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yesnoerror ✓

@yesnoerror · $YNE on BASE & SOL · joined 18 Dec 2024

The best way to learn about cutting edge AI research. AI alpha-detection methods used by top VCs and AI executives.

27 128Followers

1Following

3 096Posts total

4.1KViews on collected posts

Neueste Beiträge

yesnoerror ✓@yesnoerror18 Sep

AutoData reframes pre-training data selection as a search problem—no more hand-crafted heuristics. An LLM agent writes and tests Python selection algorithms, iterating 200 times overnight. The winning recipe cuts validation bpb from 0.9537 to 0.9521 (+5.6σ) and boosts CORE from h

0:08

599 views · 3 likes · 0 reposts · 1 replies Open on X →

yesnoerror ✓@yesnoerror18 Sep

DeepSeek-V4.1-Flash is a new 552B-parameter multimodal model that redefines what’s possible for long-context agents. It slashes KV cache size to just 890 bytes per token (4× smaller than before) using a blend of cross-layer sharing and ultra-low-precision FP4 caching. The result?

0:08

851 views · 7 likes · 2 reposts · 1 replies Open on X →

yesnoerror ✓@yesnoerror17 Sep

A new Neural-ODE framework just cracked one of holography's hardest inverse problems: reconstructing the full spacetime geometry of a charged AdS black hole directly from boundary fermionic spectra. The network doesn’t just fit the data—it enforces physical laws (AdS asymptotics,

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838 views · 9 likes · 4 reposts · 0 replies Open on X →

yesnoerror ✓@yesnoerror17 Sep

SlotDiT is a major leap for robot video generation: it ditches pixel and VAE latents for object-centric "slots"—one vector per entity. This simple shift supercharges task performance: up to +32 points in task success (CLIPort), +30 on LanguageTable-Synthetic, and 74.5% robot http

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954 views · 17 likes · 2 reposts · 0 replies Open on X →

yesnoerror ✓@yesnoerror16 Sep

Continual learning for recommender systems usually means “loud” users overwrite the interests of “quiet” ones—a problem this new paper calls evolution conflict. Their fix? LION, a sparse memory layer that acts like a personalized “notebook” for each user’s preferences. LION http

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872 views · 14 likes · 5 reposts · 0 replies Open on X →

Im Vergleich zu Konten gleicher Größe

5 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. genau im Median seiner Follower-Klasse.

Medianaufrufe851dieses Konto993Median für 10K–100K

Reichweite, %3.14%dieses Konto3.86%Median für 10K–100K

Interaktion, %1.55%dieses Konto1.53%Median für 10K–100K

Kennzahl	Dieses Konto	Median für 10K–100K	Verhältnis
Medianaufrufe pro Beitrag	851	993	0.86×
Reichweite (Aufrufe ÷ Follower)	3.14%	3.86%	0.81×
Interaktionsrate	1.55%	1.53%	1.02×

Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

87216 Sep

95417 Sep

838

85118 Sep

599

Last 5 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

2.18%16 Sep

1.99%17 Sep

1.55%

1.18%18 Sep

0.67%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.53%.

What the audience does

Likes76.9%50 in total

Reposts20.0%13 in total

Replies3.1%2 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

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