Machine Learning
Deep dives into ML algorithms, models, and applications
MusicGen: Meta's Text-to-Music Model That Runs Locally
Meta's MusicGen generates 30-second music clips from text descriptions or melody conditioning using an EnCodec audio tokenizer and autoregressive transformer - fully open and self-hostable.
LLM Compression: Pruning, Distillation, and Quantization Compared
Three techniques for making large language models smaller and faster - quantization, pruning, and knowledge distillation - each with different tradeoffs in quality, speed, and implementation complexity.
SPLADE: Sparse Neural Retrieval That Beats BM25 With Learned Weights
SPLADE uses BERT's masked language model head to produce sparse, interpretable retrieval representations that outperform BM25 while remaining compatible with inverted index infrastructure.
Nomic Embed: The First Fully Open 8192-Context Embedding Model
nomic-embed-text-v1.5 supports 8192-token context with Matryoshka embeddings at multiple dimensions - fully open training data, code, and weights under Apache 2.0.
Vector Database Benchmarks: pgvector vs Pinecone vs Qdrant in 2026
We run scale benchmarks testing search speed, indexing latency, and recall accuracy across cloud and hosted database engines.
Pyannote: Add Speaker Diarization to Whisper Transcription
Pyannote.audio provides state-of-the-art speaker diarization that identifies who speaks when in a recording, enabling meeting transcripts and podcast notes with per-speaker attribution when combined with Whisper.
Deploying ML Models on Cloudflare Workers and WebAssembly (Wasm)
Learn to compile model pipelines down to Wasm binaries to execute real-time, zero-cold-start inference at the network edge.
Graph RAG: Combining Vector Databases with Knowledge Graphs
Traditional retrieval often misses complex relationships. Learn how to map structural nodes alongside vector indices to enhance RAG systems.
Hybrid Search in Information Retrieval: Combining Dense and Sparse Vectors
Implement reciprocal rank fusion algorithms to merge keyword matching (BM25) with context-aware semantic embeddings.
Pixtral 12B: Mistral's First Vision-Language Model
Pixtral 12B processes images at arbitrary resolution without fixed patch sizes, scores 52.5% on MMMU, and is available under an Apache 2.0 license.
CLIP: Using OpenAI's Contrastive Model for Zero-Shot Image Classification
CLIP learns joint image-text embeddings from 400 million pairs, enabling zero-shot classification on any category you can describe in words - no labeled training data required.
Longformer: Process 4096-Token Documents With Sliding Window Attention
Longformer extends BERT to 4096 tokens using a combination of local sliding window attention and global attention, making it practical for document classification, Q&A, and NER on long-form text.
DeepSeek-VL2: Efficient Vision-Language Model With Mixture of Experts
DeepSeek-VL2 applies Mixture of Experts to vision-language modeling, activating only 4.5B of 27B parameters per forward pass while matching models twice its active size.
Phi-4: Microsoft's 14B Model That Beats Larger Models on Reasoning
Phi-4 at 14B parameters scores 80.4% on MATH (vs GPT-4o at 76.6%) using a synthetic data pipeline focused on textbook-quality STEM content.
DINOv2: Meta's Self-Supervised Vision Features That Beat Supervised Models
DINOv2 learns visual features from 142 million curated images without labels, producing representations that outperform supervised ImageNet models as frozen feature extractors across classification, segmentation, and depth tasks.
StableLM 2: Stability AI's Compact 1.6B Model for Edge Inference
StableLM 2 1.6B outperforms Phi-1.5 and TinyLlama at its size class and is small enough to run on a Raspberry Pi, in a browser via WebLLM, or on old consumer hardware.
Evidently AI: Monitor ML Models for Data Drift and Performance Degradation
Evidently AI generates data drift reports, quality checks, and model performance dashboards for production ML - catching distribution shifts before they silently corrupt your predictions.
BLIP-2: Bootstrap Vision-Language Models With Frozen Image Encoders
BLIP-2 bridges a frozen CLIP image encoder and a frozen LLM through a lightweight Q-Former, achieving strong VQA and captioning performance without updating the large pretrained components.
PyTorch Lightning: Write Research-Grade PyTorch Without the Boilerplate
PyTorch Lightning separates research code from engineering code - write your model logic once and get multi-GPU, mixed precision, gradient clipping, and logging for free.
Wav2Vec 2.0: Self-Supervised Speech Recognition for Low-Resource Languages
Wav2Vec 2.0 learns speech representations from unlabeled audio and can be fine-tuned with as little as 10 minutes of transcribed speech, making high-quality ASR accessible for low-resource languages.
GGUF Quantization Explained: Q4_K_M vs Q8_0 and When Each Matters
Quantization shrinks LLM weights from float32 to int4 or int8 - here is exactly what each GGUF level means, how memory usage scales, and the quality tradeoffs.
T5 and Flan-T5: The Text-to-Text Framework That Powers Many LLMs
T5 unifies all NLP tasks as sequence-to-sequence text generation, and Flan-T5 extends this with instruction tuning across 1800+ tasks, making it a practical base for fine-tuning custom generation tasks.
XGBoost vs LightGBM vs CatBoost: The Gradient Boosting Showdown
All three gradient boosting libraries beat neural networks on tabular data - but they differ in training speed, categorical handling, and GPU support in ways that matter for your specific use case.
NLLB-200: Meta's No Language Left Behind Translation Model
NLLB-200 provides machine translation for 200 languages including 55 low-resource African languages, with a distilled 600M model that outperforms Google Translate on 40+ languages. Practical guide with code examples and cost comparison.
