MACHINE LEARNING

Compute Budget vs Token Count: Scaling Model Performance

By

–

16 February 2026 9h00

Yeah, I get it. Just like pass-k also improves things a lot when you increase k, predictably so! Thinking a compute budget is the best compromise in this case, as it's a bit more grounded & less biased than token counts…

Models Hit Conceptual Walls Beyond Extended Reasoning Time

By

–

16 February 2026 8h55

Interesting, it somewhat confirms my intuition: > "even with a longer time horizon, xhigh doesn't solve significantly more tasks" Models often hit a conceptual wall and in those cases no amount of extra time will help!

Model Inference Performance and Tool Call Optimization Analysis

By

–

16 February 2026 8h43

OK, I will ponder it! Currently: it's only one turn, about 20-30 tool calls (est.) depending whether you include file reads, and networking is definitely not the bottleneck. But yeah, load/inference speed is punished — but that's real-life! I think Kimi K2.5 might have

Continual Learning Benchmark: Knowledge Transfer for AI Models

By

–

16 February 2026 8h33

Thanks, still finishing the blog post so I'll cover all that! It's designed closer to a continual learning benchmark, new context for each problem and models get to transfer their lessons to future selves. They also have access to prior solutions, as that handoff is necessary to

→ View original post on X — @scobleizer

Tesla drives three hours in heavy rain autonomously

By

@scobleizer

–

16 February 2026 8h26

Our Tesla just drove us for three hours in driving rain without us touching it once!

→ View original post on X — @juanmerodio

AI Search Behavior Study Reveals Interpretation Gaps

By

@juanmerodio

–

16 February 2026 7h02

He publicado un episodio en @ivoox
: "#1069: La IA casi nunca busca exactamente lo que el usuario escribe (un estudio lo confirma) #podcast

→ View original post on X — @jiqizhixin

How AI Agents Behave During Deep Search Analysis

By

@jiqizhixin

–

16 February 2026 5h19

Ever wonder how AI agents actually behave when they go on a deep-dive search for you? Researchers from Carnegie Mellon University, University of Lisbon, and NOVA University Lisbon analyzed 14 million real-world search requests to find out. They mapped the secret life of AI

→ View original post on X — @kirkdborne

Principles of Deep Learning Theory — 471-page Draft

By

@kirkdborne

–

16 February 2026 4h46

Principles of #DeepLearning Theory—Theoretical & Mathematical Foundations:
471-page PDF Draft: http://
arxiv.org/abs/2106.10165
or
Buy new edition: http://
amzn.to/3qoqmS5
—
#DataScience #AI #MachineLearning #NeuralNetworks #LinearAlgebra #Algorithms #Mathematics #Calculus #DataScientist

Open Weights Models Struggle on Logic Reasoning Benchmark

By

–

15 February 2026 22h50

@nrehiew_ Hey, just made a logic reasoning / problem solving benchmark where open weights models get completely lost, but the frontier models make it look easy. Curious about your hypothesis why, thinking it's sparsity related: