Channel
@oqtacore
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
41subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002172523964 |
|---|---|
| Type | Channel |
| Username | @oqtacore |
| Description | Got questions? - @oqtacore_bda_daria AI, Web3, Biotech, and Enterprise innovation |
| Created | Between 1 June 2024 and 30 September 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 10 August 2026 |
| Last confirmed live | 10 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/oqtacore |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 10 Aug 2026, 18:02 | 41 | no change |
| 7 Aug 2026, 22:51 | 41 | first reading |
16 posts held, back to 20 November 2025 — 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 pageof 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 16 posts for this entry, the most recent from 17 February 2026. An engagement rate over an empty window would be a number about nothing.
Lifetime counters from Telegram’s own channel header, read 10 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.
Measured directly from 1 video 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.
134 reactions across 16 posts, in 8 distinct kinds. The most used accounts for 41.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 56 | 41.8% | |
| 👍 | 35 | 26.1% | |
| 🔥 | 24 | 17.9% | |
| 🤝 | 10 | 7.46% | |
| 💯 | 6 | 4.48% | |
| ⚡ | 1 | 0.746% | |
| 👏 | 1 | 0.746% | |
| 💘 | 1 | 0.746% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 16 of the 16 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 134reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 16 most recent posts we hold, published 20 November 2025 to 17 February 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
How institutions adopt crypto in 2026: banks, trust funds and family offices: decisions, risks and real use cases This is the topic of our panel discussion on March 5th, 4PM (gtm +3). We will discuss how banks, funds, and family offices are working with digital assets today: what is actually being implemented, which solutions meet compliance requirements, where risks arise, and why some initiatives stall Speakers:…
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📋 The data center value chain market cap 367 companies across 45 markets in the data center value chain to spot where advantage is forming, identify investment opportunities early, and find partners before competitors. 🔗 Website | Blog | Cases | X | Linkedin
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📄 Using CB Insights predictive intelligence, we map 400+ promising AI agent startups across 16 categories to evaluate for build, buy, and partnership opportunities. 🔗 Website | Blog | Cases | X | Linkedin
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CAMBRIDGE STARTUP ECOSYSTEM 🧬 DeepTech Focus • Almost all Cambridge startups fall into DeepTech, meaning they rely heavily on science and advanced technologies. • 67% of startups are in biotech and medicine. Strong areas include oncology (16%), AI-driven drug design (16%), and genetics (14%). • Another 18% of startups are in semiconductor and chip manufacturing. • By the volume of VC investment into DeepTech, Ca…
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💡 A16Z | Big Ideas 2026 • AI-native industry Factories, energy, logistics, mining: designed from day one as software-first systems with simulation, autonomous robotics, and AI-driven operations. • Factory mindset revival Applying assembly-line thinking to construction, energy, mining, and manufacturing: modular AI, autonomy, faster design cycles, and large-scale builds (reactors, datacenters, housing). • …
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➕ Our Cases | ChainLogik We implemented ChainLogik to automate and optimize document workflows in the supply chain. ChainLogik enables bulk review, approval or rejection of purchase orders and invoices. Powered by robust APIs the system accelerates review cycles and reduces repetitive tasks significantly improving procurement efficiency. 🔗 EXPLORE MORE [15+ cases]
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🤖 Anthropic uses LLMs to find vulnerabilities in blockchain smart contracts Anthropic has begun leveraging its LLMs to detect vulnerabilities in blockchain smart contracts. The model has already identified an exploit that could result in losses of approximately $4.6M. Analysts also suggest that AI agents now have significant potential for use in hacker attacks within the crypto industry. In simulations of real-wor…
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📹 Ethereum 'Fusaka' Explained in under 2 minutes 🔗 Website | Blog | Cases | X | Linkedin
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📄 OQTACORE Digest | November 2025 Here’s a digest of the key highlights from the Web3 and deep tech world for November 2025. 🔗 OQTACORE | BLOG
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🛒 A new era of online search and shopping with AI McKinsey’s latest report shows that the way people search, discover, and buy online is being rewritten in real time. Traditional SEO is no longer enough as user journeys shift into AI-native interfaces and assistants. On top of classic search, a new discipline is emerging: GEO (Gen AI Engine Optimization) where the goal is to optimize for how gen AI engines see, int…
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How tokenisation could transform finance Larry Fink (CEO of BlackRock) and Rob Goldstein (COO of BlackRock) wrote a piece for The Economist about how tokenisation could reshape global finance. KEY POINTS: • Tokenisation lets almost any asset (stocks, bonds, real estate) exist on a single digital record that all participants can verify. • It removes friction: fewer intermediaries, faster settlement, and lower opera…
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📈 Data centers are getting really big, really quickly Data centers are getting really huge, with the largest ones under development in the 1-2 GW range. That’s roughly enough energy to power 1 million homes (according to EpochAI), and at least five of these 1GW+ data centers are expected to come online in 2026. 🔗 Website | Blog | Cases | X | Linkedin
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Showing the 12 most recent of 16 posts we hold for @oqtacore. 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 — 715,233 of 1,478,351entries 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.
Named by 1 registered channel — 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.
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 10 August 2026 — this entry's latest reading, not the date you are reading this.
“OQTACORE | Deep Tech Development Powerhouse” (@oqtacore), 41 subscribers as measured 10 August 2026. Telegram Register, tgregister.com/channel/oqtacore.
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.