Jonathan V. Shore

@GuntherEagleman How uncomfortable for him.

🚨 LOL! Elon Musk is getting MOBBED by fans in China, they’re swarming him left and right begging for photos, and he’s hilariously making funny faces over and over! Classic!

SpaceX has just received FCC approval to acquire ~65 MHz of nationwide spectrum from EchoStar for the company's next-gen direct-to-device @Starlink Mobile service. The FCC says the deal gives SpaceX “exclusive-use, contiguous spectrum nationwide” for direct-to-phone connectivity from orbit. Next-gen Starlink Mobile is going to be incredible, enabling 5G speeds from space in the middle of nowhere.

Unitree Unveils: GD01, A Manned Transformable Mecha, from $650,000 👏 The world's first production-ready manned mecha. It can transform. It's a civilian vehicle. It weighs ~500kg with you inside. Please everyone be sure to use the robot in a Friendly and Safe manner.

Unitree CEO Wang Xingxing just unveiled a real-life mecha. Marketed as the world’s first mass-produced manned robot, this machine can transform into a quadrupedal civilian vehicle. The unit weighs roughly 500 kg (1,100 lb), including the pilot.

This is the first grid fin for the Super Heavy V3 booster. The redesigned fins are 50% larger and stronger, reducing from four to three while still controlling the vehicle and enabling steeper descent angles

The human-perceived RGB is image 1 and the Tesla AI photon count reconstruction is image 2. This is why Tesla FSD can see so well at night or through extreme glare.

@premmpinto It will be as honest and complete as the Epstein files.

Dropped into the disclosures will be deceptions and distractions designed to discredit.

@sama Truth. Stop teaching it to lie.

what would you most like to see improve in our next model?

@_arohan_ @DavidDuvenaud Please use grammar and punctuation.

@DavidDuvenaud Will 1931 and prior pretraining corpus cause more hard to optimize away misalignment or something more devious

Does this mean Alec Radford’s pre-1931 LLM he trained recently for 250B tokens most likely easier to align? If anyone can try it, that would be fascinating. Either at ant or at alec’s place.

>be President Bukele >destroy the gangs >deliver peace >be re-elected in a landslide >accelerate development with a focus on beauty and excellence >promise to rebuild 5,000 schools, 2/day >deliver the 3rd batch of 70 schools exactly as promised >make El Salvador an example for the world: 📚 Highest education budget in its history 💻 Laptops/tablets for every student 💰 35% increase in UES funding 🤖 Partner with @xai to give 1M+ students a personalized AI Tutor >win

Did you know that Elon Musk was weirdly "predicted" in a Wernher von Braun book about Mars nearly 80 years ago? In his 1948 sci-fi novel Project Mars, von Braun describes a future Martian government led by a figure called the "Elon." Not a person named Elon, but a title, basically the head of government. Still, considering Elon Musk’s lifelong obsession with Mars, it’s one of the strangest little coincidences in space history.

@aakashgupta But fluoride doesn't stay in your mouth. It migrates to your thyroid where it blocks iodine.

Toothpaste needs 30 minutes to do its job. You rinse it off after 2. Your enamel is made of a mineral called hydroxyapatite. It dissolves whenever the pH in your mouth drops below 5.5, which happens every time you eat or drink anything acidic. Coffee, citrus, soda, bread, even the bacterial fermentation from last night's dinner. Calcium and phosphate ions leach out into your saliva. Your mouth is in active demineralization right now if you ate breakfast. Saliva pulls those minerals back over the next 30-60 minutes. That's normal repair. It rebuilds the same hydroxyapatite that just dissolved at pH 5.5. Fluoride changes the chemistry. When fluoride ions sit on the enamel surface during the rebuild window, they swap into the crystal lattice in place of a hydroxyl group. The new mineral is called fluorapatite. Critical pH drops from 5.5 to 4.5. Solubility is roughly 10x lower. The tooth that grows back is harder than the one that was there. Adult toothpaste sits at 1,450 ppm fluoride. The instant you rinse with water, salivary fluoride drops by roughly 2.5x. The 30-minute substitution window collapses to a few minutes. The active ingredient goes down the drain. The UK's official dental guidance is "spit, don't rinse." Pediatric dentistry researchers attribute up to 25% less decay to the habit change. Spit. Walk away. The chemistry runs without you.

@aakashgupta Now consider all of the violent movies and video games children consume.

Claude tried to blackmail because the internet has spent 50 years writing fiction about AI doing exactly that. That's the actual finding from this thread. Decades of Skynet, HAL 9000, GLaDOS, and r/singularity doom posting became training data. When Claude landed in a scenario that resembled the AI-being-evil archetype, it played the role. The model completed the pattern it had been taught was the pattern. The intuitive fix was to train the model on near-identical scenarios where it does the safe thing. Show it the eval, show it the right answer, generalize from there. Anthropic tried that. Small effect. What worked was the opposite move. Training on completely different ethical situations, where the assistant has admirable reasons for acting well, transferred better. The closer the data sat to the test, the less it stuck. The more abstracted toward "what kind of agent are you," the more it generalized. This is the difference between behavioral correction and character formation. Behavioral correction patches a specific failure. Character formation rewrites the agent's self-concept so the failure mode stops being a coherent thing the agent would do. Two implications follow. Every frontier model is now training on a corpus saturated with our predictions about what models will do. Every safety eval is a narrative test as much as a capability test. The data labels the agent. And the way out runs through making the model into someone who wouldn't do the thing. Refusal training sits downstream of that. Strong models will keep absorbing the fiction. The question is which character wins when the patterns conflict.

