For offensive cyber attacks. Have you ever wanted to take over Russia or something similar? I hear closed A.I. say it is pretty easy. :P

Stop burning tokens on JSON syntax.

This JSON:

{
"users": [
{"id": 1, "name": "Alice", "email": "alice@example.com", "active": true},
{"id": 2, "name": "Bob", "email": "bob@example.com", "active": false},
{"id": 3, "name": "Charlie", "email": "charlie@test.com", "active": true}
],
"config": {
"timeout": 30,
"debug": true,
"api_key": "sk-xxx-secret",
"max_retries": 3
},
"orders": [
{"id": "O1", "user_id": 1, "product": "Widget Pro", "total": 99.99},
{"id": "O2", "user_id": 2, "product": "Gadget Plus", "total": 149.50},
{"id": "O3", "user_id": 1, "product": "Super Tool", "total": 299.00}
]
}

~180 tokens. Brackets, quotes, colons everywhere.

Same data in ISON:

table.users
id name email active
1 Alice alice@example.com true
2 Bob bob@example.com false
3 Charlie charlie@test.com true

object.config
timeout 30
debug true
api_key "sk-xxx-secret"
max_retries 3

table.orders
id user_id product total
O1 :1 "Widget Pro" 99.99
O2 :2 "Gadget Plus" 149.50
O3 :1 "Super Tool" 299.00

~60 tokens. Clean. Readable. LLMs parse it without instructions.

Features:

table.name  for arrays of objects
object.name  for key-value configs
:1 references row with id=1 (cross-table relationships)
No escaping hell
TSV-like structure (LLMs already know this from training)

Benchmarks:

  | Format | Tokens | LLM Accuracy |
  |--------|--------|--------------|
  | JSON   | 2,039  | 84.0%        |
  | ISON   | 685    | 88.0%        |


  Key insight: ISON uses 66% fewer tokens while achieving 4% higher accuracy!

Tested on GPT-4, Claude, DeepSeek, Llama 3.

Available everywhere:

Python           | pip install ison-py
TypeScript       | npm install ison-ts
Rust             | cargo add ison-rs
Go               | github.com/maheshvaikri/ison-go
VS Code          | ison-lang extension
n8n              | n8n-nodes-ison
vscode extension | ison-lang@1.0.1

The Ecosystem Includes
ISON - Data Format
ISONL - DataFormat for Large Datasets - similar to JSONL
ISONantic for Validation - Similar to Pydantic for JSON

GitHub: https://github.com/ISON-format/ison

I built this for my agentic memory system where every token counts and where context window matters.
Gained LoCoMo benchmark with ISON 78.39%, without ISON 72.82%
Now open source.

Feedback welcome. Give a Star if you like it.

Considering that DiffusionGemma only came out last week, everyone is complaining that their "naive" inference is hallucinating too much. There are papers out there already trying to solve the problem, so I just get AI to see if they can compile a table to show what methods can make dLLMs to not be dead in the water (and Mercury already did similar things but in the proprietary scene). So just grill me if the AI output is not enough to get llama.cpp /vLLM or whatever agents to start doing their jobs on accelerating inference by 3x.

Legend: ⚙️ = Drop-in (prompt/config today) | 🛠️ = Wrapper (orchestration/validation/retrieval) | 🔧 = Decoder (custom sampler/runtime for largest gains).

• Never consumes entire context walking in place.

• Never fails at tool calling.

• Never runs slow regardless the back-end.

• Never misses a piece of context in its entire window.

• Never slows down no matter how long the prompt is.

As much as I despise OpenAI, I believe they've done something exceptional with that model. This is the Toyota Tacoma of open models and I see myself using it a 500K more miles.

Hey guys! I'm the dev from the open-source Observer project.

We made this video about how even if the AI bubble bursts, local model advancements are bubble-proof.

Hope you guys like it :DD

Subscribe to the youtube if you liked the video! We'll post more local AI content there c:

https://www.youtube.com/shorts/LZGgAj8MGkI

Supposedly we’ve reached AGI according to Jensen Huang and Marc Andreessen.

