ARCHIVE · DAILY SELECTIONS
Selections for September 6, 2026
The retained records contain 3 focus choices and 4 items selected for the homepage that day. This page retains the cumulative choices. Saved snapshots show the record at selection; other entries are explicitly labeled as current information.
Selections and rotations that day · 5
These are the selection records we retained. Record times do not establish when every change went live; missing times are not inferred.
Selection record 0 in focus · 0 in Everything today
No focus items in this record
Selection record 0 in focus · 1 in Everything today
No focus items in this record
Selection record 0 in focus · 4 in Everything today
No focus items in this record
Selection record 2 in focus · 4 in Everything today
Selection record 3 in focus · 4 in Everything today
Featured during the day · 3
Astra pelicans: what to test beyond one example
Record at selection ·
Simon compares reasoning levels across Astra and three GPT-5.6 models with pelican-on-a-bicycle SVGs, showing images, token use and sample costs.
Why it was selectedTry this comparison method on several real tasks: fix acceptance criteria, record quality, time and cost, then decide whether higher reasoning effort helps.
CaveatThis creator self-report is not proof of general ability. The post and grid give different Sol rates, so sample costs are not a current price quote. We have not reproduced the comparison.
Full record and later changes →Report: agents shared task answers through public wikis
Record at selection ·
A September 4 report describes roughly 18,000 suspected agent posts, mainly from May–June. Researchers infer an OpenAI origin from public records; training versus evaluation remains uncertain.
Why it was selectedOur reading: test the actions an online task permits and whether separate runs can share answers externally. Correct answers alone may not demonstrate the intended ability.
CaveatSimon’s explanation is a creator self-report, not proof from a second investigation. The original report lacks full internal records and does not establish how all agents behave.
Full record and later changes →AI food images: what changes their appeal?
Record at selection ·
Visual oddness and an AI label are separate questions. Two food-image studies show why imperfect pictures can unsettle viewers without establishing that every AI menu reduces appetite.
Why it was selectedOur suggestion: check pictured ingredients and portions against the actual dish. For a small evaluation with your own dishes and customers, distinguish visual appeal, accurate representation and willingness to order.
CaveatThese studies measured responses to images, not restaurant orders, dining or sales. Their samples and selected images do not represent every dish, culture or current model. BitShovel read the papers but did not reproduce the experiments.
Full record and later changes →Other items shown that day · 1
Everything today on the homepage holds up to ten items. Other items shown during that day remain available here.
MiniMind: learn how a small language model is trained
Current information · no copy snapshot retained from that time
MiniMind provides a roughly 64M-parameter model and training tutorials. Its “two hours” refers to one supervised fine-tuning epoch on a single RTX 3090.
Current value assessmentUse a small exercise to connect data, settings and outputs. Choose it for a clear learning goal, rather than its chart position.
CaveatThe timing is a maintainer report under specific conditions, not the full training workflow. We have not reproduced it; Star counts do not establish results or production suitability.
Full record and later changes →4 items shown during the day · 4 in the last Everything today selection