BTW the model is 15M param (Not-ordinary transformer), and is pre-training as we speak it's only has about 800 steps of it's max 20k training steps.

No SFT and all. I just wanted how the model stability holds, if you ever worked on pre-training LLMs is this what you see as well. (Mathematically makes sense to me as acc is about: 0.17, but I want to you know be sure, this one is expensive compute and as an independent researcher I have more to lose if I see it failing. So it is for me big loss if did not work out.)

When testing the scaled down 1k, 10k & 100k param architecture on set patterns the model showed high intelligence. Only trained on couple of steps <500 and the model learned the multiplication scheme taught to it in all test sizes and the 1k variant was perfect till it was trained but started failing as the model input was increased and was held out/never shown that data in training run (it did 64/100 on those unseen tests, still good considering a vanilla Transformer ~600k params did less than that) ; 10k and 100k showed sparks of supreme intelligence per param (outperforming pattern held out training by upto 10M digits more than it was ever trained on... the model was trained to multiply till 10000, it multiplied till `10000-(12 zeros more)` with 100% accuracy even surpassing CPU computation which is off by some float points. 10k/10k score for both 10k and 100k model. Idk how but 100k model somehow made a logical explanation on it's own for addition. It was able to add using multiplication.

I am really seeing this as something; this 15M param model as we speak outperforms Qwen-3-4B-base on this same training data in terms of same hyperparameter checks.

For training dataset being ~1.05B tokens of high quality general domain data, science/creative writing/maths/general school knowledge.

For what I can see the model is pattern recognition beast. Like it learns like crazy and at crazy fast speed. I was training it's 1M param model, you will not believe it, it learned the entire tinystories dataset which has like 2M rows (repetitive and close to `Once upon a time` types I know... since LLMs are normalised output machines "generalization" is obvious once saturation is reached.), back to the experience so it learned the format in 500steps (not accurate or too coherent) but dammit the model was really close (like even assumed the next character name perfectly) to the training data it never even get too see. those 500 steps were of 64k samples out of 2M samples.

This is why I am trying to scale as much as my budget allows me to and test this model. If it fails I may be a fool; I can only find out that after words (I may already be a stupid fool already) 😄

So if you see something strange help me please don't be afraid to ask questions apart from architecture details I can give you all the knowledge.

https://preview.redd.it/j6if0jxm8h2h1.png?width=1054&format=png&auto=webp&s=2c5be301908e4861c515b5f22d1f72974606d264

System Design SEO REPORTS

Someone showed me their monthly SEO reporting process and I couldn't let it go.

GA4 open, Search Console open, copy numbers into a doc, write a summary around them, format a PDF, send it out. Every month. Per client. Two to four hours of just moving numbers from one screen to another.

So I built a workflow to replace the whole thing.

Workflow

Here's how it's structured. An OAuth connection pulls traffic, clicks, impressions, top pages, and keyword data from both GA4 and Google Search Console. A pre-computation layer calculates period over period deltas, flags anomalies, and surfaces keyword movement opportunities... then packages everything into structured JSON so the LLM isn't just guessing, it's working from real numbers. That JSON goes to an LLM which writes a 400 to 600 word narrative report grounded in the actual data. Finally it exports a white label PDF with custom branding applied.

Start to finish, under one minutes.

The part I spent the most time on was the pre-computation layer. Sending raw GA4 output straight to an LLM produces garbage. The structured JSON step is what keeps the report grounded and makes the narrative actually useful instead of generic.

Happy to walk through any of the nodes if you have questions, especially the data transformation step before the LLM call.

Github link: n8n-workflows/SEO Reports/Automate Weekly SEO Report with AI Insights.json at main · vk-jr/n8n-workflows

Hi folks,

I am setting up local AI agents. I don't know the exact term for that but I want to know is there anyway we can evaluate the models like in which domain they are good and how good the are ? Is there any website stating that or it can be figured out using some tests or some kind of score ?

Lately I feel like the conversation around AI coding has shifted from just "which model is best" to which agent + model combination actually works well in real day-to-day development

Curious what people here are genuinely using regularly now:

• Claude Code + Sonnet?
• Codex + GPT-5?
• Cursor with Claude/OpenAI?
• DeepSeek/Qwen/Minimax setups?

