Channel
@raven_announcements
On this record: Growth · Engagement · Posts · Cite this entry
217subscribers
-5 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001214544812 |
|---|---|
| Type | Channel |
| Username | @raven_announcements |
| Created | Between 1 March 2018 and 31 July 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 7 September 2026 |
| Measurements held | 5 |
| Confirmed unchanged | 1 time, most recently 7 September 2026 |
| On Telegram | t.me/raven_announcements |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 7 Sept 2026, 21:01 | 217 | -2 |
| 23 Aug 2026, 07:07 | 219 | -2 |
| 16 Aug 2026, 03:04 | 221 | -1 |
| 7 Aug 2026, 03:16 | 222 | no change |
| 6 Aug 2026, 17:10 | 222 | first reading |
18 posts held, back to 1 May 2023 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 18 posts for this entry, the most recent from 3 January 2025. An engagement rate over an empty window would be a number about nothing.
Ravnest Dev Summary 2024: 1. Published a Research Paper about our framework Ravnest on Arxiv. 2. Built and deployed a Readthedocs website for Ravnest that contains extensive documentation, feature updates and usage examples. 3. Developed GRPC enabled communication paradigms and restructured codebase. 4. Integrated data compression techniques to our backend, reducing network overhead and improving training efficiency…
🐦⬛🪺Ravnest Dev Updates: 1. Speedups and improvements to the existing over the internet training mechanism with gRPC. 2. A new, TCP communication enabled mode for Ravnest that can run our distribution strategies over localised networks, is in the works. This will help cater to requirements which need localised training over LAN, ethernet, InifiniBand networks. 3. Comparisons are being performed against asynchrono…
Ravnest updates: 1. Active development towards roles such as Intermediary Servers for matchmaking/session status monitoring is underway in our private Ravnest repositories and will be made public soon. 2. We are benchmarking against popular LLM models like Llama, Falcon etc, which will be used to further validate our algorithm and draw open source contributions. 3. We are targeting research conferences such as NeurI…
Hi everyone! Do check out Ravnest's comprehensive documentation! With upcoming features, improvements and new developments, we plan to continuously maintain and update this website. Documentation Link: https://ravnest.readthedocs.io/ Your feedback is invaluable to us, so feel free to reach out. Do consider giving our GitHub Repository a star! Your support means lot! GitHub: https://github.com/ravenprotocol/ravnes…
Why Ravnest over Ravenverse? 1. On Ravenverse, providers hosted the entire model on their devices, and training time depended on the slowest provider, rendering faster systems idle. Ravnest improves this by using the best aspects of both data and model parallelism, so each provider hosts only a small part of the model and can train without getting impeded by straggling peers. 2. Ravnest uses GRPC for data sharing an…
Hi everyone! Here’s the meeting link for our discussion on Ravnest: https://meet.google.com/uhy-bnbr-ckk. We will be starting at 8 am GMT!
Hi everyone! Join us for an in-depth conversation on Ravnest, where we'll explore its features, technicalities, discuss best practices, and share insights on how to leverage it for your projects. What to Expect: 1. An overview of Ravnest and its capabilities 2. Technical demonstrations and use cases 3. Q&A session to address your queries 4. Networking with fellow developers, researchers and ML enthusiasts Date: 10…
Ravnest Updates 1. We have conducted a few decentralised training experiments using our algorithm across models like ResNet, Inception and BERT LLM which showed good convergence properties. These results are now available in our arXiv paper. (Link: https://arxiv.org/abs/2401.01728 ) 2. We will be regularly adding more benchmarks and results on popular models. 3. A detailed Readthedocs page is underway with complete …
Hello Everyone - Binance Research report 🐦⬛🪺🧠 We are highlighted in the latest Binance Research report as one of the few compute networks focusing on AI and Machine Learning! We highly encourage you all to check out our latest research paper on. 🐦⬛🪺Ravnest: Decentralized Asynchronous Training on Heterogeneous Devices 👉 https://arxiv.org/abs/2401.01728 Follow our GitHub implementation of that paper. Especially t…
Hello Everyone - Ravnest Update: 🐦⬛🪺🧠 Some of you open source enthusiasts are noticing activity on GitHub! We’re optimising and benchmarking our algorithm on popular deep learning models and datasets. Specifically, we are recording performance metrics of Ravnest on LLMs and CV models. We have scripts ready for distributed training of ResNet50 (a popular CV model which we are training on the TinyImagenet dataset)…
Hey everyone! We are thrilled to share the blueprints of Ravnest’s architecture. Figure 1 depicts the process of matchmaking, cluster formation and model fragmentation into submodels on an intermediary server. The requester and all available compute nodes will connect to this intermediary server to participate and get the training started. Figure 2 elucidates upon what goes on inside each cluster, running parallell…
🚀🚀🚀🚀🚀🚀🚀🚀 Hey everyone! We are happy to share the updates for implementation of the new Research Paper: - Single Cluster Zero Bubble Model Parallelism (as per new proposal in the research paper) completed and tested. - Parallel Multi Ring Reduce (One of the new mechanisms proposed in the paper) for Efficient and parallel parameter averaging implementation complete. - Dynamic Cluster formation as per benchmarking metri…
Showing the 12 most recent of 18 posts we hold for @raven_announcements. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 7 September 2026 — this entry's latest reading, not the date you are reading this.
“Raven Protocol Announcements” (@raven_announcements), 217 subscribers as measured 7 September 2026. Telegram Register, tgregister.com/channel/raven_announcements.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.