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

#Claude ...ย  #Claude ...ย  #NPMย  #Anthrop...ย  #AIย 
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
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What if your backend is failing but your UI looks perfectly fine?

Struggling with hidden bugs? Learn how application programming interface testing ensures accurate data flow, security, and performance.

api testing
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How OpenTelemetry Helps Teams Change Observability Backends Without Re-Instrumenting Everything

1. OpenTelemetry reduces backend coupling by standardizing how telemetry is generated, described, transported, and processed before it reaches any observability platform.

2. The biggest migration advantage comes from using OpenTelemetry APIs and SDKs, OTLP, and the Collector, which make telemetry pipelines more portable across backends.

3. The Collector is the operational pivot point because it can receive, process, and export telemetry to one or more destinations from a centralized control layer.

4. OpenTelemetry does not eliminate backend migration work, since teams still need to validate dashboards, alerts, queries, retention rules, and other backend-specific workflows.

5. The safest migration approach is incremental: standardize telemetry first, validate both old and new backends, then cut over gradually.

How OpenTelemetry Helps Teams Change Observability Backends Without Re-Instrumenting Everything
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Software Developer, RELIANOID

๐—๐—ฎ๐—ฝ๐—ฎ๐—ป ๐—œ๐—ง & ๐——๐—ซ ๐—ช๐—ฒ๐—ฒ๐—ธ!

๐Ÿš€ ๐—›๐—ฒ๐—ฎ๐—ฑ๐—ถ๐—ป๐—ด ๐˜๐—ผ ๐—ง๐—ผ๐—ธ๐˜†๐—ผ ๐—ณ๐—ผ๐—ฟ ๐—๐—ฎ๐—ฝ๐—ฎ๐—ป ๐—œ๐—ง & ๐——๐—ซ ๐—ช๐—ฒ๐—ฒ๐—ธ! ๐—ฅ๐—˜๐—Ÿ๐—œ๐—”๐—ก๐—ข๐—œ๐—— will be at the 23rd Information Security Expo Spring 2026 from April 8โ€“10 at Tokyo Big Sight โ€“ ๐—๐—ฎ๐—ฝ๐—ฎ๐—ปโ€™๐˜€ ๐—น๐—ฎ๐—ฟ๐—ด๐—ฒ๐˜€๐˜ ๐˜€๐—ต๐—ผ๐˜„๐—ฐ๐—ฎ๐˜€๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—ฐ๐˜†๐—ฏ๐—ฒ๐—ฟ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜† ๐˜€๐—ผ๐—น๐˜‚๐˜๐—ถ๐—ผ๐—ป๐˜€. Come see how our advanced ADC and secure application delivery solutions help protect critical infr..

japan it dx week april 26 relianoid
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Maritime Cybersecurity Is Still Too Weak โ€“ And the Risks Are Growing

๐Ÿšข Maritime Cybersecurity Is Still Too Weak โ€“ And the Risks Are Growing As ships become smarter, greener, and more connected, their cyber defenses remain worryingly outdated. ๐Ÿ“‰ Over 80% of shipowners have faced cyberattacks in the past 3 years ๐Ÿ’ธ Average cost per attack: $3.1 million ๐ŸŽฃ Phishing causes..

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 AIStorโ€™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 โ€” from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets โ€” within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.