Curious Coder
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Do join for coding resources, Handwritten notes & Quizzes! 🧑💻 Business: Curiousprogrammer12@gmail.com
Ko'proq ko'rsatish📈 Telegram kanali Curious Coder analitikasi
Curious Coder (@curious_coder) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 132 564 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 878-o'rinni va Hindiston mintaqasida 1 606-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 132 564 obunachiga ega bo‘ldi.
26 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -1 893 ga, so‘nggi 24 soatda esa -70 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 8.58% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.55% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 11 375 marta ko‘riladi; birinchi sutkada odatda 2 053 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 22 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent iit, lpa, hyderabad, patna, internship kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Do join for coding resources, Handwritten notes & Quizzes! 🧑💻
Business: Curiousprogrammer12@gmail.com”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 27 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
132 564
Obunachilar
-7024 soatlar
-4357 kun
-1 89330 kun
Postlar arxiv
132 564
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132 564
Repost from NeuralZone | AI Apps
🚀 GigaChat 3.5 Reasoning — a new open-source LLM that thinks before it answers.
It breaks problems into stages, builds a plan, checks intermediate results, and self-corrects. Built on GigaChat 3.5 Ultra, it explores multiple step-by-step reasoning paths for math & coding, using automated verification to reinforce correct answers.
⚡️ Proprietary linear attention makes it highly efficient on long contexts, retaining key points without re-matching from scratch. It’s also token-efficient: uses 37% fewer tokens than DeepSeek V4 Flash Preview on math problems!
📈 Benchmark gains over non-reasoning version:
• IFBench: 44 → 77
• Natural Plan: 64 → 80
• LiveCodeBench v6: 56 → 85
📦 MIT license. Weights on Hugging Face: fp8 | bf16
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