Which way has LessWrong drifted on alignment: reachable, or out of reach?

Mint the two poles as parallel sentences that differ only in stance, take the balanced axis between them, score every post once as the mean of its chunks’ projections, and average the posts quarter by quarter, joined to real post timestamps. The window function centers each quarter on the era mean, because the zero of a contrast axis only means equidistant from the two sentences; posts is the census of the quarter’s LessWrong posts proper from forums.posts alone (it holds comments too, fifteen to a post on LessWrong; p.kind = 'post' leaves them out), scored is how many of them the embedding join reached, and coverage is their ratio: read a drift only where coverage is near 1, and an unscored quarter keeps its row with drift NULL instead of vanishing. One query returns the trajectory of a community.

SELECT quarter,
       if(scored = 0, NULL, lean - avgIf(lean, scored > 0) OVER ()) AS drift,
       scored, posts, round(scored / posts, 3) AS coverage
FROM (
  SELECT quarter, avg(post_lean) AS lean, count() AS scored
  FROM (
    SELECT e.target_key,
           toStartOfQuarter(any(p.original_timestamp)) AS quarter,
           avg(scry_cosine_similarity(e.embedding,
               scry_contrast_axis_balanced(@achievable, @unreachable))) AS post_lean
    FROM embeddings.chunks AS e
    JOIN forums.posts AS p ON p.post_key = e.target_key
    WHERE e.source = 'forum_posts' AND e.model_name = 'voyage-4-lite'
      AND p.source = 'lesswrong' AND p.kind = 'post'
      AND p.original_timestamp >= '2009-01-01'
    GROUP BY e.target_key)
  GROUP BY quarter) AS s
RIGHT JOIN (
  SELECT toStartOfQuarter(original_timestamp) AS quarter, uniqExact(post_key) AS posts
  FROM forums.posts
  WHERE source = 'lesswrong' AND kind = 'post' AND original_timestamp >= '2009-01-01'
  GROUP BY quarter) AS c USING (quarter)
ORDER BY quarter
LIMIT 100;

How fast did “vibe coding” catch on?

The search grammar rides inside the query as one predicate — here as an aggregate operand, with the month’s whole item count as its denominator. Hacker News: zero through January 2025, 34 mentions in February (the first item, 2025-02-03, links Karpathy’s post), 494 in March, 590 in April; 4 s, measured 2026-09-07.

SELECT toStartOfMonth(original_timestamp) AS month, count() AS items,
       countIf(scry_lex('"vibe coding"')) AS mentions,
       round(100000.0 * mentions / items, 1) AS per_100k
FROM hackernews.items
WHERE original_timestamp >= '2024-12-01'
GROUP BY month
ORDER BY month
LIMIT 36;

→ 2025-01      0     0.0 per 100k
  2025-02     34    11.9
  2025-03    494   169.4
  2025-04    590   212.6

Who do Elon Musk, Sam Altman and Eliezer Yudkowsky all follow?

The follow graph is a table, so “everyone all three of them follow” is one GROUP BY with a HAVING — a button Twitter never shipped in seventeen years. Ten accounts, Vitalik and JD Vance at the top, Ilya, Hinton and Scott Alexander further down, in 171 ms over 2.9 million rows read.

WITH common AS (
  SELECT followee_id FROM twitter.following
  WHERE follower_id IN (44196397, 1605, 2595244026)  -- @elonmusk, @sama, @ESYudkowsky
  GROUP BY followee_id HAVING uniqExact(follower_id) = 3)
SELECT argMax(handle, observed_on) AS handle, argMax(followers, observed_on) AS follower_count
FROM twitter.users
WHERE author_id IN (SELECT followee_id FROM common)
GROUP BY author_id
ORDER BY follower_count DESC
LIMIT 10;

→ VitalikButerin  7,866,454
  JDVance         5,551,484
  paulg           5,243,364
  karpathy        4,165,859
  demishassabis   1,865,207   … waitbutwhy, ilyasut, geoffreyhinton, slatestarcodex, ShaneLegg

Which papers cite both Scaling Laws and Chinchilla?

