throwthrowuknow

it doesn’t need to go in the phone if it only takes a few milliseconds to respond and is cheap

these aren’t made for general chatbot use

This is simply yet another outdated analogy from haters that are failing to keep pace with the current frontier because they are too busy getting high on the anti-hype.

We’re well past the need to retry the same prompt multiple times in order to get working code. The models with their harnesses are properly agentic now, they can find the right context, make a plan, write the code, run the tests and fix the bugs with little to no intervention from a human.

The hardest part now is keeping up with them when it comes to approving the deliverables and updating the architecture and spec as new things are discovered by using the software. Not new bugs but corrections to your own assumptions you had before the feature was built.

The hard part is almost entirely management.

That’s something to seriously think about.

just wait until someone lets a local model loose on there

Now who’s saying always? Scratch the surface of anthropology and history and you’ll find those universal rules are a fiction.

So how are you as a person able to keep all of those rules in mind when you make a change? How would you train a junior engineer to do your job? Perhaps looking at it from that angle will solve your problem.

Try writing more documentation. If your project is bigger than a one man team then you need it anyways and with LLM coding you effectively have an infinite man team.

Religion isn’t based on “the greater good”, it’s always explicitly about the will of the god or gods involved and obeying or appeasing them.

Only if you are paying per token on the API. If you are paying a fixed monthly fee then they lose money when you need to burn more tokens and they lose customers when you can’t solve your problems within that month and max out your session limits and end up with idle time which you use to check if the other providers have caught up or surpassed your current favourite.

They don’t need to choose, just let them build their own power generation capacity.

What the hell is going on in this type of argument anyways? Utilities are normally private businesses so what does the state have to do with it?

Even if Opus 4.5 is the limit it’s still a massively useful tool. I don’t believe it’s the limit though for the simple fact that a lot could be done by creating more specialized models for each subdomain i.e. they’ve focused mostly on web based development but could do the same for any other paradigm.

I can agree that it doesn’t seem exponential yet but this is at least linear progression not a plateau.

Please read gingerbill’s blog to understand more about why Odin is designed the way it is https://www.gingerbill.org/article/

Odin’s design is informed by simplicity, performance and joy and I hope it stays that way. Maybe it needs to stay a niche language under one person’s control in order to do so since many people can’t help but try to substitute their own values when touring through a different language.

Going all in on AI generated code has taught me more about project management than I learned in the last decade. I also have a much better perspective on what it’s like to be a client contracting a developer to build an app for them. The best part is the AI actually follows all of the processes that you ask it to. Today was the first day that I wrote code in over a week and I still ended up asking for some review from Opus and it went perfectly. At this point no one has an excuse for shipping slop if they can afford $20 a month.

It’s all in how you use it. If you want to learn you can just tell it to walk you through the code or write a tutorial with examples and exercises or give you programming problems to solve or use the socratic method or recommend the best human written tutorials and books or review your code and suggest more idiomatic techniques or help you convert a program from one language or paradigm to another and a million other ways.

I like the AI written tutorial method, both Opus 4.5 and Gemini 3 are good at this. You just have to put the effort in to copytype, make changes, ask questions and put what you’ve learnt into practice. AI code review is also great for discovering alternatives you don’t know about.

Good to hear that’s working. When I was using copilot before Opus 4.5 came out I found it didn’t perform as well as Claude Code but maybe it works better now with 4.5 and the latest improvements to VSCode. I’ll have to try it again.

Yeah it’s really not too bad but it does get frustrating when you hit the session limit in the middle of something. I also add $20 of extra usage so I can finish up the work in progress cleanly and have Opus create some notes so we can resume when the session renews. Gotta be careful with extra usage though because you can easily use it up if the context is getting full so it’s best to try to work in small independent chunks and clear the context after each. It’s more work but helps both with usage and Opus performs better when you aren’t pushing the context window to the max.

Session limit that resets after 5 hours timed from the first message you sent. Most people I’ve seen report between 1 to 2 hours of dev time using Opus 4.5 on the Pro plan before hitting it unless you’re feeding in huge files and doing a bad job of managing your context.

I half agree, but it should be called “Hobbiest” since that’s what it’s good for. 10 minutes is hyperbolic, I average 1h30m even when using plan mode first and front loading the context with dev diaries, git history, milestone documents and important excerpts from previous conversations. Something tells me your modules might be too big and need refactoring. That said, it’s a pain having to wait hours between sessions and jump when the window opens to make sure I stay on schedule and can get three in a day but that works ok for hobby projects since I can do other things in between. I would agree that if you’re using it for work you absolutely need Max so that should be what’s called the Pro plan but what can you do? They chose the names so now we just need to add disclaimers.

Imagine if we refused to publish any material or exhibit recreations of dinosaurs because the only evidence we have are fossilized skeletons and a few skin texture impressions.

Both Anthropic and Google have clear directions. Anthropic is trying to corner the software developer market and succeeding, Google is doing deep integration with their existing products. There’s also Deepseek who seem hell bent on making the cheapest SotA models and supplying the models people can use for research on applications. Even Grok is fairly mission focused on with X integration.

their explore page is a firehose of examples created by users and you can see the prompt used so you can compare the results in other services https://www.midjourney.com/explore?tab=video_top

Man, HN really is full of shitty redditors now. Enjoy your bucket.

Node editors are also a good way to quickly compose fairly complex programs or modules even for experienced programmers. Working with them also forces you to design good interfaces for your code and eliminate tight coupling that often plagues many code bases. They do have downsides especially when using them to create complex workflows where they tend to evolve into ugly spaghetti monsters but you can prevent that by moving those workflows to code once you’ve settled on a design. This library seems like it would make that easy to do.

So what information have you read in the dossier that has helped you with your sober decision making?

The people who only use the end products will be less skillful the same way people today are less skillful at cooking, sewing, carpentry, animal husbandry, etc. because they avail themselves of modern services made possible by technology. If you are utilizing AI to its fullest you won’t be less skillful but you will have to trade your current set of skills for another. The enlightenment you’re feeling is the same you have when you’re promoted to management and don’t have time to get your hands dirty.

You have to look at the late 1800s for examples. It won’t be wars over data centres and winning won’t be simple or even possible. It would look like the wars that the U.S. Army fought against indian tribes or like the British, French, German and Dutch colonization of Africa. That is assuming there is an AI side and a non-AI side. Incidentally those conflicts did involve a lot of strategic infrastructure like railways and telegraph lines.

Fighting the expansionary actions of an AI enabled culture will not be as simple as bombing power plants, after all those are prime targets in any modern war and are well defended. How do you propose to win against an entire bloc of countries that have decided to use the products of AI to do whatever they wish with the world?

not exhausted, just not currently being collected. Generating via existing models is ok for distilling a better training set or refining existing low quality samples but won’t break out of distribution without some feedback mechanism. That’s why simulation is promising but it’s pretty narrow at the moment. There’s still a lot of space to fill in the point cloud so coming up with novel data collection methods is important. I think this is off topic though, my original contention was if you take too thin of a slice you won’t get a very useful model.

The knife edge we’ve been balancing on since 1945 is now held by weak and trembling hands who have never felt the touch of a blade. If nuclear weapons proliferate further the probability of someone using them because they think they can win with a first strike and limit the scale of a nuclear exchange will grow exponentially. Fill your pockets with salt and hope that if it happens the fallout, both literal and metaphorical, is confined to the region that starts it. Maybe a hundred million funeral pyres and a desert of glass will be a monument large enough that we’ll never forget again.