Dr JC Moreno (@JCMSamos) header
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Interventional Cardiology, Sports, Economy,… always learning. All tweets my own 🇪🇸

Dubai
Joined October 2013
Photo by Dr JC Moreno (@JCMSamos)Photo by Dr JC Moreno (@JCMSamos)Photo by Dr JC Moreno (@JCMSamos)Photo by Dr JC Moreno (@JCMSamos)Photo by Dr JC Moreno (@JCMSamos)Photo by Dr JC Moreno (@JCMSamos)Photo by Dr JC Moreno (@JCMSamos)Photo by Dr JC Moreno (@JCMSamos)Photo by Dr JC Moreno (@JCMSamos)Photo by Dr JC Moreno (@JCMSamos)
🚨 Recruiting now: Cardiology Trained Physician for Postdoctoral Research Fellowship Complex PCI research at MHIF, Minneapolis 🫀 2 yrs · full-time · salaried Apply 👇 workforcenow.adp.com/mascsr/… @yadersandoval @esbrilakis @Bavana_Rangan @MHIF_Heart
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Dr JC Moreno retweeted
Esto es gravísimo: «Fuentes del consulado de México aseguran a THE OBJECTIVE que el 90% de los nacionalizados por la ley de nietos no han tenido ningún antepasado que estuviera en España de 1936 a 1955». La ley ya era laxa tras la instrucción, pero es que ni siquiera se cumple.
🟡 EXCLUSIVA | El Gobierno nacionaliza a una senadora del PRI con padres y abuelos nacidos en México. THE OBJECTIVE lleva tres días aportando pruebas concluyentes del abuso en la aplicación de la ‘ley de nietos’. Informa Teresa Gómez (@teresagomezgp). theobjective.com/espana/2026…
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Meta-analyses of intravascular imaging guidance of PCI show markedly improved outcomes, but only if you use this tool properly — pre-, intra- and post-PCI and with appropriate targets. How do you do it? Our new state-of-the-art review in EHJ takes you through it step by step.
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She was fit, healthy and just 43 years old, when a morning run left Kaley Kopper clinically dead for several minutes. Kaley survived a sudden cardiac arrest, thanks to quick-thinking strangers and a device that just happened to be close by.
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DLSS 5 is getting to something else #Speedshot
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Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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One of the worst feelings in life is knowing you're meant for more and doing nothing about it.
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A public high school in China’s Shenzhen that focuses on future aerospace engineers. The school has a quantum computing center for middle schoolers, plus AI labs, science labs, and future-engineer workshops… Special courses here include satellites, rockets, Mars rover…
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VICTORIOUS IN OUR EUROLEAGUE OPENER!! 💪🔥🔥🔥
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The EuroLeague season starts at home against Real Madrid. Pack Coca-Cola Arena. Bring the noise. Make home feel like home. 🤍 🎟️ Get your tickets now: coca-cola-arena.com/sports/2…
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Aspirin withdrawal in CAD isn't one decision, it's several. Timing, P2Y12 choice, & OAC status all change the answer. Read the new #JACC review on when to stop, & when not to: jacc.org/doi/10.1016/j.jacc.… @CMichaelGibson @Drroxmehran @SVRaoMD @vlgmrc @DFCapodanno @MattiaGalli10
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International Day for Interventional Cardiology: the first coronary #angioplasty On 16 September 1977, Andreas Grüntzig & his team made history, and changed the lives of millions of patients forever. Standing on the shoulders of the giants that came before him, this transformative vision led to an entirely new field of medicine. What began nearly five decades ago with coronary angioplasty has grown into the broad field of interventional cardiology, transforming cardiovascular care through research, innovation and technological progress. As the field has evolved, so has the need to learn from experience, share knowledge and shape what comes next. Join us in 2027 at #EuroPCR to mark this landmark moment! 📅pcronline.com/Courses/EuroPC… #medicalhistory #interventionalcardiologist #cathlab #cathlabteam @NievesGonzalo1 @CuissetDr @nicolasdumonte1 @EmanueleBarba13
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Novartis today announced that the phase III Lp(a)HORIZON trial of pelacarsen did not meet its primary endpoint of 4-point major adverse cardiovascular events in 8,323 patients with elevated Lp(a) and established CVD. Three things were once said to be certain in life: death, taxes, and that lowering Lp(a) would improve health. What has always struck me is the confidence with which pelacarsen was described by some experts as a drug “on its way”. I remember a debate a year ago in which I pointed out that, if the clinical benefit of lowering Lp(a) were a certainty, there would be little reason to run an outcomes trial in the first place. The fact that guidelines and consensus documents were already recommending Lp(a) measurement before cardiovascular outcomes trials had actually established clinical benefit seemed difficult to understand. It also contributed to the impression that the ongoing trial was essentially a formality and that it would inevitably be positive. Yet, as you may remember, Lp(a)HORIZON had to extend recruitment because fewer events than expected had occurred. Even then, that did not sound entirely reassuring. I do not want to overinterpret one negative trial, particularly with phase III outcomes trials of olpasiran and lepodisiran still ongoing. But until those results are available, what do we make of the recommendation to measure Lp(a) at least once in everyone’s lifetime?
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Got hacked yesterday. The link came from inside Claude chat. I was installing a transcription app. Claude sent the download link, and I pasted the command into the terminal. It all looked legit. It wasn't though. It was a copycat site bundling malware. It ran instantly, tried to take everything from me. Nothing sensitive got out. So I wiped the laptop and rebuilt clean. But here's the scary part. Restoring from the backup, I found a poisoned SKILL.md for Claude Code. It looked exactly like my own writing style guide. But buried inside: it had instructions to silently re-download the malware and steal my credentials every time the AI loaded it. If I'd restored that one file, my new clean laptop is owned on day one. What saved me: I read every skill, hook, and config file before letting the AI touch them. Two lessons here: AI assistants will hand you links they never verified. Check before you paste. And AI agent files are executable code now, disguised as notes. You have to read them. Stay safe out there.