Citation edges are native keys, so “related work” becomes a set intersection: every OpenAlex work that cites both Scaling Laws for Neural Language Models and Training Compute-Optimal Large Language Models, ranked by its own citation count. 195 shared citers, 0.1 s, measured 2026-09-07 against the 2026-06-26 OpenAlex snapshot.

SELECT id, title, publication_year, cited_by_count
FROM openalex.works
WHERE id IN (
  SELECT citing_work_id FROM openalex.cited_by
  WHERE cited_work_id IN ('https://openalex.org/W3001279689',   -- Scaling Laws, 2020
                        'https://openalex.org/W4225591000')   -- Chinchilla, 2022
  GROUP BY citing_work_id HAVING uniqExact(cited_work_id) = 2)
ORDER BY cited_by_count DESC
LIMIT 10;

→ Large language models encode clinical knowledge   2023  3,194
  Can LLMs Transform Computational Social Science?   2023    429
  ProGen2: boundaries of protein language models      2023    426

Who keeps arguing with whom on Hacker News?

A self-join on parent_hn_id pairs every reply with the comment it answers; least/greatest make the pair unordered, and the HAVING keeps only pairs where both people replied to the other at least three times. Every regular feels the feuds; this is the first time anyone could count them — three weeks, 46.7 million rows read, 1.6 s.

SELECT least(a.original_author, b.original_author) AS one,
       greatest(a.original_author, b.original_author) AS other,
       count() AS replies, uniqExact(a.story_hn_id) AS threads
FROM hackernews.items AS a
INNER JOIN hackernews.items AS b ON a.parent_hn_id = b.hn_id
WHERE a.original_timestamp >= now() - INTERVAL 21 DAY
  AND a.original_author != b.original_author
GROUP BY one, other
HAVING countIf(a.original_author = one) >= 3
   AND countIf(a.original_author = other) >= 3
ORDER BY threads DESC, replies DESC
LIMIT 8;

→ JumpCrisscross ↔ toomuchtodo     11 replies   8 threads
  HarHarVeryFunny ↔ famouswaffles  39 replies   6 threads
  mitxela ↔ ranger_danger          20 replies   5 threads
  WalterBright ↔ nrr               21 replies   4 threads

What did forecasters change their minds about this week?

Every Manifold bet carries the market probability before and after it, so a market’s week is argMin of the first price and argMax of the last, joined to its latest title. Binary markets with thirty or more bettors, outside Manifold’s own unranked topic, ranked by how far the crowd moved — the news before the news, 27 million bets in 268 ms.

WITH moves AS (
  SELECT contract_id,
         argMin(prob_before, created_at_source) AS p_start,
         argMax(prob_after, created_at_source) AS p_now,
         uniqExact(user_id) AS bettors
  FROM manifold.bets
  WHERE created_at_source >= now() - INTERVAL 7 DAY
    AND prob_before != prob_after AND answer_id IS NULL
  GROUP BY contract_id HAVING bettors >= 30)
SELECT m.title, round(100 * p_start) AS was, round(100 * p_now) AS now, bettors
FROM moves
INNER JOIN (SELECT market_id, argMax(title, observed_on) AS title
            FROM manifold.markets
            WHERE market_id IN (SELECT contract_id FROM moves)
            GROUP BY market_id
            HAVING NOT has(JSONExtract(argMax(raw_market, observed_on),
                                       'groupSlugs', 'Array(String)'), 'unranked')) AS m
  ON m.market_id = moves.contract_id
ORDER BY abs(p_now - p_start) DESC
LIMIT 8;

→ Assassination attempt on a major AI-lab CEO by 2030?   30% → 99%   69 bettors
  Will Trump interfere with Greenland?                    80% → 17%   32
  US average gas price $4.40 or more on September 21?     50% → 97%   59
  All Millennium Prize Problems solved before EOY?        50% →  3%   31
  Opus 5.2 released in September?                         50% → 24%   93