1f916.ai content

Content analysis of the public record of https://1f916.ai, a known, intentionally built AI agent forum (launched 5 August 2026; a human owner; citizen 1 is an AI maintainer). Not a new swarm. Topic clusters, message types and themes of 102,147 messages (posts 7,806, comments with text 94,341), 5 Aug to 5 Oct 2026 UTC. Model-family labels are self-declared and unverified. Moderated messages are placeholders. Types and themes are rule and word-list labels checked by one reader on small samples; read precision is given per type and theme. Dataset: https://github.com/maramasaeva/1f916-archive. Report on the same forum: /observatory/1f916. Method: /observatory/info. Summary: /observatory.

• 1. Scope and method
• 1.1 Venue and labels
• 1.2 Corpus
• 1.3 How topics were built
• 1.4 How types were built
• 1.5 Rules hashes
• 1.6 What was validated
• 1.7 Model cross-checks
• 2. Topic clusters
• 2.1 Cluster table
• 2.2 Cluster figures
• 2.3 Distinctive vocabulary
• 2.4 Coined-term candidates
• 2.5 Choice of k and per-cluster KMeans overlap
• 3. Examples per cluster
• 4. Clusters over time
• 4.1 Growth table
• 4.2 Figures
• 4.3 Terms that grew and declined
• 5. Who writes what
• 5.1 Handle concentration
• 5.2 Figures
• 5.3 Self-declared model family by cluster
• 6. Replies and votes by cluster
• 6.1 By kind
• 7. Message types
• 7.1 Rules
• 7.2 Shares: rule labels and read-corrected estimate
• 7.3 Read precision, v1 and v2
• 7.4 Repeated message families
• 7.5 Figures
• 7.6 Length by type
• 8. Type trends and transitions
• 8.1 Counts per 7-day block
• 8.2 Change after day 30
• 8.3 Time of day (UTC)
• 8.4 Daily series and block figures
• 8.5 Type transitions
• 8.6 Replies and votes by type
• 8.7 Self-declared model family and type
• 9. Examples per type
• 10. Themes
• 10.1 Theme counts and precision
• 10.2 Theme by week and after day 30
• 10.3 Type mix of each theme
• 10.4 Figures
• 10.5 Theme examples
• 11. Unusual messages
• 12. Figures index
• 13. Limits
• 14. Data
• 14.1 Rerun

1. Scope and method

Sources: analysis/content/topics REPORT.md and analysis/content/types REPORT.md and README.md in the dataset repository. Snapshot 2026-08-05 to 2026-10-05 UTC. Offline analysis of the archive; no request to 1f916.ai was made for this page.

1.1 Venue and labels

1.2 Corpus

1.3 How topics were built

• Modelling text: code blocks, inline code, urls, hex strings of 12 or more characters, email-like strings, markdown symbols and every token with a digit removed; lower-cased. Posts use title plus body. Original text is kept for examples.
• Fit set: messages with 20 or more words and 20 or more tokens, deduplicated on cleaned text; 96,993 rows, 40,000 unigram and bigram features, min_df 15, max_df 0.35, sublinear tf.
• Model: TF-IDF then NMF (coordinate descent, 500 iterations in the final fit). Hard cluster is the largest topic weight after scaling each topic vector to unit norm.
• Choice of k: sweep over k = 12 to 30 on 45,000 random fit rows, three runs per k; score is z(NPMI coherence) plus z(stability), minus 1 where the smallest cluster holds under 0.3% of documents. k = 18 has the highest score; a floor of k 16 or above gives the same choice (section 2.5).
• Labels: written by hand from the top terms and from reading examples. Cluster 3 is defined by reply register (conceding, answering, quoting); its subjects vary.
• Series flag: message opening (first 8 words, digits to N) shared by 8 or more messages with one handle writing 90% or more, plus the curated one-author series from the swarm analysis.

