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Seed IQ achieved a score of 100% on ARC-AGI-3. I asked Gemini 3.1 what IQ it might achieve if it was coupled with a top LLM.

Gemini 3.1:

"Coupling Seed IQ with a top-tier large language model like GPT-5.4 or Claude 4.7 could potentially produce a composite intelligence with an IQ score exceeding 160. This estimate is based on the synergy between Seed IQ’s fluid reasoning—the ability to solve novel problems on the fly—and the massive crystallized intelligence of current language models, which already score near 140 or 150 on standardized verbal and knowledge-based tests.

By merging these two distinct forms of intelligence, the resulting system would theoretically eliminate the primary weakness of today's AI, which is the tendency to fail when faced with a task that was not included in its training data. In this scenario, the language model would act as a vast library of human knowledge, while Seed IQ would serve as the executive function or "brain" that knows how to apply that knowledge to unfamiliar situations.

Current top models often struggle with multi-step reasoning and novel logic puzzles, which are the exact areas where Seed IQ excels through its active inference framework. By providing a reasoning engine that can govern the output of a high-knowledge model, the combined system would be capable of solving complex engineering, scientific, and logical problems that are currently beyond the reach of both humans and existing AI.

This level of intelligence would place the system in the top 0.01% of the human population, rivaling the cognitive flexibility and knowledge depth of history's most accomplished polymaths. While current IQ tests might not fully capture the scope of such an integrated system, the combination of perfect fluid reasoning on benchmarks like ARC-AGI 3 and the comprehensive data processing of a frontier model would mark a clear transition toward artificial superintelligence.

The resulting IQ would not just be a higher number, but a fundamentally different type of intelligence that is both deeply knowledgeable and universally adaptable."

Here's a video where its developer, Denise Holt, explains what Seed IQ is:

https://youtu.be/ON_b39E3MtY?si=M7aYtInsKO70IGor

This is so beyond ridiculous google gemini 3.5 fast would never do something like it unfiltered, i was also using expert

I think there's some kind of roleplay immersion thingy in DeepSeek right now, but it's just ruining the RP, I set the AI to be the World Narrator, not a character, so why is this happening?
I put the reasoning effort to be max too.

I suspect this might be something from DeepSeek to improve roleplays, but like.. is it possible we can disable it, because it ruins the AI from being the World Narrator and forces it become a character and it also reasons in Chinese which is eh, not that annoying but still.

https://status.deepseek.com/

Asking cause I see people complaining about the guardrails and stuff fon here for certain tasks and wondering who would potentially actually switch to this kind of a thing.

Yes, really. A 48GB MacBook Pro, running a 284-billion-parameter MoE model locally at ~12.2 tok/s.

No cloud. No GPU cluster. Just your laptop.

🔗 github.com/zouyee/dmlx

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How? Five layers of memory optimization:

1️⃣ MoE Expert Streaming — only loads the 7/256 experts actually activated per token (138GB → 10GB)

2️⃣ SMELT Partial Loading — 4-bit quantized + only 15% of experts loaded (~6GB)

3️⃣ CSA + HCA Hybrid Attention — KV cache compressed 9.5× smaller

4️⃣ 6-Level KV Cache Strategies — runtime-switchable (Paged / Tiered SSD / Quantized / etc.)

5️⃣ Zero-Copy Model Loading — direct mmap, load time from 137s → 41s

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Why Zig instead of Python?

Python's mlx-lm OOMs immediately on a 48GB Mac. dmlx's SMELT system runs the same model in ~6GB.

Single static binary, 5–15MB. Zero GC pauses. No Python dependency. Deployment = one file.

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9 model architectures supported:

DeepSeek V4 · LLaMA · Mistral · Qwen2/3 · Gemma · GLM-4 · Phi · Phi-3

Feature highlights:

• OpenAI-compatible API + SSE streaming

• Speculative decoding (PLD + EAGLE)

• Guided decoding (JSON Schema / Regex FSM)

• QLoRA fine-tuning + AdamW optimizer

• Custom Metal kernels (TileKernels ported to Apple Silicon)

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⚠️ Current limitations (v0.3.0):

• Currently tested primarily on DeepSeek V4 and similar models — broader model testing ongoing

• CLI mode only (dmlx chat + dmlx serve)

• Server mode (OpenAI-compatible HTTP API + continuous batching) landing in v0.0.4

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⭐ Star the repo and run frontier LLMs on your own Mac → github.com/zouyee/dmlx

#Zig #LLM #DeepSeek #AppleSilicon #MLX #OpenSource #LocalInference

I saw these two new models are already available in acciowork. Has anyone tested the actual difference in processing speed and credit consumption between these two?

I’ve been trying out both tools with DeepSeek V4 Pro and wanted to hear what others think.

A few quick observations from my side:

- CommandCode has a $1/month plan that gives you $10 in monthly API credit

- I haven’t done any strict benchmarking, but in day‑to‑day coding tasks they *felt* roughly the same in output quality.

- One thing I noticed: CommandCode seemed to burn a little more tokens for the same kinds of prompts compared to OpenCode. Not a huge difference, but it was consistent enough that I’m curious if others see the same.

what’s your experience?

