AI Frontiers

After building my dual-RTX 4080 rig (which I covered in my previous post), I felt like a kid with a supercar stuck in a school zone. It was time to take it to the track. I decided to reproduce the foundational 2020 OpenAI paper: “Scaling Laws for Language Models.” Why this paper? Because it’s the “Old Testament” … Read more

After spending weeks obsessing over scaling laws and raw TFLOPS, I decided it was time to move up the stack. It’s one thing to have a powerful model; it’s another to have an Agent that knows how to use it. I took the architecture described in my recent overview of AutoMind AI Agent — an adaptive agent for automated … Read more

Alright, fellow hardware junkies and algorithm enthusiasts. You know my journey: from building my dual-RTX 4080 rig to wrestling with scaling laws and even trying to birth a local data scientist with AutoMind. Each step has been about pushing the boundaries of what one person can do with local compute. But what if the next … Read more

We’ve all seen the benchmarks. The new “Reasoning” models (like the o1 series or fine-tuned Llama-3 variants) claim to possess human-like logic. But after building my dual-RTX 4080 lab and running these models on bare-metal Ubuntu, I’ve started to see the cracks in the mirror. Is it true “System 2” thinking, or just an incredibly … Read more

There is a recurring “wall” every AI hobbyist hits when working with Novel View Synthesis (NVS). You generate a beautiful second view of a room, but as soon as you try to “walk” further into the scene, the geometry falls apart like a house of cards. Recently, I came across the paper “SceneCompleter: Dense 3D Scene … Read more

One of the most frustrating limits of early Neural Radiance Fields (NeRF) was their “statue-like” nature. They were great for static objects, but as soon as something moved, the math broke. Recently, I’ve been obsessed with the paper “Unlocking Dynamic Scene Understanding: Neural Radiance Fields for Deformable Objects.” The premise is brilliant: instead of just mapping coordinates (x,y,z) to … Read more

We’ve all been there: you ask an LLM a question about a recent event or a specific technical paper, and it either hallucinates or admits its knowledge cutoff. That’s why the paper “Enhancing Large Language Models with Retrieval-Augmented Generation: A Comprehensive Overview” caught my eye. RAG isn’t just a “feature”—it’s a fundamental shift in how we build … Read more

We’ve all seen it: a RAG system retrieves a document, but the LLM still “hallucinates” by misinterpreting a date or a name within that document. The ELEVATE paper (arXiv:2506.xxxxx) addresses this head-on with a sophisticated “Retrieve-Verify-Refine” loop. As a DIY researcher, I found this paper particularly compelling because it moves away from the “hope it works” approach … Read more

If you’ve ever opened a new app and been frustrated by its terrible recommendations, you’ve experienced the “Cold Start” problem. Traditional Matrix Completion tries to fill in the gaps of what you might like based on what others liked, but it often lacks context. The paper “Contrastive Matrix Completion: A New Approach to Smarter Recommendations” (arXiv:2506.xxxxx) proposes … Read more

One of the most human-like traits is the ability to see a new object once and recognize it forever. Standard Deep Learning sucks at this—usually, it needs a mountain of data. That’s why the paper “Unlocking Smarter AI: How Learning Conditional Class Dependencies Boosts Few-Shot Classification” (arXiv:2506.xxxxx) caught my eye. The authors argue that instead of looking … Read more

The dream of AI has always been to match human efficiency—learning a new concept from a single glance. In my Istanbul lab, I recently tackled the reproduction of the paper “Learning Conditional Class Dependencies: A Breakthrough in Few-Shot Classification.” Standard models treat every class as an isolated island. If a model sees a “Scooter” for the … Read more

If you’ve ever used a local Stable Diffusion setup, you know that long, descriptive prompts can sometimes slow down the sampling process. The research in this paper suggests that not every word in your prompt is actually “seen” by the U-Net during every step of the diffusion process. By pruning the least important tokens, we … Read more

Self-Adapting Language Models reproduction As an AI hobbyist, I’ve always been bothered by the fact that LLMs are “frozen” once training ends. You can give them a prompt, but they don’t learn from the conversation in a permanent way. That changed when I read “Self-Adapting Language Models”. The researchers at MIT introduced a framework called SEAL. Instead of waiting … Read more

As a DIY researcher, I’ve spent countless hours trying to get LLM agents to navigate websites. It’s usually a mess. You feed the agent a massive DOM tree or a high-res screenshot, and the model struggles to “see” the button it needs to click. That’s because the web was built for eyes and fingers—not for … Read more

Text-to-Image Optimization – we’ve all been there: you type a complex prompt into a stable diffusion model, and it ignores half of your instructions. It understands “a cat,” but it struggles when you say, “make the cat look slightly to the left, but keep the lighting from the previous frame.” The issue isn’t the model’s … Read more

The researchers introduced a two-part framework of Spatiotemporal activity generation AI that I found particularly elegant to implement on my rig: On my Ubuntu machine, I simulated the six MCP categories described in the paper: temporal management, spatial navigation, environmental perception, personal memory, social collaboration, and experience evaluation. Implementation the Spatiotemporal activity generation AI: Running the Parallel … Read more

It’s 2:00 AM in Istanbul, and the only thing louder than the wind off the Bosphorus is the cooling fans of my dual RTX 4080 rig. For weeks, I’ve been wrestling with a problem every LLM hobbyist knows too well: inconsistency. You ask Llama-3 a logic puzzle, it gives you a brilliant answer. You ask again … Read more

I’ve been covering AI research for a while now, but rarely does a paper make me stop everything and spend a week reproducing its experiments. Stanford’s “Mirage: The Illusion of Visual Understanding” (Asadi et al., 2026) — co-authored by Fei-Fei Li — did exactly that. The central claim was too provocative to take on faith: frontier VLMs confidently … Read more

After publishing my overview of the LLM Scaling Laws, I was left with a nagging question: Does this actually hold up when you aren’t training on a massive cluster? Theoretical comprehension is one thing, but as I’ve discussed in my previous posts, Implementation-First Research requires getting your hands dirty. So, I decided to take my local Ubuntu workstation — … Read more