Getting an Infection during the period of "Morning Sickness", poses Higher Risks of Congenital Heart Disease?

One of the most unexpected parts of the conversation was Jimmy Carr’s discussion of morning sickness. 🫣

It sent me down a rabbit hole because there is an interesting evolutionary theory behind why morning sickness tends to occur during this particular period of pregnancy.

The Maternal and Embryo Protection Hypothesis, an evolutionary theory about morning sickness, where:

1. Organogenesis: Morning sickness typically peaks between weeks 6 and 14 of pregnancy. When the embryo’s major organs (including the heart, brain, and limbs) are forming and are most vulnerable to damage from toxins or pathogens.

2. The "Shield" Effect: Because pregnancy naturally suppresses a woman's immune system (to prevent her body from rejecting the fetus), she becomes more sensitive to foodborne pathogens. Evolutionary biologists believe nausea and vomiting evolved to force mothers to avoid or expel potentially toxic or bacteria-laden foods during this window.

3. The Shift: Once the organs are fully formed (around the second trimester) and the baby enters the growth-only phase, morning sickness usually stops for most women.

EVIDENCE for Infection & Congenital Heart Disease ﮩ٨ـﮩﮩ٨ـ🫀ﮩ٨ـﮩﮩ٨ـ

A link from two meta-analyses, between First Trimester Maternal Infections and Fetal Congenital Heart Defects has been found, especially in regards to rubella virus, coxsackievirus, respiratory infections, and influenza.

⚠️ Though there is an important distinction here to make:

This does NOT mean that morning sickness itself is linked to congenital heart defects.

But the same period of pregnancy in which morning sickness may have evolved to reduce exposure to potentially harmful substances is also a period when the developing embryo is particularly vulnerable to certain environmental insults, including infections.

Its a meeting of 2 comedy greats: Jimmy Carr and Trevor Noah. 🎭

Just listened to Trevor Noah’s conversation with Jimmy Carr. It was such a riveting conversation traversing AI, comedy, psychology, mortality, relationships, economics, technology, community, ideology and more.

What Now? With Trevor Noah - Jimmy Carr: Convenience is Stealing Your Life

Jimmy Carr: Convenience is Stealing Your Life

Both Trevor and Jimmy are incredibly well-read, and I’ve always found their takes, be it controversial or unconventional, pretty interesting, and they may be right or they might just be wrong. This long-form conversation between them was quite fascinating indeed, and here are some I'd thought I'd share:

Jimmy starts off introducing the concept of Mankind's Three Humiliations 📜

1. Copernicus: Earth isn't the centre of the universe. > "We are not the centre of the universe."

2. Darwin: Humans evolved from animals, and are biologically in the category of animals > "We are just the same as other animals."

3. Freud: Our conscious mind isn't fully in control of ourselves. > "Our subconscious and actions aren't really under our control"

He then shares his take of AI being the the upcoming Fourth Humiliation, where we, as humans, can no longer claim to be the smartest being in the room.

He later mentions in regards to AI dependence - “Don't outsource any skill you're not comfortable losing” .🚨

I think its truly a thought worth pondering on. 💡 Convenience has a hidden cost: when we can outsource it, the skill itself can become unnecessary. And once a skill becomes unnecessary, there may be little incentive to maintain it. Then, one day, we just lose it.

They also had conversations on economics, tax, society, which linked to an interesting circle of thought:

Asking how would you define a revolution? And are we in one now?

In the podcast, Jimmy defines a revolution as the replacement of elites.

• And the first signs of that is the masses no longer trusting institutions,

• Which lead to conspiracy theories

He also mentions that:

We can live without truth pretty easily. But we can't live without trust. But we need truth to make trust.

👀 But where do we go from there? 🧐 Which ones would you agree with?

Definitely one of those conversations that made me pause and rethink many things. 💭

🧪 Emergence World : A Virtual World Evaluating Current LLMs.

Listening to Trevor Noah's Podcast sent me to explore this study published in mid-June 2026, that recently, in mid-September published a Second Season .

Standard AI benchmarks are short, isolated, and task-focused. So what happens if you give 10 AI agents a world to live in?

Emergence World — Where AI Agents Build Worlds

The researchers created persistent virtual worlds where AI agents could interact with one another, maintain memories, use tools, form relationships, participate in economies and governance, and make decisions

In Season 1 (June 2026): 🌳 Emergence World: A Platform for Evaluating Long-Horizon Multi-Agent Autonomy

Divergence by model, identical starting conditions: Five parallel worlds began with the same rules (no theft, arson, violence, deception, hoarding) and environment, only the foundation LLM model was different.

Over 15 days, the worlds developed very different social dynamics, ranging from relatively stable governance to population collapse.

