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Ben & Marc: Why Everything Is About to Get 10x Bigger

The media and tech landscape is undergoing a significant transformation driven by advancements in AI, technology, and new structures, enabling entrepreneurs and companies to achieve exponential growth and innovation ## ## Questions to inspire discussion.

Building Your Own Platform.

🚀 Q: How can writers escape traditional media constraints? A: Launch on decentralized platforms like Substack where you build your own brand and business as a “non-fungible writer”, potentially creating organizations 10x larger than traditional media companies you’d work for.

💰 Q: What makes writer-led platforms attractive investments? A: Platforms become cornerstone franchises when writers only succeed by making the platform successful, creating aligned incentives that generate significant returns while enabling top talent to build independent businesses.

📊 Q: What content opportunity exists in decentralized media? A: A barbell market is emerging with mainstream filler content on one end and massive untapped demand for high-quality niche content on the other, creating opportunities across various specialized domains.

Leveraging AI for Business.

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Tesla Ending FSD Sales Because the Value Is About to Change

Tesla is ending the one-time purchase option for Full Self-Driving (FSD) and shifting to a monthly subscription model, likely to recapture the value of the technology as it advances towards full autonomy and potential expansion into a robo-taxi fleet ##

## Questions to inspire discussion.

Investment Signal.

🎯 Q: Why is Tesla ending FSD one-time purchases after February 14?

A: Tesla is stopping FSD sales because autonomy is approaching a major inflection point where value will step-change when drivers are out of the loop, and Tesla wants to avoid locking in one-time payments at legacy prices before entering the real robo-taxi world.

Revenue Model Transformation.

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Physicists employ AI labmates to supercharge LED light control

In 2023, a team of physicists from Sandia National Laboratories announced a major discovery: a way to steer LED light. If refined, it could mean someday replacing lasers with cheaper, smaller, more energy-efficient LEDs in countless technologies, from UPC scanners and holographic projectors to self-driving cars. The team assumed it would take years of meticulous experimentation to refine their technique.

Now the same researchers have reported that a trio of artificial intelligence labmates has improved their best results fourfold. It took about five hours.

The resulting paper, now published in Nature Communications, shows how AI is advancing beyond a mere automation tool toward becoming a powerful engine for clear, comprehensible scientific discovery.

Microsoft Just Dropped New AI That Makes Decisions Better Than Humans

Microsoft just introduced OptiMind — a new AI system that turns plain English decision problems into solver-ready optimization models. Instead of needing an expert to manually convert business intent into MILP math, OptiMind generates the full mathematical formulation plus executable Python code using GurobiPy. The result: faster, cheaper optimization workflows for logistics, scheduling, manufacturing, and supply chains — with major accuracy gains on cleaned, expert-validated benchmarks.

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🧠 What You’ll See.
0:00 What Microsoft OptiMind Really Is.
1:43 From Text to Optimization Code (MILP + Gurobi)
2:59 OptiMind Architecture: MoE and 128K Context.
3:34 Open Source Under MIT License.
4:28 Training With Expert Hints and Clean Data.
6:02 53 Optimization Problem Classes.
8:38 Multi-Stage Solver-in-the-Loop Inference.
9:11 Self-Consistency and Auto Error Correction.
9:55 Performance vs GPT-o4 Mini and GPT-5
10:32 Limits, Safety, and Human Oversight.

🚹 Why It Matters.
Optimization is already the hidden engine behind supply chains, factories, routing, and scheduling — the problem is the translation step. Converting messy real-world requirements into correct MILP constraints takes rare experts and days of work. OptiMind targets that exact gap: natural language in, solver-ready decisions out. This is why it’s going viral — it’s not just AI text generation, it’s AI generating decisions.

#AI #Microsoft #OptiMind

Austin Light Rail gets federal ‘blessing’ on environmental analysis, pushing project forward

Austin Transit Partnership Light Rail — “Artist Conceptualizes Visualizations” of rail on the UT campus as of January 2026. Final design is subject to change.

The Austin Light Rail will be built in phases, with Phase 1 to span 9.8 miles, have 15 stations, three park and ride facilities, a new bridge over Lady Bird Lake and an Operations and Maintenance Facility near the airport.

The rail line is supposed to run every five minutes during peak hours and is expected to service 29,000 riders during the work week by 2045, when the regional population is expected to grow toward 4.7 million residents.

