[challenge][reverse engineering] https://blog.janestreet.com/can-you-reverse-engineer-an-asic/

Channel
Engineer Readings
@engineerreadings
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
530subscribers
+0 since we began measuring on 11 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
Register entry
| Telegram ID | -1001328414062 |
|---|---|
| Type | Channel |
| Username | @engineerreadings |
| Created | 16 July 2019 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 11 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 2 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/engineerreadings |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 10:07 | 530 | no change |
| 11 Aug 2026, 21:16 | 530 | first reading |
Engagement
20 posts held, back to 14 January 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 1 pageof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 44.9%
- avg views ÷ 530 subscribers
- Avg views / post
- 238
- 4 posts measured
- Reaction rate
- 1.09%
- reactions ÷ views · ER floor
- Posts in window
- 4
- of 20 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. It is computed over the 2 of 4 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 11 August 2026 |
|---|---|
| Posts held | 20 (14 January 2026 – 11 August 2026) |
| Views total | 951 |
| Reactions total | 7 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 11 Aug 2026, 21:16 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.
Reaction mix
21 reactions across 7 posts, in 4 distinct kinds. The most used accounts for 57.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 12 | 57.1% | |
| 🔥 | 6 | 28.6% | |
| 🤯 | 2 | 9.52% | |
| 🤔 | 1 | 4.76% |
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 8 of the 20 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 21reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 14 January 2026 to 11 August 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.
Recent posts
[llm][pelican on the bicycle] https://simonwillison.net/2025/Jun/6/six-months-in-llms/
[llm][harness] https://lilianweng.github.io/posts/2026-07-04-harness/
👍5
[llm][skills and evals][video] Great overview with tips for writing skills from Google Deepmind engineer: https://www.youtube.com/watch?v=0vphxNt4wyk
👍2
File, posted without a caption
[ai][world models] It’s been a while. I feel sorry for not sharing much within a 3 months period. I’ll try to get it going. Recently watched Yann LeCun presentation explaining World Models concept. Seems the deeper researched dig the further AI hype goes. Although it’s quite interesting! https://www.youtube.com/watch?v=72Xj8k5WQX4
[ai][layoff][paper] “If AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on. We show that knowing this is not enough for firms to stop it. In a competitive task-based model, demand externalities trap rational firms in an automation arms race, displacing workers well beyond what is collectively optimal. The resulting loss harms both workers an…
[ai][computer integrated to transformer] https://percepta.ai/blog/can-llms-be-computers
[ai][human brain cells] https://x.com/joshkale/status/2030719536991805595?s=46&t=eNN3Y-GKeBSlFyyj1ozvgg
🔥2
[ai][multi-behavior brain] https://x.com/alexwg/status/2030217301929132323?s=46&t=eNN3Y-GKeBSlFyyj1ozvgg
[llm][research] “We show that large language models can deanonymize users at scale. With internet access, our agent can re-identify pseudonymous Hacker News and Anthropic Interviewer users with high precision—matching hours of human investigation. In a closed-world setting, we build a scalable LLM pipeline that: 1. extracts identity clues from raw text, 2. finds candidate matches via semantic search, and 3. veri…
👍1🤔1
[research][google deepmind][llm][agents] “AI agents are able to tackle increasingly complex tasks. To achieve more ambitious goals, AI agents need to be able to meaningfully decompose problems into manageable sub-components, and safely delegate their completion across to other AI agents and humans alike. Yet, existing task decomposition and delegation methods rely on simple heuristics, and are not able to dynamically…
Showing the 12 most recent of 20 posts we hold for @engineerreadings. 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
Citation-graph rank — 1,011,360 of 1,160,990entries 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.
Forward network
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.
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.
“Engineer Readings” (@engineerreadings), 530 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/engineerreadings.
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.