Science in telegram
Science that matters: AI, space, biotech, physics, future tech β explained sharply
Show moreπ Analytical overview of Telegram channel Science in telegram
Channel Science in telegram (@science) in the English language segment is an active participant. Currently, the community unites 121 325 subscribers, ranking 104 in the Facts category and 181 in the USA region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 121 325 subscribers.
According to the latest data from 09 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -977 over the last 30 days and by -40 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 8.05%. Within the first 24 hours after publication, content typically collects 2.33% reactions from the total number of subscribers.
- Post reach: On average, each post receives 9 765 views. Within the first day, a publication typically gains 2 829 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 96.
- Thematic interests: Content is focused on key topics such as medicine, cell, researcher, scientist, u.s.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βScience that matters: AI, space, biotech, physics, future tech β explained sharplyβ
Thanks to the high frequency of updates (latest data received on 10 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Facts category.
"This is one of the most intriguing and surprising fossil discoveries of the past few years." β Dr. Steve Brusatte, University of Edinburgh π Original paper (bioRxiv) Β· Science News summary#paleontology #pterosaurs #fossil #evolution #iridescence #science
This story matters far beyond the technical achievement. First, if this approach scales, it could change the economics of AI training. A 100B-parameter model trained on geographically distributed A100 GPUs at roughly 65% of comparable datacenter efficiency is not yet a replacement for hyperscaler infrastructure β but it is a serious signal. It suggests that large-scale AI training may not always require a single billion-dollar GPU cluster. Second, the Bittensor layer is important. This is not just a distributed computing experiment; it is an incentive system. GPU owners can be rewarded for contributing compute, which creates the foundation for a market around idle hardware. In simple terms, this could become something like βAirbnb for AI trainingβ: monetizing unused GPU capacity the way Airbnb monetized unused rooms. Third, the uncomfortable part: decentralized AI training has often been dismissed by the mainstream AI community as impractical. Orion-100B does not prove that decentralized training will beat datacenters tomorrow. But it does prove that the idea deserves to be taken much more seriously. The next phase β permissionless participation from consumer hardware β will be the real test. If that works, the AI infrastructure map could become much more distributed than many people expected.Original report: https://macrocosmosai.substack.com/p/orion-100b-distributed-pretraining Summary: https://www.tao.media/macrocosmos-unveils-orion-100b-a-100b-parameter-distributed-ai-training-run/ #AI #DecentralizedAI #Bittensor #LLM #DeepLearning @science
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