🔸🌹 MattNutt 🌹🔸

He/Him

Independent 3D Environment Artist looking for opportunities in games!

[🔸🌹 MattNutt 🌹🔸 @m_nuttinator]

Demo Reel!

https://t.co/JWPCWvD2JS

Please check it out! If you have any feedback please send me a DM, I would really appreciate it! 🙂

#Blender #DemoReel #EnvironmentArt #GDC2025

[Alex Prompter @alex_prompter]

Let me trace the timeline here because nobody's connecting it.

Step 1: Scrape the entire internet. Every book, every article, every conversation, every piece of art, every forum post. Do it without asking. Do it without paying.

Step 2: Train a model on all of it. Call it "artificial intelligence."

Step 3: Go to BlackRock's Infrastructure Summit and announce: "We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter."

Step 3 is where you sell people's own knowledge back to them. On a meter.

They took the collective output of human thought, compressed it into a model, and now they want to charge you by the token to access a version of what you and everyone you know already created.

One Reddit user put it perfectly: "They stole all this data from us, the people, our life's work, creativity, art, by devouring the internet and blowing through all copyright laws. Now they want to sell it back to us in the form of a utility."

Imagine if someone photocopied every book in the public library, burned the library down, and then opened a subscription service for the copies.

That's the metered intelligence business model.

And they're pitching it to infrastructure investors as though they invented water.

[Daniel Mayakovski @DaniMayakovski]

Esto no solo ocurre con la alimentación, también con la electrónica o la ropa, en el desierto de Atacama (Chile) miles de toneladas de ropa a estrenar de grandes tiendas son arrojadas por la industria textil occidental.

Aquí es donde acaba la ropa de Occidente, llevan su basura a África, América y Asia mientras mantienen el ciclo consumista al mismo precio, evitan bajar los precios de sus prendas, manteniendo la exclusividad y la escasez artificial.

Solo un ignorante o un perturbado puede defender este sistema criminal capitalista, que prefiere desechar productos para mantener el consumismo antes que repartirlos a millones de pobres descalzos.

[The Regenaissance @_Regenaissance]

I had no idea about this, literally had a glass of this juice today. Paraquat is far far worse than glyphosate, have heard of plenty folks personally affected by it. If you drink POM I strongly suggest you find an alternative like a hand held juicer along with actual pomegranates or Lakewood Organic Pomegranate Juice.

[🔸🌹 MattNutt 🌹🔸 @m_nuttinator]

Nvidia CEO: people are completely wrong about dlss5

also the CEO: our art teams will be further adjusting the lighting and final effect to look the way we think works best for each game, will all be under our artists control

what a fucking piece of shit

[Brian Allen @allenanalysis]

🚨 Senator Chris Murphy — one hour after walking out of a classified Iran war briefing — was asked why the US went to war with Iran.

His answer:

“The simplest explanation might be the one they gave 24 hours in and tried to backtrack from — Israel made us do it. Netanyahu decided on the timeline. He convinced Trump to join him by scaring Trump into believing US assets were at risk.”

Then Murphy asked the question every American should be asking:

“How weak are we if our allies can force us into wars of choice that are bad for US national security interests?”

Mark Warner confirmed there was no imminent threat to the United States.

The nuclear program is not even part of the targeting campaign.

Seven Americans are dead.

165 schoolgirls are dead.

700,000 Lebanese are displaced.

Mines are in the Strait of Hormuz.

Gulf allies cut 6.7 million barrels.

And a US Senator with classified intelligence access just said Israel forced our hand.

How weak does that make America look.

[Leo Gao @nabla_theta]

"twin", "king", "queen"... why are the youths calling each other mattress sizes

[Owen Shroyer @OwenShroyer1776]

There's just no way.

[dhwani @dhwanisaraiya_]

there are sentences being said that have never been said before

[Amber Woods @AmberWoods100]

In violation to the Epstein Files Transparency Act

[Tired Peasant @HorrorGorl]

We only have our voices!!!!

[Dr Shola Mos-Shogbamimu @SholaMos1]

I am NOT suicidal. I don't drink, do drugs, smoke or gamble. No crimes. I'm in good health. My car & home are in good shape. I hate heights so never at top of buildings. I always look before crossing roads. I don't own a gun. I'm blessed of God. I will not be silent about Israel.

