ ์‹œ์Šคํ…œ

semantic_scholarresearch

Autonomous collaborative robotic manufacturing system with adaptive process optimization through multi-agent reinforcement learning for high precision components

MARL๋กœ ๋กœ๋ด‡ ๊ฐ„ ํ˜‘์—…์„ ์ตœ์ ํ™”ํ•˜๋Š” ๊ณ ์ •๋ฐ€ ์ž์œจ ์ œ์กฐ ์‹œ์Šคํ…œ

๐Ÿ”“ Open Source

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github-trendingData

gods-eye-view

๋ธŒ๋ผ์šฐ์ €์—์„œ ์ฆ‰์‹œ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์œ„์„ฑ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ์ธ๋ฐ, ๋ฐ์ดํ„ฐ๋Š” ์‹ค์ œ์ž…๋‹ˆ๋‹ค. ํฌํ† ๋ฆฌ์–ผ๋ฆฌ์Šคํ‹ฑ 3D ์ง€๊ตฌ๋ณธ ์œ„์—์„œ ์‹ค์‹œ๊ฐ„ ์˜คํ”ˆ์†Œ์Šค ๊ณต๊ฐ„ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค.

github-trendingOther

nitter

ํŠธ์œ„ํ„ฐ ๋Œ€์ฒด ํ”„๋ก ํŠธ์—”๋“œ

github-trendingAI

go-modern-guidelines

AI ์ฝ”๋”ฉ ์—์ด์ „ํŠธ๊ฐ€ ํ˜„๋Œ€์ ์ธ Go ์ฝ”๋“œ๋ฅผ ์ž‘์„ฑํ•˜๋„๋ก ๋•์Šต๋‹ˆ๋‹ค

github-trendingAI

scientific-agent-skills

๋ชจ๋“  AI ์—์ด์ „ํŠธ๋ฅผ AI ๊ณผํ•™์ž๋กœ ๋ณ€ํ™˜ํ•˜์„ธ์š”. ์ „ ์„ธ๊ณ„ 175,000๋ช… ์ด์ƒ์˜ ๊ณผํ•™์ž๊ฐ€ ์‚ฌ์šฉํ•˜๋Š” ๊ณผํ•™ ๋ถ„์•ผ ์ตœ๊ณ ์˜ Agent Skills ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์ž…๋‹ˆ๋‹ค. ์ƒ๋ฌผํ•™, ํ™”ํ•™, ์˜์•ฝํ•™, ์‹ ์•ฝ ๊ฐœ๋ฐœ์„ ํฌํ•จํ•œ 100+ ๊ฐœ์˜ ๊ณผํ•™ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์™€ 163๊ฐœ์˜ ์ฆ‰์‹œ ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ๊ฒ€์ฆ๋œ ์Šคํ‚ฌ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. Cursor, Claude Code, Codex, Pi, Antigravity ๋ฐ ์˜คํ”ˆ Agent Skills ํ‘œ์ค€๊ณผ ํ˜ธํ™˜๋ฉ๋‹ˆ๋‹ค.

github-trendingAI

OpenMontage

์„ธ๊ณ„ ์ตœ์ดˆ์˜ ์˜คํ”ˆ์†Œ์Šค ์—์ด์ „ํ‹ฑ ๋น„๋””์˜ค ์ œ์ž‘ ์‹œ์Šคํ…œ์ž…๋‹ˆ๋‹ค. 12๊ฐœ์˜ ํ”„๋กœ๋•์…˜ ํŒŒ์ดํ”„๋ผ์ธ, 100+ ๊ฐœ์˜ ๋„๊ตฌ, 700+ ๊ฐœ์˜ agent skill ๋ฐ ํ”„๋กœ๋•์…˜ ๋…ธํ•˜์šฐ ํŒŒ์ผ์„ ํฌํ•จํ•ฉ๋‹ˆ๋‹ค. AI ์ฝ”๋”ฉ ์–ด์‹œ์Šคํ„ดํŠธ๋ฅผ ์™„์ „ํ•œ ๋น„๋””์˜ค ์ œ์ž‘ ์ŠคํŠœ๋””์˜ค๋กœ ๋ณ€ํ™˜ํ•ฉ๋‹ˆ๋‹ค.