And beyond benchmarks, what actually holds up during real work?

Things I'm curious about:

• which setup feels the most reliable
• which one handles larger repos best
• what breaks the least during long sessions
• what gives the best balance between speed vs accuracy
• whether autonomous agents are actually useful long term or still mostly hype

I've noticed some models seem smart in isolation, but once you put them inside an agent workflow (tool calling, repo edits, terminal usage, context management, etc.) the experience changes a lot

Also feels like cost becomes a huge factor once you start using these tools heavily every day

Would love to hear honest experiences from people using these setups daily rather than just benchmark comparisons....

For the past 3 years, making AI smarter had one rule: bigger is better. More data, more compute, more power. It worked. Then it stopped working.

The dirty secret about scaling laws is that the gains aren't a straight line — they flatten. And at some point the math turns brutal. A 4-8x increase in model size requires a 5x increase in training data just to not get worse. When your model has already consumed the entire internet, where does that data come from?

That wall is here now. Right now, individual server racks the size of a fridge are consuming 11x more power than they did in 2020. By next year that quadruples again. You don't need a bigger server room anymore — you need a dedicated power plant.

So how are models still getting smarter? Two things happening quietly:

1. Synthetic data — AI generating curated training data for itself. The student teaching the student.

2. Test-time compute — Instead of spending the same energy on "hello" as on a hard math problem, models now dynamically scale how much they think before answering. That "please and thank you" token problem that was costing companies billions? Largely solved.

But the next step is weirder. Companies are already working on latent reasoning — models that stop thinking in English entirely. They'll process your prompt through thousands of simultaneous thought directions in raw mathematics, then translate only the final answer back into language. It's not science fiction. It's the next 18 months.

Which brings the uncomfortable question: if AI trains itself, scales its own compute, and thinks in math we can't read — what exactly is the human role?

My answer, which surprised me while making this video: your imperfections.

When intelligence is cheap and abundant, perfection becomes cheap too. The only signal left that something was made by a human is the rough edges — the wrong turns, the inconsistencies, the things that aren't optimal. Those stop being weaknesses. They become the asset.

The research showing that children who use AI from birth never develop certain cognitive skills isn't just a safety concern. It's the real story of what we're trading away.

Made a full video on this if anyone wants the complete picture: [link in comments]

Hey! Is anyone here into AI research or currently working on AI-related projects? I’d love to know what kind of research you’re doing and maybe learn more about the field. Feel free to share!

As a beginner wanting to learn agentic AI for free, you’re in a good spot: there are several free or mostly‑free resources that start from zero and don’t assume coding skills.

1. Start with SimplAI University (free)

• SimplAI University has a free “Fundamentals” course (50+ hands‑on lessons over about 11 chapters) that teaches agentic AI basics on the SimplAI platform, with no coding required.
• It’s a good fit if you want to learn by actually building simple AI agents and workflows, while staying inside one ecosystem.

2. Microsoft’s “AI Agents for Beginners” (free)

• Microsoft Learn offers a free 10‑lesson course called “AI Agents for Beginners”, which takes you from basic concepts to simple code.
• It’s practical, language‑agnostic in spirit, and designed so you can start with the basics and then move to frameworks like LangChain / AutoGen later.

3. Other beginner‑friendly free options

• Hugging Face Agents Course: Free interactive course that walks you through building agentic systems using Hugging Face tools and LLMs.
• YouTube crash courses: Channels like AI Agents for Beginners and Codebasics offer multi‑lesson free videos walking through agentic AI and frameworks such as LangGraph.

Simple learning path (for you)

1. Start with SimplAI University’s free Fundamentals track for a gentle, no‑code intro.
2. Parallelly, watch or skim Microsoft’s “AI Agents for Beginners” to see the underlying concepts and simple code.
3. Then pick one short YouTube crash course (e.g., LangGraph or “AI Agents for Beginners – Part 1”) to practice building a tiny agent that does something simple like answering questions or summarizing text.

If you tell me whether you’re okay with a little coding (Python) or want to stay 100% no‑code, I can map out a step‑by‑step weekly plan tailored to your comfort level.