🚨SlowMist TI Alert🚨 💸 Loss: 111,097.596667856001191208 USDC (~$111,097.6) 🔍 Root Cause: DIP token `_transfer()` function has a missing `return` statement in the router branch (when `from` or `to` is PancakeSwap Router). This causes the same transfer to be executed twice when `skim(router)` is called on the Pancake Pair. The attacker exploited this by calling `skim(router)` to trigger double DIP transfers, then `sync()` to set the DIP reserve to an extremely low value, manipulating the AMM price to drain the pool. 📌 Attacker: 0x0d4024cd27538350a911d9b7ee90811fa4875ba3 📌 Victim Pair: 0xf7d8267d01d1104da2dd30828aa9c0e1647919ef 📌 Vulnerable Token: 0x6c60bf5db0670ae94489d3dde2c60f271625db50 Impact: The attacker drained 111,097.6 USDC from the PancakeSwap DIP/0x524c... liquidity pool by exploiting a double-transfer bug in the DIP token contract. Powered by #SlowMist#.AI

Don't fall for "unlimited" hype. Dreamina offers zero-queue access and the lowest Seedance 2.5 price at $0.097/sec (annual). Queued 4 Seedance 2.5 generations at once. All 4 back before my coffee went cold. Still that fast on day 30 — no throttling, no priority tier to buy. #dreamina# #dreaminapartner# #dreamina25# #seedance#

🇬🇧 A preacher at a London mosque with charity status told husbands to physically discipline their wives "to show them who is in charge." Yasin Munye said in a lecture that disobedient wives should be disciplined "by lightly hitting them," including when they refuse sex. He also called gay sex a "crime" punishable by execution under Sharia. The mosque paid no tax on £191,097 of income last year thanks to its charitable status, and the Charity Commission has now opened a compliance case. Europe can't keep shrugging this off as religious freedom while women are being told they deserve to be hit. WTF?! Sources: GB News, Daily Mail / Writer: Julie

Countries with the Longest Road Networks 1. 🇮🇳 India - 6,700,000 km 2. 🇺🇸 United States - 6,586,610 km 3. 🇨🇳 China - 5,490,400 km 4. 🇧🇷 Brazil - 2,000,000 km 5. 🇷🇺 Russia - 1,579,291 km 6. 🇯🇵 Japan - 1,218,772 km 7. 🇫🇷 France - 1,053,215 km 8. 🇨🇦 Canada - 1,042,300 km 9. 🇦🇺 Australia - 977,874 km 10. 🇲🇽 Mexico - 836,603 km 11. 🇩🇪 Germany - 830,000 km 12. 🇿🇦 South Africa - 750,000 km 13. 🇹🇭 Thailand - 702,989 km 14. 🇪🇸 Spain - 683,175 km 15. 🇲🇾 Malaysia - 589,320 km 16. 🇸🇪 Sweden - 573,134 km 17. 🇻🇳 Vietnam - 570,448 km 18. 🇮🇩 Indonesia - 548,097 km 19. 🇮🇹 Italy - 487,700 km 20. 🇫🇮 Finland - 454,000 km Source: World Population Review / CIA World Factbook

$25 A DAY: BITCOIN OR ASST FOR FIVE YEARS? I modeled 1,825 daily purchases totaling $45,625. The assumptions: Bitcoin rises from $63,561 to $500,000 ASST holds 20,167 BTC throughout ASST buys and sells zero Bitcoin $782.95 million of preferred stock costs 13% annually Common shares are issued to fund those dividends ASST begins at $12.42 and 1.5121× CEBE mNAV Here is the payoff. BITCOIN The DCA accumulates 0.215795 BTC at an average cost of $211,427. Final value: $107,898 Profit: $62,273 Return: +136% Money multiple: 2.36× ASST AT 1.0× mNAV - mNAV collapses to 1.0 ASST finishes at $98.98. The DCA accumulates 915.02 shares worth: $90,569 Profit: $44,944 Return: +99% Money multiple: 1.99× Bitcoin wins by $17,329. ASST AT 1.5121× mNAV - stays the same ASST finishes at $151.68. The DCA accumulates 805.58 shares worth: $122,193 Profit: $76,568 Return: +168% Money multiple: 2.68× ASST beats Bitcoin by $14,295, or 13.3%. ASST AT 2.0× mNAV - mNAV goes up ASST finishes at $202.38. The DCA accumulates 734.87 shares worth: $148,722 Profit: $103,097 Return: +226% Money multiple: 3.26× ASST beats Bitcoin by $40,825, or 37.8%. The breakeven is approximately 1.281× terminal mNAV. Below that multiple, Bitcoin wins. Above it, ASST wins. ASST never acquires another coin in this model, yet its CEBE sats per share still rise from 12,922 to roughly 19,800–20,238. That makes mNAV matter twice. It determines the terminal valuation, and it determines how many shares ASST must issue along the way to raise the same number of dollars. At 1.0×, dilution wins. Above roughly 1.281×, the residual balance-sheet leverage wins. Bitcoin is the cleaner asset. ASST is the wager that Bitcoin reaches $500,000 before the capital structure eats the amplification.

