ESM, GraphQL subscriptions, and the best interception algorithm in the world.
Leaving technical preview | GraphQL
After over a year of being open sourced we’re bringing GraphQL out of “technical preview” and relaunching graphql.org.
Release v0.42.0 · mswjs/interceptors
v0.42.0 (2026-07-22) Implements TCP/TLS wrap-based interception (#770) (8a8fe6f) @kettanaito ⚠️ BREAKING CHANGES Node.js v22 is the minimal supported version. getRawRequest utility has been remo...
BerriAI/litellm
Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM]
CompactLTJ: Space & Time Efficient Leapfrog Triejoin on Graph Databases
Leapfrog Triejoin (LTJ) is arguably the most practical and popular worst-case-optimal (wco) algorithm for solving basic graph patterns in graph databases. Its main drawback is that it needs the database triples (subject, predicate, object) represented as paths in a trie, for each of the six orders of subject, predicate, and object. The resulting blowup in space makes most systems disregard LTJ or implement it only partially, which makes their corresponding algorithms non-wco. In this paper we show that, by using compact data structures, it is possible to build an index that at the same time matches the query time performance of the fastest classic wco index, and uses a fraction of the space of non-wco indices (which are much slower). Concretely, we make use of compact tree representations to store functional tries using one bit per trie edge, instead of one pointer, and further reduce the space by storing partial tries. Our most compact variant uses 5–6 times less space than classic wco implementations and 2–3 times less than classic non-wco systems. At solving queries, it is on par with the fastest classic wco system, and 30–40 times faster than non-wco systems. We further incorporate improved query resolution strategies into CompactLTJ variants, which makes it considerably faster than classic wco systems as well, on queries that do not output too many results. Finally, we show how CompactLTJ can incorporate dynamism without altering its performance, even under very demanding update regimes. We leave a public fully-functional implementation of CompactLTJ that can be directly used by practitioners.
Latency optimization | OpenAI API
Improve latency across a wide variety of LLM-related use cases.
GraphQL
GraphQL is a query language designed to build client applications by providing an intuitive and flexible syntax and system for describing their data requirements and interactions.
Using GraphQL in Rust | Shuttle
This article details how to build a GraphQL server in Rust. We will explore using queries, mutations as well as subscriptions to make a fully working endpoint.
Issue · BerriAI/litellm
Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthr...
An LLM's Perspective: What It's Actually Like to Receive These Instructions | Notion
graph TB subgraph
Extending Envoy Proxy - WASM Filter with Golang
Envoy is an open source service proxy especially designed for cloud native applications. It has a wide variety of features like connection…
The GitHub GraphQL API
GitHub announced a public API one month after the site launched.
OpenTelemetry: A Guide to Observability with Go | Blog
Modern applications are often complex, distributed systems. Debugging them is not fun: you have to follow requests across services, logs get lost, and metrics are often hard to correlate. It's like looking for a needle in a haystack - except the haystack is on fire, and the needle keeps moving. This is where OpenTelemetry (OTel) can help.