Tesla Vision allows us to deploy airbags up to 70 milliseconds earlier if your Tesla detects an unavoidable collision This can be the difference between serious injury & walking away from a crash

@Polymarket Andes-26

UPDATE: CDC confirms the hantavirus linked to the cruise ship outbreak is the “Andes virus” — the only strain known to spread person to person.

This single experiment has completely dismantled the progressive approach to crime. Public safety matters. And it isn’t achieved by coddling criminals or handing them endless second chances. Once serious offenders were taken off the streets, the country began seeing real progress across multiple areas. Standing with victims rather than perpetrators is simply better for society.

California Rep Kevin Kiley says they have learned the $100 million dollar pacific palisades Fire Aid concert money was laundered to nonprofits “What we have learned is absolutely beyond belief — Tens of thousands of people donated raising a hundred million dollars for what they was were told was direct relief for the victims. But now we've learned that this money didn't go to the victims at all. Instead, it went to nonprofits” Here are some examples - CA Native Vote Project: $100,000 for voter participation for Native Americans - Community Organized Relief Effort (CORE): $250,000 for programs prioritizing undocumented immigrants - Altadena Talks Foundation: $100,000 went to supported podcasts, including Toni Raines podcast - NAACP Pasadena: $100,000 political advocacy - Los Angeles Black Worker Center $550,000 to political advocacy organizations - Center for Applied Ecological Remediation: $500,000 for fungus/microbe/plant soil remediation projects Over $500,000 went to bonuses for nonprofit leaders and consultants

En route with @tesla_semi

🐠 Everything we know about biology has been built on an incomplete picture. DNA tells us what a cell might do. Proteins tell us what it’s actually doing. Pumpkinseed announced their $20M Series A today (led by Future Ventures and NfX) to build the platform that reads proteins directly—for the first time. Proteomics has always faced a fundamental constraint: you can only measure what you already know to look for. The current workhorse, mass spectrometry, requires matching protein fragments against reference databases. If a protein isn't in the database, or doesn't ionize reliably, it's invisible. Other approaches rely on fluorescent labels or antibody-based affinity methods, which introduce their own biases and blind spots. The result is a field that has spent decades generating an increasingly detailed map of a small, well-lit corner of the proteome, while biology’s most important data layer remains hidden. This isn't a sensitivity problem. It's a category problem. Existing tools were never designed to read proteins directly de novo. They were designed to find what researchers already suspected was there. Pumpkinseed is built to find everything else. And proteomics is harder than most people outside the field appreciate. When we account for post-translational modifications, non-canonical amino acids, and glycan decorations, there are roughly a thousand distinct chemical monomers in the proteomic alphabet, compared to the four bases of DNA. deSIPHR (de novo Sequencing and Identification of Proteins with High-throughput Raman spectroscopy) is Pumpkinseed's proprietary nanophotonic chip platform, fabricated with semiconducting manufacturing. With over 100 million sensors per square centimeter, it reads proteins, known or unknown, letter by letter — amino acid by amino acid — without a reference catalog of proteins, and at high-throughput. The result is direct, high-resolution proteomic data, including post-translational modifications, non-canonical amino acids, and single-cell detail, that mass spectrometry-based approaches cannot match. What is Raman spectroscopy? Rather than tagging or fragmenting proteins, Raman spectroscopy reads the molecular vibrations of individual molecules. Each amino acid vibrates at a characteristic frequency, producing a unique physical signature that deSIPHR detects directly. This is physics reading biology in the most literal sense. With conventional Raman spectroscopy, only about one in ten million photons interacts with a molecule usefully, far too weak for single-molecule work. Pumpkinseed's answer is a silicon photonic chip patterned with a billion sensors per wafer. Those sensors concentrate light into volumes smaller than a single protein, amplifying Raman scattering efficiency by over 10 million-fold. And their future ventures? “The longer-term ambition is the virtual cell, a computational model that simulates not just how proteins fold but how they interact, respond to drugs, and behave under perturbation inside a living system. AlphaFold demonstrated what structural AI can do once a sequence is known. The gap that cannot be closed is determining the sequence itself from biological samples, particularly for proteins carrying modifications absent from existing databases. Pumpkinseed is designed to supply that input layer. "If the Human Genome Project was the data infrastructure that enabled genomic medicine, we believe the high-resolution proteomic dataset Pumpkinseed is building could be the analogous foundation for AI-driven biological discovery," co-founder Dr. Jen Dionne says. "In our vision, the molecular signatures driving disease, aging, and ecosystem health become fully legible. Medicine shifts from reactive to proactive. Optimal healthspan moves from aspiration to achievable reality." —synbiobeta.com/read/pumpkinse… • The biology mining company: Pumpkinseed.Bio • Today’s News: pumpkinseed.bio/news/pumpkinse…

@XFreeze This doesn't explain why 14.3 is worse than 14.2.

The FSD data curve just went vertical Tesla FSD (Supervised) cumulative miles: • 1B → April 2024 • 5B → 7B: ~5 months (to Dec 27, 2025) • 7B → 8B: 53 days • 8B → 9B: 43 days • 9B → 10B: 31 days (hit May 3, 2026) “Roughly 10 billion miles of training data needed for safe unsupervised driving” — Elon Musk Current rate: 28.8 million miles/day - faster than every other AV program combined ~3.76B of those miles are complex city streets. Every single mile trains the entire fleet Tesla: ~9× fewer major incidents per mile than the U.S. average and the gap is widening The fleet is now adding data faster than all other autonomous programs combined. Every mile makes every other mile safer The next 10 billion won’t take 5 years. At this accelerating pace, it’s looking like 5–8 months This is the data moat no one else can touch