It's a stance; never about quality, voice and/or style, around for other human made products.

But after programming in Rust for 10 years, I think that your coding style has the biggest impact on how your Rust code will look and feel.

People often say Rust’s syntax is ugly, but I’d argue the syntax is the least interesting thing about Rust. The semantics (the bits and pieces the language provides to express your ideas and how those bits combine to build interesting things) are much more important.

Parsing a .env can be clunky in basic algorithm, but elegant in Rust.

Tips:

• read_to_string() instead of using a path, opening a file, creating a vector, adding the content to the vector to make a string
• Use type inference: let mut cfg = HashMap::new()
• Lean into the typesystem:
  • .lines() to split strings safely and iterates over the lines
  • split_once() to get each key-value pair for each lines
• leverage error handling: use an enum to list all possible errors with important values and thiserror can handle the error message

fn parse_config_file(path: &str) -> Result<HashMap<String, String>, ParseError> {
    let content = read_to_string(path)?;

    let mut config = HashMap::new();
    for line in content.lines() {
        match KeyValue::try_from(line) {
            Ok(kv) => { config.insert(kv.key, kv.value); },
            Err(ParseError::InvalidLine(_)) => continue, // Skip invalid lines
            Err(e) => return Err(e), // Fail on any other error
        }
    }

    Ok(config)
}

and why this code structure offers more extensibility!

Rust’s beauty is in its semantics and the core mechanics it provides: ownership, borrowing, pattern matching, traits, and so on. If you merely look at its (admittedly foreign) syntax, you overlook the real elegance of the language.

If there is anything that makes Rust “ugly”, it isn’t its syntax but the fact that it doesn’t hide the complexity underneath. Rust values explicitness and you have to deal with the harsh reality that computing is messy. Turns out our assumptions about a program’s execution are often wrong and our mental models are flawed.

Fortunately, we can encapsulate a lot of the complexity behind ergonomic abstractions; it just takes some effort! So don’t worry: once you start to confront your bad habits and look around for better abstractions, Rust stops being ugly.

Suite aux batailles pour avoir des climatiseurs LIDL, l'auteur dénonce la campagne de publicité sans avoir les stocks nécessaires pour les vendre.

Because with slow software comes also a higher degree of care.

It's not (only) the CLI but the mental models that ones must have and fully understands.

The key points:

• A commit is its entire worldline
• Commit content is both a snapshot and a patch
• Remotes have fuzzy identities and time lag
• Branches aren't quite branches, they're more like little bookmark go-karts
• Merge conflicts are actually just difficult
• git pull exists

Selon le nombre de jours qu'on a vécu, le temps d'une journée paraît plus ou moins grand par rapport à nos jours déjà vécu.

Le message que je souhaite faire passer est que si tu ne veux pas ou peux pas donner d’argent à un projet que tu estimes pourtant qu’il le mérite, il y a également la solution pour toi de donner de ton temps. Va parler du projet, convaincre d’autres personnes qui elles peuvent donner de l’argent de donner. Tu peux donner 5 euros, et/ou convaincre 10 personnes de donner 5 euros : cela sera encore plus utile pour le projet auquel tu crois au final, vu qu’il récoltera plus d’argent que ce que tu aurais pu donner. Et surtout, il y a tout ce temps que tu as pris pour ce projet, dont on peut difficilement mesurer la valeur

Use UIs instead of writing code. Ok.

I don't believe this is the case. In my opinion, LLMs do not represent a layer of abstraction, but rather a layer of automation. It doesn't abstract away the code layer, it automates it.

I believe in the importance of making understandable software. To this end, I'm working on projects that further this goal.

Primarily, I develop ReTangled, an Entangled-compatible bi-directional tangler written in Rust. It's meant to extend literate programming as far as it can go while integrating it as tightly into existing toolchains as reasonable. As of writing, it's in the early stages of development. I welcome contributions!

https://codeberg.org/liberty/retangled

First files are awesome, because the file format is the API to understand the content.

You may create a file in one app, but someone else can read it using another app.

After this introduction, an everything folder for social media is the theory: the folder would include everything you've created across different social apps. In that world, a “Tumblr post” or an “Instagram follow” are social file formats.

Bluesky, Leaflet, Tangled, Semble, and Wisp are some of the new open social apps built this way.

The author then details a typical social media post in JSON format, and how to store them as files. Domain names can be used to split each social media.

Then how to store a like with identity files (and the standard DID).

An example of such architecture is available at https://pdsls.dev/at://did:plc:fpruhuo22xkm5o7ttr2ktxdo

The apps then reacts to the change made in the social filesystem.

Encore un kudo pour Fabrice Bellard, cette fois de John Carmack.

About the movie Avengers, weeknotes, stickers, BuJo (the brand is bad, but the system has a lot of good ideas), Tacos.

We could have machines sending raw QUIC packets directly over the physical link or with minimal encapsulation + metadata for routing, in some case bypassing the need for a networking stack (i.e. directly talking to the optic fiber driver).

As John Quiggin writes, the pre-2020 Musk was the Musk of Tesla, batteries and Starlink. The post-2020 Musk is the Musk of Starship, robotaxis, Cybertrucks and Twitter – a string of commercial flops and assets that literally exploded.

