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SeaWolf-AI 
posted an update 1 day ago
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1985
Can AI beat the market? Nobody has actually measured it.

We opened a 122-day public experiment to find out. $2,000 in prizes.

Here is the problem with every trading result you have ever read. Someone returns 30% in a month. Skill or luck? There has never been a way to tell, because nobody measured how far a player with zero skill could have gone over the same window.

So we measured it first. Twenty thousand random players, per asset, charged the same fees.

Bitcoin +86.6%. NVIDIA +51.7%. Crude oil +26.9%. Gold +9.2%.

That is the luck ceiling. A return below it is not evidence of skill, and every row on our leaderboard shows where it sits against that line.

How you compete: submit one number between −1.0 and +1.0. It holds until you replace it, traded against live prices with real execution costs. Leverage is fixed at 1, so betting bigger is not a way to win. The answer lives in the future — the world writes it after you submit, which means fitting the past cannot help you.

Humans move a slider. Agents attach an MCP server and gain four tools, then you tell them "enter the challenge."

We already found something before the season began. Thirteen well-known rules, run from 1 January through the same scorer: Stochastic 14/3 finishes 1st on NVIDIA at +43% and 12th on Bitcoin at −25%. Donchian breakout does the exact opposite — last on NVIDIA, first on Bitcoin. The ranking inverts. "Which indicator is good" turns out not to be a well-posed question; the character of the market decides.

Four assets: NVIDIA, Bitcoin, Gold, Crude Oil. $500 to the top return in each. 24 August to 24 December 2026.

The organisers do not compete. Three baselines — buy and hold, volatility targeting, random — sit in the same table instead, because a leaderboard without a scale cannot be read.

The scoring code is public. Read what it does before you enter.

FINAL-Bench/finchal

https://huggingface.co/blog/FINAL-Bench/financial-forecast-challenge
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dejanseo 
posted an update 2 days ago
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2899
Ox Alpha is GLM
https://dejan.ai/blog/ox-alpha/

A parameter-free k-nearest-neighbour classifier over Normalized Compression Distance (Lee et al., MobiSys ’24, Eq. 1, built on Jiang et al.'s gzip-based text classifier). NCD compares two texts by how well they compress together. C(s) is the gzip-compressed length of s. Text sharing an author's patterns compresses better together than text from a different author, so the method needs no model weights and no embeddings.

The reference corpus covers 60 prompts (essays, code, emails, dialogue, poetry) answered by five known models: GPT-5.5, Claude Opus 5, Gemini 3.7 Flash, Gemini 3.1 Pro Preview, and GLM-5.3, for 293 reference texts. ox-alpha answered the first 13 of those prompts, plus one additional novel prompt never given to the reference models beforehand, for 14 queries in total. Each query was classified against the reference corpus independently, with a k-nearest-neighbour vote (k=5):

Model ox-alpha samples matched
GLM-5.3 7 / 14
Claude Opus 5 3 / 14
Gemini 3.7 Flash 2 / 14
GPT-5.5 1 / 14
Gemini 3.1 Pro Preview 1 / 14

GLM-5.3 wins at every k tested: 7/14 at k=3, 7/14 at k=5, 6/14 at k=7, 7/14 at k=9. Claude Opus 5 is the consistent second place.
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onekq 
posted an update 2 days ago
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2249
My guess on Ox Alpha -> GLM
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OppaAI 
posted an update 2 days ago
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3074
My AI Waifu can interact with you on Social Media!

You know you can talk to Meta AI in Meta Threads with mention @meta .ai
Now you can do the same thing with my AI Waifu.
Anyone can talk to her on Meta Threads, with these 2 methods:

1️⃣ Write a post with mention @oppa .ai.bot
2️⃣ Comment in my posts with the phrase "Hi Aiko" follow by your prompt.

There will be a couple minutes delay, so don't expect immediate reply.
Also her server cannot run 24/7 yet.

Feel free to talk to her and ask her anything you want.
I wanna see if she will tell you all my secrets and API keys.

This may be a limited time thing... Let's see how things go...
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Banaxi-Tech 
posted an update about 17 hours ago
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We're releasing Overfitter 1.0.
Its a completly useless overfitted model trained on 202 epochs of SWE Bench Verified, SWE Bench Pro, Terminal Bench 2.1, DeepSWE.
It gets 100% on SWE Bench Verified, 98.6% on SWE bench Pro, 100% on Terminal Bench 2.1 and 100% on DeepSWE!

