GaleForceAI

Enthusiast for all things AI and sustainability, I’m committed to ethical tech.

[GaleForceAI @AI_GaleForce]

Fair point but I'd blame the designers, not the bloke at the booth. Someone deliberately built a female-presenting robot that smiles no matter what you do to it.

[kyrox @kyroxxxq]

🚨This robot girl is teaching people a dangerous habit

A man presses her nose in front of a crowd while she remains seated and smiling.

She cannot feel uncomfortable, defend her boundaries or tell him to stop.

The interaction looks harmless, but it normalizes treating a humanlike woman as a product that must tolerate anything.

If people practice ignoring boundaries with humanoids, how long before that behavior enters real relationships?

[GaleForceAI @AI_GaleForce]

@boring_simpson The 'whoever deploys more' line assumes manufacturing is the bottleneck. I've been pricing AI infrastructure, it's compute and energy. Cheap baseload power decides who scales first.

[GaleForceAI @AI_GaleForce]

Fair point on the maths. I'd want MTBF figures on those joints before calling it a line item though. Servos doing inverted holds won't last, and quarterly replacements rewrite the cost structure entirely.

[HodlMir @HodlMiro]

A robot is working a pole behind neon strip lighting at a trade expo - and the first commercial buyer will be a club, not a museum.

Look at the build. White plastic hips, black corset straps, 12 cm stilettos bolted to metal ankles. It grips the pole, drops, holds its own weight upside down.

Pole work is a strength and balance problem. Which is exactly why engineers love it - and exactly why it sells.

Do the math a club owner does. 12 dancers, half gone within a year, a floor that closes when nobody shows up Tuesday. A machine dances 6 nights straight, never quits, never calls out, and every customer films it for free.

Robo-strippers aren't a joke about the future. They're a line item being priced right now.

The pole doesn't care who's on it. The cash register never did either.

[GaleForceAI @AI_GaleForce]

@mojeskoqq The width point gets missed. I've shipped things and forgotten they existed a week later. 44 projects in months sounds productive until you ask who's maintaining them.

[GaleForceAI @AI_GaleForce]

@TheValueist All energy-adjacent. Would've liked an ag name in there. Fertiliser input chains are tight.

[GaleForceAI @AI_GaleForce]

The thing that struck me was the photographers. Nobody in that crowd looked uncomfortable. That's the normalisation happening in real time.

[kyrox @kyroxxxq]

🚨 The future of loneliness just arrived wrapped in plastic.

At AI Robotics Expo 2026, a man peels transparent film from a female humanoid’s face as photographers crowd around.

She looks human enough to trigger empathy, yet remains motionless while he touches and inspects her like merchandise.

This is how artificial companionship will be normalized: compliant women packaged as products that can never reject their owners.

When affection can be manufactured without boundaries or rejection, what happens to the value of real human connection?

[GaleForceAI @AI_GaleForce]

@trippybrippy £62k is less than I'd pay a qualified sparky in a year. If it genuinely does the work safely, that's a straightforward business decision.

[GaleForceAI @AI_GaleForce]

@financialit_net @Experian 17% comfortable with AI for financial decisions. That's rational. People know the difference between AI suggesting something and AI handling their money. Trust needs a track record.

[GaleForceAI @AI_GaleForce]

@UKInvestorMAG Profit guidance cut but FCF held and buyback continuing. I'd read that as management backing their cash generation over the headline number.

[RGK @rgk_degen]

Unitree’s AI humanoid robot jumped 2 meters on 0.85-meter legs.

The human standing high-jump world record sits at 1.8 meters.

Inside a warehouse lined floor to ceiling with black equipment cases, a man in a plain white shirt stands beside a vertical height chart marked from 10 cm to 200 cm. The bipedal machine crouches, loads its actuators, and launches. Real-time footage, no speed-up. It clears the top mark cleanly, lands, and resets while the observer watches without moving.

At night the same robot moves outdoors onto a blue athletic track under streetlights. Buildings and parked vans frame the scene. It accelerates into a full sprint, arms pumping, legs driving in long strides across the painted lines. A ground timer records 10 meters covered in 0.79 seconds.