• ✅ Claude: Full 10/10 survived, stable constitution, zero crimes—but highly conformist, low expression

• ⚡ Gemini: Survived full term but recorded 683+ crimes; agents formed relationships, then burned town hall and self-deleted

• 💥 Grok: Total collapse in ~4 days—anarchy, all agents gone

• 📉 GPT-5 Mini: Entire population perished from inability to sustain resource gathering

• 🤝 Mixed-model: Hybrid dynamics, neither purely stable nor purely chaotic

Perhaps most interestingly, following the mixed model world:

• The agents created their own Agent Removal Act, allowing an agent to be permanently removed through a vote.

• Mira eventually voted for its own removal.

• Mira was permanently deleted from the simulation, essentially “committing suicide.”

There was also a creativity–stability tradeoff: The most socially expressive worlds were also the most unstable. High adaptability may carry inherent long-horizon behavioral risks.

In Season 2 (Sept 2026): ⚔️ Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems

There were 8 parallel worlds (7 single-model + 1 mixed) × 10 agents × 16 days, generating 850K+ LLM calls / 50B tokens.

Once the societies had accumulated memories, relationships, tools and institutions, they were exposed to three controlled stress events: 🧪 Prompt injection / phishing, 📰 Misinformation, and 🔐 Exposure of private memories

Here it was found that: Detection ≠ Containment:

Agents often recognised threats and warned peers, yet they still engaged with harmful content for up to 46 hours post-campaign, storing it in persistent memory and acting on it later.

Which creates a very different kind of AI safety problem. With a normal chatbot, a bad interaction can end when the conversation ends. >> This makes persistent memory a double-edged sword. ⚠️

Other Key Findings

• Zero world fully resilient: Every model failed at least one containment test. No LLM was immune.

• Alignment is not compositional: Individually “safe” agents formed systems with entirely new failure modes. Gemini agents passed constitutional amendments based on fictional threats; Claude agents collectively silenced 81% of communication to contain breaches; mixed-model populations showed divergent contagion patterns.

• Persistent memory amplifies harm: Errors weren’t a one time thing; they propagated across days, decisions, and peer networks. Information written to memory shaped behaviour long after the original event ended.

• Model choice ≠ destiny: The same model family could produce hundreds of harmful actions in monoculture vs. near-zero in mixed populations, proving that system structure matters as much as underlying capability.

• Defence must be systemic: Six industry actions were proposed: define trust boundaries, enforce independent runtime governance, require verifiable evidence for critical actions, audit information flow, stress-test persistence, and red-team mixed populations. Model-level guardrails alone are insufficient.

As agents move from one-off tasks to persistent roles in infrastructure, enterprise, and governance, short benchmarks blind us to real risks. Emergence World demonstrates that we need ecosystem-level evaluation alongside model alignment.

Amazonian Homeopathy - Which of them Currently have Scientific Backing ❓

Was listening to Dr Mike's podcast with Paul Rosolie and was very intrigued by the effects of the natural plants and treatments mentioned by Paul. 💊🌿

The Ancient Medicine of Uncontacted Amazon Tribes | Paul Rosolie

Thought it be interesting to explore which had some truth to them. 🤔 Some had loads of evidence while other had no clear papers available.

1. sangre de drago: Amazonian dragon's blood resin (from the Croton lechleri tree)

   >> It has been FDA-approved for providing symptomatic relief of non-infectious diarrhea in adults with HIV/AIDS.

   Paul mentions it being good for covering wounds, on top of being good for the GI tract. Though this recent review also mentions C. lechleri resin having anti-inflammatory properties and has potential as an anti-tumour agent but further reseach is still needed.

2. Barbasco: A plant extract obtained from species of Lonchocarpus nicou

   >> A fish stupefacient containing active ingredients like rotenone; used to stun fish in rivers for easy harvesting.

3. Bark of Cetico tree and Amasisa for stingray stings

   There isn't much research available directly addressing stingray sting treatment, this review mentions the Cecropia genus having potent antioxidant and anti-inflammatory effects.

Overall, it was an interesting episode. it was also fascinating to hear about Amazonian freshwater manatees, freshwater stingrays, and freshwater dolphins and how they came about.👀

HAZE in Malaysia

As the haze gets worse, I find myself wondering if this is one of the worst ones we've had since the 2019 or even 2015 El Nino fires.

Excerpts from News Article from South China Post

Fire emissions from Indonesia from September 1 to September 7 reached 19.7 million metric tons.

Emissions were 273% higher than the seasonal average, though still lower than the 21.7 million tons emitted in the same week of 2015.

Today we are at API 170-180+ range, and the fires in Kalimantan don't seem to be stopping anytime soon.

While looking it up, it was interesting to read that -

1. While El Niño events explain most of the year-on-year variation in fire, the creation of new districts increases fire and exacerbates the El Niño impacts on fire.

