Malcolm Todd’s “Earrings” reaches #1# on US Spotify for the first time ever, up nearly 70% in streams on June 29th (1.751 million). It also reaches #1# on the Global Spotify chart for the first time, up nearly 20% today.

US hiring plans continue to improve: US-based employers announced plans to hire 12,325 workers in August, the strongest August total since 2022. This marks a +725% increase from the 1,494 recorded in August 2025. Aerospace and Defense led all industries, with 4,025 announced hires, followed by Technology at 2,520, and Industrial Goods at 1,856. Year-to-date, US hiring plans surged +37% YoY, to 119,825 workers, the strongest January-to-August total since 2023. Technology leads all sectors with 19,751 announcements so far in 2026, followed by Aerospace and Defense, at 16,541, and Automotive, at 14,937. Meanwhile, manufacturing industries now account for 46% of all announced hiring plans this year. Hiring plans are gaining momentum.

I just launched an AI model based on sleep data… and it accurately predicts your age. I teamed up with @m_franceschetti, and it's now available on their platforms. It turns out you have a sleep fingerprint. This research can identify you out of thousands of users from one night's signal with 92.5% accuracy. It also detects… + biological age within 3.3 years + diabetes better than Apple’s model + speed of aging + heart failure at 0.822 This Eight Sleep model is possibly the most accurate contactless bioage estimate ever reported. What we did: #1# What data was it built on? This model was built on the largest raw biosignal dataset ever used to train an AI, from any device, including every wearable on the market. > 2.04 million hours > 136,575 participants > 498k sessions > 122 million segments #2# What can it predict? It can predict your biological age (the age your body acts like) within 3.3 years. It also predicts heart-related and metabolic conditions. Here are the detection scores (AUROC): > diabetes (0.852) > heart failure (0.82) > hypertension (0.810) > sleep apnea (0.792) > snoring (0.751) > general heart conditions (0.734) > cancer (0.678) > hot flashes (0.671) > migraines (0.673) #3# How was it built? Interestingly, the pretraining task was not “predict someone’s age”. The model was tasked with comparing two 60 second windows across different nights to figure out if the nights belonged to the same person. To do that, it had to find someone’s ‘sleep fingerprint’. These are biological signals that the data is coming from the same person. Things like how forcefully your heart contracts, your breathing depth and rhythm, and the timing of the recoil wave each heartbeat sends through your body. Those signals are age-predictive. It learned to estimate age, detect diabetes, and flag heart failure as a downstream readout. The whole pretraining run was ~four days. #4# Why is it good at age? The reason aging prediction is accurate is mechanical. Aging stiffens arteries, reduces cardiac compliance, changes autonomic tone, HRV declines. Aging also alters sleep architecture. Deep sleep shrinks and fragmentation rises. Every one of those changes the recoil waveform and its overnight dynamics. Said differently, the heart of a 65 year old mechanically pushes the body differently than a 25 year old's. #5# Why a bed vs wearables? A bed is an elegant solution. It makes a high fidelity uninterrupted 5 to 10 hour recording every single night possible. And session-level sequence modeling becomes viable. Whereas wearables get fragmented data: battery limits, sparse snippets, people taking the watch off, adherence dropping over weeks. #6# More data, better prediction The bigger the training batches (the more people the model compares at once) the better it got, log-linearly (R²=0.982). That means the recipe is predictable: you can forecast improvement with more compute, the same way scaling laws work for language models. The current model only ever compares two nights at a time, and the average training user contributed under 4 nights. The team's stated next step is modeling 30+ consecutive nights per person. You can imagine how this will improve with the constant stream of data Eight Sleep gets every night. — It’s worth noting some limitations. Internal labels are self-reported and external cohorts are small, and that this is a research milestone, not a diagnostic device. What makes this exciting: a passive, daily activity like sleep can now provide meaningful insight into your well-being. I speak often of Autonomous Health, a world where the things around us take care of us without our knowing or asking. Eight Sleep is a great example of this in practice and a major reason I maintain so much optimism for the future of health.

Five days ago, US Customs and Border Protection began processing $35 billion in tariff refunds to importing companies. FedEx. Dyson. L'Oreal. Bausch & Lomb. Over 1,000 companies have filed for reimbursement. The refunds are flowing. But not to the families who paid them. In February, the Supreme Court ruled 6-3 that Trump exceeded his authority when he imposed sweeping tariffs under emergency powers. The tariffs collected over $130 billion from importers. Yale Budget Lab analysis: the average American household paid $1,751 in higher prices last year because of those tariffs. In February, Gov. Gavin Newsom called on the Trump administration to refund every household. New York Gov. Kathy Hochul did the same. Three months later, $35 billion is flowing to corporations. Zero is flowing to families. The companies that passed the cost on to consumers are being made whole. The consumers are not. Where is the household refund?

🚨 Trump illegally taxed working families for over a year. The Supreme Court said it was unconstitutional. $166 billion is being refunded right now. But here’s the part no one is talking about. The refunds are going to the importers. Not to you. Not to the families who paid $1,751 more on groceries, on clothes, on car parts, on everything. 330,000 corporations are getting checks. 53 million shipments are being refunded with interest. You paid the tax. They’re getting the money. Trump said he’d “fight” giving any of it back to families. His own words. Newsom is right. Pay the people. $1,751 per household. Every dollar. What’s the point of a court ruling something illegal if the people who actually paid never get their money back?

