possibly on coding benchmarks, but see the graph in here: https://
open.substack.com/pub/garymarcus
/p/three-reasons-to-think-that-the-claude?r=8tdk6&utm_medium=ios
…
MACHINE LEARNING
-
Claude’s coding benchmark performance analysis and evaluation
By
–
-
AI Deployment Methods: Machine Learning Techniques Overview
By
–
#AI Deployment Methods by @Python_Dv #ArtificialIntelligence #MachineLearning #ML
→ View original post on X — @ronald_vanloon, 2026-04-10 02:24 UTC
-
Three reasons AI’s jagged intelligence is harder than human jaggedness
By
–
Things that make the jagged intelligence of AI harder to deal with than the jaggedness of humans:
1) Weaknesses are not always intuitive or identifiable in advanced
2) All LLMs have similar weaknesses, so you can't just hire a different one
3) Jagged frontier is moving outward -
AI Falls Short in Software Engineering Beyond Basic Syntax
By
–
Whilst AI has been super helpful in removing the burden of handling converting natural language into syntax, it hasn't as yet shown any competence at handling software engineering, beyond shallow regurgitation of basic principles. There's been no sign of this changing so far.
-
Setting up GLM-5.1 4-bit on 4x DGX Sparks cluster
By
–
Qwen3.5-397B-A17B-FP8 via OpenCode
— Ahmad (@TheAhmadOsman) 10 avril 2026
Helping me setup GLM-5.1 (4-bit)
On the 4x DGX Sparks cluster https://t.co/OTKXZ2vzkm pic.twitter.com/somZODNXQxQwen3.5-397B-A17B-FP8 via OpenCode Helping me setup GLM-5.1 (4-bit) On the 4x DGX Sparks cluster
-
AI Should Be Nurtured, Not Trained: The Promise of Neuroevolution
By
–
"AI should not be trained, but should 'grow' on its own." Sakana AI researcher @sebastianrisi appeared on the podcast @EyeOn_AI. He discussed an overview of the Neuroevolution method, which constructs neural networks through evolutionary approaches, and talked about the current state of continual learning and artificial life (ALife) research. piped.video/pPpDxB4N_mE [Translated from EN to English]
-
INSPATIO-WORLD: Real-time 4D Explorable World Simulator
By
–
INSPATIO-WORLD: Real-time 4D world simulator
— DailyPapers (@HuggingPapers) 10 avril 2026
Turn any video into an explorable, interactive 4D world you can navigate in real-time using WASD controls. Built on spatiotemporal autoregressive modeling with state-anchored world states, running at 24 FPS on NVIDIA H-series GPUs and… pic.twitter.com/BFd1l3jzMtINSPATIO-WORLD: Real-time 4D world simulator Turn any video into an explorable, interactive 4D world you can navigate in real-time using WASD controls. Built on spatiotemporal autoregressive modeling with state-anchored world states, running at 24 FPS on NVIDIA H-series GPUs and ranking #1 on WorldScore-Dynamic. [Translated from EN to English]
→ View original post on X — @scobleizer, 2026-04-10 00:28 UTC
-
AI Cracks Animal Communication Code: Future is Here
By
–
PEOPLE ARE USING AI TO CRACK THE CODE OF ANIMAL COMMUNICATION
— 0xMarioNawfal (@RoundtableSpace) 9 avril 2026
WELCOME TO THE FUTUREpic.twitter.com/12zKktDU4CPEOPLE ARE USING AI TO CRACK THE CODE OF ANIMAL COMMUNICATION WELCOME TO THE FUTURE
→ View original post on X — @ceobillionaire, 2026-04-09 23:45 UTC
-
OpenAI’s Path to Automated AI Researcher by 2028
By
–
Compute powers every layer of AI, and the investments we’ve made mean we can run more promising research experiments, train more capable models, and support broader access. @merettm talks about our progress building an automated AI researcher and what’s ahead as AI can take on… https://t.co/zM7iFZYAsK
— OpenAI Newsroom (@OpenAINewsroom) 9 avril 2026Compute powers every layer of AI, and the investments we’ve made mean we can run more promising research experiments, train more capable models, and support broader access. @merettm talks about our progress building an automated AI researcher and what’s ahead as AI can take on harder and harder problems. Jacob Effron (@jacobeffron) At @OpenAI, Chief Scientist @merettm helps lead the research roadmap to AGI including a research intern-level AI system by September 2026 and a fully automated AI researcher by March 2028. I sat down with Jakub to check on those timelines and ask him all of my top-of-mind AI questions including: ▪️ How OpenAI thinks about extending RL beyond code and math ▪️ The current state of alignment research as more powerful models loom ▪️ The future of continual learning ▪️ How startups should think about building their own models/harnesses And he also shared some great stories around OpenAI’s pioneering work on math. YouTube: piped.video/vK1qEF3a3WM Spotify: bit.ly/4sjUyrN Apple: bit.ly/41jAdrN 0:00 Intro 1:53 Research Intern Capability Timelines 4:59 Math Breakthroughs 7:59 RL Beyond Verifiable Tasks 12:32 RL vs In-Context 19:01 Allocating Compute Internally 28:18 AI for Science 31:40 Pattern Matching 33:23 Solving the Hardest Math Problems 37:40 Chain of Thought Monitoring 44:33 Generalization and Value Alignment in Models 47:57 Inside OpenAI 51:55 Quickfire — https://nitter.net/jacobeffron/status/2042234897134162077#m
→ View original post on X — @ceobillionaire, 2026-04-09 23:39 UTC
-
Discrete symbols and mathematics foundations of AI modeling
By
–
To make sense of the world is to model it in the simplest possible way. And simplicity requires discrete symbols. This is why we developed mathematics in the first place.