ThierryBuilds

What really enabled Python to spread so much into scientific computing and AI is C/C++. Most if not all performance critical libraries are not really Python. They just expose a Python API

I spent some time digging into the internal mechanics of process creation. It's one thing to call gcc or click 'Run', but I wanted to document the actual transition from a static binary on disk to the memory layout of a live process.

I tried to contrast the POSIX fork/exec model with the Windows CreateProcess approach. I’d love to hear from anyone who has had to deal with the more obscure parts of these system calls, like clone() flags or Windows process attributes.

That is the only way to represent the empty slice (similar to &s[10..10]). Interesting though to see how LLMs are fooled by that edge case.

A human will still be needed to review the AI generated PR, no?

I never had similar bug caused by hardware or compiler issue. But I had cases where I thought it could be the compiler.

This article shows that those cases are possible, even though they are very rare.

Which book is presented in the article?

Then why is there a need for a developer, that supposedly writes poorer code, to check it?

Do AI really write better React/JSX than devs?

The article is not only about security. Thanks

Agreed. Beyond security, we must consider other critical factors such as cost, ease of maintenance, and operational overhead.

Thanks for the feedback. These are typical use cases where the convenience of higher level abstractions may be less important than the benefits of direct access to the hardware.

Thank you for the feedback. I will definitely add them as example solutions for serverless.

I wrote this because I kept seeing developers (myself included) confuse language-level isolation like Python venv with OS-level isolation like Docker. I wanted to trace the actual technical boundaries between them.

The article maps out the differences between common execution environments—from physical bare metal and VMs to containers, process sandboxes, and virtual environments—to create a mental model of where the "isolation boundary" actually sits for each tool.

Great article! Thank you for sharing. Could these same strategies be applied to C programs?

Insightful. Many thanks.

Flat buffers is know to bloat client code. Was any trick used to mitigate that?

Thanks for the clarification. Happy new year

C++ Has changed so much since C++11. It feels like it is picking features from every other language

I am curious to know what was the contribution of switching to FlatBuffers in that improvement.

Thanks for your response. Wouldn't using the `_` in the enums somehow make almost all integers index "cartable" to the enums?

Balancing developer satisfaction with raw productivity is a critical trade-off. While the 'joy of coding' maintains long-term engagement, LLMs provide a necessary lift in throughput. I prefer a surgical approach: disabling LLMs for core logic to avoid 'auto-pilot' bias, while utilizing them for the high-friction work of documentation and unit testing.

Nice Article. However I fail to understand how using the enum prevents the usage of the wrong index in a data structure. Can you explain further?

Nice article. And the `Into` trick also accept &String

I’ve been using Go Expr for a while because it’s zero-dependency and fast, but I kept hitting walls that weren't obvious in the docs—like the fact that map keys in the script are treated as literals, or the "strict vs forgiving" difference between structs and maps.

I put together this deep dive into 10 specific gotchas to help others avoid the same debugging loops I got stuck in.

On a different note, the syntax for capacity hints of slices vs map can be confusing. m := make(map[string]string, 16) VS a := make([]string, 16)

They look similar but the second argument do not have the same meaning.

I got bitten by it in my early days of Go