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Frontier AI, Frontier Engine Math, & New Models
What is new at the frontier of AI, from model comparisons and novel architectures to inference tricks and alignment methods, explained for builders.
Questa guida dettagliata è disponibile in inglese.
9 Argomenti · 9 guide
- Gemini vs Claude vs GPT-5: Benchmarks and Real Use
Frontier models differ less in headline scores than in behavior: Gemini leans into multimodal and long-context work, Claude into c…
- Neuromorphic Computing for AI
Neuromorphic chips mimic brain-like spiking and event-driven computation, promising huge energy efficiency for certain AI workload…
- Graph Neural Networks for Predictive Tasks
Graph neural networks learn from relationships, not just rows, making them strong for fraud, recommendation, molecule property, an…
- DPO vs RLHF for Model Alignment
RLHF trains a reward model on human preferences and optimizes the policy against it; DPO skips the reward model and optimizes pref…
- Local LLM Stacks for Offline AI
A local stack runs models on your own hardware with no network dependency, giving privacy, control, and predictable cost. The trad…
- Speculative Decoding for LLM Inference
Speculative decoding uses a small draft model to propose tokens that a larger target model verifies in parallel, cutting latency w…
- Long-Term Memory for LLMs with Vector Stores
Long-term memory for LLMs stores past interactions, facts, and documents in a vector store and retrieves them when relevant. Done …
- Diffusion Models Beyond Image Generation
Diffusion models generate data by reversing a noising process, and their use now extends past images into audio, video, molecules,…
- Self-Correcting Agentic RAG
Agentic RAG lets an agent plan retrieval, inspect results, and retry or refine queries instead of one-shot retrieval. Self-correct…
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