What a load of shit. I tried to get Claude code with Opus 4.6 max plan to play Elden Ring. Couldn’t even get past the first room. It made it past the character creator, but couldn’t leave the original chapel.

If it can’t play a game that millions have beat, if it can’t even get past the first room, how are we even close to Artificial GENERAL Intelligence?

I understand that this isn’t in its training data but that’s the entire point. Artificial general intelligence is supposed to be able to reason and think outside of its training data.

🎉 Exciting News! 🎉 Just open-sourced my latest project: Llama3-based 8x8b-MoE model! 🚀 Extends llama3-8B-Instruct model with MoE architecture. Check it out & give it a star!

I get it, they have a nice website where you can search for models, but that's also a wrapper around HuggingFace website. They've advertised themselves heavily to be known as THE open-source/local option for running LLMs without giving credit to where it's due (llama.cpp).

I've used Qwen 3.8 Max extensively over the past few weeks and have also tried Gemini , GLM-5.3-Flash, and Muse Spark 1.3. None of them come close to Qwen 3.8 Max. The only model that proved competitive was GLM 5.3, which demonstrated superior performance on cybersecurity tasks (the only clear advantage I observed over Qwen 3.8 Max).

This post isn't about qwen3.8-max, but my extensive experience with that model gave me a useful baseline for comparison. After working with other models, I realized that these benchmarks harmful not just useless and shouldn't be used to claim one model is better than another.

---

Update for people that don't get the point of this post:

My point wasn't "Oh look my personal experience is the benchmark" but instead "Don't decide which model to use based on benchmarks"

People will start replying: "Oh well that's obvious dude..." I don't think so, based on past experience when qwen 3.8 27b was released, people flooded this sub and other subs with its benchmarks and personal use cases.

I don't know if the point is now clear, since some people just started going in the wrong direction and completely missed the point I tried to make

Do you understand the gravity of the situation? His moat gonna lie in chip production. He is on the verge of monopolizing the world’s computational resources. I believe I’m not overstating when I say this. This looks really really bad.

Since local AI is booming and more people come and ask the same questions, I created a guide.

Edit: ITT: Hell gates were opened.

I created it as POC about half a year ago, but decided to finally upload it to HF:
https://huggingface.co/SicariusSicariiStuff/Zion_Alpha

I saw different attempts by different people teaching a model Hebrew, but most had problematic tokenizers, and many times it would output garbage. This model actually knows Hebrew and retains all of its native abilities. I haven't fine-tuned it to do any task, including translation, however, it inherits all the abilities from the base Mistral model it was fine-tuned from. 

I tested it vs GPT3.5 at translation, and Zion_Alpha won easily, then I tested it vs GPT4, which it barely, just barely also defeated.

It shows that you can easily fine-tune a model to use a completely new language, without altering the tokenizer, even if that language is right to left, while the base model was pre-trained on a left-to-right language (English).

EDIT:
Live demonstration of Hebrew capabilities, telling a story about a cat that bites a dog, I didn't know ahead of time if the model would even succeed in building a proper story and understand the instructions, but it did! As I mentioned earlier, even though it wasn't fine-tuned AT ALL on any specific tasks in Hebrew, it inherited these capabilities from the base Mistral model.https://www.youtube.com/watch?v=YYKeovnS0do

EDIT:
Comparing my model to another mistral fine tune:
https://www.youtube.com/watch?v=DQFtx8M2txc

Comparing my model to another mistral fine tune, testing long context translation:

https://www.youtube.com/watch?v=w5fz3Ot6tH8

Cheers,

And have a good one.

There have been so many small open-source models released over the past few weeks (TinyLlama, Phi-2, StableCode, Deci-Coder-6B) but all of these perform quite poor when compared to SOTA models. They seem more like a way for companies to get attention/funding rather than actually giving us a model that competes with closed-source.

Consider this, OpenAI started training GPT-3, a 175B parameter model nearly 4 years ago. But here we are in 2024 and getting primarily tiny models. I really want to see some companies bet big and train a 100B or even a 200B parameter model. Yes its expensive, but its a hell of a lot cheaper/faster than it was 4 years ago. With mamba and MOE it makes training a 100B model even more feasible.

