[{"channel_id":1131189982,"post_id":3751,"date":1787654059000,"forwards":"14","views":"787","text":"Towards an AI-Augmented Textbook:


\ud83d\udcd3 Read<\/a>

@datascienceiot<\/a>","text_length":60,"media":{"root":"\/002\/pw4AAN6WbEMAAAAAjVok69lC-qU","photo":{"thumbs":{"m":{"w":320,"h":262,"hash":"aw9ijZrITC4UwDKHKZE0yQ&ts=1787762102"},"x":{"w":800,"h":656,"hash":"zqWCvFg20k-cTwI0ZRI7fQ&ts=1787762102"},"y":{"w":1152,"h":945,"hash":"oGouz3RLdLKo-IPLe_7xZg&ts=1787762102"},"i":{"bytes":"AhACg|DY5z2pDmjHNR4+v5UASY+v5mmySCMDOTTkz3z+NMmXco+vpmgaFSUOQMEZopkSFW5\/liikDsT0gB\/vUtNbr1piKkklwF+TJP8Au1aQt5YJGT3pwoIz0JH0ptiQAk9RiigAj+ImikMWiiigAooooAKKKKAP\/9k="}}}}},{"channel_id":1131189982,"post_id":3750,"date":1787652262000,"forwards":"5","views":"828","text":"\u0412\u0441\u0442\u0440\u0430\u0438\u0432\u0430\u0439\u0442\u0435 \u0441\u0438\u0441\u0442\u0435\u043c\u0443 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u043e\u0432 \u0432 \u0432\u0430\u0448 \u043f\u0440\u043e\u0434\u0443\u043a\u0442 \u0437\u0430 15 \u043c\u0438\u043d\u0443\u0442 \ud83d\ude80<\/b>

\u0415\u0441\u043b\u0438 \u0432\u044b \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0435 SaaS, \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0437\u0438\u0440\u0443\u0435\u0442\u0435 B2B-\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u044b \u0438\u043b\u0438 \u0441\u043e\u0431\u0438\u0440\u0430\u0435\u0442\u0435 \u0418\u0418-\u0430\u0433\u0435\u043d\u0442\u043e\u0432, \u0432\u043e\u043f\u0440\u043e\u0441 \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438 \u0438 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u043e\u0432 \u0432\u0441\u0435\u0433\u0434\u0430 \u0432\u0441\u0442\u0430\u0435\u0442 \u0440\u0435\u0431\u0440\u043e\u043c.

Provision OCR<\/b> \u2014 \u044d\u0442\u043e \u043c\u043e\u0449\u043d\u044b\u0439 \u0440\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0438\u0439 \u0434\u0432\u0438\u0436\u043e\u043a \u0434\u043b\u044f on-premise \u0438 private cloud \u0440\u0430\u0437\u0432\u0435\u0440\u0442\u044b\u0432\u0430\u043d\u0438\u044f.

\u26a1\ufe0f 100% \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u0438:<\/b> \u0434\u0430\u043d\u043d\u044b\u0435 \u043d\u0435 \u043f\u043e\u043a\u0438\u0434\u0430\u044e\u0442 \u0432\u0430\u0448 \u0441\u0435\u0440\u0432\u0435\u0440.
\u26a1\ufe0f \u0421\u043a\u043e\u0440\u043e\u0441\u0442\u044c:<\/b> \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0430 <3 \u0441\u0435\u043a\/\u0441\u0442\u0440\u0430\u043d\u0438\u0446\u0430 \u043d\u0430 GPU 2070 \u0438 \u0432\u044b\u0448\u0435, \u041f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 CPU , \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u0434\u043e 99%
\u26a1\ufe0f \u0411\u044b\u0441\u0442\u0440\u0430\u044f \u0438 \u0431\u0435\u0441\u0448\u043e\u0432\u043d\u0430\u044f \u0438\u043d\u0442\u0435\u0433\u0440\u0430\u0446\u0438\u044f<\/b> \u0447\u0435\u0440\u0435\u0437 REST API + gRPC.
\u26a1\ufe0f \u041f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 \u0440\u0430\u0437\u043d\u044b\u0445 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u043e\u0432 \u043d\u0430 \u0440\u0443\u0441\u0441\u043a\u043e\u043c \u044f\u0437\u044b\u043a\u0435:<\/b> \u0434\u043e\u0433\u043e\u0432\u043e\u0440\u044b, \u0441\u0447\u0435\u0442\u0430 \u0438 \u0434\u0440\u0443\u0433\u0438\u0435.

