Moon Dev
atleast 50 people have asked me to set up their openclaw

crazy request as im cooking some of the only useful openclaw flows on x

i'll set up your openclaw, 100% done for you: https://t.co/HW8RL0gLA9

as i will need to jump on a zoom with you, i have to limit this to 3 people https://t.co/vOT3LlferH
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Javier Blas
RT @citrinowicz: Very important report @BarakRavid:

Initial analysis:

A. The type of agreement Prime Minister Netanyahu is seeking or would be willing to accept remains fundamentally incompatible with Iran’s stated positions. The gap between Israeli expectations and Iranian red lines is not tactical but structural.

B. Iran is unlikely to accept any meaningful limitations on its ballistic missile program. This remains true even under heightened military pressure, including the deployment of additional U.S. naval assets. From Tehran’s perspective, missiles are a core element of deterrence and regime survival, not a negotiable bargaining chip.

C. Regarding Iranian flexibility: publicly, Iranian officials continue to reiterate their long-standing positions with little visible adjustment. That said, the absence of active enrichment inside Iran may create limited space for discussions around creative technical solutions — such as a regional enrichment consortium.

D. Even so, there is no indication of Iranian willingness to make significant concessions, particularly on missiles or proxy forces.
If the U.S. position entering negotiations becomes “no nuclear program and no missiles,” Washington’s options narrow considerably. In such a scenario, diplomatic pathways would be constrained, leaving coercive or kinetic measures as the primary alternatives. Iran, for its part, is likely to accept the risks of escalation rather than relinquish its missile capabilities.

E. President Trump’s recent statements raise the perceived credibility of a military option and increase the stakes surrounding upcoming diplomatic engagements. Should his remarks on missile limitations reflect actual administration policy — rather than signaling or negotiating leverage — the likelihood of escalation would increase substantially.

F. That said, it would be premature to dismiss the diplomatic track entirely. There remains room for creative mediation efforts, including proposals to sequence negotiations by addressing the nuclear issue first, as suggested by Turkey. Such approaches could reduce immediate escalation risks while preserving diplomatic momentum.

The coming days will be decisive.

🚨Exclusive: President Trump told me in an interview on Tuesday that he's considering sending a second aircraft carrier strike group to the Middle East to prepare for military action if negotiations with Iran fail. My story on @axios
https://t.co/JRpkoERtHw - Barak Ravid
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Quiver Quantitative
Look at this.

Ichor stock has now risen 208% since we posted the report in the first image.

Hecla has risen 312% since we posted the second.

Viasat has risen 547% since we posted the third.

New Gold has risen 854% since the trade in the fourth. https://t.co/xz1ufcZk2N
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Dimitry Nakhla | Babylon Capital®
Pick your fighter 👇🏽

$SPGI $MCO $NDAQ $MSCI
- $SPGI at 20x
- $MCO at 26x
- $MSCI at 26x
- $NDAQ at 22x
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God of Prompt
RT @godofprompt: creating ads for clients on autopilot

> simple form
> n8n worklow
> nano banana API

added this to my n8n automations bundle! https://t.co/dyPbWqMS5l
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Brady Long
RT @thisdudelikesAI: I've been copy + pasting ChatGPT responses into emails like some kind of digital assembly line worker...

Meanwhile this AI is in iMessage catching contract errors and booking flights autonomously.

I'm so behind... but Lindy's about to change that: https://t.co/ueXITRiX5v https://t.co/JYk610Rqn7

Introducing Lindy Assistant, the ultimate AI assistant.
It talks with you through iMessage, connects to 100s of apps, helps you with your meetings and emails, and proactively finds ways to save you time all day.
Check out some examples of ways Lindy assistant helps below. https://t.co/DY10QQSSfn - Flo Crivello
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God of Prompt
RT @p_song1: Thank you for sharing our work on Large Language Model Reasoning Failures!

📄 Paper: https://t.co/qKTwAsIBz9
📚 Repo: https://t.co/KOSB6811LH

🚨 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 - God of Prompt
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Moon Dev
oh codex is the same as claude code

such an interesting phenomenon rn

ai is being subsidized by investors

but at some point claude has to cut you off

and when they do

you have to switch to the other guy

to see all this ai is relatively the same

bless to be a dev rn
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