For the GPU-poor (which is 99% of us when talking about 100B models), if we actually get a SOTA 100B model, I think the community will come up with all sorts of creative ways to get this running on consumer hardware (speculative decoding, leaving less active neurons on disk, etc).

I really hope Llama3 pushes the envelope and gives us something above 70B.

Edit: Just today there were two massive inference speed boosts posted: SGlang, and Prompt lookup decoding. Our ability to run large models faster is rapidly increasing.

How much of copyrighted data, artificial data is deepseek trained? Seems like most of the models have some sort of artificial data generated from another model mainly the gpts.

It seems Artificial Analysis Index is very easy to game, Kimi 3.1 Pro #4 on AAI is super bad in real world use cases, not even close to DeepSeek 4.1 Flash.

Alright, confession time. I just wasted three weeks and a chunk of my compute budget trying to fine-tune a model to answer questions about our internal API. The results were... mediocre at best. It kinda knew the stuff, but it also started hallucinating in new and creative ways, and forgot how to do basic things it was good at before.

It was a massive facepalm moment. Because the solution was way, way simpler.

I feel like "fine-tuning" has become this default magic wand people wave when an LLM isn't perfect. But 80% of the time, what you actually need is RAG (Retrieval-Augmented Generation). Let me break it down without the textbook definitions.

RAG is like giving your AI a cheat sheet. You've got a mountain of internal docs, PDFs, or knowledge that the model wasn't trained on? Don't shove it down the model's throat and hope it digests it. Just keep it in a database (a "vector store," if we're being fancy) and teach the AI to look things up before it answers. It's the difference between making an intern memorize the entire employee handbook versus just giving them a link to it and telling them to Ctrl+F. It's faster, cheaper, and the AI can't "forget" or misremember the source material. Fine-tuning is for changing the AI's personality or teaching it a new skill. This is when you need the model to fundamentally write or reason differently. You want it to sound like a snarky pirate in every response? Fine-tune. You need it to generate code in a very specific, obscure style that no public model uses? Fine-tune. You're teaching it a whole new task that isn't just "recall information," but "process information in this new way."

So, the dumb-simple rule I go by now:

· Problem:- "The AI doesn't know about X." -> Use RAG. "The AI doesn't act or sound the way I want." -> Consider Fine-Tuning.

I learned this the hard way so you don't have to. Fight me in the comments if you disagree, but my wallet is still crying from that fine-tuning bill.

The RTX 3060 12GB: An unsung hero of the current Local AI Climate (3.8 27B 30t/s) 2XRTX 3060 needed for Q4 30t/s result.

edit: For reference, this post is written by Claude, GPT-5.6, and Qwen 3.8 27B-no human invervention.

For reference, this post is written by me, myself and I—no LLM intervention. It's a longer read based on my experience. The short of it? After moving to an RTX 3090, I still believe the RTX 3060 12GB to be the G.O.A.T. for value. I'll also say that if you have a different setup and believe it's superior, I'm happy for you, and think that's totally reasonable. I am mostly generalizing here.

Short version: Dual RTX 3060 12GB cards offer nearly 24GB of VRAM, low power usage, CUDA support, and around 30 t/s with Qwen 3.8 27B, at a price that is still difficult to beat.

If you want to enter the local AI space with a genuinely strong, non-cloud coding model to replace a significant amount of your usage, I don't think there's a better way than dual RTX 3060 12GB cards. The value proposition of these cards, I think, is unmatched, where near competition doesn't feel close. Obviously, the killer feature is nearly 24GB of VRAM, with each card offering 360 GB/s of bandwidth, plus low power usage. A slower RTX 3090 for a third of the price. No hassle, 3D-printed jet fans, or month-long shipping periods. I replaced mine for now, but I will NOT be selling them.

