What This Site Picked

What happens in a day

2026-10-15 · recorded for 69 days

1. 1,693 feed items
2. 148 last 24h
3. 89 main candidates
4. 10 published

How the 148 last-24h items split

• main-pool candidates89
• separate Korea pool52
• self-promo / discussion2
• already published0
• outside AI scope5

Checks out: 148 = 89 (main) + 52 (Korea pool) + 2 (self-promo) + 0 (duplicate) + 5 (off-scope).

The Korea pool of 52 breaks down into 38 candidates, 4 dropped as promotional, and 10 dropped as outside AI scope. Korea stories are chosen from a separate pool and never mix with the main candidates.

Where the 89 candidates went

• published 10
• outranked 69
• same story 1
• 3-per-outlet cap 9
• original unreadable 0

The bar above counts what happened at selection time. In practice 1 of these 10 started as different articles and were swapped for backups after we read the originals.

• The Decoder duplicate story

Across the 58 days on record we swapped 120 articles · duplicate story 92 · promotional 13 · original unreadable 9 · no usable facts 4 · not news 2.

Recent selection log

Only days with a recorded funnel. "Published" is the actual count; "swaps" is how many were replaced by backups. Days before swap-tracking show —.

Stories with a next step

On 2026-10-15 it was 1 of them, and 91 across the 50 days on record. We only write one when there is a screen to open or a setting to change today. When there is nothing to do we leave it out. Ten out of ten every day would mean writing sentences that look like actions rather than actions.

What came in, what ran: 69-day total

Across the 69 days with candidate records. Bar length is what came in; the solid part is what ran.

The "% of candidates published" is not a quality score for an outlet. It is affected by story volume, duplicate coverage, daily competition, and the per-outlet cap.

• AI Times 2101→120 5.7% AI Times, 120 of 2101 candidates published, 5.7 percent of candidates
• TechCrunch AI 600→155 25.8% TechCrunch AI, 155 of 600 candidates published, 25.8 percent of candidates
• The Decoder 388→90 23.2% The Decoder, 90 of 388 candidates published, 23.2 percent of candidates
• The Verge AI 316→81 25.6% The Verge AI, 81 of 316 candidates published, 25.6 percent of candidates
• Hacker News (AI) 292→0 0.0% Hacker News (AI), 0 of 292 candidates published, 0.0 percent of candidates
• SiliconANGLE AI 271→10 3.7% SiliconANGLE AI, 10 of 271 candidates published, 3.7 percent of candidates
• Wired AI 181→80 44.2% Wired AI, 80 of 181 candidates published, 44.2 percent of candidates
• Ars Technica AI 153→67 43.8% Ars Technica AI, 67 of 153 candidates published, 43.8 percent of candidates
• VentureBeat AI 70→8 11.4% VentureBeat AI, 8 of 70 candidates published, 11.4 percent of candidates
• OpenAI News 61→1 1.6% OpenAI News, 1 of 61 candidates published, 1.6 percent of candidates
• MIT Technology Review AI 58→25 43.1% MIT Technology Review AI, 25 of 58 candidates published, 43.1 percent of candidates
• Google AI Blog 27→17 63.0% Google AI Blog, 17 of 27 candidates published, 63.0 percent of candidates
• Google DeepMind Blog 22→18 81.8% Google DeepMind Blog, 18 of 22 candidates published, 81.8 percent of candidates
• MarkTechPost 4→2 50.0% MarkTechPost, 2 of 4 candidates published, 50.0 percent of candidates

Which outlets we ran

14 sources in total. No outlet can take more than 3 of the 10 slots in a day. Percentages are of all 830 published stories.

• TechCrunch AI 20424.6% TechCrunch AI, 204 of 830 published stories, 24.6 percent
• AI Times 12515.1% AI Times, 125 of 830 published stories, 15.1 percent
• Wired AI 9511.4% Wired AI, 95 of 830 published stories, 11.4 percent
• The Verge AI 9411.3% The Verge AI, 94 of 830 published stories, 11.3 percent
• Ars Technica AI 9111.0% Ars Technica AI, 91 of 830 published stories, 11.0 percent
• The Decoder 9010.8% The Decoder, 90 of 830 published stories, 10.8 percent
• MIT Technology Review AI 273.3% MIT Technology Review AI, 27 of 830 published stories, 3.3 percent
• Google DeepMind Blog 232.8% Google DeepMind Blog, 23 of 830 published stories, 2.8 percent

What the stories were about

Each percentage is the share of all 830 published stories that carry that topic. A story can have more than one topic, so the counts can exceed the story total and the percentages can add up to more than 100%.

• Safety & Alignment 21525.9% Safety & Alignment, 215 of 830 published stories, 25.9 percent
• Business & Funding 21225.5% Business & Funding, 212 of 830 published stories, 25.5 percent
• Products & Services 21225.5% Products & Services, 212 of 830 published stories, 25.5 percent
• Agents & Dev Tools 15518.7% Agents & Dev Tools, 155 of 830 published stories, 18.7 percent
• Policy & Regulation 13916.7% Policy & Regulation, 139 of 830 published stories, 16.7 percent
• Models & Research 13215.9% Models & Research, 132 of 830 published stories, 15.9 percent
• Society & Work 10712.9% Society & Work, 107 of 830 published stories, 12.9 percent
• Infrastructure & Chips 8710.5% Infrastructure & Chips, 87 of 830 published stories, 10.5 percent
• Open Source 384.6% Open Source, 38 of 830 published stories, 4.6 percent
• Generative Media 364.3% Generative Media, 36 of 830 published stories, 4.3 percent
• Robotics & Physical AI 263.1% Robotics & Physical AI, 26 of 830 published stories, 3.1 percent
• Data & Training 222.7% Data & Training, 22 of 830 published stories, 2.7 percent

Terms we explained

Each term is explained on the day it actually appeared in a briefing. We don't write a dictionary separately. Repeats across days are counted once. Browse the glossary →

How these numbers are counted

You can download this page's aggregates as machine-readable JSON: selection data (JSON)