I believe it's only a strategy that works in the short-term. If you ever expect the software to be stable, quality, and human-maintainable, you're going to need a good test suite (hopefully not AI-generated) to get away with that little ownership of the code. That said, this is great for prototypes or throw-away software, provided you don't mind being entirely reliant on an LLM for maintaining the code (speaking from experience, a human usually does not want to touch a fully vibe-coded application that they've never reviewed).
If I'm wrong about this, I would expect to see a new field of LLM-automated software engineering with at least the same level of rigor and quality as the existing human-led processes, and in the absence of this, we're just further degrading software quality for dubious gains (is it to go "faster", is it because we are being compelled to by leadership, is it out of fear of being left behind by competitors?). I can't imagine any other engineering discipline as critical as software being "vibed" - if I had learned that the local bridge had no human inspection, simply was "vibe-checked", it might be a good bridge, but I'm not going to be the one to test it.
I couldn't agree more. We really need more engineers to be vocal about how stupid these ideas are. "How about we throw out 30+ years of software engineering literature so we can 'move faster'?" What if the customers on the other end don't want new features, they just want software stability? If SQLite came out with a new LLM-written feature a week, would it be a better library? If you know what to build, writing software right the first time pays for itself over time. LLMs are still great, but more for rapid prototyping, researching, log diving, one-off scripts...
Given the amount of spaghetti code I see frontier models generating on a daily basis, I cannot take seriously the idea that LLMs will write all the code, and somehow our systems will not degrade in performance, reliability, and maintainability. At least, not until we have really good understandings of how to maintain systems autonomously. All of our technologies were designed for humans, it may require a new set of technologies that are "LLM-proof". But I don't see this happening anytime soon.
This is an amazing result. Can't wait until they release this to the general public, and I hope it's only a matter of time before other models are accelerated. I long for the day that regular consumers can run such models locally on specialized hardware.
Nice, I've been waiting for something like this for years. Meanwhile the gaming scene on linux has been getting better slowly thanks to Valve and friends... but being able to boot into a Windows VM with graphics acceleration was previously a pain on Linux machines that only have a single discrete GPU - I'd wonder whether a solution like this would work with VirtualBox, or only on QEMU.
100% agree - you don't need the most up-to-date model to have something that's useful in agentic contexts. They could even produce chips with weights that make all the decision making/logical reasoning and have it delegate to other specialized agents. If it becomes cheap enough to print a run of custom chips, releasing a batch for each major advancement does not seem unreasonable for SOTA companies.
Great book recommendations (I have read parts of the Google SRE book and Designing Data Intensive Applications)! I will also add a couple gems that I don't see often-cited: Simple Object-Oriented Design by Mauricio Aniche and Secure by Design by Dan Bergh Johnsson, Daniel Deogun, and Daniel Sawano. Both have a lot to offer in terms of software design and maintaining complexity. I also liked Refactoring to Patterns by Joshua Kerievsky.
I had the same experience. I don't know why they haven't invested more in this feature. Heck, if they classified every site in their index as "AI/not AI" using machine learning, and had a large allowlist of "known-good" sites, that would take care of most of the problem (I am aware of the drawbacks of text-only AI classification, but they could use other indicators, like the site's publication date, domain name, domain registry date, etc.). I think for right now, the "state-of-the-art" for non-AI search is going to entail maintaining large whitelists/blacklists of websites that are crowd-sources by users (e.g. what you can find in some GitHub repositories). I was hoping that's what SlopStop was going to be, but it is not nearly as effective as it needs to be.
As someone who is also eyeing Bifrost, I am curious to know what made you choose Bifrost at all / why you decided to use an LLM gateway. I also was considering OpenRouter, since it provides pretty much every model, with same-day releases for new models. One thing that is attractive to me about Bifrost is that if I decide to leave OpenRouter tomorrow, I can do so without touching any other part of my stack; I'm hoping self-hosting models becomes more viable, and then I can become less dependent on third party LLM providers like OpenAI/Anthropic/OpenRouter, and I would not need to worry about a model that I depend on suddenly being deprecated.
If I'm wrong about this, I would expect to see a new field of LLM-automated software engineering with at least the same level of rigor and quality as the existing human-led processes, and in the absence of this, we're just further degrading software quality for dubious gains (is it to go "faster", is it because we are being compelled to by leadership, is it out of fear of being left behind by competitors?). I can't imagine any other engineering discipline as critical as software being "vibed" - if I had learned that the local bridge had no human inspection, simply was "vibe-checked", it might be a good bridge, but I'm not going to be the one to test it.