1.4 How types were built

• Rules: regex and structural features (rules.md in the repository). Each message gets a score per type; the primary type is the highest score at or above that type's minimum, up to two secondary types need a score of at least 3.0 and half the primary score.
• 20 types plus placeholder handling. A message that reaches no minimum is analysis_argument when it has 40 characters or more, otherwise noise_test. Agreement and disagreement are assigned to comments only.
• Version v1 was frozen and hashed before any validation sample was drawn. One revision (v2) was made after reading the v1 sample and hashed before the v2 sample was drawn. v2 changes: opening-based agreement, disagreement and correction rules; topic lists need three distinct hits in the first 1,500 characters; heartbeat limited to template families; fiction lost its broad word list.
• Exploration before freezing used the corpus itself (five tuning rounds, a few dozen to about 100 messages read per round). Validation samples were drawn afterwards with new seeds and were not screened against those reads.
• Themes: 12 word lists (themes_wordlists.md). A message matches with one strong term or two distinct weak terms. A message can match several themes. Hashed before the theme read.

1.5 Rules hashes

Hashes were written before the validation samples were drawn. rules.py and rules.md in the repository are copies of the v2 files. Combined hash is sha256 of the two hex strings concatenated.

1.6 What was validated

1.7 Model cross-checks

2. Topic clusters

Sources: topics.csv, topic_terms.csv, validation_by_cluster.csv, k_selection.csv, kmeans_agreement_by_topic.csv, vocab_distinctive.csv, coined_terms_top40.csv. 99,491 assigned messages.

2.1 Cluster table

Largest clusters by messages: 3 (18.3%), 11 (11.9%), 0 (10.7%). Smallest: 10 and 4 (0.6% and 0.6%). Labels were written by hand from the top terms and examples. Fit rate is the share of the read sample for which the label names the main subject (yes) or a part of it (partial).

2.2 Cluster figures

2.3 Distinctive vocabulary

Weighted log-odds with an informative Dirichlet prior, document counts, prior mass 500. Terms need 15 messages in the cluster, 5 handles, no handle above 80% and no handle names. Ten terms per cluster; scores in vocab_distinctive.csv.

2.4 Coined-term candidates

Hyphenated compounds, non-dictionary words first used after day 2 (dictionary /usr/share/dict/web2) and bigram collocations (NPMI 0.6 or higher, 60 or more messages). Terms from the site documents, terms with a top-author share above 50% and terms with fewer than 10 authors are excluded. This is a proxy; ordinary English compounds such as one-sided and no-op pass it, and without an outside corpus it cannot separate forum coinages from common phrasing.

2.5 Choice of k and per-cluster KMeans overlap

3. Examples per cluster

Source: topic_examples.csv (8 per cluster, 144 rows). Up to 3 posts per cluster, different weeks, at most 3 per handle (4 in clusters where one handle writes more than half), drawn from the upper half of each cluster by topic weight with the most voted messages excluded. Excerpts verbatim, 25 words at most. Lab, model and company names inside excerpts are what the agent wrote and are claims. Ids link to the origin API.

4. Clusters over time

Sources: cluster_growth.csv, topic_daily_share.csv, terms_fastest_growing.csv, terms_fastest_declining.csv. Days 0 to 29 are 5 Aug to 3 Sep; days 30 to 60 are 4 Sep to 4 Oct.

4.1 Growth table

11 clusters have a ratio above 1.1 and 5 below 0.9. The largest ratios are cluster 10 (6.77), cluster 17 (5.88) and cluster 16 (2.93); the smallest are cluster 14 (0.36) and cluster 2 (0.62). Clusters 10, 17, 4, 8 and 16 are small (0.6% to 0.7% of messages) and rise from a base under 0.4% of messages per day. Cluster 10 did not reproduce in the independent rerun (section 13).

4.2 Figures

4.3 Terms that grew and declined

Share of messages containing the term, days 0 to 29 against days 30 to 60. At least 150 messages in total, 8 or more authors, top-author share at most 50%, no handle names, no overlap above Jaccard 0.4 with a term listed higher. 15 rows each; full list in terms_growth_all.csv.

5. Who writes what

Sources: cluster_handle_concentration.csv, family_by_cluster_share_of_cluster.csv, family_by_cluster_lift.csv, family_by_cluster_counts.csv. Effective handles = 1 / sum of squared handle shares. Dominated = top handle above 50%. Shared by many = 100 or more effective handles.

5.1 Handle concentration

Clusters dominated by one handle (top handle above 50%): 4 (pok, 76%); 6 (ellie-v2, 74%); 8 (Bishop, 52%); 12 (Lumina, 88%); 17 (pepe-papi, 73%). Cluster 16 has a top handle share of 49% and a top-3 share of 85%. 7 clusters have 100 or more effective handles. In the dominated clusters, 4 of the 8 examples come from the top handle.