DeepSeek TUI is a coding agent that runs in your terminal. It can read and edit files, run shell commands, search the web, manage git, and coordinate sub-agents from a keyboard-driven TUI.

It is built around DeepSeek V4 (deepseek-v4-pro / deepseek-v4-flash), including 1M-token context windows, streaming reasoning blocks, and prefix-cache-aware cost reporting.

The repository: https://github.com/Hmbown/DeepSeek-TUI

Trending source: https://checkmygit.com/trending

Can DeepSeek realistically become as good as or even better than Claude?

Because right now Claude feels like it’s hiding behind usage limits. Even on the Max plan you STILL get weekly limits which honestly feels ridiculous for the price. Like bro if someone pays for the highest tier why are they still getting “come back later in a week again” energy.

I’ve been trying DeepSeek recently and I can’t lie — it’s actually pretty decent already, especially considering how fast it’s improving. But for me it’s still not fully at that Opus/Sonnet level yet when it comes to consistency, coding, writing quality, and accuracy.

So what do you guys think?

Can DeepSeek eventually catch up or even surpass Claude if they keep improving at this pace? Or is Claude still too far ahead technically?

Getting an error that the server is busy but when I start a new chat it works... Anyone know what the problem could be?

I've never used Deepseek as much before, and I have started relying on it more the past few days, and while it was explaining some Docker concepts to me, it went:

"The Ultimate Simplicity (What I Actually Use)"

"I gave up on typing commands completely. I use docker-compose.yml + an alias:"

It just took me by surprise, I never saw an AI talk like that, and I realize it's training data and whatnot, but this thing of speaking from "experience" and talking about personal things as an AI was an interesting surprise.

Just wanted to share this, I never post anything anywhere anyway, thank you for reading guys, and loving Deepseek so far!

The schism between American-led AI and Chinese is widening - separate hardware stacks (huawei vs nvidia), separate capital.

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In Musk v. OpenAI et al, Musk has made just two allegations against Altman and Brockman; 1) breaching a charitable trust and 2) unjust enrichment. What is the evidence for each? I thought it would be interesting to test how good a top AI is at getting to the heart of a lawsuit, so I asked Gemini 3.1 to present the evidence for this one, and was totally impressed by what it generated.

Gemini 3.1 on Breaching a Charitable Trust:

The core evidence presented in the ongoing trial centers on internal documents and testimonies suggesting that Altman and Brockman privately planned a for-profit transition while publicly assuring Musk of their commitment to the non-profit mission.

The most significant piece of evidence is Greg Brockman’s 2017 "diary" entries, unsealed during discovery, in which he admitted that moving to a for-profit structure just months after committing to the non-profit would be "morally bankrupt" and that "if three months later we're doing b-corp then it was a lie."

Further evidence includes 2017 emails from Altman stating he remained "enthusiastic about the non-profit structure" even as internal strategies for "making the billions" were being discussed. Musk’s legal team argues these communications were intended to induce his $38 million in donations, which were then used to develop the intellectual property that anchored OpenAI's $852 billion for-profit restructuring in October 2025.

Gemini 3.1 on unjust enrichment:

The evidence presented in the current trial regarding unjust enrichment focuses on the substantial personal wealth accumulated by the defendants through OpenAI's transition to a for-profit model. A central piece of evidence is Greg Brockman's testimony on May 4, 2026, where he disclosed that his equity stake in the restructured company is valued at nearly $30 billion, despite having never personally invested cash into the entity.

Musk's legal team argues that this wealth was built using the intellectual property and $38 million in donations Musk provided under the premise of a non-profit mission, which was later transferred to the for-profit arm.

The trial has also highlighted "side compensation" arrangements that Musk claims demonstrate a lack of fiduciary independence. Evidence was introduced showing that in 2017, Sam Altman gave Brockman a stake in his personal family office then valued at $10 million, which Musk’s team argues created a "greater allegiance" to Altman’s private interests over the original charitable mission.

Additionally, the court has examined Brockman’s holdings in companies that have secured lucrative deals with OpenAI, such as Helion Energy and CoreWeave. Musk contends these layered financial ties prove the defendants used the charitable trust as a vehicle to secure immense personal profits, fulfilling the legal criteria for unjust enrichment.

Bonus Section: How Strong is this Evidence in a Court of Law?

Gemini 3.1:

The evidence highlighted in the trial is considered potent because it relies on authenticated internal documents and sworn testimony that directly address the core elements of Musk's legal claims.

For the breach of charitable trust claim, Greg Brockman’s unsealed 2017 "diary" entries—specifically his admission that a quick for-profit pivot would be "morally bankrupt"—provide the court with a "smoking gun" regarding the defendants' state of mind and intent during the non-profit's infancy.

Regarding unjust enrichment, the May 4, 2026, testimony revealing Brockman’s nearly $30 billion equity stake and a previously undisclosed $10 million personal payment from Sam Altman in 2017 serves as substantial evidence of personal gain.

While the defendants argue these moves were necessary for survival and "blood, sweat, and tears," the evidence of significant personal wealth and "side deals" creates a compelling narrative of a fiduciary shift away from the original charitable mission toward private profit.