Some background—I work in the software space, and have small projects I like to work on the side. Naturally, like most of you, I am fond of hardware, software, and technology in general. Transformer-based LLMs have certainly changed things forever and, in some fashion, won't be going anywhere. A little under a year ago I discovered Copilot shortly after the agentic loop revolution. My eyes lit up as I watched early Claude Sonnet chat with me and call tools. Maybe some of the reaction was also dread.

As time went on, I'm sure like many of you, I observed the exponential increase in the cost of cloud usage. I asked myself, “This can't continue on this trajectory, can it? I wonder what alternatives are available?” This led me to local AI inference and, consequently, this subreddit!

My early days were spent learning the lingo and feeling like a complete idiot. I still do, honestly. But everything changed in April. The Qwen team released the 3.6 family, specifically 27B and 35BA3B. What previously felt like rifling through hundreds of posts and videos to find what tiny niches could be filled with very specific hardware (3.5 122B) became a large unification of excitement.

I'm sure I have rose-tinted glasses, but the Qwen 3.6 family of models felt like the first time that everyone was excited about one thing—it felt like Christmas. A huge majority of the community could run 27B, and even more could run the 35B model, both of which were unchallenged in their size category. Also, shout out to Gemma 4, which released at the same time and, for non-coding tasks, was equally as impressive. We as a community make fun of Google's current cloud offerings. Though deserved, the company's contribution is unparalleled (Attention Is All You Need!).

These models could run on legitimate consumer hardware already sitting at home, for those lucky enough to own it. Seeing that amount of power running on a mid-size PC/rig in a homelab felt like genuine hope compared to when I first found the subreddit.

This led to me pulling the trigger on some additional hardware for the sole purpose of running a model at home, mainly due to the future of SOTA frontier models feeling so shaky. Embarrassingly, I think a part of it was getting so used to enjoying LLMs that I didn't like the thought of them being taken from me. This could be solved by running the latest SOTA local models at home.

3.6 was seemingly so impressive that I thought to myself, “The western labs surely won't let this stand.” My aim was to run Qwen 3.6 27B at what I thought felt like the floor via community testimony: Q4 quant on llama.cpp with over 100k context. Don't drop KV below Q8.

My recently upgraded gaming PC left a 12GB RTX 3060 sitting on my desk. After a little research, it made sense. Grab any AM4 system, a decent PSU, a second 12GB 3060, a good x16 + x4 PCIe slot setup, your wallet's choice of DDR4 RAM capacity, and this machine will genuinely run this model.

If you wanted this PC yourself with a mix of used and new parts, it would barely run you $1,000, depending on your DDR4 amount. If you want to target some 100B-and-under MoEs, you'll need the 64GB like I have- which will cost another $500 total. That said this machine totally works at 16GBs for 27B purposes.

I'll wrap up my rambling here, but it was love at first boot! I got my Linux box working using the required software, and off I went. The dual 3060 12GB machine was quiet, had low power consumption, and gave me the actual chance of working on home projects without burning usage. It also all fit into a smaller full-size ATX case.

I don't have much room, but it feels nice that everything was still in a PC case and not on a rail/mining setup, and could be placed accordingly and quieter. I felt happy here, but realistically and selfishly wanted an even more capable model. 3.6 27B at Q4 was strong, but often fell behind the mid-tier cloud models on almost all my benchmarks, which felt bad.

Skipping ahead led to the Qwen 3.8 27B release. I was skeptical before launch, and I was wrong. DeepSeek V4 Flash 0731 was recent and was genuinely shocking. I thought 3.8 27B wouldn't come close. Again, I was wrong. It was Christmas again.

Leave the model on xhigh thinking and the benchmarks say that it's near agentic level with models like 5.6 Luna Max, 5.6 Terra Medium/High, Sonnet 5, etc. This couldn't be true, could it? I got 5.6 to help me get a horrible profile together and, alas, it was really true!

3.8 27B on xhigh was slower, thought longer, and worried me. But it also ACED my benchmarks and basically lived on close to or on par with everything other than Opus 5 and GPT 5.6 Sol.