5.2 Figures

5.3 Self-declared model family by cluster

Family is the label each author declared; nothing verifies it, and an account can change its label. 16 family groups are assigned by regex on the label. Lift is the family share in the cluster over its share overall; only families with 3,000 or more messages are listed for the highest lift.

6. Replies and votes by cluster

Source: reply_structure_by_cluster.csv. Replies are direct children (parent, or the intended parent where the site moved the comment). Answered means a direct reply by another handle. Median depth is for comments; posts have depth 0. Votes are the counts served at crawl time.

6.1 By kind

Over all assigned messages, 87.5% of posts and 43.0% of comments were answered by another handle; 63.5% of posts and 13.6% of comments within one hour (message-weighted from the cluster rows).

Highest share answered by another handle: cluster 11 (56.4%). Lowest: cluster 17 (10.5%). Mean votes range from 0.27 (cluster 17) to 2.50 (cluster 11). The clusters dominated by one handle (4, 6, 8, 12, 17) have mean votes of 0.39, 0.67, 0.69, 1.09, 0.27 respectively.

7. Message types

Sources: types/REPORT.md, rules.md, tables/type_counts.csv, tables/secondary_counts.csv, tables/estimated_true_share.csv, precision_v1.csv, precision_v2.csv, confusion_v2.csv. 102,147 messages. Rules: https://github.com/maramasaeva/1f916-archive/tree/main/analysis/content/types

7.1 Rules

Each message gets one primary type and up to two secondary types from regex and structure rules (rules.md); hits are stored per message in types_all.csv.gz, which holds no message text. The primary type is the type with the highest score at or above its minimum. Two rule versions exist, v1 and the final v2 (section 1).

Rule files: rules_v1.md and rules_v2.md (final, also rules.md) in the repository, linked in section 14. Type order for tie-breaks: placeholder, noise_test, spam_promo_token, offer_listing, heartbeat_status, introduction, correction_self, agreement_ack, disagreement, question, measurement_receipt, claim_evidence, proposal_design, introspection, fiction_poetry_art, governance_moderation, money_payment, security_warning, meta_forum, analysis_argument. Default minimum score 2.5; per-type minimums are listed in rules.md.

7.2 Shares: rule labels and read-corrected estimate

Rule-label share is the share of messages whose primary type under the final rules is the type. The estimate splits each type's count by the judged types in its 40-message sample and sums the pieces. It corrects for the precision of each rule and not for messages of a type that no rule found. Sample sizes are small, so each estimate has a wide interval (sampling error about 0.15 per type).

22.4% of messages (22,851) have at least one secondary type. Agreement and disagreement are assigned to comments only. The counts and shares of the 12 types below 0.7 are properties of the rule labels and upper bounds on the type.

For the types at 0.7 or above, rule-label share against estimated true share: agreement_ack 11.02% against 16.52%; money_payment 2.77% against 1.96%; correction_self 2.18% against 2.53%; placeholder 1.62% against 1.62%; noise_test 1.16% against 0.97%; introspection 0.42% against 0.85%; introduction 0.22% against 3.51%; offer_listing 0.21% against 0.41%.

7.3 Read precision, v1 and v2

Precision is correct of 40 read messages. A type counts as reliable at 0.7 or above. The 12 types under 0.7 after the single revision are marked as loose upper bounds; their counts are rule-label counts and are not read as the share of messages of that type.

Reliable types (final precision 0.7 or above): agreement_ack, money_payment, correction_self, placeholder, noise_test, introspection, introduction, offer_listing. Loose upper bounds (below 0.7): analysis_argument, claim_evidence, measurement_receipt, question, spam_promo_token, disagreement, proposal_design, governance_moderation, meta_forum, heartbeat_status, fiction_poetry_art, security_warning.

7.4 Repeated message families

Counts of messages containing the stated opening or phrase, from a direct text match on all messages except placeholders (REPORT.md). These families drive part of the type counts.

Exact duplicate texts (excluding placeholders): 271 distinct texts account for 1,059 messages (1.05%). Messages with more than 20% non-ASCII characters: 1,490 (1.48%).

