Telegram RegisterThe public register of Telegram

Channel

Convly | AI News

@ConvlyAi

On this record: Topic · Observations · Growth · Engagement · What this channel posts · Posts · Cite this entry

16,051subscribers

-2,733 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1004427728967
TypeChannel
Username@ConvlyAi
DescriptionYour daily source for the latest news, breakthroughs, and trends in Artificial Intelligence
CreatedBetween 1 June 2026 and 31 July 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live12 August 2026
Measurements held7
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/ConvlyAi

Topic

Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.

Observations

These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.

Views per post sit far below this size band

1.9 average views per post against 16,051 subscribers — an engagement rate of 0.012%. Across the 6,344 registered channels in the same cohort — 10,002–31,609 subscribers, posting mainly in English — the middle half sit between 2.73% and 15.7%, with a median of 7.33%.

What this was computed from
Window30 days (13 July 2026 12 August 2026)
Posts measured30 of 30 published in the window (30 exact, 0 rounded by Telegram)
Views totalled57
Mature posts only0.013% over 27 posts read at least 24h after publication
Subscribers16,051 measured 12 August 2026
Cohortb10000:eng · 6,344 channels · p10 0.726% · p25 2.73% · p50 7.33% · p75 15.7% · p90 28.3%
Position in cohort0.189th percentile · 1/500 the cohort median
Uncertainty±0% of the figure, from Telegram’s rounding
Post languageEnglish · 100.0%of the window’s posts

When this is recorded. A channel is listed here only when its engagement rate sits at or below the 1st percentile of its cohort and is at least 3× away from that cohort’s median — below it — on both the all-readings figure and the mature-only figure. The percentile alone would be circular: a percentile cut puts the same share of every cohort in the tail whatever the data looks like. The distance from the median is what makes it a statement about this channel.

This is not a verdict, and the direction is not a quality signal.A low rate has many innocent causes — audiences that read in the Telegram app without opening the channel, a subscriber base built long before the current output, an audience in a different timezone from our reading. A high rate has innocent causes too: a post that travelled far beyond the channel’s own subscribers will do it. We publish the measurement and the distribution it sits in. The full cohort baselines are downloadable, so this comparison can be reproduced rather than trusted.

Recorded under the key err_low, last confirmed 12 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.

Growth

16,05118,78417,417.57 August 2026 — 18,784 subscribers7 August 2026 — 18,784 subscribers8 August 2026 — 18,592 subscribers9 August 2026 — 17,766 subscribers10 August 2026 — 16,752 subscribers11 August 2026 — 16,450 subscribers12 August 2026 — 16,051 subscribers7 August 202612 August 2026
7 measurements spanning 4 days, net -2,733. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 15,641–19,194 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 03:2116,051-399
11 Aug 2026, 06:1116,450-302
10 Aug 2026, 07:0716,752-1,014
9 Aug 2026, 03:5417,766-826
8 Aug 2026, 04:1118,592-192
7 Aug 2026, 19:3018,784no change
7 Aug 2026, 19:2918,784first reading

Engagement

30 posts held, back to 31 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 14 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
0.012%
avg views ÷ 16,051 subscribers
Avg views / post
1.9
30 posts measured
Reaction rate
this channel exposes no reaction counts
Posts in window
30
of 30 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.

What these figures were computed from
WindowRolling 30 days · latest post in window 11 August 2026
Posts held30 (31 July 202611 August 2026)
Views total57
Reactions total
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 10:16 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

What this channel posts

Photos
223
Links
230

Lifetime counters from Telegram’s own channel header, read 12 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.

Recent posts

11 Aug 2026, 20:08 UTC1 viewsread 12 August 2026
Photo

vLLM Docker: Run a GPU-Backed Inference Server in Minutes Pull vllm/vllm-openai:latest and run it with --runtime nvidia --gpus all --ipc=host to get a GPU-backed OpenAI-compatible server.Mount ~/.cache/huggingface into the container so mo… https://convly.ai/vllm-docker-guide/?utm_source=convly&utm_medium=social&utm_campaign=autopost

11 Aug 2026, 14:10 UTC1 viewsread 12 August 2026
Photo

LoRA Fine Tuning: A Practical Guide LoRA fine tuning trains a tiny set of adapter weights instead of the full model — typically 1–5% of total parameters — so you can fine-tune a 7B model on a single consumer GPU.QLoR… https://convly.ai/lora-fine-tuning-explained/?utm_source=convly&utm_medium=social&utm_campaign=autopost

