👋🏻 Durov "USERNAME"lar haqida!
"Yaqin vaqtgacha Telegram’dagi barcha foydalanuvchi nomlarining 70 foizi Erondan kelgan kibersquatterlar tomonidan faol bo‘lmagan kanallarda saqlangan. Bu qidiruv natijalarini chalkashtirib yuboradigan o'lik foydalanuvchi nomlari qabristonini yaratdi va millionlab Telegram foydalanuvchilariga o'z akkauntlari, guruhlari va kanallari uchun tegishli umumiy manzillarni tanlashiga to'sqinlik qildi.
Ushbu zaxiralangan foydalanuvchi nomlarini olishni istagan foydalanuvchilar ko'pincha hech qanday javob olmagan yoki aldanib qolishgan.
Yaxshiyamki, bu vaziyat o'zgara boshladi. Avgust oyi oʻrtalarida biz oʻtgan yil davomida boʻsh yoki faol boʻlmagan kanallarga bogʻlangan barcha ochiq Telegram manzillarini olib tashladik. Biz bu manzillarning 99 foizini asta-sekin qaytadan umumiy foydalanishga kiritamiz, bu safar algoritmik va geolokatsiya cheklovlari bilan faqat bir nechta foydalanuvchilar emas, balki ko‘proq foydalanuvchilar foyda ko‘rishi mumkin.
Eng yuqori baholangan qisqa foydalanuvchi nomlariga kelsak, ularni tarqatishning eng samarali va adolatli usuli men avvalgi postimda aytib o'tgan auktsion bo'lib tuyuladi. Shunday qilib, ushbu jozibali havolalarni qo'lga kiritganlar ularni yaxshi foydalanishga va taniqli t.me manzillarida joylashtirilgan original kontent bilan foydalanuvchilarimiz uchun qadrlashga undaydi.
Telegram foydalanuvchi nomlarini yig‘ib olganlar hafsalasi pir bo‘lganiga shubha qilmayman, lekin bu o‘zgarish foydalanuvchilarning katta qismiga foyda keltiradi. Men millionlab ajoyib Telegram manzillari qanday qayta tiklanishini va nihoyat bizning hamjamiyatimizga xizmat qila boshlashini intiqlik bilan kutaman.
P.S. Kelgusi voqealarni kutgan holda, bugun biz Telegramdagi har bir foydalanuvchi nomi uchun sindor.t.me kabi maxsus havolalarni qo'llab-quvvatlashni boshlaymiz. Ushbu veb-saytlar allaqachon istalgan brauzerda ishlaydi." - Pavel Durov
#username#yangilik#hulosa
💚@TGraphUz | YouTube
#python#agents#graph#llms#rag
Graphiti helps AI systems handle constantly changing information by building real-time knowledge graphs that track relationships and historical data, allowing them to integrate user interactions, business data, and external sources seamlessly. Unlike traditional methods, it updates information instantly without needing full recomputations, enabling precise historical queries and efficient hybrid searches. This helps AI applications stay context-aware, automate tasks effectively, and manage complex, evolving data with minimal delay.
https://github.com/getzep/graphiti
#typescript#csv#diagrams#graph#json#nextjs#react#tool#visualization#yaml
JSON Crack is a free, open-source tool that instantly turns complex JSON, YAML, CSV, XML, or TOML data into clear, interactive graphs, making it easier to explore and understand your information. It lets you convert between formats, validate data, generate code (like TypeScript interfaces), run queries, and export visuals as images—all while keeping your data private since everything processes locally on your device[1][2][5].
https://github.com/AykutSarac/jsoncrack.com
#cplusplus#arduino#cansat#csv#embedded#graph#ground_station#iot#microcontroller#network#projects#qt#serial#serial_studio
Serial Studio is a free, easy-to-use tool that lets you visualize real-time data from devices like microcontrollers via serial ports, Bluetooth, or network connections. It works on Windows, macOS, and Linux, and offers customizable dashboards with various widgets to monitor sensor data, debug info, or telemetry. You can quickly plot data, export it as CSV for analysis, and even use advanced features like checksum validation and JavaScript data processing. It supports hobbyists, educators, and professionals by simplifying data monitoring and debugging, saving you time and effort in understanding your device’s output. Pro versions add commercial use and extra features[1][4][5].
https://github.com/Serial-Studio/Serial-Studio
#rust#ai#ai_ocr#attention_mechanism#gnn#gnn_model#gnns#graph#graph_neural_networks#llm_inference#low_latency#mincut#neo4j#ocr#onnx#rust#vector#wasm
RuVector is a free, open-source vector database that gets smarter with every query. Unlike static databases, it learns from usage via GNN layers, runs LLMs locally with no cloud costs, supports graph queries like Neo4j, scales freely across nodes, and deploys as a single self-booting file (125ms startup). Run with `npx ruvector`. You benefit from faster, more accurate AI search that improves automatically, zero operating costs, full offline/privacy control, and easy scaling—perfect for RAG, agents, or edge apps without vendor lock-in.
https://github.com/ruvnet/ruvector