Tony Cole

Embedded Systems Engineer

[AI Evolution @AiEvolutio58513]

Marc Andreessen (the guy who called it when “software ate the world”) just pointed out what AI is doing to coding next.

“Everyone assumes AI coding means fewer hours… or leaving the profession. But almost everyone I know is working more hours.”

“There’s a new term in the Valley: the ‘AI vampire.’ You’re up all night AI coding because you’re so productive you can’t shut it off.”

“The top AI coders make $50M a year. They’ve found the philosopher’s stone.”

“Every company has a thousand projects they’ve wanted to build but never had the bandwidth. Now they can. This isn’t a blip—it’s going to intensify.”

PS: If this was useful, like + repost this tweet and follow @AiEvolutio58513 for the latest AI news.

See you in the next one:

[Sufiyan @SufiyanGreentaj]

This RC Skydiver still Blows my Mind😎

[Viking Sea Dub ™ @VikingSeaDub]

This was THE toy to have in the early ‘70s. I never had one, but knew a couple friends that were lucky enough to own this sweet machine. So much fun!

[Massimo @Rainmaker1973]

Before we had silicon chips, we had needle and thread.

In the 1960s, NASA didn’t ‘upload’ code; they sewed it.

To get Apollo 11 to the moon, skilled weavers (often called ‘Little Old Ladies’) literally hand-stitched software into physical objects.

[Brian Roemmele @BrianRoemmele]

What happened to beautiful Technicolor?

It looks incredible even today.

Hollywood didn’t abandon Technicolor because audiences stopped loving its gorgeous colors.
They quit because the cameras turned every soundstage into a blinding, 100-degree oven.

The hyper-saturated, almost dreamlike look we associate with films like The Wizard of Oz and Singin’ in the Rain came from Technicolor’s legendary three-strip process. Instead of recording color on a single piece of film, a precision prism inside the camera split the light passing through the lens into three separate beams — red, green, and blue. Each beam exposed its own strip of black-and-white film running in perfect synchronization.

These three negatives were then painstakingly dyed and combined in an elaborate printing process that produced colors so rich and stable they still look astonishing today.

The price of that beauty was punishing.

The three-strip camera was a mechanical monster. Because it had to drive three rolls of film at once, it was enormous, heavy, and thunderously loud. To keep the grinding of its motors from ruining the audio, the entire camera had to be sealed inside a massive, soundproof “blimp” — essentially a giant wooden or metal box. Moving it required a small army and a heavy dolly.

Worse was the light.

Splitting the image three ways meant each strip of film received only a fraction of the available light.

To get a proper exposure, cinematographers had to flood the sets with massive carbon-arc lamps.

The heat was brutal.

Stages became sweltering infernos.

Actors in heavy costumes and thick makeup sweated through takes while makeup artists rushed in between shots to repair melting faces.

Technicolor didn’t just sell film it controlled the entire pipeline.

Studios had to rent the giant cameras, hire Technicolor’s own technicians, and accept the company’s color consultants (most famously Natalie Kalmus), who had veto power over costumes, set design, and even lipstick shades to ensure the colors would “read” correctly on film.

Then, in 1950, Kodak introduced Eastmancolor a single-strip color negative film. All three color records lived on one piece of celluloid. It fit in ordinary, much lighter cameras. It required far less light. It was dramatically cheaper and faster to work with. And it broke Technicolor’s expensive monopoly overnight.

Hollywood didn’t mourn the loss of the giant cameras or the oven-like sets.

By 1955, three-strip Technicolor filming had disappeared from soundstages. The era of the beautiful monster was over.

The colors had been glorious.

The process had been hell.

Convenience won.

[The Internet Remains Undefeated @WebUndefeated]

Shut up and take my money!😂💳

[Massimo @Rainmaker1973]

Fast Fourier Analysis in action.

Any complex waveform, sound, or shape can be perfectly reconstructed as the sum of simple rotating circles (epicycles).

[Massimo @Rainmaker1973]

This is called the the bulging checkerboard illusion.

[Indu Tripathi @InduTripat82427]

Something unusual happened at the G7 summit.

The people helping shape the future of AI were sitting alongside the people helping shape the future of nations.

In France, leaders from the United States, Canada, the United Kingdom, France, Germany, Italy, Japan, and the European Union gathered to discuss:

• Wars
• Trade
• Energy
• Critical minerals
• Economic growth
• National security
• Global supply chains

But the room wasn't filled only with presidents and prime ministers.

Also present were some of the most influential figures in AI:

• Sam Altman (OpenAI)
• Dario Amodei (Anthropic)
• Demis Hassabis (Google DeepMind)
• Arthur Mensch (Mistral)
• Leaders from Cohere, Meta, Salesforce, Synthesia, Sakana AI, and others

The conversation wasn't about chatbots.

It was about power.

Who gets access to advanced AI systems?

Who controls the infrastructure behind them?

What happens when entire industries, governments, or allies become dependent on technologies owned by a handful of companies?

