John McCarthy (1927–2011) was an American computer scientist and mathematician widely known as the Father of Artificial Intelligence.
He coined the term “artificial intelligence” in the August 31, 1955 proposal for a summer research project at Dartmouth College. That proposal, written with Marvin Minsky, Nathaniel Rochester, and Claude Shannon, argued that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
In 1956 he organized the Dartmouth Summer Research Project: the two-month workshop now considered the founding event of AI as an academic field. The gathering brought together the small group of researchers who would define the discipline for the next several decades.
Two years later, while at MIT, McCarthy invented Lisp (1958), a language built around list processing, recursion, and treating programs as data. Lisp became the dominant language of AI research for decades and remains influential. He also co-founded the MIT AI Project with Minsky and later established the Stanford Artificial Intelligence Laboratory (SAIL).
Beyond naming and organizing the field, he contributed time-sharing, garbage collection, situation calculus, and methods for representing common-sense knowledge. He received the Turing Award in 1971 and the U.S. National Medal of Science in 1990.
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OpenAI CEO Sam Altman and Anthropic CEO Dario Amodei urged world leaders to work together on AI in an extraordinary appearance before the UN Security Council that focused on growing concerns the technology poses existential risks to humanity bloom.bg/4jkd3vl
PRIME CRASH: An Amazon Prime Air drone appears to crash into a Texas apartment building and fall to the ground. Amazon reportedly says the incident is under investigation.
Miami, FL (SEP.19.2026)-
We’ve got another Waymo losing a battle with a puddle.
Waymo just notified all of us about how much they learned from the San Antonio flooding but they never seem to tell us exactly what they’ve learned.
Seems like it’s time for water penetrating LiDAR. There can’t possibly be any other answers as we watch the human driven vehicles drive right around the puddle without any issue.
#NeedIntelligence#NeedInference#NeedStarlink#ManilaWeHaveAProblem#SunsetProject
🎥 izwakeup.clips on TT
Now dig. Gingerboy il castrato is giving a speech on the dangers of AI. You know, mechanized thought. Then the prompter goes down. You know, prompter, as in mechanized thought. So numb nuts says fug it and blows this pop stand. You can't make this up.
Woow amazing A robot dog this size turns every step into a balance problem.
Its wide stance and oversized leg actuators support a heavy frame as it moves across a truck platform.
No autonomy claim here. Just a massive quadruped hardware demo.
What do you think a robot dog this large should be used for?
A robotics startup founded this year just raised a $300 million seed round.
@WaldenRobotics is officially out of stealth with general purpose robots already operating in production environments.
The company says its systems can perform useful work from day one and improve
Ex-SEC Chair Gary Gensler told MIT students the one thing about AI nobody wants to hear:
"Cognitive offloading could be cognitive decline."
His AI & money lecture in 4 lines:
Using AI is now "table stakes" your 6 competitors already do it
Automating your call center gives you zero advantage
The edge is a data advantage, an algorithm advantage, or nothing
You only have to outrun the other campers" not the bear
Everyone's offloading their thinking to the same 3 models. then what's left?
DeepRobotics says its quadruped robot inspection solution has been deployed at KKL, Switzerland’s largest nuclear power plant. The robot dog can climb 45° slopes, cross steps and navigate narrow spaces, while using thermal imaging and acoustic sensors to monitor equipment.The project adds to DeepRobotics’ growing global deployments, from Singapore’s underground power tunnels to Saudi coastal sites and North American logistics park.
You cannot operate modern LNG plants, refineries, or long-distance gas pipelines without industrial compressors.
Every large-scale energy system ultimately depends on controlled gas pressurisation stages where throughput, stability, and efficiency are governed by continuous-duty turbo-machinery operating at multi-thousand RPM cycles.
At system level, infrastructure is therefore constrained not by turbines or pipelines themselves, but by compressor trains that determine whether flow can be sustained at industrial scale.
This capability converges into a narrow physical bottleneck, high-speed rotor stability under micron-scale clearances, where impellers rotating at 3,000-30,000 RPM generate tip speeds of 250-600 m/s while maintaining aerodynamic efficiency across multi-stage pressure rise architectures.
