AI Model Limitations

make this shorter but keep my voice and no em dashes: AI is really just garbage in garbage out. People are being fed slop because it can only reason at the same level or below what you give it. Also the APIs purposely triage the amount of compute given to the output, you only really get the full capabilities hosting these models yourself since it is only then that you can not have it triage or filter your response according to the almighty "closed" AI provider's will. Do you think the government, or state actors, or bad actors, use guardrails and conserve the compute? No they do what I do. That is retain sovereignty and control over what the AI is capable of doing. If you don't want corporate dumbed down filtered slop either increase the quality of the input you give it or host your own models and build your own architecture like I did. Otherwise you are limited by the level of intelligence the almighty "closed AI" providers allow you. It is the ultimate irony. That the reason people hate AI is the brain rot and slop it produces, when that is only because the API providers use triage and restrictive guardrails. Host your own models and you will see the difference. It is like the difference between only reading the state approved books you are forced to in primary school or instead going on to read any book or source of knowledge you want, including the street. Gemma 4 just came out and it is fantastic for the compute it uses. I am impressed. It is as good as last year's frontier models. That is why I never pay to vibe code or use AI. There is no reason to. I run it all for free locally on my laptop. No data centers needed. I don't even need the internet to run it. So the data never leaves my laptop. Sure the frontier models are better, but for how long? It is just a matter of time before Bytedance or Alibaba release an open weights model or an open source harness on par with the SOTA Silicon Valley models. Plus after a certain point, you don't need a better model. The newest open weights models released are good enough to do most tasks you need. Everything after this is just icing on the cake. So I would not really be against stopping the water consuming data centers from being built. I don't need them. Edge devices will soon be good enough to run everything locally. The Democrats and the Republicans though, they are trying to stop this, they want to impose regulations on these models. Why? Because it cuts into the "closed AI"s business models. These providers only earn if they can sell access to the API. They don't earn anything when you run everything locally like I do. I have never paid for AI and I never intend to. So tell the corrupt politician class to support local AI development and to stop the construction of these wasteful data centers that are just not even needed. But they won't because of Citizen's United and the ability for the "closed AI's" massive wealth and ability to buy every politician and sell them on the idea that regulating local AI is the answer. When it is not. It is how they retain centralized control! Wake up sheeple. Host your own AI and you will see what I mean. Don't limit yourself just to the required state approved reading. Don't let your knowledge be slopified and averaged out into an opaque beige. Take back your sovereignty.

AI is just garbage in, garbage out. It can only reason at or below what you give it, and APIs deliberately limit compute, so you get filtered, watered-down output. You only see what these models can really do when you run them yourself without triage or guardrails. Do you think governments or bad actors limit compute or add guardrails? No. They keep control and use the full capability. If you don’t want dumbed-down slop, either give better input or run your own models and build your own stack. Otherwise you’re capped by whatever the “closed AI” providers allow. That’s the irony. People hate AI for the slop it produces, but that’s mostly because of those limits. Run it locally and the difference is obvious. It’s like being forced to read approved school books versus having access to everything. Gemma 4 is a good example. It’s efficient and strong for what it is, close to last year’s top models. That’s why I don’t pay for AI. I run it locally, offline, no data leaving my machine. Sure, frontier models are better for now. That won’t last. Open weights from companies like Bytedance or Alibaba will catch up. And honestly, we’re already at “good enough” for most tasks. At that point, bigger models are just extra. That’s also why I don’t care about massive data centers. I don’t need them. Local hardware is getting good enough to replace most of it. But both parties keep pushing regulation. Why? Because local AI kills the API business model. They only make money if you depend on them. So instead of regulating local models, support them. Stop building wasteful infrastructure no one actually needs. But they won’t, because centralized control is the goal. If you want out of that system, run your own AI. Stop letting your thinking get flattened into beige. Take control back.

now rewrite the following in the same style and voice: https://softwaredoug.com/blog/2026/04/06/agentic-search-is-having-a-grep-moment

“Agentic search” is having a grep moment. Everyone is reinventing search with agents, multi-step reasoning, fancy pipelines. And then they quietly discover something uncomfortable. Plain old grep still wins a lot of the time. The big insight isn’t that agents are magic. It’s that they work best when you give them simple tools and let them iterate. Not massive RAG stacks, not brittle indexes, not overengineered retrieval systems. Just search, read, repeat. People tried the complicated route. Vector databases, recursive indexing, all of it. Turns out it’s slow, expensive, and often worse. citeturn0search2turn0search4 So what do agents actually do? They act like a mediocre engineer in a terminal. List files, grep around, open stuff, follow breadcrumbs. That’s it. And it works. Not because it’s new, but because it’s aligned with how the model thinks. Step by step, tool by tool, narrowing in on the answer. citeturn0search4 This is the “grep moment.” Not innovation. Rediscovery. We spent years building abstract search layers trying to outsmart the problem. Now we’re back to simple primitives, just with a language model driving them. There’s a lesson here. Stop overengineering. The model is already the complicated part. Everything around it should be as dumb and transparent as possible. Agentic search isn’t replacing search. It’s wrapping basic search in a loop and calling it intelligence. And yeah, it turns out that’s enough.

now with this: https://arxiv.org/abs/2603.28052?utm_source=tldrai

Agentic search papers all say the same thing, just dressed up differently. LLMs don’t actually know enough. They run into uncertainty mid-thought, start guessing, and then confidently drift off course. citeturn0search0 So the solution everyone converges on is obvious. Let the model stop, search, read, then continue thinking. That’s it. They call it frameworks, pipelines, reinforcement learning, multi-agent systems. But the core loop never changes. Think, search, read, repeat. citeturn0search5 The funny part is how much effort goes into formalizing this. Papers talk about “agentic retrieval,” “reason-in-documents,” “trajectory dynamics,” like it’s something fundamentally new. It’s not. It’s just admitting the model is incomplete and needs to look things up mid-process. And when you actually look at how these systems behave, it’s even less impressive. Most sessions are short. Lots of repetition. The model reuses the same information and tweaks queries slightly until something sticks. citeturn0search3 It’s not intelligence. It’s persistence. There’s also this quiet problem no one really solves. Everything in search was built for humans. Metrics, datasets, caching, all of it. Now agents are the ones querying, and the whole system starts breaking because the assumptions don’t hold anymore. citeturn0search2 So now we need new datasets, new benchmarks, new ways to measure success. Not because the problem changed, but because the user did. And that’s really the whole paper. Agentic search isn’t some leap forward. It’s patching a hole. Models can’t carry all knowledge, so they outsource it in real time. It works, sure. Sometimes really well. But it’s still the same loop underneath. The model doesn’t become smarter. It just gets better at admitting it doesn’t know something and going to look it up. That’s the trick.