\ud83d\udca1 \u0414\u0438\u0441\u0442\u0440\u0438\u0431\u0443\u0442\u0438\u0432 \u0432 \u043d\u0430\u0448\u0435\u043c Telegram-\u043a\u0430\u043d\u0430\u043b\u0435:<\/b> @ProvisionLabs<\/a>

\ud83d\udcbb \u041f\u0440\u0438\u043c\u0435\u0440\u044b \u043a\u043e\u0434\u0430 \u043d\u0430 GitHub:<\/b>
https:\/\/github.com\/orgs\/Provision-Labs\/repositories<\/a>

#OCR #ProvisionOCR #\u0414\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u044b #\u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430 #Python #AI #OnPremise","text_length":798,"media":{"root":"\/003\/pg4AAN6WbEMAAAAAUMjeC166baU.qqwAAMeiYUjDum0LETysVgI.AkNslt4AAA6mao1p-2ChPsgqMiBZ4Z_wbKuLiaU","video":{"duration":15,"w":1080,"h":1920,"is_animation":false,"is_round":false,"mime_type":"video\/mp4","size":4293949,"thumbs":{"m":{"w":180,"h":320,"hash":"hULg9hXpxtmk6kyye9dp9Q&ts=1787762102"},"i":{"bytes":"AoABY|DLEdNK4qftUclW0QmRUUtFSUP8zim53GmU5eDTBIeEop2aKqyGQjrUgAxRRUoljGODRRRSGf\/Z"}},"streamable":true,"televisor_hash":"vHBfd8I1gtW50IPVeeHRUw&ts=1787762102"}}},{"channel_id":1131189982,"post_id":3749,"date":1787584858000,"forwards":"65","views":"1.3K","text":"Mathematical Theory of Deep Learning: <\/b>

333-page PDF.

\ud83d\udcd3 Read<\/a>

@datascienceiot<\/a>","text_length":79,"media":{"root":"\/00c\/pQ4AAN6WbEMAAAAAdh2LGnu5dkg","photo":{"thumbs":{"m":{"w":320,"h":180,"hash":"YtusRjY2ezuZtvQFlHgGYw&ts=1787762102"},"x":{"w":800,"h":451,"hash":"ag2BhlVShwhcTdidyTP2JQ&ts=1787762102"},"y":{"w":1152,"h":649,"hash":"9HZh4oufGZ2MKPg5Hs-QyQ&ts=1787762102"},"i":{"bytes":"AXACg|DWckFcd896b5hIOAOBnntSyZ3Lzjr3phjOCdoPFAEYmm4ztqwzhFLEE47CqwRv+eVWSpK4B2n2qVew2rDYZhLnCkY9aKdGjJnLls+tFNXtqIXaf8mjb7miimAbD\/eNGw\/3jRRQAbT6miiigD\/\/2Q=="}}}}},{"channel_id":1131189982,"post_id":3748,"date":1787414421000,"forwards":"60","views":"1.9K","text":"10 \u0431\u0435\u0441\u043f\u043b\u0430\u0442\u043d\u044b\u0445 \u043a\u043d\u0438\u0433 \u043f\u043e \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044e, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0436\u043d\u043e \u0447\u0438\u0442\u0430\u0442\u044c \u043f\u0440\u044f\u043c\u043e \u0441 GitHub<\/b>

1. The Rust Programming Language<\/b>
https:\/\/github.com\/rust-lang\/book<\/a>

2. Rust by Example<\/b>
https:\/\/github.com\/rust-lang\/rust-by-example<\/a>

3. The Little Book of Rust Books<\/b>
https:\/\/github.com\/lborb\/book<\/a>

4. Python Data Science Handbook<\/b>
https:\/\/github.com\/jakevdp\/PythonDataScienceHandbook<\/a>

5. Dive Into Deep Learning<\/b>
https:\/\/github.com\/d2l-ai\/d2l-en<\/a>

6. Probabilistic Machine Learning<\/b>
https:\/\/github.com\/probml\/pml-book<\/a>

7. Algorithms for Decision Making<\/b>
https:\/\/github.com\/algorithmsbooks\/algforopt-notebooks<\/a>

8. Operating Systems: Three Easy Pieces<\/b>
https:\/\/github.com\/remzi-arpacidusseau\/ostep-homework<\/a>

9. Crafting Interpreters<\/b>
https:\/\/github.com\/munificent\/craftinginterpreters<\/a>

10. Free Programming Books<\/b> \u2014 \u043e\u0433\u0440\u043e\u043c\u043d\u0430\u044f \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 \u0431\u0435\u0441\u043f\u043b\u0430\u0442\u043d\u044b\u0445 \u043a\u043d\u0438\u0433 \u043f\u043e Python, Java, C++, Go, AI, SQL, Linux \u0438 \u0434\u0435\u0441\u044f\u0442\u043a\u0430\u043c \u0434\u0440\u0443\u0433\u0438\u0445 \u0442\u0435\u043c
https:\/\/github.com\/EbookFoundation\/free-programming-books<\/a>

\u041f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0439 \u0440\u0435\u043f\u043e\u0437\u0438\u0442\u043e\u0440\u0438\u0439 \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e \u043f\u043e\u043b\u0435\u0437\u0435\u043d: \u0442\u0430\u043c \u0441\u043e\u0431\u0440\u0430\u043d\u0430 \u043e\u0433\u0440\u043e\u043c\u043d\u0430\u044f \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e \u043e\u0431\u043d\u043e\u0432\u043b\u044f\u0435\u043c\u0430\u044f \u0431\u0430\u0437\u0430 \u043b\u0435\u0433\u0430\u043b\u044c\u043d\u043e \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b\u0445 \u043a\u043d\u0438\u0433 \u0438 \u0443\u0447\u0435\u0431\u043d\u044b\u0445 \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u043e\u0432 \u043f\u043e \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044e.","text_length":1124},{"channel_id":1131189982,"post_id":3747,"date":1787407313000,"forwards":"7","views":"1.6K","text":"\ud83d\udd25 \u0425\u043e\u0447\u0435\u0448\u044c \u0431\u044b\u0441\u0442\u0440\u0435\u0435 \u0440\u0430\u0441\u0442\u0438 \u0432 IT? \u0425\u0432\u0430\u0442\u0438\u0442 \u0443\u0447\u0438\u0442\u044c\u0441\u044f \u0432 \u043e\u0434\u0438\u043d\u043e\u0447\u043a\u0443<\/b>

