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@varbear shared a link, 4 weeks, 1 day ago
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Reversing YouTube's "Most Replayed" Graph

An engineer cracked open YouTube’s “most replayed” heatmap. Turns out it runs onsampled view frequency arrays, client-sidenormalization, andSVG renderingstitched together withCubic Bézier splinesfor that smooth, snappy curve. Behind the scenes, playback gets logged with adifference array + prefix su.. read more  

Reversing YouTube's "Most Replayed" Graph
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@laura_garcia shared a post, 4 weeks, 1 day ago
Software Developer, RELIANOID

🚨 Join RELIANOID at the Dallas Cybersecurity Conference 2026! 🚨

📍 Dallas, Texas | 🗓 January 22, 2026 Securing the Future starts here. We’re excited to be part of FutureCon Dallas, a high-impact event bringing together CISOs, C-suite leaders, and senior security professionals to tackle today’s most pressing cyber threats. 🔹 Why attend? Gain actionable insights in..

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@kaptain shared a link, 4 weeks, 1 day ago
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v1.35: Restricting executables invoked by kubeconfigs via exec plugin allowList added to kuberc

Kubernetes v1.35 lands with acredential plugin allowlist, now in beta, no feature gate needed. It lets you lock down whichexecplugins your kubeconfigs can run. Tighter leash, lower risk. Especially when the credential pipeline gets sketchy... read more  

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@kaptain shared a link, 4 weeks, 1 day ago
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From Bare Metal to Containers: A Developer's Guide to Execution Environments

A sharp look at how execution environments evolved - from bare metal to VMs, containers, sandboxes, and language-level runtimes. The focus: isolation. Hardware, kernel, processes, runtimes - each adds a boundary. Modern stacks mix and match layers to dial in the right amount. VMs, containers, venvs... read more  

From Bare Metal to Containers: A Developer's Guide to Execution Environments
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@kaptain shared a link, 4 weeks, 1 day ago
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A Brief Deep-Dive into Attacking and Defending Kubernetes

A sharp teardown of Kubernetes’ attack surface maps out where things go sideways: pods, the control plane, RBAC, admission controllers, and etcd. Misconfigurations like anonymous API access, wildcard roles, and hostPath mounts aren't just sloppy- they're attack vectors. Fixes? ThinkFalco,RBAC lockdo.. read more  

A Brief Deep-Dive into Attacking and Defending Kubernetes
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@kaptain shared a link, 4 weeks, 1 day ago
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Run Your Project in a Dev Container, in Zed

Zed v0.218 addsDev Containersupport with Docker. Projects can now spin up in clean, spec-compliant environments built from.devcontainer.json. It hooks into theDevelopment Containers CLI, with a Zed remote server running backend ops and piping through standard IO. Fast and clean. The bigger picture?L.. read more  

Run Your Project in a Dev Container, in Zed
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@kala shared a link, 4 weeks, 1 day ago
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Reading across books with Claude Code

A custom LLM agent, built withClaude Codeand some hard-working CLI tools, chewed through 100+ nonfiction books by slicing them into 500-word semantic chunks - and then threading excerpt trails by topic. Under the hood: Chunk-topic indexes lived inSQLite. Topic embeddings flowed throughUMAPfor clust.. read more  

Reading across books with Claude Code
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@kala shared a link, 4 weeks, 1 day ago
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The Complete Guide to CLAUDE.md

Claude Code just got smarter withCLAUDE.md- a project-level file that loads every time a session starts. Drop in your team's coding quirks, custom commands, naming rules, or traps to avoid. Claude reads it, remembers it, and quietly tailors responses to fit. Think of it likeAGENTS.md, seen in Cursor.. read more  

The Complete Guide to CLAUDE.md
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@kala shared a link, 4 weeks, 1 day ago
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Recursive Language Models: the paradigm of 2026

Prime Intellect dropped a fresh take on long-range LLM workflows with itsRecursive Language Model (RLM)scaffold. It pulls off two smart moves: folds context to free up tokens and spins off sub-LLMs to handle chunkier tasks. Think persistent Python REPL meets lightweight agent swarm... read more  

Recursive Language Models: the paradigm of 2026
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@kala shared a link, 4 weeks, 1 day ago
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FinePDFs: Liberating 3T of the finest tokens from PDFs - a Hugging Face Space by HuggingFaceFW

Hugging Face introduces FinePDFs, a large open dataset built by extracting and cleaning text from millions of PDF documents, reaching trillions of tokens across many languages. The post explains how the pipeline handles messy PDF structure, layout noise, duplication, and low-quality content to produ.. read more  

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.