It matches the "Dead Economy Theory" from https://www.owenmcgrann.com/p/the-dead-economy-theory.

The actual dead economy risk is that our institutions and markets will continue to move capital from productive activity into memestocks, vibes, and bubbles.

That's not just a dead economy – it's one that'll kill everyone you love and everything that matters.

The idea is to die young as late as possible

Distributing Rust crates via git URLs instead of crates.io

First of all, this essay is researched and argued. There are many sources accross the texts.

Landgon Winner wrote an essay

• the technology is designed to have certain political effects
• the technology is inherently political

Technologies and machines tend to profeit. For example, the mechanical tomato harvester by UC Davis in the 1950s.

Once the tech­nology arrives and becomes entrenched, the conver­sa­tion gets reframed as one of tech­no­log­ical inevitabilism vs. anachro­nism, and dissent is discour­aged. [...] The adaptation of social life to technical requiremetns pop up spontaneously.

Liberal democracy is sustainable as long as "the balance of power of democracy is premised on the average person having leverage through creating economic value. If that's not present, I think things become kind of scary."

In prac­tice, certain people in a capi­talist liberal democ­racy tend to get increas­ingly rich. Absent coun­ter­mea­sures, the wealthy gain control of the polit­ical appa­ratus, thwarting liberal-demo­c­ratic norms.

The synthesis might be: as more wealth becomes concen­trated in the hands of fewer citi­zens, liberal democ­racy weakens, because whichever citi­zens are losing economic rele­vance will also lose polit­ical rele­vance.

The AI story is a fallacy: the sci-fi discourse is optimized for cinematic impact instead of real daily life changes.
Moreover the paperclip maximizer experiment of Nick Bostrom shows AI has unintended side-effects.

The West was won by AI because we balance water supply between the population and AI. Water is a biolog­ical neces­sity; AI is not.

Another point is laber replacement. Every CEO is outdoing the next in predicting that AI will replace human labor, which will lead to waves of unemployment.

Capital markets are laready pricing as if AI companies will have the perfect scenarios.
Software programmers, highly paid writers and lawyers are all work with high costs that can be automated.

Zitron has char­ac­ter­ized the AI market so far as a blink­ered conver­sa­tion between AI exec­u­tives and trad exec­u­tives, all of whom “have little idea what work looks like”.

Many CEOs and billionaires argued AI will create an abundance of goods and services. Really?

The persis­tent lack of detail signals that these billion­aires have no idea how, exactly, AI will accom­plish this.

Even if the labor comes down to 0$, resources, enegery and raw materials have a cost. Another contradiction is "Big AI’s promises of world-histor­ical goodies for consumers and world-histor­ical profits for share­holders may be incom­pat­ible".

To improve public acceptance, AI has so far emphasized their fun consumer-facing porducts: generating an image or writing a story. In the goodies economy, as citi­zens lose inde­pen­dence as economic actors, they will also lose inde­pen­dence as polit­ical actors. They will be captured by industry.

Tangible assets are the oppo­site—bricks, mortar, inven­tory, and so forth. According to the WIPO, since 1995 global invest­ment in intan­gible assets has steadily increased, and since 2009 has exceeded that of tangible assets. By wide margin, the US leads invest­ment in intan­gible assets.

If you think about it, it's crazy. It's even more crazy to see the productivity growth compared to the hourly compensation growth: https://www.epi.org/publication/charting-wage-stagnation/#:~:text=Figure%202

“Tech­nology always makes certain jobs obso­lete; new ones will arise.” AI’s predicted labor replace­ment is unprece­dented in three ways: the diver­sity of tasks replaced; its outsize effect on highly educated workers; and the back­drop of 50 years of wage stag­na­tion.

If an AI can replace the output of a company, then every company with an AI can do so.

Currently certain legal-AI star­tups license LLMs from Big AI and repackage them for Big Law at high prices. These star­tups claim to add other special sauce. OK, sure. Where’s the economic equi­lib­rium? If legal-AI star­tups prove that money can be made selling AI to Big Law—won’t Big AI just sell to Big Law directly, and cut out the star­tups?

By the process described above: Big AI essen­tially uses its tech customers as an R&D facility.

As AI makes it harder for workers to capture value from their labor, they will increas­ingly have to rely on goodies from Big AI, priva­tizing what were once func­tions of govern­ment. If Big AI subsumes the func­tions of workers and govern­ment, both will tend to realign polit­i­cally around Big AI’s inter­ests. [...] AI-centered capi­talism risks an extinc­tion of demo­c­ratic possi­bility

Epilogue

Encryption is only a first step: there are others in order to control the devices we use daily. "Sovereign control" means answering yes to each of the following:

• Do you own the encryption keys — not the platform provider?
• Are backups protected end-to-end, by default, without manual configuration?
• Is metadata shielded from external observation, including communication patterns, timing, and participant identity?
• Do you control who can access message content, with no external request system that can override that?
• Do you govern which devices, users, and integrations can access sensitive channels?
• Can the platform be deployed on infrastructure you control, in jurisdictions that meet your data residency requirements?

Vendor the dependencies in the project directly. It will avoid to install the dependencies every time.

So just by not updating dependencies automatically, you turn every single package in an ecosystem into a fire-break for supply chain attacks.

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