\u041e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0440\u0435\u0448\u0430\u0435\u0442 \u0431\u043e\u043b\u044c\u0448\u0435, \u0447\u0435\u043c \u043a\u0430\u0436\u0435\u0442\u0441\u044f.

\u0421\u043e\u0431\u0440\u0430\u043b \u043f\u0430\u043f\u043a\u0438 \u0438 \u043a\u0430\u043d\u0430\u043b\u044b, \u0433\u0434\u0435 \u043c\u043e\u0436\u043d\u043e \u0431\u044b\u0441\u0442\u0440\u0435\u0435 \u0432\u043b\u0438\u0442\u044c\u0441\u044f \u0432 \u043d\u0443\u0436\u043d\u043e\u0435 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435, \u0441\u043b\u0435\u0434\u0438\u0442\u044c \u0437\u0430 \u0442\u0440\u0435\u043d\u0434\u0430\u043c\u0438 \u0438 \u043d\u0435 \u0432\u0430\u0440\u0438\u0442\u044c\u0441\u044f \u0432 \u0441\u0432\u043e\u0451\u043c \u043f\u0443\u0437\u044b\u0440\u0435.

AI: t.me\/ai_machinelearning_big_data<\/a>
Python: t.me\/pythonl<\/a>
Linux: t.me\/linuxacademiya<\/a>
\u0425\u0430\u043a\u0438\u043d\u0433: t.me\/linuxkalii<\/a>
DevOps: t.me\/DevOPSitsec<\/a>
Docker: https:\/\/t.me\/+90Z5TAyfuNU5YmRi<\/a>
Golang: t.me\/Golang_google<\/a>
Rust: t.me\/rust_code<\/a>
C++: t.me\/cpluspluc<\/a>
C#: t.me\/csharp_1001_notes<\/a>
Java: t.me\/javatg<\/a>
JavaScript: t.me\/javascriptv<\/a>
React: t.me\/react_tg<\/a>
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PHP: t.me\/phpshka<\/a>
Android: t.me\/android_its<\/a>
\u041c\u043e\u0431\u0438\u043b\u044c\u043d\u0430\u044f \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430: t.me\/mobdevelop<\/a>
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Data Science: t.me\/data_analysis_ml<\/a>
Big Data: t.me\/bigdatai<\/a>
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Kubernetes: t.me\/kubernetc<\/a>
GameDev: https:\/\/t.me\/gamedev<\/a>
Haskell: t.me\/haskell_tg<\/a>

\u0421\u043e\u0431\u0435\u0441\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u0438 \u043a\u0430\u0440\u044c\u0435\u0440\u0430:<\/b>

DS \u0441\u043e\u0431\u0435\u0441\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f: t.me\/machinelearning_interview<\/a>
Python \u0441\u043e\u0431\u0435\u0441\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f: t.me\/python_job_interview<\/a>

\u041f\u0430\u043f\u043a\u0430 \u0441 \u0432\u0430\u043a\u0430\u043d\u0441\u0438\u044f\u043c\u0438: t.me\/addlist\/_zyy_jQ_QUsyM2Vi<\/a>
\u041f\u0430\u043f\u043a\u0430 Go \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0430: t.me\/addlist\/MUtJEeJSxeY2YTFi<\/a>
\u041f\u0430\u043f\u043a\u0430 Python \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0430: t.me\/addlist\/eEPya-HF6mkxMGIy<\/a>
\u041f\u0430\u043f\u043a\u0430 ML: https:\/\/t.me\/addlist\/2Ls-snqEeytkMDgy<\/a>
\u041f\u0430\u043f\u043a\u0430 Frontend: https:\/\/t.me\/addlist\/mzMMG3RPZhY2M2Iy<\/a>

\u041f\u043e\u043b\u0435\u0437\u043d\u043e\u0435 \u0441\u0432\u0435\u0440\u0445\u0443:<\/b>

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\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0441\u0442\u043e\u0432: t.me\/english_forprogrammers<\/a>
\u0418\u0418 \u0438 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438: t.me\/vistehno<\/a>
954 \u0413\u0411 open-source \u043a\u0443\u0440\u0441\u043e\u0432: https:\/\/t.me\/+rKBQEMccAA01MTcy<\/a>
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Max Ai<\/b>: https:\/\/max.ru\/ai_machinelearning_big_data<\/a>
Max python: https:\/\/max.ru\/pythonl<\/a>
\u0422\u0415\u0425\u041d\u041e: https:\/\/max.ru\/vistehno<\/a>
Max Go: <\/b>https:\/\/max.ru\/Golang_google<\/a>
Max Linux:<\/b> https:\/\/max.ru\/linuxkalii<\/a>
Devops: https:\/\/max.ru\/DevOPSitsec<\/a>
C#: https:\/\/max.ru\/csharp_ci<\/a>
C++: https:\/\/max.ru\/cpluspluc<\/a>
SQL: https:\/\/max.ru\/sqlhub<\/a>
Java: https:\/\/max.ru\/javatg<\/a>