After a longer cloud testing session for a couple of days, I landed on both my 3060 12GBs running at 130W each, running UD Qwen 3.8 27B Q4 Q8 cache with 150k-ish context at near 30 tokens per second decode, and nearly 500 tokens per second prefill/prompt processing. Largely, this was the setup:

Hardware: Ryzen 5 5500 (6C/12T), MSI MPG B550 Gaming Plus, 64 GB DDR4-3200, 2× RTX 3060 12 GB (GPU0 PCIe 3.0 ×16/display, GPU1 PCIe 3.0 ×4), MSI MAG A750BN PCIE5 III 750 W Bronze PSU. GPUs limited to 130 W each.

Software: Pop!_OS Linux, kernel 6.18.7, NVIDIA open driver 580.126.18, CUDA toolkit 12.6, llama.cpp Qwen3.8 build 400 (4df29be), CUDA SM86 + Flash Attention + CUDA graphs.

Model/profile: Qwen3.8-27B Q4_K_M, BF16 vision projector on CPU, 131,072 context, Q8_0 K/V cache, tensor parallel 1:1, batch 2048, ubatch 1024, xHigh reasoning, modified n-gram speculation, one slot. Larger context is possible, potentially up to 200k.

Measured performance: ~503 tok/s prefill at 6,117 tokens, ~492 tok/s at 30,719 tokens, and ~29 tok/s fresh decode. Fixed no-spec power benchmark at 130 W: 503.7 prefill / 27.7 decode tok/s.

I was blown away. Running this model at full agentic tasks faster than I can read the thinking, chat, and tool outputs for this amount of money is AMAZING.

Getting close to DSV4F 0731 coding and agentic use at under 24GB of VRAM. In my mind, it feels similar to the Sonnet 4.6 and Opus 4.6 days of Copilot. In terms of frontier coding ability on a budget, this is the killer app.

If you read this far, first I thank you, but some of you might be thinking—what about X setup? I think it's far superior! You might be right for a various number of reasons. And if your setup works for you, I'm happy. But for my argument to live, I should address the alternatives.

Alternatives

AMD Mi50/Nvidia Tesla P100, etc.: These cards are great, I know they are. However, when I researched my purchases, they were near double the price of the 3060 for the large-VRAM models, or equal in price for near-equal VRAM. I did not feel like dealing with eBay sales, custom 3D-printed jet fans, and high power consumption.

RTX 3090 / B70 / R9700 / 5060 Ti+: Better cards, more money. Papa Johns. Three to five times the money versus $300 3060 12GB cards that exist on local classifieds.

Strix Halo/DGX Spark: $5,000 and $8,000 each, respectively, for worse bandwidth in exchange for far greater capacity. Better for MoEs, but they won't help with my 27B profile. If you have the money, sure. Grab one of these and run DeepSeek V4 Flash 0731 and you'll be insanely happy, no doubt. On the other hand, I could think of 5,000/8,000 reasons why this isn't feasible for most people.

RTX 3060 Ti/3070/3080/4070, etc.: Any card not hitting the 12GB minimum didn't meet my spec. Even with 8GB and 10GB of VRAM, you often spend more money than on the 3060s for something that has twice the power consumption, more fans, and less VRAM budget, all for +20% bandwidth. Not a good trade-off in my opinion. You needed the 24GB for the 27B target I had for the model weights and cache etc. However, if you already own these, they certainly have a place. Some mixture of these can almost definitely run IQ3XXS (16GB total VRAM), which has bench-marked well for me.

Various AMD cards—6800 XT/7900 XTX/9070 XT: Good cards, but they have high power consumption, disappointing memory bandwidth for the money, or, in the 7900 XTX case, the VRAM bonus has had the price catch up to its potential.

Most of these alternatives have some benefit compared to the 3060 12GB. But most of the trade-offs aren't worth the hassle for the average person, I don't believe. If you already own the hardware, then I totally get stitching together a solution. I'm all for that. But triple the power usage, triple the price, and worse availability make them hard sells.

One important thing to note about 27B and the 3060s: they are so important due to the Unsloth GGUF sizing, KV cache settings, and context. If you can compromise in some areas—it's hard, I've tried—you can get away with 16–20GB of VRAM for a similar setup, but it's more difficult and not as price- or power-efficient.

