LLMs & Generative AI - Page 7 of 55
Latest breakthroughs in large language models and generative AI shaping the future of artificial intelligence and machine learning.
Latest breakthroughs in large language models and generative AI shaping the future of artificial intelligence and machine learning.
Token-by-token generation is the bottleneck that has kept large language models tethered to a serial fate. DFlash breaks that chain.
Testing AI agents for security holes is a manual, brittle mess. Each new framework demands a custom audit; crafted attacks are often obsolete before they run. RIFT-Bench proposes a different path.
For centuries, philosophers and novelists have argued over what makes an agent. The fight is no longer academic. Every major tech firm now markets an “AI agent,” but most are just fancy phone trees.
We keep stacking AI judges into panels, hoping a crowd of models will be wise. According to new research from Apple, it isn't. These committees are functionally useless.
OpenAI's GPT-5.5-Cyber just beat Anthropic's Mythos on key cybersecurity benchmarks. That’s the flashy result. Look past it. The consequential move is Daybreak’s evolution.
The 9.6 GB download is a promise. A 128K context window, a 4-bit quantized model, and the raw power of Gemma 4 sitting right on your NVIDIA RTX 2000 Ada. This isn't about cloud dependencies or API keys.
Anthropic and Micron are making their partnership official. The stated goal is simple: build better memory for artificial intelligence.
Sakana's new Fugu model is powerful. It is also a complicated bet on a very specific future. The multi-model system claims "frontier performance," a statement that has ignited a practical debate among developers.
You watch a coding agent move through a browser like it owns the place. It opens tabs, fills forms, clicks buttons, all without a single line of traditional automation script. The mechanics are deceptively simple.
Adding neurons should, in theory, grant a network more power. It doesn't. A 1970 textbook by Minsky and Papert holds the stubborn math: combine any number of purely linear neurons, and your final output is just addition and multiplication.
Most natural language processing is a war against noise. You feed in text, and your tokenizer's first instinct is to break everything apart. For technical or specialized language, that's a disaster.
The relentless push for bigger AI models is stalling. In Tokyo, a startup named Sakana AI is scrapping that entire blueprint. Their answer? Ditch the single, lumbering giant.
Samsung is wiring AI directly into its corporate spine. The mandate is clear: deploy ChatGPT and OpenAI's Codex now, across software development, marketing, product design, and manufacturing. This isn't a pilot.
Retrieval quality is the silent killer of parametric memory. The model’s weights hold vast stores of language, reasoning, and world knowledge, frozen in time at training.
We keep calling them AI agents. Really, they're just readers with a single, weird book. A new study clarifies the process. The model's decision-making isn't mystical or pre-programmed. It parses text.
The best ChatGPT trick is to stop talking. Your next prompt is less important than the silence you leave for the machine to fill. When your request is shapeless or half-formed, forget clever phrasing.
Sam Altman thinks the smartest people in AI got the most important thing wrong. The OpenAI chief is still betting everything on making language models bigger.
Quantizing a model? It's math, not magic. The real trick is running that math on Windows with a Radeon card. Everyone targets the Q4_K_M format for their LLMs—a proven compromise between shrunken file size and acceptable performance.
The engineering class is finally catching up to the hype. A new five-course program from the IEEE aims to turn people who use AI into people who can actually build and fix it.
Imagine an AI that answers your question not by rifling through a library, but by scanning every book in a single glance. That’s the promise of keeping your entire retrieval corpus resident on the GPU.
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