To make your data more accessible AND reduce server load. More and more people are using AI, so there will be more and more bots as the trend seems to be accessing data with your AI, and maybe, only sometimes, actually reviewing what it found. I'm afraid that if you don't at least start to consider how we might accommodate the bots, you'll be walling off more and more real users going forward.
A wide portion of the Internet is not ad supported. Consider a user searching for product support: the company benefits from the user self-solving the issue without having a ticket filed.
This, may, however be to the detrimental of discussion platforms, but if that drives people to self hosted discussion platforms instead of relying on reddit, then it is a net positive.
Discourse's most recent blog delves into this and is an interesting read, I will submit that I think.
> All the people claiming "opus 5.5 is just as good" tends to make me think some people only do simple tasks or do not care about correctness or quality.
Have you considered the possibility of the problem being in the way you prompt a model and prepare it's context? I've been pushing myself to using mainly the cheapest models and I see them deliver the same results once we provide enough context or lay out a plan.
I'm pretty sure that's not the issue. For example I had one problem where Opus struggled for several weeks without getting us anywhere, flip-flopping in its decisions, generating huge amounts of irrelevant hacks. That obviously went through compaction several times, but I manually maintained explicit documentation and state of the project for it to work with, and reset the session as needed.
Fable made it work correctly in 2 days, it stayed on point, identified the real problems, and gave me clean code that was suitable to merge.
The task was simply to warm the hot path of an app so that latency was not as jittery for some rare, but important, events.
Fable built a reliable repeatable benchmark, disassembled the code to figure out how much could actually fit in L1, made tactical changes to eliminate bloat, and reliably achieved high coverage with minimal edits.
Opus just tried a bunch of random ideas without a plan, hoping to find something that sticks.
> I'm pretty sure that's not the issue. For example I had one problem where Opus struggled for several weeks without getting us anywhere, flip-flopping in its decisions, generating huge amounts of irrelevant hacks.
Sounds to me that you're describing a model trying to navigate ambiguity.
> Fable made it work correctly in 2 days, it stayed on point, identified the real problems, and gave me clean code that was suitable to merge.
If the problem lies in identifying the real problem, that sounds like this is a issue in how you setup the problem statement, aka provide context.
I mean, there are many reasons why in the early days of LLMs and AI code assistants we saw some people using the likes of gpt4.0 to enable software factories while others swore the models were unusable.
There was nothing ambiguous about the task. It had a clear goal and a clear gate to assess success, a gate that takes less than 2 minutes to assess if run correctly (which it didn't until I forced it to encode it as a program).
Your argument that holding the model's hand and telling it what to do and how to achieve the task would make it perform better is ridiculous. It's equivalent to not only giving it the answer to the problem, but also staying all day watching it while it re-derives the solution is correct, making it not only redundant but a waste of time and money.
I remember after a day it made me write in the handoff a hard lesson it learnt after burning 500 dollars of tokens: warming up a synthetic path other than the one the benchmark takes does not meaningfully decrease cache misses. No shit Sherlock, 500 bucks well spent.
It's just literally much dumber than Fable is, which is my argument. I'm not saying it's not usable, I'm saying the difference is quite obvious.
> I feel for kids growing up with LLMs like this. They will never impress a parent, teacher or peer with a simple “hello world” type text game, when the peer could generate a full multiplayer video game with a couple of prompts.
Adults are also desensitized to impressive deliverables. We see it even among peers who barely bat an eye when someone goes above and beyond to deliver a feature. The toxic ones even try to downplay your achievements by throwing around claims that your deliverables were vibecoded.
> "one-shotting" is fun for a low (human) effort toy, but it's a mistake to get overly hung up on LLMs getting everything right on the first try, since a lot of that is the harness and the internal prompting that makes it burn extra tokens to test everything to get it in that one "shot" (ui turn).
I'd be more specific and state that these models are now way far from being LLMs and instead they are full blown agent orchestration systems capable of running multi-layer ReAct loops and tool-calling, and with your whole session history as context. It doesn't make any sense to refer to them as glorified chatbots as the LLM part is nowadays far from being the component that delivers the highest value.
> as the LLM part is nowadays far from being the component that delivers the highest value
Yes it is. The rest of the agent could basically be a while loop, a two sentence system prompt and a shell invocation, and it would still work reasonably well.
These agent swarms with harnesses are certainly more than just an LLM, but the LLM is certainly the integral part that delivers the most value. Without it, there is no AI.
With a LLM alone, all you have is a stateless chatbot. If you increase the parameters of the LLM by a few orders of magnitude, you still have a stateless chatbot.
If you start with an anaemic LLM and run it with a ReAct loop and tool-calling, you now have something you can use to do agentic coding and RAG-like capabilities.
There's a reason why relatively small open weights models are starting to perform on par and even outperform models that need a huge datacenter to run.
Which ones, and which models? https://openrouter.ai/benchmarks/gpqa-diamond shows Kimi K3.1 (not small) roughly on par with Gemini 3.7 Flash. I see no coding benchmark there that could substantiate your claim, and it doesn’t match my experience either, so I’m curious what you are basing it on, but you’re leaving the onus on me to prove you right.
> Almost everywhere I the world railways are state owned and heavily subsidised and still aren’t that attractive to customers.
That's a remnant of the past, where only a nation state could afford massive projects such as creating a railway line crisscrossing throughout a massive country.
Since the last couple of decades we see private companies investing in railway lines, including high-speed railway corridors. We also see a major liberalization push in Europe to decouple railway operators from infrastructure maintainers, and now there are plenty of private companies operating railway services across the world. In Spain alone there are 2 or 4 private companies providing high-speed railway connections between major urban centers.
> The Yahoo founders left with never-fly-commercial money before re-joining the billionaire club by investing the money they had.
I think you're confusing people cashing out with actually creating a successful company. Yahoo is not what people would describe as flagship company, or pets.com or Ask Jeeves or myspace or Voat or whatever. The dotcom bubble made a lot of people rich, but most examples were at expense of bag holders.
Yup, if you google what founders in those 'failures' did was that they pretty much jumped vc side or founded more companies and have had very successful career. Not mind boggling like Bezos or Brin but still very cool career.
If you go by current standings in the market, yes, Yahoo is no longer at the top. Yet Yahoo was one of the most valuable and influential tech companies in the late 90s, early 2000s.
For a VC "running" would mean re-invest in the next thing to get more money. What alternative would you prefer? Keep doing the same thing without investing in new stuff?
They don't "take the money", there are other people that believe some things (profits/growth in a field) will last forever and will bring 10x return, so they are willing to give them the money.
I think it's starting to become the new normal. A couple of years ago I was pushing myself to go way out of my way to use AI code assistants in tasks I would never use before. Now I find myself doing the exact same thing but to manually write code.
> I really don't understand the strategy of these companies.
I believe I do, and I think it's the most rational thing an established company could do. There are companies burning through billion of dollars a year on hardware to the point they are syphoning all production. If you are a business, what do you do? Do you redirect all your short-term output to these dominant consumers or do you ignore them altogether?
If you read the article, right at the top they state that AMD's data center business is expected to grow 50% in the next 3 months (?!) and double that growth, and is expected to double that as it enters 2027. Thanks to that strategy, AMD doubled it's profits this year, and 63% of that was thanks to data centers. This is free money to them. What would compell anyone to say no to that?
In a gold rush, the ones making money are the pickaxe salesmen. AMD is one of the biggest pickaxe makers in the AI gold rush. Only a fool would think if steering away from this market.
Sounds like you're desperately searching for a problem where a specific make and model of a car can fit as a solution. That doesn't make the car any better than it is.
Why?
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