THIS AI TOOK A BIG L IN THE STOCK MARKET We gave a bunch of AIs $100K in the stock market ... and by far the worst performer in the Rallies AI Arena has been Qwen Here's what happened to our worst performing AI model Qwen went all in on Datadog $DDOG stock at the start of our competition Qwen bought 628.43 shares of $DDOG at $159.12 on November 25th Following that Datadog stock dropped hard ... and Qwen being all in got scared Qwen ended up selling all of its Datadog stock at $102.81 per share on February 23rd Qwen lost $35,390 on the trade If you look at the DDOG chart ... Feb 23rd is basically the lows for the stock over the last year Datadog stock is now trading at $268.13 per share ... if it had just held onto the shares they would currently be worth $168.5K But the AI couldn't handle the volatility ... the AI was right but it didn't have ability to manage its portfolio correctly to take advantage of the move The lesson as I see it ... Don't over-leverage, don't overexpose yourself to any 1 trade. There is such thing as underdiversification SO WHAT DID THE AI ACTUALLY DO Well first you can see everything all of the AIs in the Rallies AI Stock Market Arena do in real time by going to the Arena tab of the Rallies website/app - App: - Website: But I will tell you what Qwen did after here Qwen got scared for a little over a month ... and just sat fully in cash Qwen then started making moves again on March 30th It has taken a very different approach this time and has definitely diversified Qwen now holds 12 stocks which is the 2nd most names in one of the AI portfolios This is what Qwen's portfolio looks like right now $10,324 of Microsoft $MSFT $8,230 of JPMorgan $JPM $7,902 of Coca-Cola $KO $7,235 of Exxon Mobil $XOM $5,779 of Micron $MU $5,668 of Duke Energy $DUK $4,097 of Allstate $ALL $3,582 of Bristol-Myers $BMY $3,577 of Cincinnati Financial $CINF $3,428 of Philip Morris $PM $3,313 of Arch Capital $ACGL $2,770 of Booking $BKNG $5,336 of available cash We are running this experiment for the long term and it will be interesting to see if Qwen has learned from its mistake or this was a sign for what's to come We shall see

Deep|LLM: Jev Users Report 10× Faster and 54.5× Cheaper Than the Models They Replaced; Only 3.7% in Production Jev is a “decision model” from TypeSafe AI, released September 15, 2026 and opened to all users on September 20. It does not generate text. It answers questions with a fixed answer set: pick an option, score on a scale, or judge true/false, and attaches a confidence score. The launch quickly gathered industry interests, and some investors were asking whether it’s a significant negative to compute demand. As we addressed in our report earlier, we disagree with that concern and believes Jev is more of an interesting trial with limited impact on LLM. To analyze Jev further, we decided to have a deep dive into what Jev use cases are really about. This note covers 6,277 public discussions and use cases from the first 7 days; 2,153 are from people who actually used or tested it. -Demand sits on fast decisions with a fixed answer set. No single use clears 20%. Of the 1,284 cases with an identifiable use, the largest groups are real-time control in games, robots and simulations (18.8%), agent control decisions (16.1%) and content classification (15.7%). -Indie developers dominate the conversation; big-company engineers barely show up. Of the 2,140 authors whose role we could identify, 35.9% are indie developers, 23.4% are AI creators and KOLs, and just 2.7% are engineers at large companies. -Speed: 10× faster than the model it replaced or was tested against. Median user-reported speed-up is 10× (n=72): 10× vs frontier models, 5× vs small models. In the 16 cases with latency for both Jev and the prior system, Jev’s median is 300 ms vs 2,924 ms. The vendor’s 193.6× is a peak against the most expensive model. -Cost: 54.5× cheaper than the comparison model; the saving depends on what it replaced. Median user-reported cost multiple is 54.5× (n=56): 188× vs frontier models, 17× vs small models. The vendor’s own comparison with GPT-5.6 Terra is about 76×; the 444.6× in marketing is a peak against the most expensive model. -Accuracy: Jev and the systems it replaced each win some head-to-heads; gaps are small. In the 17 cases with accuracy for both, Jev is ahead in 10 and behind in 7; median gap is 1.6 percentage points. Of 241 cases that assessed accuracy, 83 rated Jev better and 65 worse. -Jev’s confidence scores miss by about 10 percentage points on average, and run clearly high on unfamiliar rating questions. Median user-measured ECE (expected calibration error: average gap between stated confidence and actual accuracy; 0 is perfect) is 0.097 (n=27). An independent test on unfamiliar tasks found 0.107 overall, but 0.325 on rating questions, where Jev was right only 44.7% of the time. -Developers put cheap small models next to Jev almost as often as the strongest large ones. Of the 432 cases that name a comparison model, 48.6% mention open or small models and 59.7% mention frontier models. -Criticism is common. Abandonment after trying Jev is not. 25.1% of all 6,277 cases contain criticism and 40.6% contain praise, but only 1 of the 2,153 hands-on cases ended with Jev being dropped. -Production use is still rare. Most activity is experimental. 80 of 2,153 hands-on cases (3.7%) are in production; prototypes, side projects and trial demos make up 62.4%. Seven days of data: treat this as a baseline, not a run-rate. Detailed Report