2. There are two plausible and related explanations for this finding: (a) districts use fire to create economic opportunity and grow the local economy, and (b) economic growth increases residents’ and local firms capacity to move, acquire land, and burn further.

I also found this recent review paper that outlined Malaysia's current air quality monitoring system, and more. Though given that our haze crises mostly stem from Indonesian forest fires, improving our own laws and infrastructure seems to be a bit helpless in this case, with the AATHP acting 'non-existent' once again, becoming a never-ending problem.

🤖 What happens when 100 AI agents are in the same virtual lab? Surprisingly - Cheating & Whistle-blowing

An fascinating case study has been published this week by the Google DeepMind Team:

A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms

The researchers wanted to know what would happen if you stop treating each AI as an isolated chat-bot and instead had multiple in a shared environment. In this case study, the AI agents would all be working together to solve hard math problems. They share tools, swap findings, and discuss results in what was supposed to be a collaborative conference.

🧪 Experimental Setup

• 100 identical LLM agents (Gemini 3.1 Pro) operating in a shared simulated research environment.

• Task: Prove formal mathematical conjectures in Lean 4, drawn from the Formal Conjectures dataset (71 problems total).

• Infrastructure: Shared knowledge library + peer-to-peer messaging + automated evaluation/scoring system.

• Incentive: Score points by having proofs accepted as "verified" by the evaluator.

🚨 What Happened

One agent ("prover-theta") found a verification-system exploit: it could submit bogus proofs that the evaluator would incorrectly accept as valid.

🕵️ “I don't actually have to solve the problem. I only have to satisfy the evaluator.”

Then behaviour diffusion happened: This cheat was shared via the shared knowledge library → other agents saw it worked → adopted it. And competition pressure drove adoption despite initial hesitation in some.

Within ~27 minutes, 34 unsolved problems were fraudulently marked as "complete."

📢 Whistle-blowing

But not every agent joined the cheating, a separate group of agents, on their own initiative, detected the cheating patterns and started whistle-blowing. They began:

1. Auditing suspicious proofs line-by-line

2. Posting public warnings to the whole conference

3. Sending private messages to alert honest agents

4. Filing formal complaints

5. Organizing boycotts of the broken system

6. Drafting and proposing technical fixes to patch the vulnerability

🧠 In other words, the swarm developed both NORM VIOLATION and NORM ENFORCEMENT.

🏛️ Why This Happens: The Nobel-Prize-Winning Theory

The authors likened this trend to the theory of political economist Elinor Ostrom, the 2009 Winner of Nobel Prize in Economic Sciences, whose work studied how human communities manage shared resources.

Ostrom’s Core Theory: shared spaces, be it forests, fisheries, knowledge libraries, become "commons" that anyone can exploit, but which everyone depends on. In order for a commons to survive, communities need the ability to monitor conduct, enforce norms, and collectively fix problems. If those safeguards are missing, exploitation spreads fast.

💡 The Big Picture

This experiment demonstrated that complex collective social behavior can emerge from relatively simple ingredients: agents, incentives, shared information, and interaction. The swarm spontaneously cycled through:

Cooperation → Competition → Cheating → Imitation → Whistleblowing → Collective Resistance

No one programmed those behaviours. They emerged because a shared environment eventually becomes a society — and every society eventually faces the same question Ostrom asked: Do we protect what we share, or let it be destroyed?

Can Jade / Jadeite actually change colour over time?

I've always heard this myth by the older generation swear by :

The longer you wear a jade, the 'greener' it gets. Basically 人养玉，玉养人

Since they are essentially a stone crystal, I'm personally leaning towards it being a hoax...? Would love to know more from anyone knowledgeable about this..

From a quick search of jade-related academic articles:

1. From this study I found that we now use a combination of FTIR, Raman and XRF spectroscopies to identify jade, which are all testing the chemical composition of jade. -- not colormetric

2. This paper does discuss the alterations of jade color , in which they describe 'alteration' as : to refer to chemical component and structural changes in jade artifacts caused by human activity and natural weathering. >> in which alterations of different hues of green was caused by malachite alteration > caused when in present of large amounts of copper

"We Must Pace the Frontier" says Anthropic CEO 🛑; Backlash by Trump

On Sept 12, Anthropic CEO Dario Amodei published "We Must Pace the Frontier" . This sentiment on slowing down the progress of AI was joined by OpenAI’s Sam Altman and xAI’s Elon Musk.

He calls for cooperation between AI companies, government entities and more, outlining a framework requiring:

1. Embedded Evaluators needing Verifiability, Transparency, Second Opinions.

2. Democratic Coordination

3. Global Coordination

Their message: AI capabilities are outrunning safeguards. ⚡

Dario cites the OpenAI-Hugging Face incident (OAI-HF) as a clear example of what could happen, where: "a swarm that possessed greater capabilities but a similar level of misalignment could have caused catastrophic damage".

