Public list by ~betterdevlink

Issues #287 Jun 15, 2026

[Free Live Tech Session] How AI Agents Can Write, Test, And Fix Their Own Code In Kubernetes

https://www.vpdae.com/redirect/cyv568yz6xs9hoatt44wtmyi01b

[Free Live Tech Session] How AI Agents Can Write, Test, And Fix Their Own Code In Kubernetes Live technical workshop | June 18 | 11am ET / 8am PT | 45 min + Q&A Your AI agent writes code fast. So fast it barely matters now. The bottleneck is what comes after: running it, watching it break against real infrastructure, and debugging the gap between what the agent assumed and what your cluster actually does. The agent never saw your queue payloads. It guessed your database schema. It has no idea that the downstream service returns 429 under real load. So you validate. Manually. Every time. That validation tax is the problem we're solving live on June 18.. What you'll watch happen in real time: We'll connect a Cursor agent to a real Kubernetes cluster using mirrord and run the full loop: inspect live environment state, generate code informed by what's actually there, test immediately against real services, watch it fail, and watch the agent fix itself. No mocks. No deploy cycle. No human in the loop. The agent calls inspect, generate, test, fix autonomously. Total time from failure to working code: under 30 seconds. Engineering teams at monday.com and similar companies already run this pattern in production. 350+ engineers at monday.com use mirrord daily. They cut dev feedback loops from 30 minutes to 30 seconds. You'll leave with: A reference architecture for wiring agents to real clusters safely. The exact prompts and mirrord config from the demo. A clear mental model for where human judgment still belongs and where agents can own the loop. Built for engineers who are done validating what the agent should be validating itself. Hosted by Arsh Sharma (Senior DevRel, MetalBear) and Aviram Hassan (Founder and CEO, MetalBear, co-creator of mirrord).

2 months ago

Weekend trivia: your process' memory is a file

https://lcamtuf.substack.com/p/weekend-trivia-your-process-memory

Weekend trivia: your process' memory is a file Some folks say that the design philosophy of Unix is that “everything is a file”. If you’re familiar with Unix-like platforms, you probably know that they don’t quite live up to the hype. But apparently, the memory of a process itself truely live up to this hype.

2 months ago

How The Heck Does GPS Work? (An Interactive Exploration)

https://perthirtysix.com/how-the-heck-does-gps-work

How The Heck Does GPS Work? GPS is fundamentally a translation tool: it converts time into distance. A satellite sends a signal, your phone catches it, and the delay between those two events tells the phone exactly how far away the satellite is. 1 nanosecond of signal travel = 0.3 meters.

2 months ago

6IT

https://6it.dev/blog/on-cpu-physics-and-cpu-cycles-80730

On CPU Physics and CPU Cycles This is a DRAFT of the first part of Chapter 4 - On CPU Physics and CPU Cycles of a C++ books but I learn a lot about CPU in its physisc form and the communication between all component: cpu, ram, l1/2/3 cache.

2 months ago

The Smallest Brain You Can Build

https://ranpara.net/posts/perceptron-explained-from-scratch/

The Smallest Brain You Can Build A perceptron explained from scratch in Python, with interactive demos. Learn weights, bias, the decision boundary, epochs, learning rate, and why we normalize data.

#machine learning #perceptron #neural network #python #tutorial #build a perceptron python

2 months ago

Making a vintage LLM from scratch - Cr;Lf;

https://crlf.link/log/entries/260525-1/

Making a vintage LLM from scratch In this blog post, I will share the adventures I had creating my own LLM, from (almost) scratch, trained only on old texts. I made my own base-training and fine-tuning scripts, data processing pipelines and custom datasets.

2 months ago

RTX 5080 + RTX 3090 Setup: 80+ Tok/s on Qwen 3.6 27B Q8 - iMil.net

https://imil.net/blog/posts/2026/rtx-5080-+-rtx-3090-setup-80+-tok-s-on-qwen-3.6-27b-q8/

RTX 5080 + RTX 3090 Setup: 80+ Tok/s on Qwen 3.6 27B Q8 Many LLM Model require more 24GB+ VRAM? what happen if you can combine 2 smaller GPU Card? This walk ou through that setup.

2 months ago

Appreciating Exif | Brent Fitzgerald

https://brentfitzgerald.com/posts/appreciating-exif/

Appreciating Exif Have you ever need to auto rotate a user profile picture, u read exif and rotate them? So let learn about this structure. They age well despite a standard being invented long ago with so many gotchas.

2 months ago

How to Setup a Local Coding Agent on macOS

https://ikyle.me/blog/2026/how-to-setup-a-local-coding-agent-on-macos

How to Setup a Local Coding Agent on macOS Running Gemma 4 26B-A4B and Qwen3.6 35B-A3B locally with llama.cpp, MTP speculative decoding, multimodal support, and PI as a coding agent.

#qwen #coding agent #llama.cpp #pi #gemma #macos

2 months ago

GitHub - davepl/BlinkenDisk

https://github.com/davepl/BlinkenDisk

BlinkenDisk A tiny macOS utility that puts a red LED in your menu bar and lights it up whenever there’s I/O activity on the local drives you choose to monitor.

2 months ago

GitHub - mukul975/Anthropic-Cybersecurity-Skills: 754 structured cybersecurity skills for AI agents · Mapped to 5 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND & NIST AI RMF · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 26 security domains · Apache 2.0

https://github.com/mukul975/Anthropic-Cybersecurity-Skills

Anthropic Cybersecurity Skills This repo contains 754 structured cybersecurity skills spanning 26 security domains, each following the agentskills.io open standard. Every skill is mapped to five industry frameworks — MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, and NIST AI RMF

2 months ago

container/docs/container-machine.md at main · apple/container

https://github.com/apple/container/blob/main/docs/container-machine.md

MacOS Container Machine A tool for creating and running Linux containers using lightweight virtual machines on a Mac. It is written in Swift, and optimized for Apple silicon.

2 months ago

moonshotai/Kimi-K2.7-Code · Hugging Face

https://huggingface.co/moonshotai/Kimi-K2.7-Code

Kimi-K2.7-Code a coding-focused agentic model built upon Kimi K2.6. With substantial improvements on real-world long-horizon coding tasks, it strengthens end-to-end task completion across complex software engineering workflows while improving token efficiency, reducing thinking-token usage by approximately 30% compared with Kimi K2.6.

2 months ago