Scaling Ascent Peak: The Seven Summits of Artificial Intelligence In my latest article, I chart the evolution of AI—from the early days of symbolic logic to today’s autonomous agents, and what lies beyond. This journey is more than a history lesson: it’s a strategic framework
RESEARCH
-
Is a Model Trained on Claude Outputs Without Permission Open Source?
By
–
Would you still call a model open source if it was trained on Claude's outputs without permission?
-
Why Looped Transformers Excel at Multi-Step Reasoning
By
–
Researchers just figured out why looped transformers are so powerful. Most language models store huge amounts of knowledge but fail to combine facts in a single pass. Ask one a 10-step reasoning question and it breaks. A new paper explores looped transformers, an architecture
-
Open Source AI Models and the Secret of Knowledge Distillation
By
–
Some "open source" AI models have a secret. They were trained using outputs from closed models like ChatGPT and Claude. The weights are free. The code is public. Anyone can run them.
But the intelligence inside came from somewhere else. This technique is called distillation.
A -
YC Bench: AI Agent CEO Simulation Benchmark for Startups
By
–
YC Bench by @CollinearAI
: Benchmark for Agents who play CEO of an AI startup for 1 simulated year via CLI tool use against a deterministic discrete-event simulation. Score = final $$ amount achieved by @nazneenrajani and team Also a good opportunity to showcase this recent hf -
Embedding Hybrid Deterministic AI With Transformers for Neurosymbolic AI
By
–
All the more a reason to embed hybrid AI with deterministic traits alongside ANNs in particular Transformers with Self-Attention mechanism into Neurosymbolic AI
-
YOLOE Enables Zero-Shot Open-Vocabulary Real-Time Object Detection
By
–
YOLOE = real-time object detection with NO retraining.
— Satya Mallick (@LearnOpenCV) 1 mai 2026
Type "delivery driver in a red jacket" → it finds them. Zero-shot. Open vocabulary. YOLO speed.
The closed-world era of computer vision is over. 🧵👇
🔗 https://t.co/MQ6NbUde7Y#YOLOE #ComputerVision #AI #DeepLearning #YOLO… pic.twitter.com/ROqCzxZXckYOLOE = real-time object detection with NO retraining.
Type "delivery driver in a red jacket" → it finds them. Zero-shot. Open vocabulary. YOLO speed.
The closed-world era of computer vision is over. https://
vist.ly/42jd3
#YOLOE #ComputerVision #AI #DeepLearning #YOLO -
Agentic Harness Engineering for Automatic Coding-Agent Evolution
By
–
Paper: Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses https://
arxiv.org/abs/2604.25850 https://
github.com/china-qijizhif
eng/agentic-harness-engineering
… -
Practical Path to RSI: Improving the Model Harness and Tools
By
–
The near-term path for RSI (recursive self-improvement) is not necessarily about the model directly improving its own weights. A more practical path is improving the harness around the model: tools, middleware, long-term memory, skills, evals, feedback loops. I saw a recent
-
Grok 4.3 Scores 53 on AI Index with Lower Pricing
By
–
Grok 4.3 is a very good model especially when you think its only 500m parameters! xAI's Grok 4.3 scores 53 on the Artificial Analysis Intelligence Index with ~40% lower input and ~60% lower output pricing vs Grok 4.20, making it one of the most cost-efficient models at its