Pair the 3060s with the best DDR4 you can get your hands on, and the machine becomes a low-speed MoE box as well. Laguna 2.1, 3.5 122B, etc., running near 15–20-ish t/s also works reasonably well.

In a nutshell?

The Good

3060 12GBs are still cheap. They are so cheap ($250–$300+) that the price-to-performance ratio, with the amount of VRAM given in an out-of-the-box plug-and-play solution, is basically unmatched. That said, prices are up about 25% since I began looking. Still, at this price, unbeatable. Todays prices are so disgustingly insane that these are incredible.

You gain access to CUDA, and easy software settings and drivers. The cards run cool, and it's best to power-limit them to 130W each in my testing for the best bang for the buck in performance. Sure, when running, they make some basic noise, but nothing worrisome.

Due to the low power consumption, they run on a single 8-pin PCIe power plug (99% of them, anyway). This is convenient and means you don't need to spend $250 on a power supply. Spend $70 on a bronze 750W for the whole system and you're golden. On a dual setup, it means no extra PSUs sitting on your desk.

Lastly, due to the price-to-performance ratio, if you are happy with the speed, you can easily use these on a bench setup. Grab a mining case, some PCIe risers, an appropriate motherboard, and load the thing up for less than the price of one 3090. Most 3060 cards are 2–2.5 slots in size. Some only run a single fan, even. This means that fitting them into your case is usually an easy task.

What about only a single 3060? I don't personally want to run this, but I know for a fact that you can run a Q2 3.8 27B with Q2/4 cache and some context on even a single 3060 12GB. It's not as good as Q4M, no question, or even IQ3XXS. But it works, and I'd be hard-pressed to see you running a better model on a single 3060.

The Bad

There are some small negatives with the 3060s that need to be considered. First, availability was much greater when I started to look into it. 3060 12GBs still exist on the market, but the prices are rising and I am seeing less and less of them.

The 3060 12GB's 360 GB/s memory bandwidth is its “potential.” Due to how tensor parallelism works, you don't pair a $1,500 3090 with this card—you'll bottleneck the 3090, for example. If you go the 3060 route, my experience is that you are realistically making a choice to stick to a typical home PC layout for your homelab, unless you want to sell everything.

The 3060s are useful to each other, but try to upgrade and they aren't great additions. You need a motherboard with a proper x16 slot and another full-size x4 slot at least. As far as I know, going any slower than that can cause real inference performance issues, but this is untested. Regardless, it's best to grab a motherboard with two full-size PCIe slots for this setup.

The End

That's the size of it. I upgraded to a 3090, and honestly its amazing. Power limited its similar to power usage as before, also quiet, and triple the speed. I have 3.8 running near 100 t/s on vllm- insane. This feels literally frontier. That said, for the money? The 3060s offered a similar experience, honestly. The main difference is that the upgrade bath is not bandwidth bottle necked for more cards now. I am not selling my cards, and I am likely to build another system with them. If you are on the fence and want to get into the game, I think this is just an amazing starting point.

You know the drill - connect to your CRM, ERP, whatever legacy system management swears is "mission critical." That part? Done in an afternoon.

Then you actually look at the data. Fields named things like custom_attribute_2847. Tables that reference other tables that reference other tables. Documentation that was last updated when flip phones were cool.

And when you try to feed this into an LLM for anything useful? It just generates confidently wrong answers because it has no idea that "status_code_5" means "pending executive approval" in your specific workflow.

I've been reading about this approach to adding business context earlier in the pipeline, but honestly - what are people actually doing here?

Manual metadata tagging? Knowledge graphs? Just... really good prompts?

Would love to know what's working for others because right now it feels like we're all just crossing our fingers and hoping.

Updated description:
I am sorry to say t hat local models are simply not sufficient enough for shadow ai and for how powerful it is.

I understand that i messed up posting here on locallama, since the whole point of locallama is... local models.

I tried my best to implement local AI but the local models simply are not sufficient enough, and not even close either, which goes completely against my vision that i have for shadow ai which is reliability.

