Here's my 4+ hour conversation with Pavel Durov (@durov), founder and CEO of Telegram. This was one of the most fascinating and powerful conversations I've ever had in my life.
We discuss everything from his philosophy on freedom to government bureaucracies, intelligence agencies, human nature, mathematics, encryption, great engineering & design, education, family, and his philosophy on life.
It's here on X in full and is up everywhere else (see comment). It is translated and dubbed into Russian, Ukrainian, French, and Hindi.
Timestamps:
0:00 - Introduction
3:07 - Philosophy of freedom
6:15 - No alcohol
14:20 - No phone
20:16 - Discipline
41:28 - Telegram: Lean philosophy, privacy, and geopolitics
56:50 - Arrest in France
1:13:01 - Romanian elections
1:23:56 - Power and corruption
1:33:29 - Intense education
1:45:29 - Nikolai Durov
1:49:58 - Programming and video games
1:54:11 - VK origins & engineering
2:11:24 - Hiring a great team
2:20:40 - Telegram engineering & design
2:39:42 - Encryption
2:44:39 - Open source
2:49:26 - Edward Snowden
2:51:58 - Intelligence agencies
2:53:10 - Iran and Russia government pressure
2:56:19 - Apple
3:03:16 - Poisoning
3:29:28 - Elon Musk
3:35:31 - Money
3:44:23 - TON
3:54:13 - Bitcoin
3:57:12 - Two chairs dilemma
4:03:52 - Children
4:15:02 - Father
4:19:33 - Quantum immortality
4:26:05 - Kafka
Introducing Qwen3!
We release and open-weight Qwen3, our latest large language models, including 2 MoE models and 6 dense models, ranging from 0.6B to 235B. Our flagship model, Qwen3-235B-A22B, achieves competitive results in benchmark evaluations of coding, math, general capabilities, etc., when compared to other top-tier models such as DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro. Additionally, the small MoE model, Qwen3-30B-A3B, outcompetes QwQ-32B with 10 times of activated parameters, and even a tiny model like Qwen3-4B can rival the performance of Qwen2.5-72B-Instruct.
For more information, feel free to try them out in Qwen Chat Web (https://t.co/bg4tAU1p74) and APP and visit our GitHub, HF, ModelScope, etc.
Blog: https://t.co/Z8YgHerTXz
GitHub: https://t.co/Ij0Vne5b5K
Hugging Face: https://t.co/V1WxhQ0fad
ModelScope: https://t.co/Z9Z37FODVN
The post-trained models, such as Qwen3-30B-A3B, along with their pre-trained counterparts (e.g., Qwen3-30B-A3B-Base), are now available on platforms like Hugging Face, ModelScope, and Kaggle. For deployment, we recommend using frameworks like SGLang and vLLM. For local usage, tools such as Ollama, LMStudio, MLX, llama.cpp, and KTransformers are highly recommended. These options ensure that users can easily integrate Qwen3 into their workflows, whether in research, development, or production environments.
Hope you enjoy our new models!