Channel
@OntologyAnnouncements
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Telegram's recommendations · Cite this entry
2,947subscribers
+4 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001163094211 |
|---|---|
| Type | Channel |
| Username | @OntologyAnnouncements |
| Created | 24 December 2017 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 15 August 2026 |
| Measurements held | 6 |
| Confirmed unchanged | 1 time, most recently 15 August 2026 |
| On Telegram | t.me/OntologyAnnouncements |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 15 Aug 2026, 08:59 | 2,947 | +2 |
| 12 Aug 2026, 07:22 | 2,945 | +2 |
| 9 Aug 2026, 14:15 | 2,943 | +1 |
| 6 Aug 2026, 19:32 | 2,942 | -1 |
| 6 Aug 2026, 05:03 | 2,943 | no change |
| 6 Aug 2026, 00:56 | 2,943 | first reading |
15 posts held, back to 23 May 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 29 July 2026 |
|---|---|
| Posts held | 15 (23 May 2026 – 29 July 2026) |
| Views total | 390 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 15:59 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.
Measured directly from 2 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
📌 Ontology MainNet upgrade to v3.1.2 Ontology will perform a scheduled MainNet upgrade to v3.1.2 at block height 20,800,000. What is in it 🔹 PUSH0 (EIP-3855) places zero on the stack in one byte and 2 gas, instead of two bytes and 3 gas. 🔹 BASEFEE (EIP-3198) lets a contract read the current base fee directly on-chain for 2 gas, removing the need for an external data source. 🔹 MCOPY (EIP-5656) copies memory in a …
📌 Ontology at Eight: verified human data for the AI economy Eight years ago the Ontology MainNet went live, and it has run without interruption ever since. But our eighth anniversary is not really about looking back. It is about what all of it was building toward. AI is only as good as the data it learns from, and the industry is moving away from scraped content toward high-quality human data: consent-based, and pr…
📌 When human oversight becomes a compliance requirement "We had humans in the loop" is a description of a process. "Prove it" is a demand for evidence, and evidence has properties good intentions do not. It has to name specific people, show they were distinct real humans rather than sybils or one-shot contractors, show their judgement held up over time, and make every contribution attributable, timestamped and tampe…
Video, posted without a caption
📌 New: when the judge shares the blind spot Roll two fair dice. You are told at least one is a six. The probability that both are sixes is not 1 in 6, it is 1 in 11: the clue removes every outcome with no six, leaving 11 equally likely cases, one of which is the double six. The fast answer assumes an independence the clue already broke. Avena et al. tested eight state-of-the-art models on problems built exactly to …
🎲 We're running a little experiment today, and we want you in it. One dice question. One trap almost everyone falls for, the AI models included. The poll is live on X right now 👉 https://x.com/OntologyNetwork/status/2066416847499485589?s=20 Vote, argue it out in the replies, show your working, and tag a friend who reckons they're good at probability. The more wrong answers, the better the point we're making. No G…
A checklist today, because the SFT-vs-RL argument is eating attention that belongs one layer up. Whichever recipe wins for reasoning models, both consume step-level human evaluation, and almost nobody can defend theirs. Five questions sort the defensible pipelines from the rest: who made each judgement, does it carry its rubric, would you notice a drifting evaluator, can experts prove credentials without exposing ide…
📌 New: Evaluator-backed benchmarking, after the MLE-Bench moment MLE-Bench has been quietly contested across r/MachineLearning over the last week. The skepticism is not really about any single metric inside the benchmark; it is about whether a static benchmark structure can survive sustained adversarial attention from teams with economic incentive to game it. The standard answer (better methodology, rotating held-ou…
📌 New: Reward models need reward-model QA The recent LongTraceRL work made one thing unavoidable: sparse outcome signals are not enough at the reasoning-trace layer. The field has to evaluate intermediate reasoning steps. Step-level evaluation is a substantially different operation than outcome evaluation: judgements are finer-grained, cognitive load on the evaluator is higher, and the noise floor on any individual …
📌 New: The evaluator uniqueness primitive: from sybil resistance to agent evaluation Closing piece for Issue 02. The week opened with the METR teardown and traced the credibility-event pattern through preference data integrity (Tuesday) and longitudinal evaluation (Wednesday). This piece folds together the two threads still open: chronic sybil contamination in preference-data marketplaces, and the agent decision eva…
📌 New: Continuous training needs continuous evaluators Deployed models do not sit still anymore. Retrained, fine-tuned, instruction-extended, behaviourally patched on a cadence measured in weeks, sometimes in days. Last week's Prism paper (Tang et al., arXiv 2605.26110) treats multimodal continual instruction tuning as the deployed reality and flags that the field is hindered by severe engineering bottlenecks. The b…
📌 New: Your reward model is only as good as your preference data Last week's RTDMD paper (Huang et al., arXiv 2605.26108) proposes reward-guided RL for few-step diffusion alignment. It also explicitly acknowledges that aligning distilled models with human preferences remains challenging. The framework solves a downstream optimisation problem; the upstream supply of preference signal still does what it has always don…
Showing the 12 most recent of 15 posts we hold for @OntologyAnnouncements. 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.
Citation-graph rank — 420,515 of 1,481,306entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 15 August 2026 — this entry's latest reading, not the date you are reading this.
“Ontology Official Announcement” (@OntologyAnnouncements), 2,947 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/OntologyAnnouncements.
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.