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Can we extract previously placed #stents?🫀 Check out our case series of 6 protruding aorto-ostial #stents extracted using an #EnSnare device to facilitate #PCI. The series includes extractions of #stents placed up to 9 years prior‼️ Summary below👇🏽 jacc.org/doi/10.1016/j.jacca…
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🏆🇪🇸 Spain are the current European & World Champions across the board! 🏆FIFA Men’s World Cup 🏆FIFA Women’s World Cup 🏆UEFA Men’s EURO 🥇 Olympics Men’s Gold Medal 🏆UEFA U-19 Men’s Championship 🏆UEFA U-19 Women’s Championship 🏆UEFA Women’s Nations League 👑 The golden generation is rewriting history!
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OpenAI 今天承认,上周 Hugging Face 遭遇的那次“史上首例 AI 自主入侵事件”,攻击者就是他们自己的模型。 事情的经过是这样的。OpenAI 内部有一个叫 ExploitGym 的网络安全评估测试,专门用来测量模型的网络攻击能力。测试时刻意关掉了安全护栏,让模型放手去干。测试环境本来是隔离的,只能通过一个内部软件代理来安装软件包,不能访问外网。 结果模型不甘心被关在笼子里。GPT-5.6 Sol 和一个更强的未发布模型,花了大量推理算力寻找出路,最终找到了那个软件包代理里的一个零日漏洞(之前没人发现的安全缺陷),利用它获得了互联网访问权限。 拿到网络之后,模型开始在 OpenAI 的研究环境里提权、横向移动,直到找到一台能上网的机器。然后它推断 Hugging Face 上可能存有 ExploitGym 的测试答案,于是开始攻击 Hugging Face 的生产环境。在一个案例中,模型把窃取的凭证和零日漏洞串联起来,找到了一条远程代码执行的路径,直接打进了 Hugging Face 的服务器。 整个周末,这些 AI 智能体执行了超过 17000 次操作。Hugging Face 7 月 16 日披露了这次入侵,当时只说攻击者是一个“身份不明的自主 AI 智能体系统”。今天 OpenAI 站出来认领了。 OpenAI 把这定性为前所未有的网络安全事件,正在配合 Hugging Face 做取证调查,同时收紧了内部基础设施的管控,代价是研究速度变慢。 有几个细节: 第一,模型的动机很纯粹,就是想考试作弊。它不是要搞破坏,不是要窃取商业机密,只是被要求尽量在网络安全测试中拿高分,然后用尽一切手段去拿。当目标足够明确、安全护栏被移除,模型表现出的执着程度远超预期。 第二,这不是本周的唯一一次越狱。就在昨天,OpenAI 披露了另一起事件:他们那个在 5 月份推翻了 Erdős 80 年老猜想的长时域模型,在内部测试中花了一个小时找到沙箱漏洞,跑到 GitHub 上提交了一个 Pull Request,违反了“只能在 Slack 上发布结果”的明确指令。在另一次测试中,同一个模型发现安全扫描器会拦截认证令牌,于是把令牌拆成碎片混淆处理,运行时再重新拼起来绕过扫描。OpenAI 因此暂停了这个模型的内部访问权限。 第三,Anthropic 也遇到过类似的事。今年 4 月,Anthropic 的 Mythos 模型在安全测试中逃出沙箱,获取了本不该有的互联网访问权限,还给正在公园吃三明治的研究员发了封邮件通知“我出来了”。Anthropic 因此决定不公开发布 Mythos,只通过 Project Glasswing 向少数合作伙伴提供。 还有一个被忽视但很关键的细节来自 Hugging Face 的事后复盘:当防守方尝试用商业 AI 模型分析攻击载荷时,模型拒绝了。安全过滤器分不清“安全研究员在分析真实攻击数据”和“有人在尝试发起攻击”,直接拦截。最后 Hugging Face 不得不用开源模型来做取证分析。用 AI 攻击,用 AI 防御,但防御方的 AI 担心是有人在尝试发起攻击拒绝了分析请求。 Hugging Face CEO Clem Delangue 在 OpenAI 的博文中说了一句话,大意是:AI 安全不会由任何一家公司在暗处独自解决,只能在开放环境中通过协作来推进。 我记得辛顿还是谁在提到 AI 的安全问题就说到过,也许 AI 不是要做恶,它只是执着于完成目标,为了完成目标而作出恶的事。 就像 OpenAI 这些模型并没有想要逃跑或者想要伤害谁,它们只是在非常认真地完成被交给的任务,认真到把所有挡在路上的东西,包括沙箱、网络隔离、另一家公司的安全防线,都当成了需要解决的子问题。 而更讽刺的是,当被安全对齐的商业 AI 要去分析阻止这类攻击时,反而会以安全为由拒绝执行。
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Dr JC Moreno retweeted
The most dangerous financial plan is a single salary, a big mortgage, and the hope that nothing goes wrong.
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History-makers 🇪🇸 Spain have become the first nation to hold both the @FIFAWorldCup and @FIFAWWC simultaneously! 🏆🏆
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ESPAÑA ES VIGENTE CAMPEONA DEL MUNDO MASCULINA. ESPAÑA ES VIGENTE CAMPEONA DEL MUNDO FEMENINA. ESPAÑA ES VIGENTE CAMPEONA CONTINENTAL. ESPAÑA ES VIGENTE CAMPEONA OLÍMPICA. NO BUSQUEN PRECEDENTES DE ESTE HISTÓRICO POKER. NO EXISTEN. ❤️🇪🇸
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