\u041f\u043e\u0434\u043f\u0438\u0441\u044b\u0432\u0430\u0439\u0441\u044f \u043d\u0430 \u043d\u0443\u0436\u043d\u044b\u0435 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0438 \u0441\u043e\u0431\u0438\u0440\u0430\u0439 \u0441\u0435\u0431\u0435 \u043b\u0435\u043d\u0442\u0443, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0440\u0435\u0430\u043b\u044c\u043d\u043e \u0434\u0432\u0438\u0433\u0430\u0435\u0442 \u0432\u043f\u0435\u0440\u0451\u0434.","text_length":1939,"media":{"root":"\/00d\/ow4AAN6WbEMAAAAAZSaOGr9Qd0g","photo":{"thumbs":{"m":{"w":312,"h":320,"hash":"lvS3bAPcP_NIUMgnZDyFWg&ts=1787762102"},"x":{"w":780,"h":800,"hash":"MhdpipGOkJB35rQ2rztInQ&ts=1787762102"},"y":{"w":1239,"h":1270,"hash":"OuDesbUkBaWBsG3fLlMOFg&ts=1787762102"},"i":{"bytes":"AoACc|CkIkS2WR4dwI+9uP8A+qkZIQW\/dgc7eWPBpglLQiNm+XGMAds59KRmBB3E4Jyfr+VK5SSCdFSQ4UBcAgAk0FEIG0Hp+tNZi7s+c\/XmnDLJk4znqBzTJGN6AY49KKcwYjBY+vNFAxqA8df0pWHH\/wBemqOPuk\/jSlf9kj8aQWETGD604EkBBUY4NSbT1H86YhMEHB60UgOPY0UAKrAUu9feiigZF2pwY4xmiigAJzRRRQI\/\/9k="}}}}},{"channel_id":1131189982,"post_id":3745,"date":1787311084000,"forwards":"13","views":"1.9K","text":"What Does Context Compression Cost an Agent? Interaction Costs Unrevealed by Task-Completion Metrics<\/b>

5\u00d7 context compression did surprisingly little to GPT-5.5\u2019s final task result.

\ud83d\udcd3 Read<\/a>

@datascienceiot<\/a>","text_length":206,"media":{"root":"\/006\/oQ4AAN6WbEMAAAAAMX8jIVt6Sok","photo":{"thumbs":{"m":{"w":320,"h":295,"hash":"FA2F4nx9GVtAg9WCJ9feJA&ts=1787762102"},"x":{"w":800,"h":737,"hash":"nhRmxxFBFjLHErt82csZqA&ts=1787762102"},"y":{"w":999,"h":920,"hash":"L879-xASk8J2BQ2Gyy8FEg&ts=1787762102"},"i":{"bytes":"AlACg|DYxkUm3\/aNLnik3fWgBD1xk0uPc0b\/AK4+lG4e9ADgMe9FIDkUUAH8JqOnn7pqPJ9KTdgFpcn1pvPpRhvQ0uYCVenNFImdvNFUAv8ACaj70UUAO2e9G33oooAcowKKKKAP\/9k="}}}}},{"channel_id":1131189982,"post_id":3744,"date":1787303080000,"forwards":"24","views":"1.9K","text":"\u0412\u044b\u0440\u0430\u0441\u0442\u0438\u043b\u0438 \u0446\u0435\u043b\u043e\u0435 \u0434\u0435\u0440\u0435\u0432\u043e \u0438\u0437 \u0430\u0433\u0435\u043d\u0442\u043e\u0432 \ud83c\udf33<\/b>

\u041c\u044b \u0432 \u0410\u0432\u0438\u0442\u043e \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043b\u0438 \u0435\u0434\u0438\u043d\u0443\u044e \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0443 \u0434\u043b\u044f \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0432. \u0421\u0430\u043c\u044b\u0435 \u043b\u0443\u0447\u0448\u0438\u0435 \u0438 \u043f\u043e\u043f\u0443\u043b\u044f\u0440\u043d\u044b\u0435 \u0441\u0442\u0430\u043d\u043e\u0432\u044f\u0442\u0441\u044f \u0430\u0433\u0435\u043d\u0442\u0430\u043c\u0438. \u0412 \u0438\u0442\u043e\u0433\u0435 \u043a\u0430\u0436\u0434\u044b\u0439 \u0441\u043e\u0442\u0440\u0443\u0434\u043d\u0438\u043a \u043c\u043e\u0436\u0435\u0442 \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0441\u0432\u043e\u0439 \u043f\u0440\u043e\u0446\u0435\u0441\u0441 \u0438\u043b\u0438 \u0432\u043e\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u0433\u043e\u0442\u043e\u0432\u044b\u043c. \u041a\u043b\u0430\u0441\u0441\u043d\u043e? \u041d\u0435 \u0442\u043e \u0441\u043b\u043e\u0432\u043e! \u041f\u043e\u044d\u0442\u043e\u043c\u0443 \u043c\u044b \u043d\u0430\u043f\u0438\u0441\u0430\u043b\u0438 \u0441\u0442\u0430\u0442\u044c\u044e, \u043a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0432\u0430\u043b\u0438 \u044d\u0442\u043e \u0440\u0435\u0448\u0435\u043d\u0438\u0435.