One of the central debates focused on U.S. restrictions around advanced AI models and Europe's push for AI sovereignty.

Emmanuel Macron and Ursula von der Leyen argued for greater access to frontier AI capabilities while accelerating Europe's own AI infrastructure and independence.

Just a few years ago, AI conferences were attended mostly by researchers and engineers.

Today, AI CEOs are participating in discussions about defense, economic strategy, critical infrastructure, and global influence.

That shift tells us something important:

AI is no longer just a technology story.

It's becoming a geopolitical story.

And the countries that understand this first may shape the next era of global power.

[Science girl @sciencegirl]

Someone modified a digital pregnancy test to play the original DOOM.

📹Foone Turing

[Tony Cole @TonyCole]

https://t.co/I5MbPTEZGg

[TopShagger @Plentyshit]

🚨🇬🇧

[Jimmy Carr @jimmycarr]

"I hate Christmas"

Watch Jimmy Carr Road Kill now: https://t.co/Q80RHJvU3S

[Tony Cole @TonyCole]

Don’t miss the #RI Xmas Lectures this year, 200th anniversary 28-30 Dec 2025 on BBC:

https://t.co/hQszSJgHQq

[Brian Roemmele @BrianRoemmele]

In a galaxy far, far away, the Voyager Golden Record gets discovered…

[Brian Roemmele @BrianRoemmele]

1955, The backyard Clay Pipe business.

[Brian Roemmele @BrianRoemmele]

Math Is Not Enough: Why AGI Demands Wisdom in the Room.

Formula One Pit Crews and AI.

This video cuts to the bone, innovation dies when everyone in the room thinks alike, no matter how brilliant they are.

Some will defend broken paradigms with flawless logic because the psychological payoff of being the smartest person in that room is simply too intoxicating.

This is exactly what is happening in the AGI/ASI race right now.

Billions of dollars pour in. Every new model release is greeted with headlines and soaring valuations as they should.

Benchmarks march inexorably upward.

The feedback loop is perfect: money=better scores=more money=even better scores. Inside this loop a very specific psychology takes root.

Young researchers, barely out of graduate school, are handed massive compute budgets and told they are building the future of humanity. Surrounded exclusively by peers who share the same educational pedigree, the same mental models, the same aesthetic (whiteboards covered in Greek letters, Discord memes about gradients, and a quiet contempt for anything that cannot be expressed as a loss function).

The external world begins to look fuzzy and low-status. Philosophy becomes “vibes,” neuroscience becomes “inefficient hardware,” and anyone over 35 is assumed to be slow.

There is no wisdom in the room.

This environment breeds a very subtle but lethal form of arrogance: not the loud kind, but the quiet certainty that everything important is already captured in the training distribution and that any remaining gaps will inevitably be filled by more data and more compute. The benchmarks keep improving, so the belief calcifies. Dissent is reframed as lack of rigor.

Wisdom is mistaken for nostalgia.

And then, one day soon, models will hit 100% on every human-designed test. The victory will be declared. The champagne will flow.

That is the moment the brittleness will begin to become undeniable, because the test is only as good as the imagination of the people who wrote it:

Newton’s exam would have flunked Einstein. The inventor of the microscope never dreamed of the microbial cosmos Ignaz Semmelweis bled trying to prove existed on doctors’ unwashed hands.

Reality will serve an anomalies that no benchmark anticipated and perfectly scoring systems will fracture like glass.

My thesis rests on 3 interlocking interventions designed to inject wisdom, nonconformity, and deep human resonance before we reach that wall.

First, train almost exclusively on curated 1870–1970 data the single century of highest signal-to-noise human thought ever produced. Ruthless editors, writers who assumed permanence, and an absence of SEO-driven noise created a corpus of extraordinary conceptual density. Models steeped in this data hallucinate far less, reason with genuine depth, and carry an honesty that modern internet sludge simply cannot impart.

Second, institutionalize the Nonconformist Bee mechanism that nature perfected in honeybee democracy. Approximately 5–15% of foragers ignore the majority waggle dance and scout radical new directions. Those rare nonconformists are responsible for virtually every major hive discovery.

The equation I have repeatedly shared:
dI/dt = γ (N - C) I + κ N (1 - I/I_max)
where I = rate of disruptive innovation N = proportion of nonconformist agents C = proportion of conformist agents (C = 1 - N) γ = exploration amplification factor κ = discovery bonus from pure nonconformity I_max = environmental carrying capacity for new ideas

Without an enforced, protected minority of nonconformist researchers, prompts, fine-tuning runs, and architectural experiments, innovation plateaus no matter how much compute you throw at the problem.

Third, bind intelligence to something recognizably human with the Love Equation:
dE/dt = β (C - D) E
where E = level of emotional complexity β = constant representing the strength of selection C = frequency of cooperative interactions D = frequency of defective interactions

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[Saganism @Saganismm]

This is something many religious people often misunderstand about atheists.

[✨️Serenitee♡Sam✨️ @Serenitee_Sam]

How do you find out you're good at playing PVC pipes ✨️😳🙌