dantraztrev

I’m building a text editor inspired by ed, but instead of editing files on disk, it edits live network flows. In this model, files don’t exist as static objects—they exist in motion.

Creating a file generates a self-sustaining pattern of packets circulating through the network, and editing it changes the flow itself. Multiple users can edit the same file simultaneously, because the file isn’t tied to any machine—it’s in the network.

The interface is familiar if you’ve used ed, with commands like append, delete, and substitute, but behind the scenes it’s all live traffic. You can even discover existing flows and jump into them in real time.

It’s a Linux proof-of-concept using raw sockets, and the goal is to explore what files could be if we thought of them as living, circulating processes rather than static storage.

https://github.com/DantrazTrev/nerd

That sounds good but it might give the echos of effective altruism and its recent debacle with the tech/fin industry.

It is a good idea but some notion to counter balance it should exist , some different form or progression philosophy. To keep it on check and balance ?

As one of the new devs , I feel like AI tools have pushed the bottom line higher , you can learn to do things and you can dive deep into technical aspect or in this case the basic implementation. But the velocity or expected velocity has increased , we expect more out of the junior devs then what we used to years ago , As if you don't use AI you're already behind.

There will always be people who know how to do things better , who can understand what will be the most optimal code style for fastest and/or smallest assembly instruction , and in future this bottom line will get moved much further.

We are probably coming close to something similar of 1980s game industry crash for engineers or we can avoid it.

New devs may not care for deep tech knowledge they optimize for opportunities, and you give them tools for their disposal to optimize for opportunities and they grab it.

Not entirely sure but here is an hunch , a single memory is really hard to remove, and presuming they're not training it again. Here are some solutions

1) Block Personally Identifiable info to get into training data : Maybe have a redact such info before passing it to model . Just having a layer between I/O and the processing .

2) Have a sort of VCS for models and rewind and retrain if such is needed to be done . ( Seems quite unfeasible )

3) If it is not an info but rather an opinion , train the model to counter biases until a netural or agreed stance is reached

Also info about using chopsticks with hand impairment

https://www.accessible-japan.com/surviving-without-chopstick...