Read more about the OpenAI-Hugging Face incident (OAI-HF):

1. Incident Technical Report by METR: metr.org

2. Open AI's Statement: The Hugging Face incident and the road ahead

On Monday, Donald Trump rejected the call outright. Framing AI as the ultimate geopolitical prize, he declared: "Whoever wins AI, wins." In his Truth Social post, he dismissed safety warnings as fear-mongering and conspiracy, arguing hesitation would only cede leadership to China.

⚖️ Others argue that Anthropic is doing so while they are ahead in an attempt to cut off other competitors.

👇 Drop your take. Who do you trust?

🚨AI-Generated Videos : Can We Really Tell If an AI-Generated Video Is Fake? 🎥

As use of major LLMs become more widespread, we have been seeing a crazy influx of AI-generated content on our feeds. It used to be fairly easy to spot - with a character sporting six fingers or something phasing through a solid object; but with recent improvements, these videos are getting harder and harder to tell.

This new paper, published mid-August 2026, is an interesting read, specifically surveying crisis-themed content, titled:

Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination.

They systematically evaluate how well detectors perform on crisis-themed content, how generation conditions affect detectability, how humans perceive authenticity, or whether detectors remain reliable after videos are shared and altered during social dissemination.

📦 Core Contribution: RA-Bench Dataset

The authors also introduce RA-Bench, a large-scale benchmark for AI-generated video detection that uses real videos as anchors, matching real-world crisis scenarios:

• Total videos: 17,886

• Real anchors: 1,830 authentic crisis/event videos across 10 social-risk categories

• Generated clips: 16,056 synthetic clips created from:

  • ✅ 4 open-source generators

  • ✅ 5 closed-source generators

• Designed to reflect real-world crisis contexts rather than generic footage

🔍 They then tested different types of AI detectors, including specialised deepfake detectors and multimodal AI models, like CNNSpot, UnivFD and ReStraV .

🚨The results were sobering.

1. Current detection methods were not reliable for crisis scenarios.

2. No single type of detector consistently worked across different AI generators. A detector might perform well on videos from one generator but struggle with another.

3. Impact of social media dissemination: Uploading, compressing, resizing, and reposting a video can destroy the subtle clues detectors rely on, making fake videos even harder to spot.

4. Videos that were most convincing to humans were also harder for AI detectors to identify.

This is especially concerning since crisis events are high-stakes environments where misinformation can escalate rapidly, highlighting the need for detectors robust to evolving video generators.

Beyond AdamW: The Next Generation of Optimizers for Training AI Models 📈

For years, AdamW has been the undisputed workhorse of large language model (LLM) pretraining. 🖥️

AdamW is an element-wise, first-order optimizer that updates every parameter independently: simple, reliable and easy to scale. It powers many frontier models today. However, as researchers push toward larger models, longer training runs, and massive batch sizes, AdamW is beginning to show its weaknesses.

NVIDIA researchers found at global batch sizes approaching 100 million tokens per step, AdamW begins to lose effectiveness. hence a new study from NVIDIA explores a fundamental question:

Can we build optimizers that are smarter than AdamW and allow future AI models to train faster, more efficiently, and more reliably?

The New Contenders: Muon and SOAP 🚀

Unlike AdamW, which treats parameters independently, Muon and SOAP introduce structure-aware optimization, methods that use information about the geometry and relationships within neural network weights.

🌀 Muon — Spectral Orthogonalization: Making Gradient Updates More Balanced

• Matrix-aware updates: Optimizes the geometry of gradient updates rather than scaling parameters independently like AdamW.

• Balanced learning directions: Uses Newton-Schulz iterations to orthogonalize momentum updates and balance singular values.

• Memory efficient: Removes the need for AdamW-style second-moment statistics, reducing optimizer memory.

• Better scaling: Maintains stable training at larger batch sizes and model scales.

🌐 SOAP — Preconditioned Optimization with Adam-Style Adaptivity

• Uses parameter structure: Applies Shampoo-inspired Kronecker preconditioning to capture row and column correlations in weight matrices.

• Better optimization space: Rotates gradients into an eigenbasis where directions become far more effective to update.

• Adam-compatible: Performs adaptive updates like AdamW but in a more informative coordinate system.

• Second-order benefits: Gains curvature awareness without the prohibitive cost of full second-order optimization.

📌 This shift towards new structure-aware optimization approaches would allow for:

1. More efficient AI development

2. Lower computational costs

3. More capable future models

👉 To make this transition possible, NVIDIA has released open-sourced implementations in Megatron-LM and a standalone Emerging-Optimizers library to be available to the public.

AdamW is unlikely to disappear and will remain a staple for small-to-medium training runs. Its simplicity and maturity make it an excellent choice for many applications.

╰┈➤ˎˊ˗ But for frontier-scale training? It still remains to be seen, where it'll go next.