AI & Deep Learning
All about Deep Learning, LLMs #deeplearning #deep_learning #AI #ML Follow for quality content amid all the noise in #AI.
Show moreπ Analytical overview of Telegram channel AI & Deep Learning
Channel AI & Deep Learning (@deeplearning005) in the English language segment is an active participant. Currently, the community unites 11 116 subscribers, ranking 10 714 in the Technologies & Applications category and 34 618 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 11 116 subscribers.
According to the latest data from 15 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 159 over the last 30 days and by 3 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 12.69%. Within the first 24 hours after publication, content typically collects 2.48% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 411 views. Within the first day, a publication typically gains 276 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
- Thematic interests: Content is focused on key topics such as developer, openai.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βAll about Deep Learning, LLMs #deeplearning #deep_learning #AI #ML
Follow for quality content amid all the noise in #AI.β
Thanks to the high frequency of updates (latest data received on 16 September, 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 Technologies & Applications category.
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| Date | Subscriber Growth | Mentions | Channels | |
| 15 September | +4 | |||
| 14 September | +9 | |||
| 13 September | +8 | |||
| 12 September | +7 | |||
| 11 September | +15 | |||
| 10 September | +10 | |||
| 09 September | +3 | |||
| 08 September | +5 | |||
| 07 September | +3 | |||
| 06 September | +8 | |||
| 05 September | +3 | |||
| 04 September | +5 | |||
| 03 September | +11 | |||
| 02 September | +12 | |||
| 01 September | +11 |
| 2 | https://deepmind.google/models/model-cards/gemini-3-8-flash/ | 754 |
| 3 | Kronos: A Foundation Model for the Language of Financial Markets
Kronos is the first open-source foundation model for financial candlesticks (K-lines), trained on data from over 45 global exchanges.
https://github.com/shiyu-coder/Kronos | 936 |
| 4 | https://qwen.ai/blog?id=qwen3.8 | 1 104 |
| 5 | https://openai.com/index/gpt-6-astra/ | 1 228 |
| 6 | https://z.ai/blog/glm-5.3-flash | 1 442 |
| 7 | Atomic chat has made running local models very efficient, they ship with turboquant and kv cache baked in, giving much better performance.
https://atomic.chat/ | 1 871 |
| 8 | Great News! MCP will now be stateless, this was a much awaited change. Read full blog:
https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/ | 1 887 |
| 9 | https://gemini.google/overview/agent/spark/ | 1 919 |
| 10 | Grok 4.6 achieves frontier intelligence across several agentic coding and knowledge work benchmarks. It matches GPT-5.6 Sol on the Artificial Analysis Intelligence Index, which is a composite score of nine benchmarks.
https://x.ai/news/grok-4-6 | 2 038 |
| 11 | https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model | 1 963 |
| 12 | https://github.com/yc-software/qm | 1 784 |
| 13 | https://qwen.ai/blog?id=qwen3.8 | 1 788 |
| 14 | https://thinkingmachines.ai/news/introducing-inkling/ | 1 871 |
| 15 | TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification.
At inference time, TabFM does not require training parameters on your dataset; instead, it leverages in-context learning by reading your training data as "context" to make instant predictions on new test samples.
https://github.com/google-research/tabfm | 1 872 |
| 16 | https://www.anthropic.com/news/claude-opus-5 | 1 708 |
| 17 | https://www.zdnet.com/article/claude-ai-shared-chats-indexed-by-google/ | 1 694 |
| 18 | https://github.com/hkuds/lightrag | 1 774 |
| 19 | TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification.
At inference time, TabFM does not require training parameters on your dataset; instead, it leverages in-context learning by reading your training data as "context" to make instant predictions on new test samples.
https://github.com/google-research/tabfm | 1 |
| 20 | https://platform.kimi.ai/docs/guide/kimi-k3-quickstart | 1 923 |
