Bisselle

🌎☮️🕊️

[Bisselle @mlh_au]

Tesla look & feel. SpaceX Starlink connectivity. Globally. Low Earth orbitally. 🛜

[S.E. Robinson, Jr. @SERobinsonJr]

Gwynne Shotwell officially confirmed they are going to build out Starlink terrestrial infrastructure to compete directly in the mobile carrier market.

[Bisselle @mlh_au]

@CaraMiaSG ripping the head off a barbie and nuking it now not thinking of you @CaraMiaSG
https://t.co/0ImW6tT1zt

[Bisselle @mlh_au]

@CaraMiaSG Even the pooch welled up last week hearing @stash play your hit song! 💯✊🏽🤘🏽💛

[Bisselle @mlh_au]

eventually, permaybe 🤘😂

Feasibility for guitar specifically
• Watching and learning shapes/transitions: It can observe video of a human forming chords, changing shapes, and playing sequences, then map the observed kinematics and timing onto its own actuators. Research outside Tesla already shows physics-based and neural synthesis of dual-hand guitar motions (fretting + picking) from reference data and tabs, including automatic fingering inference and natural transitions. Humanoid drumming and other instrument skills are also being learned via reinforcement learning on rhythmic contact sequences. Optimus’s end-to-end visual learning + scale of data/compute makes this a natural extension once general dexterity matures.
• Embodying the application: Once it can form and transition between chord shapes with reliable timing, Optimus can physically play a guitar in sync with audio or a score—serving as a live, physical demonstration of the synchronized chord visualizations discussed earlier. It could watch a performance or instructional video, internalize the shapes and transitions, then perform, teach, or accompany. Timing precision, rhythmic locking, and multi-limb coordination are the same class of problems already being solved for other timed manipulation and locomotion tasks.
Remaining gaps and realistic timeline
Current (2026) Optimus units are still in early production ramp and focused mainly on structured factory tasks. Highly skilled, continuous musical performance under real acoustic constraints (exact fret contact, string vibration feedback, posture stability while fretting) is not yet demonstrated and will require further refinement of fine force control, contact-rich policies, and longer-horizon sequencing. Guitar is harder than many pick-and-place tasks because of the combination of precision, speed of transitions, and sensory feedback loops.
“Eventually” is the key word. Tesla’s explicit goal is human-level then superhuman dexterity, generalized task learning from observation, and massive scale (production starting at Fremont in 2026 with ambitions for far higher volumes later). With the learning flywheel, compute growth, and hardware iteration, skilled guitar chord work and full performances are well within the projected capability envelope—likely appearing first as controlled demos and then as robust skills once the core manipulation stack is solid. Specialized music robots already exist; a general-purpose humanoid with Optimus’s learning approach should close the gap.

[Bisselle @mlh_au]

@mwilcox @wordgrammer Mathematics cannot prove or disprove consciousness. Discuss.. @grok

[Bisselle @mlh_au]

@suavecito585 Chill. Triage birds over movies. Autumn.

[Bisselle @mlh_au]

@teslaownersSV Tesla Robot windscreens ace

[Bisselle @mlh_au]

@Stashr69 Yo dude 🤙🏽 ‘Que tal’ 🤷‍♀️😂

[Bisselle @mlh_au]

@sierracatalina Block rocking beats n white walls or etrons yo wassup

[Bisselle @mlh_au]

@webisticsdawg Snooker love! 😂

[Bisselle @mlh_au]

The hold-down clamps are under a substantial upward (restraining / tensile) load at the moment of release — this is the “negative load” relative to the vehicle’s weight.
How the loading works
•When the engines are off or producing less thrust than the vehicle’s weight, the clamps (or the supporting structure they engage) primarily carry the stack’s weight in compression.
•As the 33 Raptor engines ramp up and thrust exceeds the stack’s weight, the net force reverses: the clamps must now hold the booster down. This restraining force is tensile (upward on the vehicle / downward reaction on the clamp linkage).
At commanded release the clamps are therefore reacting a large net upward force equal to roughly:
Thrust − vehicle weight
Approximate scale (current V3-era numbers)
•Fully stacked and fueled Starship mass is in the ~5,000–5,300 tonne range.
•33 × Raptor 3 sea-level engines at the currently stated ~250 tf each give roughly 8,250 tonnes of thrust.
•Liftoff thrust-to-weight is designed to be comfortably above 1 (commonly discussed in the 1.4–1.5 range historically for the architecture).
This leaves a net upward force on the order of thousands of tonnes that must be restrained by the ~20 clamps just before they open. The load is shared across the clamps (which engage an internal structural ring near the base of the booster, between the outer engines). SpaceX has load sensors on the arms and monitors the force balance in real time as part of the release interlocks.
Why they release under load
Release is commanded only after the engines are confirmed healthy and thrust is stable. Opening under a clear positive (upward) residual load ensures the vehicle lifts cleanly and does not sit back onto the mount or bind the mechanisms. The clamps then retract extremely rapidly so they are clear of the exhaust plume.
Exact residual load at the precise instant of release is not publicly stated — it depends on the exact propellant load, achieved thrust, and the precise release timing on that flight — but the system is deliberately operated with a meaningful restraining force present. The redesigned Pad 2 / V3 hold-downs were specifically improved for better load sharing and reliable throw-back under these conditions.