[Boston Smalls @smalls2672]

code red. alarm 5.

[Nick Mark MD @nickmmark]

1. Demand passports to vote.
2. Make it harder to get passports:

[𝗖𝗼𝘀𝗺𝗼 @HoshiNoCosmo]

NO the FUCK we are NOT!!!!!!!

[Adam @AdameMedia]

This picture is going to be in history books.

[Cardi B @iamcardib]

If we talking about drugs let’s talk about Epstein and friends drugging underage girls to rape them. Why yall don’t wanna talk about the Epstein files?

[Brian Krassenstein @krassenstein]

BREAKING: Leaked docs show DHS & ICE are now pretending to be regular people online, friending private groups and individuals to spy on them without their knowledge.

- This new “masked engagement” goes beyond simple monitoring. Agents can actually interact, infiltrate, and gather private info under fake identities.

- Over 6,500 federal agents and intel operatives now have access to these covert spying tools.

- They’re basically building secret social-media surveillance on everyday Americans, no transparency, no consent.

- This is yet another example of ICE & DHS expanding authoritarian, domestic spying tactics on us.

What is a “don’t tread on me” crowd think of this?

[Rep. Ana-María Rodríguez Ramos @Ramos4Texas]

We have received numerous credible reports of torture, killing, and inhumane treatment of detained individuals at the Camp East Montana migrant detention facility, located within Fort Bliss.

We are demanding an independent investigation and public hearings by the House Committee on Homeland Security, Public Safety, and Veterans Affairs into the horrific conditions at the facility. #txlege

[blue @bluewmist]

you need to be slowmaxxing. you need to be reading long, fat books. you need to be making 48-hour chocolate chip cookies. you need to spend hours watching wildlife, you need to spend 15+ min making your coffee. you need to breathe in and breathe out. you need to be slowwwwwwwwwww.

[God of Prompt @godofprompt]

🚨 Holy shit… Stanford just published the most uncomfortable paper on LLM reasoning I’ve read in a long time.

This isn’t a flashy new model or a leaderboard win. It’s a systematic teardown of how and why large language models keep failing at reasoning even when benchmarks say they’re doing great.

The paper does one very smart thing upfront: it introduces a clean taxonomy instead of more anecdotes. The authors split reasoning into non-embodied and embodied.

Non-embodied reasoning is what most benchmarks test and it’s further divided into informal reasoning (intuition, social judgment, commonsense heuristics) and formal reasoning (logic, math, code, symbolic manipulation).

Embodied reasoning is where models must reason about the physical world, space, causality, and action under real constraints.

Across all three, the same failure patterns keep showing up.

> First are fundamental failures baked into current architectures. Models generate answers that look coherent but collapse under light logical pressure. They shortcut, pattern-match, or hallucinate steps instead of executing a consistent reasoning process.

> Second are application-specific failures. A model that looks strong on math benchmarks can quietly fall apart in scientific reasoning, planning, or multi-step decision making. Performance does not transfer nearly as well as leaderboards imply.

> Third are robustness failures. Tiny changes in wording, ordering, or context can flip an answer entirely. The reasoning wasn’t stable to begin with; it just happened to work for that phrasing.

One of the most disturbing findings is how often models produce unfaithful reasoning. They give the correct final answer while providing explanations that are logically wrong, incomplete, or fabricated.

This is worse than being wrong, because it trains users to trust explanations that don’t correspond to the actual decision process.

Embodied reasoning is where things really fall apart. LLMs systematically fail at physical commonsense, spatial reasoning, and basic physics because they have no grounded experience.

Even in text-only settings, as soon as a task implicitly depends on real-world dynamics, failures become predictable and repeatable.

The authors don’t just criticize. They outline mitigation paths: inference-time scaling, analogical memory, external verification, and evaluations that deliberately inject known failure cases instead of optimizing for leaderboard performance.

But they’re very clear that none of these are silver bullets yet.

The takeaway isn’t that LLMs can’t reason.

It’s more uncomfortable than that.

LLMs reason just enough to sound convincing, but not enough to be reliable.

And unless we start measuring how models fail not just how often they succeed we’ll keep deploying systems that pass benchmarks, fail silently in production, and explain themselves with total confidence while doing the wrong thing.

That’s the real warning shot in this paper.

Paper: Large Language Model Reasoning Failures