\u041e \u0447\u0451\u043c \u0443\u0437\u043d\u0430\u0435\u0442\u0435, \u0435\u0441\u043b\u0438 \u043f\u0440\u043e\u0447\u0438\u0442\u0430\u0435\u0442\u0435:<\/b>
\ud83d\udd38\u043a\u0430\u043a \u0443\u0441\u0442\u0440\u043e\u0435\u043d\u0430 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430 \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u044b,
\ud83d\udd38\u043a\u0430\u043a \u043f\u0440\u043e\u0446\u0435\u0441\u0441 \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u0441\u044f \u0430\u0433\u0435\u043d\u0442\u043e\u043c,
\ud83d\udd38\u043a\u0430\u043a \u0438\u0437\u043c\u0435\u0440\u044f\u0442\u044c \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u043e \u0430\u0433\u0435\u043d\u0442\u043e\u0432.

\u0427\u0438\u0442\u0430\u0442\u044c \u0441\u0442\u0430\u0442\u044c\u044e<\/a> \ud83d\ude80","text_length":453,"media":{"root":"\/00b\/oA4AAN6WbEMAAAAAAwWAih5cEr0","photo":{"thumbs":{"m":{"w":320,"h":180,"hash":"2CcLyfJiqsJ82pMxAPPOzA&ts=1787762102"},"x":{"w":800,"h":450,"hash":"NnuktWH4JPAm3OQ0iUUcZA&ts=1787762102"},"y":{"w":1280,"h":720,"hash":"y25IHYgNZmBuSrHylV2Law&ts=1787762102"},"i":{"bytes":"AXACg|DVJCgknAFQSySDftAx\/C3YfWluJAgClSVI5wDUPACMrFUfB5fBz+VCGL5zgLub5sYIHanrJJsbAJORgkdqpkEXoABznH5ip1Rd8jeYTxhjxjFMC2GVumDRUVuIwG8tcAHHeik0BKWwKiZgOVgB\/KiigCk8UzSbhHj5s4yMVcVgwwYNufTFFFMCVcAADNFFFID\/2Q=="}}}}},{"channel_id":1131189982,"post_id":3742,"date":1787150665000,"forwards":"29","views":"2.5K","text":"Deep systemic analysis of AI constraints from context to internal weight editing.<\/b>

\ud83d\udcc2 PDF<\/a>

#AIRedTeaming #AISafety #LLMs #AIJailbreak #Pliny","text_length":141,"grouped_id":14297205325395682,"media":{"gallery":[{"root":"\/002\/ng4AAN6WbEMAAAAAjVok69lC-qU","photo":{"thumbs":{"m":{"w":226,"h":320,"hash":"ledQveNxSK-29mQVbGGjVw&ts=1787762102"},"x":{"w":565,"h":800,"hash":"jPJ3kwqrHaXpTdAjmk9UsQ&ts=1787762102"},"y":{"w":641,"h":908,"hash":"yG-niZWZRuSobVZHTBrJdw&ts=1787762102"},"i":{"bytes":"AoABw|DZoopNw9aAFooBB6UUAFNI9KdRQAgGKWiigAooooAKKKKAP\/\/Z"}}}},{"root":"\/012\/nw4AAN6WbEMAAAAA6PxnK-FfnKo","photo":{"thumbs":{"m":{"w":226,"h":320,"hash":"rdKinI40lJegNaDztf1xIA&ts=1787762102"},"x":{"w":565,"h":800,"hash":"d7tYdsRMbY9zRQz8gKo8MA&ts=1787762102"},"y":{"w":640,"h":906,"hash":"5-WW-Jm2DIyJObYgYYaQTw&ts=1787762102"},"i":{"bytes":"AoABw|DYLAdSBS01jj0pQ2fSgBaKKKAGSHGOn5UJ\/wAB\/CiTtSIee9AD6UdKSlHSgBrLupAhHc\/nRRQA\/HvRRRQB\/9k="}}}}]}},{"channel_id":1131189982,"post_id":3741,"date":1787147108000,"forwards":"50","views":"2.3K","fwd_from":[],"text":"\u0411\u0435\u0441\u043f\u043b\u0430\u0442\u043d\u0430\u044f \u043a\u043d\u0438\u0433\u0430 \u043f\u043e performance engineering<\/b>

\u0412 Algorithmica \u0445\u043e\u0440\u043e\u0448\u043e \u0440\u0430\u0437\u043e\u0431\u0440\u0430\u043b\u0438, \u043f\u043e\u0447\u0435\u043c\u0443 \u043a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u043e\u0446\u0435\u043d\u043a\u0430 \u0441\u043b\u043e\u0436\u043d\u043e\u0441\u0442\u0438 \u0432\u0441\u0451 \u0445\u0443\u0436\u0435 \u043e\u0442\u0440\u0430\u0436\u0430\u0435\u0442 \u0440\u0435\u0430\u043b\u044c\u043d\u0443\u044e \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043d\u0430 \u0441\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u043c \u0436\u0435\u043b\u0435\u0437\u0435.

