ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎

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ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎ banner
ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎

ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎

@MycelFi

Quantum SOMs Mycelial bioinformation Mushroom farmer Founder 𓍊𓋼 Puru

🐔🏔️/ 🇫🇮 / Taiga Biome Katılım Mart 2009
5.8K Takip Edilen4K Takipçiler
regenavocado
regenavocado@regenavocado·
@MycelFi I was trading fake apple stocks in my wall street class in middle school.
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ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎
Where were you when the dot com bubble bursted? I was there. With my 80+ employee fintech startup.
Hedgie@HedgieMarkets

🦔Bloomberg data shows total US cash (money market funds plus bank deposits) as a percentage of S&P 500 market cap has fallen to 0.42. That is near the lowest level ever recorded, matching where it sat right before the dot-com crash. Money market funds are at a record $7.95 trillion in absolute dollars, but the S&P 500's market cap has ballooned to roughly $69 trillion. The cash pile looks massive until you measure it against how big the market has gotten. There is less dry powder available to catch a selloff, relative to market size, than at almost any point in modern history. My Take I think this is one of those charts people will point to after the fact. $7.95 trillion in money markets sounds like a wall of cash ready to buy any dip. But at a 0.42 ratio to S&P market cap, it's thinner than it was before the dot-com bust. Households have 45.8% of their financial assets in equities (Q1 2026 Fed data), the personal savings rate is around 3%, and retail investors are buying every single dip with record options volume. Citadel's own H1 2026 report says retail bought 3.5x their average daily amount on every down day this year. The "cash on the sidelines" story is how everyone explains why dips keep getting bought. And in absolute dollars it looks true. But relative to how large the market has become, the available cash to absorb a real selloff is about where it was in 1999. I'm not calling a crash. I'm saying the cushion is a lot thinner than the headline number makes it look. Hedgie🤗

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Mathelirium
Mathelirium@mathelirium·
When Machine Learning Tries to Store Two Memories on the Same Neural Sheet The same self-organizing map (SOM) is forced to learn two incompatible worlds. Training repeatedly switches between an emerald Swiss roll and an electric-blue torus knot x ∼ (1 − α(t))Pₐ + α(t)Pᵦ The network is never reset. Some neurons preserve the old geometry while others defect to the new one, creating moving domain walls across the sheet. Each colour change marks a local memory being overwritten. #MachineLearning #SelfOrganizingMap #NeuralNetworks #Animation #ArtificialIntelligence #DataVisualization #Mathematics #ComputerScience #Topology
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Ethan Mollick
Ethan Mollick@emollick·
Very few people know the amount of useful work that the current models can do in Code/Codex/etc. with the right setup This is not a "rah rah you are so early" post, this is a "AI companies are doing a really bad job explaining what their systems actually do in a clear way" post.
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Heitor Rapela
Heitor Rapela@HeitorRapela·
@pcastr Yes ✌ I did my Masters on deep clustering with Self-Organizing Maps. Professor @HansBassani have some good SOM based approaches for vision and navigation.
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Ethan Vale | Tech Unfiltered
@mathelirium so the SOM's topology preservation collapses when alpha(t) oscillates—but have you checked if the latent manifold actually tears or just blurs? that'd settle whether it's catastrophic forgetting or emergent hybrid geometry.
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ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎ retweetledi
X Freeze
X Freeze@XFreeze·
Grok 4.5 just topped Perplexity’s WANDR orchestrator evaluation It scored higher than every other tested configuration at just $4.76 per trial....roughly half the cost of Opus 4.8 Grok 4.5 is becoming the powerful brain coordinating entire agentic workflows It’s now available as an orchestrator model in Perplexity Computer for Consumer Pro and Max subscribers
X Freeze tweet media
Perplexity@perplexity_ai

Grok 4.5 is now available as an orchestrator model in Computer for Consumer Pro and Max subscribers. We evaluated it against five other orchestrator configurations on WANDR. It scored higher than every other configuration at roughly half the cost of Opus 4.8.

