VictoriaMetrics: Reading the Go Source of a Time-Series Powerhouse
Anyone who has trained or served a model at scale knows the feeling: the GPU node is hot, the loss curve looks fine, and then the run silently stalls because some unattended metric — host memory, NVLink errors, an overloaded...
Valkey Source Tour: How the Community-Governed Redis Fork Rewired Its Core
In March 2024 the Redis project moved its source tree to a source-available license, and the open-source world responded the way open-source worlds do: it forked. The result is Valkey, a high-performance data structure server that, per its own README,...
Thanos: Giving Prometheus Unlimited Memory with Go and Object Storage
If you run even a modest AI or ML fleet, your metrics outgrow your monitoring faster than your models outgrow your GPUs. A single GPU export loop that scrapes utilization, memory pressure, KV-cache occupancy and request latency every fifteen seconds...
Inside the OpenTelemetry Collector: The Go Engine That Routes Every Trace, Me...
Modern AI systems are telemetry furnaces. An LLM gateway emits metrics that classic web apps never had: tokens per second, time-to-first-token, queue depth per model replica, cache hit ratios on a prompt store. Every step of an agent — tool...
NATS: The Featherweight Messaging Backbone for Distributed AI
Every serious AI system turns into a fan-out problem embarrassingly fast. One “answer the user” request becomes a burst of calls to an embedding service, a retriever, a reranker, and a model provider; a training run becomes millions of checkpoint,...
Inside Memcached: The C Event Machine That Still Wins the Latency Game
If you build anything around models, you spend your life answering the same question over and over: the same prompt prefix, the same tokenizer call, the same feature vector, the same session lookup. Doing that work twice is pure waste,...
Grafana Loki: Log Aggregation That Indexes Labels, Not Lines
If you have ever trained a model overnight and woken up to a cryptic CUDA OOM at 3 a.m., you already know the real product of an ML system is not just the checkpoint — it is the log trail....
Jaeger: Tracing Every Hop of Your Distributed AI Stack
Modern AI systems are distributed systems in disguise. A single “ask the model a question” request in a production RAG application usually touches an API gateway, an embedding service, a vector database, a retriever, maybe a reranker, and finally an...
InfluxDB 3: A Source Tour of the Rust, Arrow, and Parquet Time-Series Engine
Anyone who has trained a model or operated a GPU cluster knows the feeling: the metrics are everywhere and the answers are nowhere. GPU utilization counters, temperature probes, token latencies, loss curves from experiment trackers, queue depths from an inference...
etcd: The Tiny Consensus Machine Holding Kubernetes Together
If you train or serve machine-learning systems on Kubernetes, every meaningful thing you do passes through a small Go program you have probably never looked at. When the scheduler decides which node runs your GPU job, it reads and writes...
Typesense Source Tour: How the C++ Search Engine Is Wired, From HTTP to Inver...
Typesense is a compact, open-source search engine written in C++ that has quietly become the default answer for teams that need typo-tolerant, millisecond-latency search without operating a distributed JVM cluster. Its promise, repeated in the project’s own README, is simplicity:...
TimescaleDB Source Tour: How Hypertables, Chunk Routing, Columnstore, and Con...
TimescaleDB is a PostgreSQL extension for high-performance real-time analytics on time-series and event data, developed by Timescale (now Tiger Data). Unlike a fork of Postgres or a separate storage engine, it is a loadable extension written in C that hooks...
Tantivy source tour: how the Rust full-text search engine library works
Most search tutorials stop at the API surface. This one goes the other way: we open the crate itself. Tantivy is a fast full-text search engine library written in Rust — closer in spirit to Apache Lucene than to a...
SurrealDB: One Rust Binary for Documents, Graphs, and Vectors
If you have ever built an AI-flavored application, you know the drill: documents go into a document store, the relationship graph goes into a graph database, embeddings go into a dedicated vector engine such as Qdrant or Milvus, and then...
Redis Under the Hood: A Guided Tour of the C Source Behind the World's Favori...
If you have ever cached an LLM response, rate-limited an API, or served precomputed features to a model at inference time, there is a very good chance Redis was sitting somewhere in the request path. Because the dataset lives in...
Prometheus in Go: How the Monitoring Engine Behind Modern Infrastructure Actu...
If you run GPU boxes for training, serve models behind inference gateways, or orchestrate batch jobs across a cluster, you already know the uncomfortable truth: things fail silently unless something is watching. A saturated NVLink, a queue that stops draining,...
ParadeDB Source Tour: How pg_search Turns Postgres into a Tantivy-Powered Sea...
Most teams that need full-text search eventually make an uncomfortable choice: keep everything in Postgres and accept the limits of its built-in text search, or stand up a separate engine like Elasticsearch and figure out how to keep two copies...
MinIO Under the Hood: How a Go Object Store Turns Disks into an S3 Cloud
Modern AI and data workloads live and die by storage. Training corpora, checkpoints, dataset shards, model weights, Parquet lakes, backup archives — all of it eventually lands in object storage because that is the one abstraction every tool agrees on....
Meilisearch Source Tour: How the Rust Search Engine Works Under the Hood
Meilisearch is one of those projects that feels deceptively small from the outside. You POST some JSON documents to it, you POST a query, and half a second later you get typo-tolerant, ranked results with facet counts. Under the hood,...
Kafka Under the Hood: How the Distributed Commit Log Actually Works
Every AI system eventually hits the same wall: your models are only as good, and only as fresh, as the events that reach them. Feature vectors generated a few seconds after a user click, GPU utilization samples scraped off a...
Vanna Source Tour: Inside the Text-to-SQL Agent Framework
Vanna is an open-source Python framework that turns natural-language questions into SQL queries and, ultimately, into charts and summaries. You feed it your schema (DDL), business documentation, and sample queries; it embeds and stores them, then retrieves the most relevant...
Ray Source Tour: How Tasks, Actors and the Object Store Fit Together
Ray began as a research project at UC Berkeley and grew into what its README now calls “a unified framework for scaling AI and Python applications.” The pitch is simple: the same Python you run on your laptop should run...
Pydantic source tour: how BaseModel turns type hints into validators and seri...
Pydantic is the quiet dependency under a remarkable amount of the modern Python stack. If you have ever declared a FastAPI endpoint, asked an LLM for structured output with instructor, defined tools with the MCP Python SDK, or let an...
Arize Phoenix Source Tour: Inside the Open-Source OTel-Native LLM Tracing Server
If you have ever shipped an LLM application and then asked “what did the model actually see, and what did it answer?”, you already understand the problem Arize set out to solve with Phoenix. Phoenix is an open-source, OpenTelemetry-native observability...