\u0420\u0430\u043d\u044c\u0448\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u0431\u044b\u043b\u0430 \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u043b\u043e\u0433\u0438\u0447\u043d\u043e\u0439: \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440 \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442 \u0438\u043d\u0441\u0442\u0440\u0443\u043a\u0446\u0438\u0438 \u043f\u043e\u0447\u0442\u0438 \u043f\u043e\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u043d\u043e, \u0443 \u043a\u0430\u0436\u0434\u043e\u0439 \u0435\u0441\u0442\u044c \u0441\u0432\u043e\u044f \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c, \u0430 \u0437\u043d\u0430\u0447\u0438\u0442 \u043c\u043e\u0436\u043d\u043e \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u043e\u0446\u0435\u043d\u0438\u0442\u044c \u0432\u0440\u0435\u043c\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e\u043c \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439.

\u041f\u043e\u0442\u043e\u043c \u0432\u0441\u0451 \u0443\u043f\u0440\u043e\u0441\u0442\u0438\u043b\u0438 \u0434\u043e \u0430\u0441\u0438\u043c\u043f\u0442\u043e\u0442\u0438\u043a\u0438. \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0432\u043c\u0435\u0441\u0442\u043e \u0442\u043e\u0447\u043d\u043e\u0433\u043e \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u0430 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439 \u0432 \u0443\u043c\u043d\u043e\u0436\u0435\u043d\u0438\u0438 \u043c\u0430\u0442\u0440\u0438\u0446 \u043c\u044b \u043f\u0440\u043e\u0441\u0442\u043e \u0433\u043e\u0432\u043e\u0440\u0438\u043c O(n\u00b3)<\/code> \u0438 \u0438\u0433\u043d\u043e\u0440\u0438\u0440\u0443\u0435\u043c \u043a\u043e\u043d\u0441\u0442\u0430\u043d\u0442\u044b. \u0414\u043b\u044f \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044f \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432 \u043d\u0430 \u0431\u043e\u043b\u044c\u0448\u0438\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u044d\u0442\u043e \u0443\u0434\u043e\u0431\u043d\u043e.

\u041d\u043e \u0441\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 CPU \u0443\u0441\u0442\u0440\u043e\u0435\u043d\u044b \u043d\u0430\u043c\u043d\u043e\u0433\u043e \u0441\u043b\u043e\u0436\u043d\u0435\u0435: \u043a\u044d\u0448\u0438, \u043a\u043e\u043d\u0432\u0435\u0439\u0435\u0440\u044b, \u043f\u0430\u0440\u0430\u043b\u043b\u0435\u043b\u044c\u043d\u043e\u0435 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 \u0438\u043d\u0441\u0442\u0440\u0443\u043a\u0446\u0438\u0439, SIMD, prefetching, \u043f\u0430\u043c\u044f\u0442\u044c \u0441 \u0440\u0430\u0437\u043d\u043e\u0439 \u0437\u0430\u0434\u0435\u0440\u0436\u043a\u043e\u0439.

\u041f\u043e\u044d\u0442\u043e\u043c\u0443 \u0434\u0432\u0430 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430 \u0441 \u043e\u0434\u0438\u043d\u0430\u043a\u043e\u0432\u044b\u043c O(n)<\/code> \u043c\u043e\u0433\u0443\u0442 \u043e\u0442\u043b\u0438\u0447\u0430\u0442\u044c\u0441\u044f \u043f\u043e \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438 \u0432 \u0440\u0430\u0437\u044b.

\u0410 \u0438\u043d\u043e\u0433\u0434\u0430 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u0441 \u0444\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e \u00ab\u0445\u0443\u0436\u0435\u00bb \u0441\u043b\u043e\u0436\u043d\u043e\u0441\u0442\u044c\u044e \u043d\u0430 \u0440\u0435\u0430\u043b\u044c\u043d\u044b\u0445 \u0440\u0430\u0437\u043c\u0435\u0440\u0430\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442\u0441\u044f \u0431\u044b\u0441\u0442\u0440\u0435\u0435.

\u0425\u043e\u0440\u043e\u0448\u0430\u044f \u0441\u0435\u0440\u0438\u044f \u0434\u043b\u044f \u0442\u0435\u0445, \u043a\u0442\u043e \u0445\u043e\u0447\u0435\u0442 \u043f\u0435\u0440\u0435\u0439\u0442\u0438 \u043e\u0442 \u00ab\u0443 \u044d\u0442\u043e\u0433\u043e O(n), \u0437\u043d\u0430\u0447\u0438\u0442 \u0431\u044b\u0441\u0442\u0440\u043e\u00bb \u043a \u043f\u043e\u043d\u0438\u043c\u0430\u043d\u0438\u044e \u0442\u043e\u0433\u043e, \u043a\u0430\u043a \u043a\u043e\u0434 \u0440\u0435\u0430\u043b\u044c\u043d\u043e \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442\u0441\u044f \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u043e\u043c.

