Oracle expressed the opposite. Within a year or two Larry killed OpenSolaris, let the MySQL, Hudson and OpenOffice communities fork away, sued Google over Java, and watched Gosling and most of the engineers who embodied Sun walk out.

The part that rankles the most, and undermines many a retrospective I think, is that Larry the lawnmower turned out to be a better steward of Java & MySQL than Sun was. Coldbloodedness is a survival trait.

The relentless demand for justification is indistinguishable from a demand for others to stop doing things you don't like.

A great point that’s also true in reverse.

On the note of science, I'll credit Java's maintainers for treating their language as a technology and not merely as a product. Many languages respect the real-world physics of computers to stay relevant. Few respect the "metaphysics" until weird bugs keep emerging in the compiler and ecosystem. Bugs that can't be solved with more building, engineering, or even design. Because those bugs are not defects; they come from the concepts & axioms of the language itself, not its implementation.

Feature flags are gates for whether a piece of code runs; basically, an if-condition. Remote configs are a mechanism for changing runtime values without redeploying[1].

For example:

  # Feature flag — variant gate for rollout
  flag = sdk.check_gate(user, "checkout_flow")
  if flag == 'open':
      render_new_checkout()
  elif flag == 'warning':
      render_warning_checkout()
  else:
      render_old_checkout()

  # Raw remote config pulled — structured values for tuning behavior
  config = sdk.get_config(user, "checkout_settings") # if the config changes based on user or context, this "remote" config is considered "dynamic"
  timeout_ms   = config.get("timeout_ms", 5000)
  max_items    = config.get("max_items", 50)
  allowed_tlds = config.get("allowed_tlds", [".com", ".org"])

As I've seen it: the forcing function that separates the concepts are experimentation platforms: when human-control of feature flags is shared (via dynamic configs) with automated & randomized assignments. That's how Statsig built their system and, in part, why they could sell for a billion. Whereas companies that ignored the difference, like LaunchDarkly, struggled outside of feature flags.

[1] https://engineering.atspotify.com/2020/10/spotifys-new-exper...

[2] https://docs.statsig.com/dynamic-config/overview https://blog.x.com/engineering/en_us/topics/infrastructure/2...

Lower-case internet is ok as a tool for making spaces. But I reckon humane-ness, or really, virtue, is a habit built from within. And the habits the Internet rewards are generally the wrong ones.

My partner is a H1B tech worker employed by Meta; not working on AI/ML.

Given the Chinese government opposition to the Manus acquisition, is there any risk they could encounter for their upcoming H1B visa stamp in China?

Big Tech teams want to ship features fast, but measuring impact is messy. It usually requires experiments and traditionally every experiment needed one Data Scientist (DS) to ensure statistical validity, i.e., "can we trust these numbers?". Ensuring validity means DS has to perform multiple repetitive but specialized tasks throughout the experiment process: debugging bad experiment setups, navigating legacy infra, generating & emailing graphs, compensating for errors and biases in post-analysis, etc. It's a slog for folks involved. Even then, cases still arise where Team A reports wonderful results & ships their feature while unknowingly tanking Team B's revenue— a situation discovered only months later when a DS is tasked to trace the cause.

Experimentation platforms like Statsig exist to lower the high cost of experimenting. To show a feature's potential impact before shipping, while reducing frustrations along the way. Most platforms will eliminate common statistical errors or issues at each stage of the experiment process, with appropriate controls for each user role. Engs setup experiments via SDK/UI with nudges and warnings for misconfigurations. DS can focus on higher-value work like metric design. PMs view shared dashboards and get automatic coordination emails with other teams if their feature is seen as breaking. People still fight but earlier on and in the same "room" with fewer questions about what's real versus what's noise.

Separating real results from random noise is the meaning of "statsig" / "statistically significant". I think it's similar to how companies define their own metrics (their sense of reality) while the platform manages the underlying statistical and data complexity. The ideal outcome is less DS needed, less crufty tooling to work around, less statistics learning, and crucially, more trust & shared oversight. But it comes at considerable, unsaid cost as well.

Is Statsig worth $1B to OpenAI? Maybe. There's an art & science to product development, and Facebook's experimentation platform was central to their science. But it could be premature. I personally think experimentation as an ideology best fits optimization spaces that previously achieved strong product-market fit ages ago. However, it's been years since I've worked in the "Experimentation" domain. I've glossed over a few key details in my answer and anyone is welcome to correct me.

Business-wise, I think getting acquired was the right choice. Experimentation is too small & treacherous to build a great business, and the broader Product Analytics space is also overcrowded. Amplitude (YC 2012), to date, only has a 1.4B market cap.

Joining the hottest name next door gives Statsig a lot more room to explore. I look forward to their evolution.

I’m curious (as a solo dev) if there’s a market for such a product.

Smells like a strong bias against employees in favor of management and founders.

If the OP consulted with Turso on this blogpost, then Turso probably believes the reported behavior is indeed a failure or a flaw, which they think a local db should be responsible for.

The confusion is that Limbo, their solution to this presumed problem, is not mentioned in the article which means that everyone has to figure out where this post is coming from.

It makes sense for our age. Amid a thousand distractions, typing on the keyboard gives the illusion of getting a grip. Note-taking on my computer gives the illusion of a second brain. Ululating on the internet gives the illusion of sharing thoughts.

Instead of "writing is thinking", I prefer "thought precedes speech" https://inframethodology.cbs.dk/?p=1127; it fits the small human mind better though I've yet to learn it properly.

It's the organizing principle of the second generation of Rust's leadership[1]. Formally, it means "zero runtime cost"[2], but the now-former maintainers operated as though it meant Rust could get rid of all cost. The belief was that they can have a language that's faster than C, safer than Ada, more ergonomic than Java, more memory safe than Go, by either making the compiler do more work, or working more on the compiler. In practice, I think this belief caused massive complexity in the compiler, trade-off dishonesty in the community, and bad evangelism in domains unsuited for memory safety (e.g. games programming)

[1] Graydon, the original author of Rust, was against this idea.

[2] The term originates from C++ as "zero overhead" which was smaller in scope, and not a governing principle of the C++ language.

The Chinese Government backed up their mandate with money. Lots of money, allocated well, over a long period of time. In the absence of that sustained political will, I think this initiative would have succumbed the infighting and finger-pointing that the article above describes.

[1] https://en.wikipedia.org/wiki/Electric_vehicle_industry_in_C...

Rather, I saw the start of a flamewar below (not caused by you) and I figured I'd say my piece. But it came out wrong and you got flagged undeservingly.

Sorry about that :\