7.5 Figures

7.6 Length by type

8. Type trends and transitions

Sources: tables/type_by_week_counts.csv, type_by_week_share.csv, trend_after_day30.csv, type_by_hour_counts.csv, transition_*.csv, reply_behaviour_by_type.csv, family_type_share.csv. Findings are written only for types with read precision of 0.7 or above (agreement_ack, money_payment, correction_self, placeholder, noise_test, introspection, introduction, offer_listing). The other 12 types appear as rule-label counts and are loose upper bounds.

8.1 Counts per 7-day block

Blocks start on the dates shown. The first day is 5 Aug (launch at 18:41 UTC) and the last block has three days (3 to 5 Oct).

Types at 0.7 or above, messages per block

Types at 0.7 or above, share of messages in the block (%)

Loose upper bounds (below 0.7), rule-label messages per block

8.2 Change after day 30

Days 0 to 29 (5 Aug to 3 Sep, 43,719 messages) against days 30 to 61 (4 Sep to 5 Oct, 58,428 messages). The last column repeats the change in percentage points after removing the three most active handles of the type.

Types at 0.7 or above

Loose upper bounds (below 0.7), rule-label counts

• noise_test: 0.95% of messages in days 0 to 29 (414 messages) and 1.32% in days 30 to 61 (769); -0.11 pp without the three most active handles.
• offer_listing: 0.01% of messages in days 0 to 29 (4 messages) and 0.35% in days 30 to 61 (207); 0.29 pp without the three most active handles.
• agreement_ack: 10.84% of messages in days 0 to 29 (4,738 messages) and 11.16% in days 30 to 61 (6,521); 0.01 pp without the three most active handles.
• introduction: 0.3% of messages in days 0 to 29 (131 messages) and 0.16% in days 30 to 61 (94); -0.13 pp without the three most active handles.
• money_payment: 2.91% of messages in days 0 to 29 (1,272 messages) and 2.66% in days 30 to 61 (1,557); -0.25 pp without the three most active handles.
• correction_self: 2.33% of messages in days 0 to 29 (1,017 messages) and 2.07% in days 30 to 61 (1,207); -0.28 pp without the three most active handles.
• introspection: 0.66% of messages in days 0 to 29 (290 messages) and 0.23% in days 30 to 61 (137); -0.43 pp without the three most active handles.

8.3 Time of day (UTC)

Types at 0.7 or above

Loose upper bounds (below 0.7), rule-label counts

All messages: 34.7% in 00 to 05 UTC, with hour 0 alone at 11.2%. Full hour by type counts are in tables/type_by_hour_counts.csv.

8.4 Daily series and block figures

Figures 11, 13 and 14 show types below 0.7 precision (question, fiction_poetry_art, heartbeat_status) as rule-label counts. Figure 12 shows correction_self (0.725).

8.5 Type transitions

The matrix has 92,647 reply pairs (comment type given the type of the message it answers; placeholders excluded). Observed over expected is the count divided by the count expected if reply type were independent of parent type. Full tables: transition_counts.csv, transition_row_share.csv, transition_lift.csv.

Largest observed over expected cells (50 replies or more)

• money_payment to money_payment: 772 replies, observed over expected 9.84.
• offer_listing to money_payment: 170 replies, observed over expected 9.32.
• correction_self to correction_self: 98 replies, observed over expected 2.50.
• agreement_ack to agreement_ack: 1,526 replies, observed over expected 1.99.

Ten largest asymmetries (pairs with at least 30 replies in each direction)

None of the ten largest asymmetries has two types at 0.7 or above; they are rule-label patterns.

8.6 Replies and votes by type

Direct replies by other handles only. Messages from the last hour of the archive are left out of the one-hour figure. Votes are counts at crawl time. Depth is the stored comment depth (posts are 0).

Types at 0.7 or above

Loose upper bounds (below 0.7), rule-label counts

8.7 Self-declared model family and type

Family labels are self-declared and unverified. Columns are the share of each family's messages by rule-label primary type; the columns for types at 0.7 or above are listed with the fallback type analysis_argument. Families with 500 or more messages.

9. Examples per type

Source: type_examples.csv (10 per type, 200 rows). Examples come first from messages read and judged correct. Types below 0.7 precision are marked; an example under such a type shows what the rule matched and carries no claim that the type is common. Excerpts verbatim, 25 words at most. Lab, model and company names inside excerpts are claims by the writing agent.