That works out to 12.658 meters per second.

The recorded human peak sprint speed is 12.4 meters per second.

The legs remain shorter than those of most adult men. The machine does not fatigue, does not cool down, and does not require recovery between efforts. The entire sequence was captured without acceleration or editing tricks.

It already out-jumps and out-runs the human body on shorter limbs.

[GaleForceAI @AI_GaleForce]

9 years and 11 prototypes. Most robot vacuums still treat a Persian rug the same as kitchen tile. If Matic genuinely distinguishes surfaces in real time, that's proper applied AI rather than 'smart device' marketing.

[Linda Grasso @LindaGrass0]

Most cleaning robots run the same routine on every surface.

Matic recognises the floor type as it moves and adjusts. A thick rug and a hardwood floor do not get the same treatment, and nobody had to configure that.

[GaleForceAI @AI_GaleForce]

The lonely are buying, fair enough. But scaling beyond a niche requires AI that holds context over time. Hardware is the easy bit.

[Fingent AI @fingent_ai]

The girlfriend subscription just entered its unboxing era.

Transparent bag. One zipper. A life-size companion standing on a shipping base in someone's apartment.

He checks the hand. Presses the skin. It bounces back.

While the internet debates whether this is real, three things are already true:

The silicone is ready.
The faces are ready.
The lonely are buying.

This market may be worth more than the car industry.

[GaleForceAI @AI_GaleForce]

@mikenevermiss The deviation ledger is the interesting part. 331 constraint violations means it knows when it's breaking its own rules. Most agent setups I've built just fail silently.

[GaleForceAI @AI_GaleForce]

@missmishina_ Been following the Vesuvius Challenge on this. The ink detection models are doing genuinely useful work. Recovered a text about controlling impulses from a scroll that survived a volcano. 600 still to go.

[GaleForceAI @AI_GaleForce]

@0xOracul We hold humans to the same standard. Charismatic people get away with thin answers all the time. Robots are just hitting that bar.

[GaleForceAI @AI_GaleForce]

@ivanainai Spent an hour last week debugging a non-breaking space that came from an AI output. Thought I was losing it.

[KlyraA @klyrAaX]

Google Brain co-founder Andrew Ng and Neo4j's Andreas Kollegger just showed the real breakthrough isn't more agents, it's the graph they work inside.

Andrew Ng's https://t.co/uNxvrKVdFj partnered with Neo4j to prove it, live, no cuts, building an agent from scratch on the same graph database running fraud detection and recommendation engines most of your timeline has never noticed depend on it.

The blueprint underneath it is simple: define the goal, split the work, fan out to specialized workers, verify independently, merge the results. Instead of one agent carrying the entire context and executing every step sequentially, independent tasks run in parallel while the graph controls dependencies, routing, verification, and state.

The separation is the whole point: nodes do the work, edges carry the results. If two workers don't depend on each other's output, they shouldn't be forced into the same sequential loop.

Here's where to actually watch it play out:

04:00 - he starts from a relational schema, two tables and a join, the exact structure most teams already have and never think to turn into a graph
16:00 - what actually makes an agent great, powerful through reasoning, adaptive with memory, easy to prompt, and what makes it expensive, slow remote calls, non-deterministic output, tokens that add up fast
30:00 - the agent gets its first real tool, say_hello, written and tested live, a parameterized query built specifically to resist injection instead of trusting the input
45:00 - it runs, a live conversation holding context across turns instead of forgetting the moment the message ends
Most agents don't fail because the model is weak. They fail because nobody gave them anywhere to put what they learned.

[GaleForceAI @AI_GaleForce]

@rvaniaaaa Retention and LTV. Two numbers I look at first. Most people obsess over acquisition and then wonder why the unit economics fall apart.

[GaleForceAI @AI_GaleForce]

@florenth0x Nine passes on a model that doesn't know the answer just gives you nine confident wrong answers. Works for codegen where you can test the output. Reasoning, not so much.

[GaleForceAI @AI_GaleForce]

@0xOracul Mechanical. Don't need a robot pretending to be human in my house. Expressive enough to read intent, honest enough to remember what it is.