📚 All Articles
60 guide(s) — regularly updated
GPIC : Stanford releases 28 trillion pixels to train image generation models
Stanford releases GPIC, a 28-trillion-pixel dataset for training image generation models. Discover this permissive dataset.
LLMSurgeon: this ACL 2026 paper opens the black box of LLM pre-training
Discover LLMSurgeon, the ACL 2026 paper that opens the LLM pre-training black box to reveal their secret data mix.
Qwen3-Coder-Next : 80B MoE with 3B active, the open-source code agent that rivals Claude Sonnet
Discover Qwen3-Coder-Next: an 80B MoE (3B active) open-source code model rivaling Claude Sonnet on SWE-Bench.
OSCAR: Together AI open-sources a 2-bit KV cache quantization that reduces memory by 8x
Discover OSCAR: Together AI's open-source 2-bit KV cache quantization that cuts memory by 8x and optimizes LLM serving.
Stanford AI Index 2026 : the 5 figures that show AI has passed a point of no return
Discover the Stanford AI Index 2026 and 5 key figures proving AI has crossed a point of no return.
Gated DeltaNet-2 : the Yejin Choi paper that solves the oldest problem of linear attention
Discover Gated DeltaNet-2, Yejin Choi's paper that finally solves the oldest problem of linear attention in AI models.
Cursor Composer 2.5: The coding model that rivals Opus 4.7 at a tenth of the price
Discover Cursor Composer 2.5, a coding model rivaling Claude Opus 4.7 at a tenth of the price. AI price war analysis.
DeepWeb-Bench: The new benchmark that exposes the weaknesses of AI search agents
Discover DeepWeb-Bench, the new benchmark proving AI search agent scores are inflated and exposing their true weaknesses.
Gemini 3.5 Flash : the fast model that beats Opus 4.7 and GPT-5.5 on agent benchmarks — 289 tokens/second
Discover Gemini 3.5 Flash: the ultra-fast model at 289 tokens/sec beating Claude Opus 4.7 and GPT-5.5 on agent benchmarks.
General Preference RL: this paper unifies reinforcement learning and preference optimization for LLMs
Discover the General Preference RL paper unifying reinforcement learning and preference optimization to solve LLM post-training.
OpenAI Parameter Golf: The challenge that proves small models are the future of AI
Discover the OpenAI Parameter Golf challenge: why compressing an LLM into 16 MB proves small models are the future of AI.
Meta Muse Spark: why Meta betrayed open-source — the first closed model from the Superintelligence Lab
Discover why Meta Muse Spark is a turning point: the first closed model from the Superintelligence Lab that betrays Meta's open-source promise.