Machine Learning
Deep dives into ML algorithms, models, and applications
11 min readVector 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.
7 min readPyannote: 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.
10 min readDeploying 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.
9 min readGraph 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.
8 min readHybrid Search in Information Retrieval: Combining Dense and Sparse Vectors
Implement reciprocal rank fusion algorithms to merge keyword matching (BM25) with context-aware semantic embeddings.
9 min readReal-Time Feature Stores: Implementing Feast for Low-Latency ML Features
How to synchronize offline analytical feature stores with online key-value databases to feed real-time models under 10ms.
7 min readPixtral 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.
7 min readCLIP: 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.
7 min readLongformer: 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.
7 min readDeepSeek-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.
7 min readPhi-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.
7 min readFeast: The Open-Source Feature Store for Real-Time ML
Feast solves training-serving skew and enables feature reuse across models - define features once and serve them consistently from both batch and real-time data sources.
7 min readStableLM 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.
7 min readDINOv2: 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.
7 min readEvidently 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.
7 min readBLIP-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.
7 min readPyTorch 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.
7 min readWav2Vec 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.
7 min readOptuna: Modern Hyperparameter Optimization That Beats Grid Search
Optuna uses Tree-structured Parzen Estimators to learn from previous trials and focus on promising regions - finding better hyperparameters in fewer trials than grid or random search.
7 min readGGUF 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.
7 min readT5 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.
7 min readXGBoost 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.
5 min readNLLB-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.
7 min readScikit-learn in 2026: Still Relevant and What's New in v1.4+
Scikit-learn remains the best library for classical ML on tabular data - v1.4+ adds HDBSCAN, TunedThresholdClassifierCV, and better Pipeline verbosity while staying beginner-friendly.