Where directories are code and drive space is infinite.

I live in two places: a terminal running Claude Code and Neovim. For months I bounced between them. Edit in Neovim, alt-tab to the terminal, paste a file path, ask Claude something, alt-tab back, apply the change by hand. The friction was small but constant, and constant friction is the kind that wears a groove in your day.

cosh doesn't impose a philosophy: attach or detach its built-in tools and choose your model to shape the agent you want. Works with Claude, OpenAI, Gemini and many more providers. Boots in milliseconds.

I have been using DeepSeek 4.1 Flash for about a month, heavily, across a dozen projects. It is super capable, and orders of magnitude cheaper than the "frontier" models. When I'm mid-session, if I don't look at the model name, I honestly could not tell you if I'm using DeepSeek or Opus. Whether it's our conversations, the work, or the speed, I don't notice a difference. I don't care that there is no 4.1 "Pro". I treat this like a frontier model because it behaves like one. I'm coming at this from my subjective usage experience, but you can see more complete benchmarks here if that floats your boat.

When we made our strategic bet on Rust back in 2022, we focused on Rust for backends and cloud systems. At the time, that was not an obvious choice. Rust was mostly seen as a systems language only, a safer replacement for C and C++ in operating systems, embedded devices, or browser engines. Suggesting to use it also for the kind of web backends that teams would typically build with Rails, Django, or Node.js required some explaining.

My first session with Claude Code was practically magical. I was speaking to my computer, telling it with natural language what I wanted it to do, and it was able to just do it. It did ( and still does ) feel like a completely new form of input, a new way to control my machine. I have misgivings about using AI in this way, but I still think this is a great tool for sufficiently low-level tasks. I’m waiting eagerly for the day that I can spin up a local LLM that can perform this function as well as Claude Code does.

L'agente di coding IA open source

Modelli gratuiti inclusi o collega qualsiasi modello da qualsiasi provider, inclusi Claude, GPT, Gemini e altri.

Bash strict mode refers to a set of options and practices used in Bash scripting to make scripts more robust, reliable, and easier to debug. By enabling strict mode, you can prevent common scripting errors, detect issues early, and make your scripts fail in a controlled way when something unexpected happens.

A Jupyter notebook client for your terminal. It aims to emulate the functionality of the classic Jupyter notebook. Built on the excellent textual framework with image support from textual-image.

The role of the IC (Individual Contributor) is evolving fast—and AI is accelerating the shift. As AI tools become deeply integrated into development workflows, many engineers find themselves stepping into responsibilities once reserved for engineering managers. This isn’t a hypothetical trend—it’s already happening in high-performing teams.

Developers looking to continue working in the C and C++ programming languages amid the global push to promote memory-safe programming now have another option that doesn't involve learning Rust. Filip Pizlo, senior director of language engineering at Epic Games, has created his own memory-safe flavor of C and – because why not …

People seem to think that writing a garbage collector is really hard, a deep magic understood by a few great sages and Hans Boehm (et al). Well it’s not. In fact, it’s rather straight forward. I claim that the hardest part in writing a GC is writing the memory allocator, which is as hard to write as it is to look up the malloc example in K&R.

This program consists of a single print statement wrapped in a while loop. You would not think that this would amount to much, but you would be very, very wrong. A clue to what is happening and how this works is encoded in the ASCII art of the program source.

Codon is a high-performance Python compiler that compiles Python code to native machine code without any runtime overhead. Typical speedups over Python are on the order of 100x or more, on a single thread. Codon supports native multithreading which can lead to speedups many times higher still.