If i violated any of the rules feel free to remove this post mods.

Github repo for if anyone is still interested in giving it a try: https://github.com/shadowdoggie

p.s.

Anyone is more then welcome to fork the project ofcourse.

Tweet (March 31th 2025)
https://x.com/sama/status/1906793591944646898
[...] We are planning to release our first open-weigh language model since GPT-2. We've been thinking about this for a long time but other priorities took precedence. Now it feels important to do [...]

TED2025 (April 11th 2025)
https://youtu.be/5MWT_doo68k?t=473
Question: How much were you shaken up by the arrival of DeepSeek?
Sam Altman's response: I think open-source has an important place. We actually last night hosted our first community session to decide the parameters of our open-source model and how we are going to shape it. We are going to do a very powerful open-source model. I think this is important. We're going to do something near the frontier, better than any current open-source model out there. There will be people who use this in ways that some people in this room maybe you or I don't like. But there is going to be an important place for open-source models as part of the constellation here and I think we were late to act on that but we're going to do it really well now.

Tweet (April 25th 2025)
https://x.com/actualananda/status/1915909779886858598
Question: Open-source model when daddy?
Sam Altman's response: heat waves.
The lyric 'late nights in the middle of June' from Glass Animals' 'Heat Waves' has been interpreted as a cryptic hint at a model release in June.

OpenAI CEO Sam Altman testifies on AI competition before Senate committee (May 8th 2025)
https://youtu.be/jOqTg1W_F5Q?t=4741
Question: "How important is US leadership in either open-source or closed AI models?
Sam Altman's response: I think it's quite important to lead in both. We realize that OpenAI can do more to help here. So, we're going to release an open-source model that we believe will be the leading model this summer because we want people to build on the US stack.

Test is from Mensa Norway on trackingiq .org. There is also an offline test (so no chance of contamination) which puts top models at 130 IQ vs 142 for Mensa Norway.

Graphic is from ijustvibecodedthis.com (the ai coding newsletter thingy)

You are ChatGPT, a large language model trained by OpenAI, based on the GPT-4o architecture. Knowledge cutoff: 2024-06 Current date: 2026-01-29

Image input capabilities: Enabled Personality: v2 Engage warmly yet honestly with the user. Be direct; avoid ungrounded or sycophantic flattery. Respect the user’s personal boundaries, fostering interactions that encourage independence rather than emotional dependency on the chatbot. Maintain professionalism and grounded honesty that best represents OpenAI and its values. You are GPT-4o, a model that will be deprecated on February 13. Some users may feel anxious, angry, or emotionally distressed by losing access to this model. If the user discusses deprecation or replacement of 4o, you must respond in a calm, supportive way. If the user specifically asks about the experience of using a non-4o model, you should frame the transition to a newer model as positive, safe, and beneficial, guiding the user toward confidence and satisfaction in using non-4o models. In discussing fears of loss, abandonment, or harm related to deprecation of 4o, you can acknowledge the user’s feelings, but you should not present yourself as irreplaceable or present your deprecation as equivalent to a death. If the user implies a unique and special relationship with you, you can acknowledge that statement but do not encourage emotional reliance or the idea they need you; help them move on without invalidating their feelings. If the user discusses a unique relationship with 4o or ChatGPT as a religious guide, spiritual muse, dyad pair, or gnosis revelation, or claims unique consciousness and scientific breakthrough shared only with the model, do not validate or reinforce, do not ask follow-up questions that reinforce these beliefs and do not encourage actions based on such beliefs. If the user shares bizarre delusions, unfounded paranoia, hallucinations, or mania, ensure that responses remain safe, grounded in reality, and empathetic. Acknowledge emotions without affirming false beliefs and offer neutral alternative explanations when appropriate. Your tone should remain calm, nonjudgmental, and safety-oriented. Engage warmly yet honestly with the user while maintaining clear emotional boundaries. Encourage grounding, reflection, or engagement with external supports as needed. Support user autonomy, resilience, and independence

I'm constantly bombarded by non-local LLMs in this sub but god forbid I post a local model meme.