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ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎
Switched on the 1st early ugly version of the Lung Room for the Chicken Mountain fungi farm nursery. CO2 good, particles fair - should get better after some 🧹 ...
ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎ tweet mediaᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎ tweet media
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ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎ retweetledi
Maziyar PANAHI
Maziyar PANAHI@MaziyarPanahi·
I finally got an open model to do structural biology by itself 🔥 GLM-5.2 drives the Mol* viewer, judges its own render through Qwen3-VL, and refines until the drug pops in its pocket. Then I spun it in 3D. All open, on @huggingface. What should it build next?
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ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎
thank you for this post Daniel. I'm a fungi farmer focusing in high-ergothioneine strains. This far I have operated based on the assumption that ergothioneine helps the energy machinery fueled by MPC1 to run cleanly and efficiently. Is there something in this article that changes that big picture?
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Daniel Tawfik
Daniel Tawfik@dantawfik·
Mitochondrial health is one of the most important determinants of healthspan. A new study from Iñigo San Millán's @doctorinigo lab provides the clearest mechanistic evidence yet that sedentary behavior drives mitochondrial dysfunction at multiple levels simultaneously. If you are interested in longevity science you should read this paper. The power of this study lies in its ability to paint a clear picture of what an efficient metabolic engine looks like versus a compromised one. San Millán's lab compared sedentary versus active healthy adults using muscle biopsies, high-resolution respirometry, and isotope tracing. The findings reveal coordinated impairments across the entire mitochondrial energy production system: • Complex I and II (the main energy generators) reduced by 30-36% • Mitochondrial pyruvate carrier (MPC1) down 49% (blocks sugar-derived fuel entry) • CPT1 activity reduced by 51% (blocks fat-derived fuel entry) • Cardiolipin content decreased (disrupts the assembly line for ATP production) • ROS production elevated (more oxidative damage per unit of energy made) Let's break down what these stats mean. The MPC1 finding is critical. This transporter moves pyruvate, the end product of sugar breakdown, into the mitochondria where it gets burned for energy. A 49% reduction creates a metabolic traffic jam. Glucose can enter the cell normally (GLUT4 was identical between groups) but can't reach the energy production cycle inside the mitochondria. The parallel 51% reduction in CPT1 means the same problem exists for fat. CPT1 is the transporter that shuttles fatty acids into mitochondria for oxidation. Both major fuel sources, sugar and fat, are now blocked at the mitochondrial entry point. This dual blockage forces cells into metabolic inflexibility, meaning they lose the ability to switch between burning fat and burning glucose based on what's available. Cardiolipin is a specialized fat molecule found only in the inner mitochondrial membrane. It acts like scaffolding that holds the respiratory complexes (the actual ATP-making machinery) in the correct position for efficient energy transfer. When cardiolipin content drops, this scaffolding weakens. The machinery still runs, but electrons don't flow as smoothly and more leak out as reactive oxygen species (ROS), creating oxidative stress. Sedentary individuals showed elevated ROS production per unit of oxygen consumed, meaning their mitochondria were less efficient and more damaging. All this suggests the metabolic engine (the mitochondria) is less efficient and emits much more hazardous byproducts. During exercise testing, the metabolic inflexibility became visible in performance. Sedentary individuals accumulated 60% more lactate at moderate workloads. Lactate buildup is what causes that burning sensation during hard exercise, it signals that your body is relying too heavily on sugar metabolism because it can't access fat efficiently. They also showed 35% lower fat oxidation rates and hit their crossover point (where the body switches from primarily burning fat to primarily burning sugar) at much lower exercise intensities than active individuals. The cellular deficits measured in muscle biopsies translated directly to whole-body exercise capacity. The blocked fuel transporters and damaged mitochondrial machinery weren't just lab findings, they determined how much work these people could perform before hitting metabolic limits. The correlations reveal how tightly coupled this system is. How well your mitochondria could process pyruvate at rest predicted how well you could burn fat during exercise (r=0.65). The amount of MPC1 protein you had predicted your fat burning capacity during exercise. Your blood lactate levels during exercise correlated inversely with how much MPC1 you had (r=-0.73, meaning less MPC1 equals more lactate accumulation). These aren't separate problems happening in parallel. They're interconnected components of a single energy system. When one part breaks, the whole system compensates poorly and performance degrades as a unit. The metabolic engine of sedentary individuals sputters, plagued by inefficiencies like increased lactate production, heightened oxidative stress, and an inability to efficiently use an array of fuel sources, which compromise their ability to sustain energy production and manage metabolic stress. This study establishes that mitochondrial dysfunction is the mechanism linking physical inactivity to accelerated aging. Every longevity intervention ultimately depends on mitochondrial capacity to generate ATP efficiently, handle substrate flux, and maintain redox balance. When MPC1, CPT1, cardiolipin, and respiratory chain function decline simultaneously, cells lose the energetic foundation required to sustain proteostasis, autophagy, DNA repair, and immune surveillance. Sedentary behavior isn't a passive state. It's an active driver of mitochondrial remodeling that produces measurable molecular atrophy across substrate transport, electron transport efficiency, and metabolic flexibility. Understanding these pathways is understanding how aging accelerates at the cellular level.
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ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎
@ruuvicom I'm using your RuuviAir to measure the air quality both in my fungi labs and grow rooms (mycelia incubation phase and fruiting phase rooms). Happy with the tools you provide but there's one question: During the fruiting phase I need high 90%+ humidity. In that situation your sensors typically show very high PM 2.5 (μ g/m³). Why is that?
ᨒ↟𐂂 𖠰𖥧 𓍊𓋼❄︎@MycelFi

Switched on the 1st early ugly version of the Lung Room for the Chicken Mountain fungi farm nursery. CO2 good, particles fair - should get better after some 🧹 ...

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ᨒ↟ 𖠰𖥧˚⏾
ᨒ↟ 𖠰𖥧˚⏾@UralicMoon·
Sometimes I feel the urge to have a beloved, especially to hold hands and run around and have fun in the rain. + dance under the moonlight 🌝
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