en.algorithmica.org\/hpc\/complexity\/<\/a>

@machinelearning_books<\/a>","text_length":1103,"media":{"root":"\/00b\/nQ4AAN6WbEMAAAAAAwWAih5cEr0","photo":{"thumbs":{"m":{"w":320,"h":205,"hash":"ONcaTUm3jBy4B2-HKnP54w&ts=1787762102"},"x":{"w":503,"h":323,"hash":"BcogIbUGA2hWKGYQTsDSDA&ts=1787762102"},"i":{"bytes":"AaACg|DSYszkr06Yz6U9XO07gcimDln+p\/pSqWAbAbr7elAEm9cEnIx7VGJgzEK464xg02Qths5xgdfrUKnEgx6igCzuffg9PYUU7fhwrY5ooAjwQ7DbuPJpyBtpI4yaeVUk5A6elOoAYU3Z3dwKaIEDA45FS0UAQSDMyfXPSip6KAP\/2Q=="}}}}},{"channel_id":1131189982,"post_id":3740,"date":1787143054000,"forwards":"2","views":"1.0K","text":"\u041a\u0430\u043a \u0431\u0443\u0434\u0443\u0442 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0442\u044c\u0441\u044f \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430 \u0432 \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0435 \u0433\u043e\u0434\u044b? \u041a\u0430\u043a\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u0441\u043a\u0438\u0435 \u0438\u0434\u0435\u0438 \u0443\u0436\u0435 \u043f\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u044e\u0442\u0441\u044f \u0432 \u0440\u0430\u0431\u043e\u0442\u0430\u044e\u0449\u0438\u0435 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u044b? \u0421 \u043a\u0430\u043a\u0438\u043c\u0438 \u0437\u0430\u0434\u0430\u0447\u0430\u043c\u0438 \u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0441\u0442\u0430\u043b\u043a\u0438\u0432\u0430\u044e\u0442\u0441\u044f AI-\u043a\u043e\u043c\u0430\u043d\u0434\u044b?

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\u0412 \u0446\u0435\u043d\u0442\u0440\u0435 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u2014 NextGenAI<\/b>, \u043f\u0440\u043e\u0435\u043a\u0442, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0438\u0441\u0441\u043b\u0435\u0434\u0443\u0435\u0442 \u043d\u043e\u0432\u044b\u0435 \u0432\u044b\u0437\u043e\u0432\u044b \u0438 \u0444\u043e\u0440\u043c\u0438\u0440\u0443\u0435\u0442 \u043e\u0431\u0440\u0430\u0437 \u0431\u0443\u0434\u0443\u0449\u0435\u0433\u043e \u0418\u0418.

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17 \u0441\u0435\u043d\u0442\u044f\u0431\u0440\u044f \u2014 \u041c\u043e\u0441\u043a\u0432\u0430 (\u043e\u0447\u043d\u043e) \u0438 \u043e\u043d\u043b\u0430\u0439\u043d.
\u0420\u0435\u0433\u0438\u0441\u0442\u0440\u0438\u0440\u0443\u0439\u0442\u0435\u0441\u044c<\/a>
#\u0440\u0435\u043a\u043b\u0430\u043c\u0430
\u041e \u0440\u0435\u043a\u043b\u0430\u043c\u043e\u0434\u0430\u0442\u0435\u043b\u0435<\/a>","text_length":936,"media":{"root":"\/006\/nA4AAN6WbEMAAAAAMX8jIVt6Sok","photo":{"thumbs":{"m":{"w":320,"h":320,"hash":"zc-guiXsEQLlMHUtA246HA&ts=1787762102"},"x":{"w":720,"h":720,"hash":"QdNUKOxo6z6jeKRbnC7s5g&ts=1787762102"},"i":{"bytes":"AoACg|DIXPYE1NHkZJ4HpRDFLOSIxn2qRbScjheM9f8AP1q0iWMIGeGFIfnlz2WpWtZU7DPOOfSnJZzEYVc4680SERsfeimzRvE2Gx+BoqbE2GRnnBZh9Kl3YOfOYn6j\/GkitJZ87ELAcEintplzniFvzqrliKzOTmZyD6kf40MxyP30hx9P8ab9luEbYYyHxnHtTvslz\/zzNITYjkNgFmJFFK0UsYHmBlBopkkkNxJCrBJAATyNuaebu46eYgz\/ALA\/wooosF7DfPmM255ASV2j5R09OlPMz7Scrx\/sj\/CiigdrjHkeRRlwVHYDFFFFBJ\/\/2Q=="}}}}},{"channel_id":1131189982,"post_id":3739,"date":1787051876000,"forwards":"25","views":"2.4K","text":"Title: \"A Year in LLM Serving: Workload Evolution, Caching and Load-Balancing\"\n\narxiv. org\/abs\/2608.13573","text_length":105,"media":{"root":"\/006\/mw4AAN6WbEMAAAAAMX8jIVt6Sok","photo":{"thumbs":{"m":{"w":320,"h":258,"hash":"HFG_fzYpMxcl2FaiCL7Nfw&ts=1787762102"},"x":{"w":800,"h":645,"hash":"Ky-a8w3t7XC0ryrLAzzBpA&ts=1787762102"},"y":{"w":959,"h":773,"hash":"AadHmWeHR-aXbpetM9Byig&ts=1787762102"},"i":{"bytes":"AgACg|DXbg9TzTSrEZVj+NKZMEjHSm7h\/tfnQAYkJ64pQGA5LZ9qC3A9\/enLgd8\/U0ACj3P40U4GigCFvvHmjI96cRyflpNh\/u0ABxtFJx6frTmHAGKbgUAPTGeBRSR\/e\/CigD\/\/2Q=="}}}}},{"channel_id":1131189982,"post_id":3738,"date":1787045642000,"forwards":"5","views":"2.5K","text":"\u0427\u0430\u0441\u0442\u043e \u0437\u0430\u043a\u0430\u0437\u0447\u0438\u043a\u0438 \u043e\u0442\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442\u0441\u044f \u043e\u0442 \u0432\u043d\u0435\u0434\u0440\u0435\u043d\u0438\u044f \u0418\u0418-\u0430\u0441\u0441\u0438\u0441\u0442\u0435\u043d\u0442\u043e\u0432 \u0438\u0437-\u0437\u0430 \u00ab\u0433\u0440\u044f\u0437\u043d\u044b\u0445\u00bb \u0432\u0445\u043e\u0434\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445: PDF \u0441 \u0442\u0430\u0431\u043b\u0438\u0446\u0430\u043c\u0438 \u0438\u043b\u0438 \u0441\u043a\u0430\u043d\u044b \u043f\u043b\u043e\u0445\u043e \u0438\u043d\u0434\u0435\u043a\u0441\u0438\u0440\u0443\u044e\u0442\u0441\u044f, \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u044c \u0433\u0430\u043b\u043b\u044e\u0446\u0438\u043d\u0438\u0440\u0443\u0435\u0442 \ud83e\udd2f