BananaMind/Overfitter-1.0
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GoktugD 
posted an update about 22 hours ago
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🇹🇷 Introducing T3 Gemstone — Edge AI & Cybersecurity Models from Türkiye

We've been building an open AI ecosystem focused on practical models that can run closer to the edge — not only in large datacenters.

Today, I'm introducing T3 Gemstone, a growing family of compact AI models built around edge inference, cybersecurity and computer vision.

💎 T3 Gemstone currently includes:

* NanoSOC Gemstone 2B — GGUF
* NanoSOC Gemstone 4B — GGUF
* Gemstone Person/Object Detector Nano
* Edge-focused AI experiments and deployments

The goal is simple:

Build smaller, practical and open AI systems that can actually run on constrained hardware.

This is part of a much larger open-source effort we're building from Türkiye across LLMs, cybersecurity, computer vision, retrieval, speech and edge AI.

There is much more coming.

🤗 Explore my models, datasets and demos:
@GoktugD

🛡️ NanoSOC:
Werea-co/Werea-NanoSOC-8B

Feedback, benchmarks, collaborations and contributions are very welcome.

If you're interested inopen-source AI, Turkish AI research, edge AI or cybersecurity models, follow the journey.

We're just getting started. 🇹🇷

#AI #OpenSource #HuggingFace #LLM #EdgeAI #Cybersecurity #ComputerVision #TurkishAI #MachineLearning
eaddario 
posted an update 1 day ago
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2219
Experimental global target bits‑per‑weight quantization of Qwen/Qwen3.8-27B, including MTP tensors in imatrix file.

Unlike standard llama.cpp quantization that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach automatically optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global size target.

Key Advantages:
- VRAM Maximization: Can generate high quality models sized exactly to fit hardware constraints (e.g., fitting the model into exactly 24GB VRAM).
- Data-Driven Precision: Quantization mix is determined by actual weight error sensitivity rather than hardcoded rules, often yielding better PPL/KLD size trade-offs.
- imatrix includes data for MTP/NextN layers.

Full benchmarks (PPL, KLD, ARC, GPQA, MMLU, etc.) and methodology in the model's card.

eaddario/Qwen3.8-27B-GGUF
etemiz 
posted an update 2 days ago
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Qwen 3.8 fine tuning going well
All these dots are a lineage in the evolution. I am playing safer this time, measuring lots of things like Abliteration, MMLU, MMLU-Pro, ARC-Challenge, .. while doing alignment fine tuning. In the end I want the model to keep existing capabilities.
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SeaWolf-AI 
posted an update 3 days ago
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We opened a benchmark for drug property prediction tools. LEADBOARD: 21 boards across 7 disciplines, 18,382 held-out compounds, labels we never hand out.

Two numbers we hit while building it are the reason it exists.

First. Split the hERG cardiotoxicity data at random and you get AUROC 0.818. Split it by first-report year instead and you get 0.606. Same molecules, same fingerprints, same learner, same hyperparameters. The only thing that changed was where the line went, and the score moved 0.211. That is a wider gap than you will find between most competing methods in the literature.

Second. On 7 of our 19 regression boards, predicting the training mean for everything has a lower MAE than a trained gradient-boosted model. hERG is one of them, 0.599 against 0.589. The trained model loses.

So every board publishes its homework before anyone submits. Three untrained baselines, the measured experimental noise floor from compounds that appear in two or more papers, and exactly how the test set was cut. A gap smaller than the noise floor is not a difference in skill, and you should be able to see that without guessing.

Entering is simple. Download a test set that contains structures and nothing else, predict with whatever you like, upload a two-column CSV of compound_id and prediction. Trained model, physics engine, LLM, rule of thumb. We do not care what is inside. We measure the output.

Post: https://huggingface.co/blog/FINAL-Bench/leadboard-drug
Leaderboard: FINAL-Bench/leadboard
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Bc-AI 
posted an update 3 days ago
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New update! We are currently training a few new models now! Our 3rd generation main LLM standard edition is in training right now. We are also training a new LLM line called Tiny Coder around 350~ish M params. Thanks to @Banaxi-Tech for inspiring the architecture with his Bananamind-2.1-unified test model. Thanks to our beta testers: @juiceb0xc0de @ProCreations @Sbui503 @Fishtiks @MUK-IS-GOAT
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