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How In-Memory Caching Works in Redis

Redis isn’t just a cache anymore. Sure, it still owns the in-memory speed game—with **key expiration**, **data persistence**, and **horizontal scaling** via **replication** and **clustering**. But if you're only using it to stash a few keys, you're missing the point. This thing handles **streams**,.. read more  

How In-Memory Caching Works in Redis
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Experimenting with local LLMs on macOS

Running **open-weight LLMs locally on macOS**? This post breaks it down clean. It compares **llama.cpp**—great for tweaking things—to **LM Studio**, which trades control for simplicity. Covers what fits in memory, which quantized models to grab (hint: 4-bit GGUF), and what’s coming down the pipe: *.. read more  

Experimenting with local LLMs on macOS
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TIOBE Programming Index News September 2025: Perl Regains the Spotlight

Perl 5 has risen to **10th place in the TIOBE Index**, increasing in popularity even though the exact reason is unknown. Perl 6, or Raku, lags behind Perl 5 in rankings and has not seen the same rise in attention. Other top languages like C and Java have experienced slight falls in rankings... read more  

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Using Claude Code to modernize a 25-year-old kernel driver

A long-dead Linux kernel driver for QIC-80 tape drives just got dragged into the present—with help from **Claude Code** and a lot of tinkering. It now builds cleanly and runs as a **standalone module** on **Linux 6.8**, playing nice with modern setups like **Xubuntu 24.04**. **The bigger picture:**.. read more  

Using Claude Code to modernize a 25-year-old kernel driver
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Building an AI Server on a Budget ($1.3K)

A developer rolled their own AI server for $1.3K—Ubuntu 24.04.2 LTS, an Nvidia RTX GPU, and a sharp eye on Tensor cores, VRAM, and resale value. The rig handles small models locally and punts big jobs to the cloud when needed. Local-first, cloud-when-it-counts... read more  

Building an AI Server on a Budget ($1.3K)
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You Vibe It, You Run It?

Vibe Coding lets developers create software by chatting with AI, skipping traditional coding. But the non-determinism of AI prompts poses significant risks for reliability and maintainability, potentially leading to addiction-like dependence on this new tool. Think twice before fully embracing this .. read more  

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Understanding LLMs: Insights from Mechanistic Interpretability

LLMs generate text by predicting the next word using attention to capture context and MLP layers to store learned patterns. Mechanistic interpretability shows these models build circuits of attention and features, and tools like sparse autoencoders and attribution graphs help unpack superposition, r.. read more  

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The LinkedIn Generative AI Application Tech Stack: Extending to Build AI Agents

LinkedIn tore down its GenAI stack and rebuilt it for scale—with agents, not monoliths. The new setup leans on distributed, gRPC-powered systems. Central skill registry? Check. Message-driven orchestration? Yep. It’s all about pluggable parts that play nice together. They added sync and async modes.. read more  

The LinkedIn Generative AI Application Tech Stack: Extending to Build AI Agents
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GitHub Copilot on autopilot as community complaints persist

GitHub's biggest debates right now? Whether to shut down AI-generated "noise" fromCopilot—stuff like auto-written issues and code reviews. No clear answers from GitHub yet. Frustration is piling up. Some devs are ditching the platform altogether, shifting their projects toCodebergor spinning upself-.. read more  

GitHub Copilot on autopilot as community complaints persist
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Building Agents for Small Language Models: A Deep Dive into Lightweight AI

Agent engineering with **small language models (SLMs)**—anywhere from 270M to 32B parameters—calls for a different playbook. Think tight prompts, offloaded logic, clean I/O, and systems that don’t fall apart when things go sideways. The newer stack—**GGUF** + **llama.cpp**—lets these agents run loc.. read more  

GPT-5.3-Codex is OpenAI’s advanced agentic coding model, designed to go beyond writing code and operate as a general-purpose collaborator on a computer. It builds on GPT-5.2-Codex by combining stronger coding performance with improved reasoning and professional knowledge, while running about 25% faster. The model is optimized for long-running tasks that involve research, tool use, and complex execution, and it performs at the top of industry benchmarks such as SWE-Bench Pro and Terminal-Bench.

Unlike earlier Codex models that focused primarily on code generation and review, GPT-5.3-Codex can reason, plan, and act across the full software lifecycle. It supports activities such as debugging, deploying, monitoring, writing product requirement documents, creating tests, and analyzing metrics. It can also autonomously build and iterate on complex applications and better interpret underspecified prompts, producing more complete and production-ready results by default.

A defining feature of GPT-5.3-Codex is its interactive, agentic workflow. Users can steer the model while it is working, receive progress updates, and adjust direction without losing context, making it feel more like a teammate than a batch automation tool. The model was even used internally to help debug its own training and deployment processes. GPT-5.3-Codex is available through paid ChatGPT plans in the Codex app, CLI, IDE extension, and web, with API access planned for the future.