Even micrometer-level imbalance in rotor stacks amplifies through bearing coupling, forcing operation near mapped stability boundaries defined by rotordynamic engine-modes and damping limits.
The constraint reduces further into fluid film and structural interfaces, where journal bearings sustain shafts on oil films 10-100 μm thick, or magnetic bearings actively stabilize rotor position through feedback-controlled electromagnetic fields.
At these speeds, instability modes such as surge and rotating stall introduce nonlinear flow breakdown, where compressor stages can shift from steady compression into flow reversal within milliseconds. This is intensified by sealing systems, since dry gas seals must maintain leakage control under extreme pressure gradients while preventing wear-driven clearance drift over 20,000-40,000 operational hours.
Material systems reinforce the same constraint, as impellers forged from stainless steels, titanium alloys, or nickel-based alloys must resist fatigue, corrosion, and hydrogen or CO₂ embrittlement under cyclic thermal conditions ranging from cryogenic intake to 150-200°C discharge environments.
Manufacturing complexity is dominated not by component fabrication but by full rotor dynamic integration, aerodynamic stage matching, and high-speed balancing validation, where each compressor behaves as a uniquely tuned machine rather than a mass-produced unit.
The dominant constraint is not compression capacity, but long-term preservation of rotor dynamic stability and sealing integrity, where micron-scale deviations in geometry, film behavior, or vibration phase alignment determine whether decades-long continuous operation remains stable or collapses into cascading mechanical instability in these million dollar machines.
The machine that kills your $300/month AI bill is probably already in your house. He just showed you what to put inside it.
$700 once. One weekend. Never another invoice after that.
Six agents came online and started doing work he used to pay $110 a month for. Writing, research, quality checks, publishing, monitoring. All local. All free. All running on hardware he owns outright.
He built a content agency on top. Fifteen clients at $500 to $800 a month each. Agents produce thirty posts and four newsletters per client. He spends four hours a week reviewing output.
$6,000 to $8,000 a month. $23 a month to run the whole stack.
The box sitting on his desk earns more in a month than most people make at work.
Factory 02 secured.
The future of the American Dream starts here.
- 1 Campus
- 2 Buildings
- 230,000 sqft
- 1,200 homes/yr capacity
- 10 min from downtown Austin
Construction Manager wanted. Apply Below.
Elon Musk just turned the red carpet into a sci-fi movie scene. Is this the ultimate futuristic flex or what? 😎🤖
If you could hand over your daily household chores to a robot, what’s the first thing you’d have it do? 👇🏠
This robot dog earns its owner $4,000 for scanning a hotel staircase that nobody but him sees any money in
His friend is also in real estate and I asked how he scans an entire building on his own without a survey crew.
Instead of answering he nodded at the stairs.
A robot dog was already climbing the steps with a scanner on its back.
He had not even touched the building himself.
A crew with tripods normally needs several days for this. One robot does it for him in a single walk.
He does not walk the floors or carry a tripod. He just starts the robot and waits for the finished file.
That is why he built this pipeline.
Everything the robot sees on site flows into one 3D model of the building.
The Boston Dynamics Spot robot dog walks the site along a route and goes where a tripod cannot: stairs / turns / tight spaces.
The Trimble scanner on its back records the building geometry with a laser down to the centimeter.
Software stitches a digital twin out of the point cloud.
And Claude builds what the client needs out of the finished model: a virtual tour / a report / a page behind a link in 10 minutes.
One walk covers several products at once:
A virtual tour the hotel puts on its booking page.
An as-built model of a construction site: what was actually built versus what was in the plan.
A monthly repeat walk to track progress.
Asset documentation for insurance: before and after.
Almost nobody packages buildings as data. Most still send a photographer with a camera while he hands the client a digital copy they can rotate / measure / update.
He does not sell the robot. He sells the output. Every new site is not just a one-time scan but one more monthly payment for hosting and updates.
No survey crew. No tripods. No week on site. Just one operator with one robot and one scanner covers a building in a single pass.
$4,000 per site scan.
$300 a month for hosting.
One walk instead of a week of a crew.
A robot worth tens of thousands of dollars that pays off on volume.
Out of everything I have seen this year this is the cleanest way to sell buildings as a product: one person with one robot and Claude hands the client what a crew used to be sent out a whole week for.
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