Ever Upward ☝🚀✨✝⚔️⚡🔛 @TimBallFL

In the era of #ArtificialIntelligence, when human dignity is threatened by new forms of dehumanization, ours is the pressing duty to remain profoundly human. We must lovingly safeguard the grandeur of humanity bestowed upon us and revealed in its fullness in Christ, the splendor of which no machine can ever replace. #MagnificaHumanitas vatican.va/content/leo-xi…

@SSN_UCF @rejoyceknights Absolute fact.

In 1303, Pope Boniface VIII was arrested by King Philip IV of France. The Pope was jailed and beaten for 3 straight days, leading to his death a few weeks later. To even make a passing reference to that period takes a level of evil we haven't seen on earth for a very long time.

Shawn Ryan @ShawnRyan762

3 months ago

The Pentagon summoned the Pope's ambassador, told him the United States has the military power to do "whatever it wants," and warned that the Church better take its side. They even invoked the Avignon Papacy, a dark chapter in history when a government used military force to bend

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@DisneyClipsGuy Baseline.

Christ is risen! But Christ's victory doesn't shield us from death. It frees us to face it: to witness to difficult truths, to stand up to injustices, to sacrifice for others...not b/c we won't die, but b/c death has no more sting. So celebrate Easter, and give your life away.

Pope Leo's Easter message: "Let those who have weapons lay them down! Let those who have the power to unleash wars choose peace! Not a peace imposed by force, but through dialogue! Not with the desire to dominate others, but to encounter them!"

As communities gather this weekend, @AstroVicGlover reflects on the shared spaceship we all call home: Earth.

Bought a new @craftsman lopper at @AceHardware today. Battery won't charge. Craftsman says, "Oh, your battery is out of warranty; the date code is from 5 years ago Not our problem." First and last Craftsman tool I will own. @stanleytools you've destroyed a great American brand.

@Tulsabill55 @CatholicFQ I've never seen data to support that idea. Only conjecture.

C.S. Lewis predicted AI in That Hideous Strength. N.I.C.E., that’s AI. Fans know what I’m talking about. AI’s problem is not going to be its inability to do anything, but that it ruins everything. It creates a cultural problem. It sterilizes everything and makes nothing desirable. The real danger isn’t incompetence; it’s efficiency without soul. Lewis imagined a world where technique replaced wisdom, where power outran virtue, and where the language of progress masked the erosion of meaning. AI can generate endless words, images, and music, but culture has never been about endless production. Culture is inheritance. It is formed slowly, through discipline, memory, imitation, and love of what is beautiful and true. When creation becomes instantaneous, the risk is not scarcity but saturation and a flood of content so frictionless that nothing feels earned, and therefore nothing feels worth longing for. A world where you can make anything at any time can easily become a world where nothing feels necessary. That is the paradox Lewis hinted at…the more perfectly we manufacture expression the more we risk hollowing out the human longing that gives culture life in the first place. The task ahead is not to reject AI outright, but to resist letting it become N.I.C.E.

Today is the feast of the 21 Coptic martyrs - 20 Egyptian and 1 Ghanaian - who were beheaded by ISIS for not recanting their faith. They were construction workers - ordinary fathers, brothers and sons - with an extraordinary faith. Jesus, give me faith like theirs

@Faithful2Pray @theblessedsalt @HumnDcncyREBORN You haven't been keeping up

Forever our 2026 @NBAAllStar Celebrity Game MVP 🏆 Proud of @tackofall tonight #ChargeOn

Give me solidarity and subsidiarity over the singularity

@LifeSite @CarriePrejean1 She's a wolf in Catholic clothing. Shame on you, LifeSiteNews.

C. S. Lewis tried to warn us about AI

🚨 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

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If the halftime show was Weird Al with the Muppets, I bet the Outrage Machine would find something wrong with it, because if they aren’t making you angry, they can’t afford the payments on their Arizona mansions

MrTate @MrTate

5 months ago

@emzanotti I would settle for somebody who can play and sing...

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Reminder: FREE IRAN.

@EYakoby Pray for us, Nigerian martyrs.