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Story FAUN.dev() Team Trending
@eon01 shared a post, 6 days, 1 hour ago
Founder, FAUN.dev

16 Things Anthropic Didn't Want You to Know About Claude Code

Claude Code

Earlier today (March 31, 2026), Anthropic accidentally shipped the full source code of Claude Code inside an npm package. The 512,000 lines of TypeScript have since been picked apart by the developer community, and what's inside is more revealing than anyone expected.

Claude Code Leaked
News FAUN.dev() Team Trending
@kala shared an update, 6 days, 2 hours ago
FAUN.dev()

Anthropic Accidentally Leaks Claude Code's Entire Source Code via npm

Claude Code

Anthropic shipped a source map file inside the latest npm release of Claude Code - and with it, the full source code of its flagship AI coding CLI. The leak exposed 512,000 lines of TypeScript across 1,900 files, 43 built-in tools, 44 feature flags, 26 hidden slash commands, and over 120 secret environment variables. It is one of the most detailed accidental exposures of a commercial AI product's internals to date.

Claude Code leaked source
AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.