NikitaKoptiev
251 posts

NikitaKoptiev
@AllSeeingNik
AI content creator | Tools & automation |


World Cup Golden Boot Race: the Final Showdown Scenario 1 has fully played out: 🇦🇷Argentina pulled off a 2-1 comeback against 🏴England Messi picked up two assists and took the lead on tiebreakers: he now has (8G, 4A) compared to Mbappe's (8G, 3A) On @bananagun 🇦🇷Messi - 59% 🇫🇷Mbappe - 40% Yes, Messi is the favorite, but he enters the final against a rock-solid Spanish defense where scoring will be a massive challenge Meanwhile, Mbappe is headed to the 3rd-place match against England, where there is way less pressure Historical Context: the 3rd-place match has secured or decided the award four times It locked in the Golden Boot for Muller (2010), Suker (1998), and Schillaci (1990), while Fontaine scored four goals here in 1958 to set his all-time record (13 goals) In my opinion, Mbappe just makes more sense on paper If you're trading these finals on Banana Predicts, speed is everything. The terminal lets you hit live lines instantly as the action happens Plus, you can just copy-trade the top of the leaderboard to mirror the sharpest accounts Who wins this final sprint: 🐐 or 🐢?




Polymarket trader earned $481,587 in 1 day He joined Polymarket in July 2026, or to be precise, a day ago. I have already made 24 predictions, and all of them are related to the Crypto markets. Recently alone, he has managed to earn $481,587.94. At the same time, he earned on 5m markets by trading with a large amount and using limit orders for a 10-20% chance. Here are his best deals: > $14,005.63 —> $140,056.31 > $20,980.68 —> $104,903.41 > $9,194.37 —> $91,943.74



🫵🏼🇦🇷 Leo Messi: “It’s crazy what this group has been doing: five finals!”. “We’re coming off winning the World Cup, we’ve been the best team over the last four years, and today we’re among the two best teams in the world, another final”.
















Before AI agents became the hype, Karpathy explained the loop that made Tesla Autopilot work Not by coding every turn, lane and pedestrian by hand. By building a data engine: stage 1 → cars drive, and every car on the road is a sensor stage 2 → the model fails on the weird 1%: strange lanes, shadows, half-covered signs stage 3 → those failures get collected and labeled by humans stage 4 → the nets retrain on exactly what broke them stage 5 → the better model ships back to the fleet, and the loop restarts The fleet is the sensor. The failures are the dataset. The loop is the product Swap cars for agents and nothing changes: observe → act → fail → learn → redeploy. Karpathy described agent engineering before agents existed Bookmark now and watch it



