perrygeo
For positive things, for future hope and dreams, one has to be careful. We know that visualizing a positive outcome can be counter-productive...
Those who daydream about the perfect fitness routine don't go to the gym.
Those who daydream about building anything are less likely to take the step necessary to actually build it.
It's as though strong visualization of a positive outcome becomes a satiating reward in itself. Without any changes to reality, we become attached to the mere idea of possible future. Just as those dwelling on the past are dissociating from reality, those hoping for the future are willfully disengaging engaging with primary reality.
All that is to say, it IS all about attention. So pay attention to the present.
LLMs are pattern matching machines. Your code is the base template that will determine all patterns moving forward. No amount of context engineering or prompting can save your code from itself.
Pragmatically, it suggests we need a much greater investment in code quality, and much less hand-wringing over AI tooling.
Am I offending you personally by having used AI at all? Or is my contribution just running afoul of your technical judgement?
In order to get to "Is AI coding useful?" you need to personally work through "Is AI even acceptable?" - socially, environmentally, legally, biologically, militarily, technically, philosophically - I can absolutely respect those who say "hell no" to any of it!
- You can say "hell no" to contribution with any AI, due ethical concerns. Say it with your voice, not soft ban language. State your ethical objections.
- Much different to saying "hell no" to any AI contributions because of quality concerns; you know what's best for quality, security, bugs, etc.
- Much different still to saying "it depends" based on the nature of the contribution.
- Much different still to saying "yolo claude can commit to main".
They are all valid viewpoints. But deserve significantly different AI policies. Don't make me guess.
I'll call my phenomenon "management driven development" or MDD - a project that runs on hierarchical control, where management decisions are made on the hour and no one takes accountability.
Sadly, AI vibe coding is far worse. Following your manager's vibes, there was at least some semblance of accountability. Now when an AI vibe codes something, I'm not even sure whose ideas we're vibing on, or if they belong to anyone.
This is a perfect storm for enshittification. No one is accountable for the quality of what was delivered, only that it was delivered. As long as the latest fire is out, all is well. Right?
This might be the key point. There's legal ambiguity surrounding these licenses. Large companies with legal resources can draw their own line in the sand to protect themselves.
But small companies don't have the resources or the bandwidth to address the ambiguity. So a blanket ban (Zero AGPL and GPL for Internal or SAAS, Linux generally gets a pass) is rational.
Likewise, I see small dev shops without the resources to address anti-AI policy ambiguity are going to defer contributions until further notice.
If you're serious about an anti-AI policy, you owe the world a definition. It's not hard. You are making a strong statement - an outright ban. Make an equally strong case for what is and is not in scope. You must get in the weeds, since it is you yourself that is banning the weeds.
To quote Gustave Flaubert: “The art of writing is the art of discovering what you believe.” Failing to write the definition is a strong signal that no one thought through the problem.
Put simply, if you're so convinced that AI contributions will ruin your project, you owe the world a full and complete definition of what "AI contribution" means.
This reminds me of the legal ambiguity behind the GPL and its definition of "derivative work". A careful analysis of the GPL shows that using GPL software doesn't trigger any other clauses. But distribution does. What is the legal definition of distribution? Who knows!?! That's why most software companies have a partial (edit) ban on GPL software. Even companies that are willing to comply with the GPL, they cannot feasibly come into compliance with such a vague definition. So it's safer not to engage.
Same will happen with anti-AI policies. Even contributors who are fully willing to do things "by hand" will start avoiding these projects. No way to know if you'll accidentally run afoul of someone's AI radar. There's nothing to protect enforcement from becoming a witch hunt.
Code as an artifact is different in that it can be given agency, ie turned into software. The output of that software can be valuable despite the code being unintelligible to the vast majority of people. Very few people have read e.g. the source code for the TLS encyrption algorithms, yet billions of people's lives depend on it.
Consider how trivial it would be to write a program that would destroy every computer on the world. Any competent developer could write this in a hour. An LLM in 5 minutes. The trick is actually getting the code onto every computer in the world and running it! Until someone grants it agency and runs it IRL, the artifact has zero impact.
The models can produce whatever scary text they want. But if a human acts on it, connects that with their email or their drone weapon, it's the human who bears total responsibility. We desperately need some legislation to enforce this; otherwise I see a future where almost any accountability can be avoided by AI-washing the problem.
Instead of prompting, I hack on code in my editor.
My harness gather everything it needs from the local context - my git diff, my open editor buffers, etc. to assess what I've been doing. No chat. This is fed into phase 2 which tries to guess my intent. Then phase 3, it presents a plan to complete the work. The only user interaction is reviewing the plan and typing yes or no.
The quality of the plan of course depends on the quality of my uncommitted ideas. As it should be. If the plan goes off the rails, it's my fault. Do not chat your way to a solution! Abort the session and continue fleshing out the idea in source code/markdown.