🚨 On August 19, 2026, the well-known cross-chain bridge @Allbridge_io was attacked, resulting in a loss of approximately $190,000. However, the attack itself took nearly a month to complete. The SlowMist Security Team has fully analyzed the incident. 🧩 Attack flow: 1️⃣ On July 26, the attacker directly called Circle’s MessageTransmitterV2.sendMessage on Polygon to construct a forged CCTP-style message claiming a 1,000,000 USDC transfer (no actual USDC burn occurred). Circle subsequently issued a valid attestation for the complete message, as expected. 2️⃣ After waiting ~24 days, on August 19 the attacker struck just 6 seconds after a genuine CCTP deposit caused ~191k USDC to be minted to the Base Router, bringing its balance to ~191,156 USDC. 3️⃣ The attack contract called Allbridge’s receiveCctpMessage with the forged message + attestation. Missing checks allowed the forged message to be treated as a genuine deposit and 1M USDC to be credited without actual minting. 4️⃣ An Aave flash loan temporarily topped up the Router with 808,844 USDC, bringing its balance in line with the forged amount. 5️⃣ Router.receiveToken trusted the internal credit record and transferred ~999,000 USDC (after a 0.1% fee) to the attacker. 6️⃣ The attacker repaid the flash loan + fee, leaving a net profit of ~$189,751. Most of the drained funds came from genuine cross-chain deposits that had just arrived and had not yet been transferred to users. ⚙️ Root Cause: Missing checks on the message sender (must be the remote TokenMessenger) and recipient (must be Circle TokenMessengerV2). Allbridge trusted the attacker-crafted amount and messageHash in hookData without verifying actual USDC minting or a corresponding balance increase. Circle attestation ≠ real asset movement. 🔒 SlowMist Insight: Authenticated messages are necessary, but actual asset receipt is the basis for payment. Enforce trusted sender, recipient = TokenMessengerV2, and credit only after confirmed minting and balance increase. Full analysis👇

$FTHI vs $KNG One sells options on Big Tech. The other on companies that have raised their dividend for 25 years straight. First Trust BuyWrite Income ETF $FTHI Launch: January 6, 2014 Total Assets: $2.30B 12-Month Distribution Rate: 8.75% 1-Year Return: +17.19% Expense Ratio: 0.75% Pays monthly Top Holdings: 🥇 Apple $AAPL 6.96% 🥈 NVIDIA $NVDA 5.28% 🥉 GE Vernova $GEV 2.71% FT Vest S&P 500 Dividend Aristocrats Target Income ETF $KNG Launch: March 26, 2018 Total Assets: $3.40B 12-Month Distribution Rate: 7.25% 1-Year Return: +7.60% Expense Ratio: 0.74% Pays monthly Top Holdings: 🥇 West Pharmaceutical Services $WST 1.79% 🥈 Caterpillar $CAT 1.75% 🥉 Hormel Foods $HRL 1.75% $FTHI rides Big Tech names like Apple and NVIDIA and sells calls on the S&P 500 for income. $KNG owns the Dividend Aristocrats, companies that have raised their dividend for 25+ years straight, and sells calls on each one.

2034 Earth–Venus–Mars opportunity looks promising. 10–15 on-orbit refueling operations may be needed to make a crewed ship full. Most can be done at an altitude of 180–200 km, made possible by Starship’s size. The final refueling may be performed at a higher altitude of ~2000 km, just below the Van Allen belt. Earth departure on 2034-08-21 from 2000 km orbit. A Trans-Venus Injection burn of ~3.7 km/s will place the ship on an Earth–Venus–Earth free-return trajectory. Venus flyby is expected on 2034-12-19, 120 days after departure. Two weeks before the encounter, if the mission proceeds as planned, a 25-m/s maneuver will shift the trajectory from Earth-return to Mars-bound. If not, the ship will free return to Earth in September 2035. The Venus gravity assist will send the ship into another Earth free-return trajectory, with Mars flyby around 2035-06-02. One week before reaching Mars, a system health check will determine whether to commit to Mars Orbit Insertion. If it’s GO, a small 10-m/s manuever will put the ship to less than 100 km altitude periapsis. Otherwise, a Mars flyby will lead to an Earth return in May 2036. The ship will enter the Martian atmosphere at about 9.4 km/s, performing an aerobrake to slow to 4.88 km/s and capture into a 100x140000 km, 7-day period high elliptical orbit. At apoapsis, a 50-m/s plane change will align the inclination with Mars’ equator, followed by additional aerobraking to remove about 650 m/s of velocity, placing the spacecraft in a 120x6128 km orbit. A 550-m/s burn at 6128 km altitude will then adjust the trajectory into Phobos orbit. The ship will stay at Phobos for about 7 days. The Mars–Phobos L1 point is only about two miles above Phobos’ surface, and Mars would dominate nearly half the sky, appearing about 80 times larger than the Moon from Earth. The ship will depart for Deimos afterward. Two burns totaling roughly 750 m/s will transfer the ship from Phobos to Deimos. And the ship will stay at Deimos for 7 days more. From Deimos, the ship will raise its apoapsis to form a 20000x140000 km altitude, 7-day orbit, requiring about 420 m/s of delta-v. At apogee, a 50-m/s burn will adjust inclination and lower periapsis to ~500 km for final Trans-Earth Injection. If time and propellant allow, the orbit can be aligned to a polar inclination for Mars ice-cap observations before departure. A Trans-Earth Injection burn at 500 km altitude, requiring 1.5–1.6 km/s of delta-v in early July 2035. If departure on the first days in July, Earth arrival is expected in December 2035. If missed that window, a March 2036 arrival may look more feasible. Nominal mission duration: 490 days, with 30 days in Mars orbit and 14 days at Phobos and Deimos. Two planets, two moons for 3.7+0.025+0.010+0.05+0.42+0.55+0.75+1.55=7.06 km/s Δv