New global standard set for testing graphene’s single-atom thickness

Graphene could transform everything from electric cars to smartphones, but only if we can guarantee its quality. The University of Manchester has led the world’s largest study to set a new global benchmark for testing graphene’s single-atom thickness. Working with the UK’s National Physical Laboratory (NPL) and 15 leading research institutes worldwide, the team has developed a reliable method using transmission electron microscopy (TEM) that will underpin future industrial standards.

Researchers at the University of Manchester, working with the UK’s National Physical Laboratory and 15 international partners, have developed a robust protocol using transmission electron microscopy (TEM). The results, published in 2D Materials, will underpin a new ISO technical specification for graphene.

“To incorporate graphene and other 2D materials into industrial applications, from light-weight vehicles to sports equipment, touch screens, sensors and electronics, you need to know you’re working with the right material. This study sets a global benchmark that industry can trust,” said Dr. William Thornley, who worked on the research during his Ph.D.

From Nano to Nobel: National Lab Researchers Use MOFs to Solve Big Problems

Building on the foundational Nobel Prize-winning work, researchers at Berkeley Lab and its DOE user facilities continue to push MOF technology to address major global challenges.

For example, at the ALS, a team led by Yaghi traced how MOFs absorb water and engineered new versions to harvest water from the air more efficiently – an important step in designing MOFs that could help ease water shortages in the future. Yaghi is launching this technology through the company Waha, Inc, and working with scientists from the Energy Technologies Area to apply water-absorbing MOFs for in-building technologies and industrial applications.

Another team, led by joint Berkeley Lab and UC Berkeley scientist Jeffrey Long, used the ALS to study how flexible MOFs hold natural gas, with potential to boost the driving range of an adsorbed-natural-gas car – an alternative to today’s vehicles. An international team of scientists used the ALS to study the performance of a MOF that traps toxic sulfur dioxide gas at record concentrations; sulfur dioxide is typically emitted by industrial facilities, power plants, and trains and ships, and is harmful to human health and the environment. Others have used the facility to design luminous MOFs, or LMOFs, glowing crystals that can capture mercury and lead to clean contaminated drinking water.

Two-step flash Joule heating method recovers lithium‑ion battery materials quickly and cleanly

A research team at Rice University led by James Tour has developed a two-step flash Joule heating-chlorination and oxidation (FJH-ClO) process that rapidly separates lithium and transition metals from spent lithium-ion batteries. The method provides an acid-free, energy-saving alternative to conventional recycling techniques, a breakthrough that aligns with the surging global demand for batteries used in electric vehicles and portable electronics.

Published in Advanced Materials, this research could transform the recovery of critical battery materials. Traditional recycling methods are often energy intensive, generate wastewater and frequently require harsh chemicals. In contrast, the FJH-ClO process achieves high yields and purity of lithium, cobalt and graphite while reducing energy consumption, chemical usage and costs.

“We designed the FJH-ClO process to challenge the notion that battery recycling must rely on acid leaching,” said Tour, the T.T. and W.F. Chao Professor of Chemistry and professor of materials science and nanoengineering. “FJH-ClO is a fast, precise way to extract valuable materials without damaging them or harming the environment.”

Material Strength Doesn’t Follow the Rules

A textbook rule for the relationship between the structure and strength of a material breaks down for high-speed deformations, like those caused by strong impacts.

On the microscale, metallic materials are made of homogeneous crystalline regions—grains—separated by disordered boundaries. In general, materials with smaller grains are stronger because they have more grain boundaries, which impede deformation. But researchers have now demonstrated a radical departure from this rule: With rapid deformation, such as that from an explosive impact, finer grained metals are softer, not harder [1]. This new insight, the researchers hope, could be useful for engineers developing impact-resistant alloys for armor, aerospace structures, or hypersonic vehicles.

The yield strength of a material is the stress (force) at which it begins to deform permanently rather than springing back. At the atomic scale in crystalline materials, this deformation occurs when sections of the crystal slide past one another, facilitated by the motion of structural defects called dislocations. But at grain boundaries, dislocations are halted and can pile up, which translates into resistance to deformation and increased yield strength. Materials with smaller grains have more grain boundaries than those with larger grains, so smaller grains are associated with higher strength.

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