Robust call-bound JavaScript intrinsics, using `call-bind` and `get-intrinsic`.
If available, the `Object.prototype.__proto__` accessor and mutator, call-bound
If available, the `Object.prototype.__proto__` accessor and mutator, call-bound
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A utility for managing a prototype chain
Open Node Streams on demand.
Pattern-matching on byte buffers
Simple Semver and SemverRange classes
> Some friendly semver range utilities
Import a module while bypassing the cache
Handle continuous steam of events in Promise fashion.
Editing commands for ProseMirror
A expression-bound function-factory actor
Find a bounding box for a set of points
The core of the packages included in the `@react-input` scope.
React library for Cloudflare's Turnstile CAPTCHA alternative
Robustly `.call.bind()` a function
Helper functions around Function call/apply/bind, for use in `call-bind`
Like lodash isEqualWith but for shallow equal.
require a whole directory of trees in bulk
Utility for easy setup and access of SAP HANA XS Advanced environment variables
The time-based use-recency-unaware cousin of [`lru-cache`](http://npm.im/lru-cache)
Helper function to optimise call expression
View-specific transforms for Vega dataflows.
Gem that adds asynchronous method calls for all methods on every object to aid in throughput on I/O bound processes. This is intended to improve throughput on I/O bound processes like making several HTTP calls in row.
Gem that adds lazy method delegation methods. Using this gem you can easily define lazy loading or asynchronous versions of specific methods. Lazy loading is useful when used with caching systems while asynchronous methods can improve throughput on I/O bound processes like making several HTTP calls in row.
Ruby bindings (via Rust/magnus) for the Zstandard compressor with persistent ZSTD_CCtx / ZSTD_DCtx contexts that are reused across calls. Provides Zstd frame compress/decompress at module level and a stateful Dictionary class for dict-bound compression. Designed to be safe to call from multiple Ractors and competitive with rlz4 on small messages, where per-call context allocation in zstd-ruby dominates the cost.
RLM.rb is a Ruby runtime spine for Recursive Language Models. It runs bounded, typed, auditable AI jobs over files, records, and application context. RLM.rb includes RubyLLM provider access, a dspy.rb signature adapter, the recursive prompt loop, file/context mounting, recursive sub-LM calls, typed final output, budget controls, trace events, and a best-effort trace_store hook.
Watermark's library for interfacing with Arena ChMS's web API
In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. It provides near-constant-time operations (bounded by the inverse Ackermann function) to add new sets, to merge existing sets, and to determine whether elements are in the same set. In addition to many other uses (see the Applications section), disjoint-sets play a key role in Kruskal's algorithm for finding the minimum spanning tree of a graph. A disjoint-set forest consists of a number of elements each of which stores an id, a parent pointer, and, in efficient algorithms, a value called the "rank". The parent pointers of elements are arranged to form one or more trees, each representing a set. If an element's parent pointer points to no other element, then the element is the root of a tree and is the representative member of its set. A set may consist of only a single element. However, if the element has a parent, the element is part of whatever set is identified by following the chain of parents upwards until a representative element (one without a parent) is reached at the root of the tree. Forests can be represented compactly in memory as arrays in which parents are indicated by their array index. Disjoint-set data structures model the partitioning of a set, for example to keep track of the connected components of an undirected graph. This model can then be used to determine whether two vertices belong to the same component, or whether adding an edge between them would result in a cycle. The Union–Find algorithm is used in high-performance implementations of unification. This data structure is used by the Boost Graph Library to implement its Incremental Connected Components functionality. It is also a key component in implementing Kruskal's algorithm to find the minimum spanning tree of a graph. Note that the implementation as disjoint-set forests doesn't allow the deletion of edges, even without path compression or the rank heuristic. Sharir and Agarwal report connections between the worst-case behavior of disjoint-sets and the length of Davenport–Schinzel sequences, a combinatorial structure from computational geometry.
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