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#\u0440\u0435\u043a\u043b\u0430\u043c\u0430
\u041e \u0440\u0435\u043a\u043b\u0430\u043c\u043e\u0434\u0430\u0442\u0435\u043b\u0435<\/a>","text_length":937,"media":{"root":"\/001\/mg4AAN6WbEMAAAAAiDalmCGnFvQ","photo":{"thumbs":{"m":{"w":320,"h":320,"hash":"N3BvpsZYhAqvo9uD5TcEsA&ts=1787762102"},"x":{"w":719,"h":720,"hash":"_55xQZhcRXcqUs-qkLZHWw&ts=1787762102"},"i":{"bytes":"AoACg|CkBb\/Zxnd5uecUwKh4AbNAWpoYyWzitrFjfK5qN0xVzY28fypk0fyg4p2BalPFFSlKKY7EiL6jNW1MYQKp5x0J5quqO6fKD9RUD29xGxO1j70r2FOKaLkjgcA4qHJk4HSoT5h5MbbvpSx29w7BirKPpRzLoEdNCVlopzArwevvRTNbDYZEC4YjPutSiWM5BIx9KKKLEIZtg3Z4z9P\/AK9K0wH3cH8KKKEhkbyZH3QKKKKqwH\/\/2Q=="}}}}},{"channel_id":1131189982,"post_id":3737,"date":1786540452000,"forwards":"54","views":"3.9K","text":"From Worm to Human:
Scaling Brain Emulation<\/b>


\ud83d\udcd3 Read<\/a>

@datascienceiot<\/a>","text_length":70,"media":{"root":"\/002\/mQ4AAN6WbEMAAAAAjVok69lC-qU","photo":{"thumbs":{"m":{"w":279,"h":320,"hash":"TTYAINVz5hM0bQzuG_VmtQ&ts=1787762102"},"x":{"w":697,"h":800,"hash":"rKSknQJXmUCrEXnPSsZSDg&ts=1787762102"},"y":{"w":1116,"h":1280,"hash":"1t_XhwnH92pzd3xkyVwLUA&ts=1787762102"},"i":{"bytes":"AoACM|DZIBHNJ7UppO9ABQDTSfwpwzQAtFFFABTCx9OPen0wt16\/lQAm4npg08cj0pit6Z\/LNPHI\/wDrUALRRRQAxycim5OfxoooAfxSgCiigBaKKKAP\/9k="}}}}},{"channel_id":1131189982,"post_id":3736,"date":1786193992000,"forwards":"42","views":"5.1K","text":"Kimi Delta Attention.
<\/b>
\u2014 a minimal implementation of the Kimi K3 architecture.
http:\/\/k-a.in\/KDA.html<\/a>","text_length":101,"media":{"root":"\/00d\/mA4AAN6WbEMAAAAAZSaOGr9Qd0g","photo":{"thumbs":{"m":{"w":320,"h":221,"hash":"wdxKGqJ4PdDT8ozTlXtMFg&ts=1787762102"},"x":{"w":800,"h":552,"hash":"9XJy40wEyg-HV63LC4eAjA&ts=1787762102"},"y":{"w":1280,"h":883,"hash":"op3OYZU8cjrQhp2uNj2QaA&ts=1787762102"},"i":{"bytes":"AcACg|DZooooAh8xvP8AL2jHrn+lTVF5I87zdzZ9M8flUtABRRRQAUmBS0UAJgUYpaKACiiigD\/\/2Q=="}}}}}]