Latest AI and tech news
One question — “is the problem what your model knows, or how it behaves?” — decides between a $20,000 training run and a retrieval step…...
Not long ago I didn’t know what the word deploy meant. Now there’s a model running on my laptop that I trained myself. Here’s how it went…...
What vector quantization actually costs at one million vectors, which is not where you think...
Bolt.new, StackBlitz’s browser-based AI development platform, is testing a new trade with developers: more coding-model usage in exchange for training data....
Explore five free Microsoft GitHub courses covering data science, machine learning, artificial intelligence, generative AI, LLMs, RAG, fine-tuning, and AI agents.
FINAL-Bench•1 day ago•12
Your AI agent is almost there. The prompt is getting longer, the examples keep multiplying, and every new edge case seems to need one more instruction....
In most companies running LLMs today, somewhere there is a fine-tuned model or a customized prompt sitting in production that nobody remembers registering anywhere....
How Domyn built sovereign AI models (Italia, Colosseum, Domyn Large/Small) using NVIDIA NeMo, Megatron-Core & Leonardo supercomputer. Technical insights on pruning, distillation, RL & EU AI Act compliance.
In Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy, we compressed a Llama 3.1 8B Instruct model from 14.9 GB to 8.0 GB using 8-bit integer (INT8) W8A8 quantization with SmoothQuant and Generative Pre-trained...
When we introduced co-operative time-slicing in llm-d, we made a claim: if RL phases become schedulable units, independent jobs can share accelerators with near-zero waste. Today we're backing that claim with a measured, end-to-end proof. For researc...
Can you train a cyberattack detector without ever showing it a real cyberattack?...
I'm building a resume screening/ranking system (matching resumes to
job descriptions using pretrained sentence embeddings + cosine
similarity, no fine-tuning at this stage) as a learning project aimed
at becoming a market-ready NLP practitioner....
Guidelines that read clearly can still produce annotators who disagree. Rubric design is the missing layer, and this post shows how we make it explicit, inspectable, and testable before labeling starts.
Introduction: The Quiet Threat Hiding Inside Your Data...
black-forest-labs•Jun 4•29
The Amazon Web Services (AWS) Open Data Sponsorship Program has hosted the Common Crawl open repository of web data at no cost since January 2012. It has become one of the most important sources of training data for the large language models (LLMs) reshaping industries. This is the story of a 14-yea
bartowski•25 days ago•17
The file inside @lenml/tokenizer-claude is md5-identical to the one Anthropic shipped in anthropic-sdk-python v0.38.0 and has since…...
How AI-Generated Data Helps Train Machine Learning Models, Protect Privacy, and Solve Real-World Data Challenges....
The large language models (LLMs) that power generative AI work by drawing upon the patterns and information present in their training data. Without access to the right data, LLMs struggle to comprehend context—like our internal corporate vocabulary—a...
bartowski•about 14 hours ago•15
Fine-tuning a large language model sounds like something that requires a room full of GPUs....
Nvidia and Palantir announced on Thursday that they’re working together to bring “sovereign AI to critical supply chains,” kicking off initially with Nvidia’s own sprawling supply chain....
NeoHorse-1-4B is a 4B-parameter causal language model from TokenRhythm, derived from Qwen3.5-4B and adapted through routing-guided agentic post-training for text-based agent harnesses, tool use, coding, and instruction following. Its key distinction ...
A practical directory of MiniMax H3 models, runtimes, acceleration tools, quantization, LoRAs, fine-tuning, high-resolution workflows…...