The reason I like this is it forces me to at least take a stab at the work. I treat the AI like a relief pitcher to come in and close out the game.
I can't say this is the way to hyper productivity. I'm still slow. But at least I'm spending exactly 0 hours a day arguing with an LLM!
"The Strange Last Voyage of Donald Crowhurst" - This story is about the first round-the-world solo yacht race, and the one contestant who died at sea. His story is really strange - an anti-hero tragedy with a touch of mental illness. The author does a great job bringing the reader on this descent into madness.
"Protocols" - Andrew Huberman's recent book. Like his podcast, it's full of solid advice backed by published science - but also with some repetitive filler and product name drops. The sections on exercise and diet were not very informative to me, mainly because I've already incorporated most of that into my life already. The sections on light exposure and its relationship to sleep, melatonin and cortisol is the most fascinating new-to-me info. I've started morning/evening walking without my sunglasses to get those sweet sweet photons.
> Some 2.3 billion people are current drinkers. Alcohol is consumed by more than half of the population in only three WHO regions – the Americas, Europe and Western Pacific.
Globally, the majority of people are non-drinkers. It's not even close. The maps obviously tell a different story on a per-country basis. And if you live in an area with a super-majority of drinkers - yeah it's going to feel like everyone in the world drinks. But the global data says otherwise.
So to answer the original question, "Where does all this supposed productivity go?": Sitting in a github repo somewhere, undeployed.
Needing alcohol to socialize is not normal (though it has been normalized by some cultures). If you need alcohol to have fun, that's a sign of a serious problem that requires professional help.
It used to be a universal social lubricant, but now people stay home - the only people drinking in public are drunkards. Not a great look.
It used to be considered "healthy" to drink a bit. Twenty-one drinks a week was considered normal. Now we have decades of research meta-analysis concluding, in no uncertain terms, that the only safe amount of alcohol is zero. https://hsph.harvard.edu/news/alcohol-is-the-root-of-62-dise...
Alcohol increases anxiety. Given the zeitgeist and state of the world, it's not surprising consumers want to avoid spiking their cortisol in the evenings.
Alcohol is expensive. The cost of living is going up and wages are not. The budget has to adjust somehow - alcohol is an obvious place to make cuts.
Alcohol prevents the body from metabolizing real food. Fat burning stops while the metabolism shifts to rid the body of the toxins you just dumped in. Aside from the health impacts, you're gonna see this directly in your gut circumference via visceral fat. Considering the GLP-1 trend, beer gut is out of fashion.
So for me personally, and anyone else paying attention, there is no reason to touch alcohol. It's strictly poison and has no valid role in a human diet. Sorry farmers - maybe switch to growing something that provides nutrition and isn't toxic?
I'm trying to say that making such claims in the first place is invalid.
There is no language that is objectively more or less readable. Yes, I've read the studies. No, none of them come close to adequately addressing the confounding factor of prior exposure/education. A randomized controlled trial starting from childhood could establish such truths, but such an experiment has not been done. Until then, small-n studies that don't address this flaw in any way yet continue to make broad claims - "pop" science is perhaps not dismissive enough.
Cynically, this might be the real reason managers and investors love AI. It diffuses responsibility. No one is accountable. "Oops the AI messed it up" is a convenient excuse for bad management.
Difficulty is, by definition, relative to one's skill. You cannot so quickly discount the fact that 99% of programming is taught in Java/C style language syntax. If you've had lifelong exposure to Lisp, you might feel exactly the opposite. The author does a poor job of justifying why these pop-cognitive-psych theories should have more weight than prior exposure.
Personally, as someone with decades of exposure to both styles, I look at the factorial example and see everything I love about Lisp syntax - consistent, no magic keywords and syntax to memorize, it represents a tree just like my mental model of code, there's no way to fall through and forget an else, expressions instead of statements, no early returns ... literally everything about the Lisp example is more readable to me. YMMV.
I've heard that one before. This one is the last pedal, I swear :-)
What about a slightly different angle: a custom language runtime? The language itself stays true to the original but you build your own custom compiler and dev tooling: an expanded stdlib, LSP, linting, formatting rules, build systems, test runners, package manger, host extension system, etc.
This is becoming somewhat of a reality in the Clojure world. https://clojure.cc/dialects/ lists ~30 languages which are recognized as "Clojure" but have radically different host environments. Of course Clojure has macros too so nothings stopping you from going overboard on the DSL weirdness.
I could see the following scenario: Your company picks Typescript. You evaluate Bun and Deno and others but nothing really works. You take the most promising one, build a little test suite to make sure it stays consistent with the language spec, fork it, and add the runtime features you need. Your dev team still writes Typescript but you have full control over the tooling and how that code works at runtime.