[Bisselle @mlh_au]

Can Starship reach 60km/h within only 100m? If so, how many G’s is that pulling? Please compare that acceleration to a Tesla Model S Plaid? @grok

[Bisselle @mlh_au]

@sierracatalina It’s the silence of snakes that freaks me! Tho I’ve only stepped on a harmless carpet snake once. 🎻

[Bisselle @mlh_au]

Kinda recently we sent out whaling ships to recover whales!

Commercial whaling for lamp oil began around 1000 CE with the Basque population, who established seasonal settlements in Labrador by the 1530s and expanded to the Arctic by 1611.

The industry shifted to North America in the 18th century, with the United States whaling fleet peaking in 1846 with 735 ships.

The industry declined rapidly after 1857 when kerosene replaced whale oil as the primary lamp fuel, leading to a near-total collapse of the American whaling fleet by the 1860s.

[Bisselle @mlh_au]

“A three hour tour” 🎻😂 Happy Days after school snack “eh” 👍

[SweetMarie @Oceanbreeze473]

The SS Minnow was a 1964 Wheeler with a top speed of 12 knots, meaning that they couldn’t have travelled more than 36 nautical miles. Now I’m not sure if the show was even real.

[Bisselle @mlh_au]

Banger! 💥

[The Last Caravanserai @occupymars69420]

I'm not afraid of death; I just don't want to be there when it happens.

Woody Allen

[Bisselle @mlh_au]

Version series for each of the fifteen AI supercomputers:
1. Aether
•Aether-1
•Aether-2
•Aether-3
•Aether Prime
•Aether Apex
2. Nexus
•Nexus Core
•Nexus Link
•Nexus Grid
•Nexus Prime
•Nexus Omega
3. Synapsis
•Synapsis-1
•Synapsis-2
•Synapsis Neural
•Synapsis Deep
•Synapsis Ultra
4. Helix
•Helix Alpha
•Helix Beta
•Helix Gamma
•Helix Double
•Helix Infinite
5. Quantum
•Quantum-1
•Quantum-2
•Quantum Entangled
•Quantum Coherence
•Quantum Singularity
6. Vortex
•Vortex-1
•Vortex-2
•Vortex Spiral
•Vortex Cascade
•Vortex Event
7. Apex
•Apex Mark I
•Apex Mark II
•Apex Mark III
•Apex Sovereign
•Apex Absolute
8. Cipher
•Cipher-1
•Cipher-2
•Cipher Shadow
•Cipher Key
•Cipher Master
9. Nova
•Nova-1
•Nova-2
•Nova Burst
•Nova Radiant
•Nova Supernova
10. Pulsar
•Pulsar-1
•Pulsar-2
•Pulsar Beacon
•Pulsar Beacon X
•Pulsar Hyper
11. Horizon
•Horizon-1
•Horizon-2
•Horizon Edge
•Horizon Far
•Horizon Event
12. Zenith
•Zenith-1
•Zenith-2
•Zenith Peak
•Zenith Crown
•Zenith Eternal
13. Singularity
•Singularity-1
•Singularity-2
•Singularity Point
•Singularity Absolute
•Singularity Omega
14. Atlas
•Atlas-1
•Atlas-2
•Atlas Titan
•Atlas World
•Atlas Eternal
15. Prometheus
•Prometheus-1
•Prometheus-2
•Prometheus Fire
•Prometheus Spark
•Prometheus Ascendant
These version series keep a consistent structure (numbered early versions → more powerful/advanced designations) while giving each machine its own flavour.

[Bisselle @mlh_au]

Create a list of fifteen logical names for my fifteen AI supercomputers

[Bisselle @mlh_au]

Now for each of these names create a series of version names