Science in telegram
Science that matters: AI, space, biotech, physics, future tech — explained sharply
Show more📈 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 120 856 subscribers, ranking 106 in the Facts category and 176 in the USA region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 120 856 subscribers.
According to the latest data from 30 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -793 over the last 30 days and by -10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.04%. Within the first 24 hours after publication, content typically collects 2.21% reactions from the total number of subscribers.
- Post reach: On average, each post receives 6 091 views. Within the first day, a publication typically gains 2 665 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 33.
- Thematic interests: Content is focused on key topics such as medicine, cell, researcher, scientist, u.s.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Science that matters: AI, space, biotech, physics, future tech — explained sharply”
Thanks to the high frequency of updates (latest data received on 01 July, 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.
The Mamba moment has arrived — but not as a revolution overnight. NVIDIA did not ship a pure state-space model. It shipped a pragmatic hybrid. That is probably the pattern to watch: keep attention where it creates value, replace it where it becomes too expensive. Open-weight frontier models are now strategic infrastructure. NVIDIA is not just selling GPUs anymore. By releasing serious open models, datasets, and recipes, it is pulling developers deeper into its full-stack AI ecosystem — hardware, software, inference, agents, and deployment. The next AI race may be less about raw parameter count and more about architecture, inference efficiency, data quality, and agentic reliability. A 55B-active model with strong benchmark results is a signal that “useful scale” is becoming more nuanced than simply making models bigger.The honest caveat: these are NVIDIA’s own benchmark numbers, and real-world agentic performance is always messier than leaderboard scores. A 71.9% SWE-Bench Verified result is impressive, but it still means the model fails a meaningful share of real software-engineering tasks. The big takeaway: the Transformer is not dead. But its monopoly may be ending. The future of frontier AI may look less like one dominant architecture — and more like modular systems where attention, state-space layers, MoE routing, long-context memory, and inference-time reasoning are mixed together for efficiency and performance. Sources: • NVIDIA Nemotron 3 Ultra Model Card https://build.nvidia.com/nvidia/nemotron-3-ultra-550b-a55b/modelcard • NVIDIA Research: Nemotron 3 Ultra https://research.nvidia.com/labs/nemotron/Nemotron-3-Ultra/ • NVIDIA Technical Blog https://developer.nvidia.com/blog/nvidia-nemotron-3-ultra-powers-faster-more-efficient-reasoning-for-long-running-agents/ #Nemotron3 #NVIDIA #MambaArchitecture #AI #OpenWeights #StateSpaceModels #Transformers
The significance of Cosmos 3 is not the model itself — it’s what it represents. For the past few years, the AI race has focused on making language models larger and more capable. NVIDIA is betting that the next battleground will be Physical AI: systems that can see, understand, predict, and act in the real world. If this shift succeeds, the winners of the next decade may not be the companies with the smartest chatbots, but those building the best robots, autonomous machines, industrial agents, and digital-physical ecosystems. The most important question is no longer: “Can AI think?” It’s becoming: “Can AI reliably interact with reality?” That is a far more difficult challenge — and a far larger market.📎 AIapps June 2026 roundup · SingularityMoments Top 10 #AI #NVIDIA #PhysicalAI #Robotics #EmbodiedAI #ArtificialIntelligence #science
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