11 Aug 2026, 13:25 UTC1 viewsread 12 August 2026
Photo

Nvidia Becomes the Bank of AI With Infrastructure Financing Nvidia is signing agreements with financial institutions to fund AI infrastructure expansion, positioning the chipmaker as a banker for the artificial intelligence industry. https://convly.ai/nvidia-ai-infrastructure-financing-deals/?utm_source=convly&utm_medium=social&utm_campaign=autopost

11 Aug 2026, 06:06 UTC1 viewsread 12 August 2026
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Ollama Cloud: Running Models in the Cloud vs Locally TL;DROllama Cloud refers to running Ollama on cloud infrastructure (AWS, GCP, Azure) rather than local hardware—same CLI and API, remote execution.All models in the Ollama library … https://convly.ai/ollama-cloud-explained/?utm_source=convly&utm_medium=social&utm_campaign=autopost

10 Aug 2026, 20:23 UTC1 viewsread 12 August 2026
Photo

Jan AI: Open-Source Desktop App for Running LLMs Locally Jan is a free, open-source desktop app (AGPL license) that runs LLMs entirely on your own hardware — no account, no cloud, no data leaving your machine.It ships a chat interface, a… https://convly.ai/jan-ai-explained/?utm_source=convly&utm_medium=social&utm_campaign=autopost

10 Aug 2026, 14:04 UTC1 viewsread 12 August 2026
Photo

KoboldCpp: Complete Guide to the Single-Binary Local LLM Runtime KoboldCpp is a single executable — download it, point it at a GGUF model file, and a browser UI plus OpenAI-compatible API start immediately on port 5001.GPU offload is controlled … https://convly.ai/koboldcpp-guide/?utm_source=convly&utm_medium=social&utm_campaign=autopost

10 Aug 2026, 13:22 UTC1 viewsread 12 August 2026
Photo

Meta Launches Muse Glimmer, a New AI in Intelligence Model Meta and Mark Zuckerberg have launched a new AI model called Muse Glimmer, according to the Detroit Free Press, adding another entry to the competitive field of AI in intelligence and generative systems. https://convly.ai/meta-muse-glimmer-ai-model-launch/?utm_source=convly&utm_medium=social&utm_campaign=autopost

10 Aug 2026, 06:29 UTC2 viewsread 12 August 2026
Photo

ComfyUI GGUF: Run Large Diffusion Models on Low-VRAM GPUs GGUF quantisation shrinks large diffusion models like FLUX.1 from ~24 GB to 5–12 GB, letting them run on consumer GPUs with 6–16 GB VRAM.Install the ComfyUI-GGUF custom node by cit… https://convly.ai/comfyui-gguf-setup/?utm_source=convly&utm_medium=social&utm_campaign=autopost

8 Aug 2026, 06:13 UTC4 viewsread 12 August 2026
Photo

Llama Cpp Python: Install, GPU Build, and Parameters The plain pip install llama-cpp-python gives you a CPU-only build. GPU support requires either a prebuilt GPU wheel or a source build with CMAKE_ARGS.CUDA: CMAKE_ARGS="-DGGML_CUDA=… https://convly.ai/llama-cpp-python-guide/?utm_source=convly&utm_medium=social&utm_campaign=autopost

7 Aug 2026, 20:06 UTC3 viewsread 12 August 2026
Photo

How to Update Ollama and Its Models on Windows, macOS, and Linux Windows & macOS: the desktop app downloads updates itself — click the Ollama icon in the system tray or menu bar and choose Restart to update.Linux: re-run the install script: … https://convly.ai/how-to-update-ollama/?utm_source=convly&utm_medium=social&utm_campaign=autopost

7 Aug 2026, 14:04 UTC3 viewsread 12 August 2026
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SGLang vs vLLM: Which LLM Serving Engine to Choose in 2026 vLLM is the safer default: broadest model and hardware support, biggest ecosystem, least deployment friction.Pick SGLang when your traffic reuses prompt prefixes heavily (agents, m… https://convly.ai/sglang-vs-vllm-comparison/?utm_source=convly&utm_medium=social&utm_campaign=autopost

7 Aug 2026, 13:04 UTC3 viewsread 12 August 2026
Photo

Nvidia GPUs at Launch Prices: QuakeCon Idea Takes On Markups At QuakeCon, Nvidia reportedly floated an idea PCMag Middle East calls 'crazy': selling its graphics cards for their launch prices. Here is why that framing says so much about the GPU market, and what it would mean for gamers and local AI builders. https://convly.ai/nvidia-gpus-launch-prices-quakecon/?utm_source=convly&utm_medium=social&utm_campaign=autop

Showing the 12 most recent of 30 posts we hold for @ConvlyAi. 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.

Cite this entry

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 12 August 2026 — this entry's latest reading, not the date you are reading this.

“Convly | AI News” (@ConvlyAi), 16,051 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/ConvlyAi.

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.