A $599 Mac mini might be the best Claude credit saver nobody talks about. Not because it replaces Claude. Because it stops you from wasting Claude on tasks that never needed a frontier model. Run Ollama locally. Connect Claude Code through LiteLLM. Let Qwen handle routine coding, Gemma handle quick tasks, and local agents run summaries, tests, log checks, and cron jobs without charging you per request. Then route the difficult work back to Claude: architecture, complex debugging, production code, high-stakes writing, anything that needs real judgment. That is the setup. Your boring 80% runs locally. Your expensive 20% stays in the cloud. Lower bills, private code, no idle API spend, and a small AI team running quietly under your desk. Full Mac mini setup and routing guide below.

👀 slowly but surely

1/ Meet M1llionz (RichMilly666), a French cybercriminal allegedly laundering assets from two violent home invasion robberies in France in April 2026 that netted $667K total, with a history of openly posting about bank and retail fraud on social media. My work has led to a $93K Tether freeze directly tied to these attacks so far.

Thank you for this opportunity @predictdotfun @dingalingts

A 14-year-old Chinese student sold his first Python script for $40, then watched his teacher present it as a “professional AI development case study” without realizing the author was sitting in the third row. He built the project during winter break. For two weeks, he waited for his parents to fall asleep, opened Claude and kept asking questions until the script worked. It was an AI agent that could search websites, summarize information and generate structured reports. He uploaded it to GitHub with a broken-English README: “ai agent that does homework and finds answers from any website.” Zero stars. Then someone bought it for $40 through GitHub Sponsors. The buyer’s real name was hidden, so the kid never knew who it was. He spent the entire payment on Fortnite skins and went back to coding. A few weeks later, school started. During the first computer science class, his teacher opened an AI agent demo and told the room: “I found this script online. It automatically searches websites, summarizes the content and uses Claude to generate structured reports. Something that used to take me two hours now takes three seconds.” The student recognized everything. The variable names. The folder structure. Even the Chinese comments he had been too lazy to translate. His teacher was presenting the script to 40 students as an example of professional AI development, while its creator sat silently in the third row. He checked the repository after school. 847 forks. A university in Beijing had adapted it to grade papers in bulk. A tutor in Shanghai had turned it into a homework-checking service that charged parents $15 per month. A company in Hangzhou had modified it into a customer-support bot for online stores. All of it came from a small script written by a bored kid during winter break with Claude. In class, he still wrote code by hand and received a B-minus. At home, the code he built with AI was being purchased, deployed and presented as professional work. Same student. Same code. The only difference was who was looking.

Opus 4.8, GPT 5.5 and Gemini 3.1 Pro are now free to test from one place. No card required. 50 credits on signup. The platform behind it is reportedly valued at $4B, and the timing makes sense. Most people are still paying for multiple subscriptions just to compare prompts across models: Opus for one task, GPT for another, Gemini as backup. That workflow is expensive and messy. A single model gateway makes the comparison much cleaner. You can run the same high-value task across different frontier models, see which one actually performs better and avoid paying three separate subscriptions just to test basic workflows. The smart way to use it is simple. Register, use the free credits on expensive tasks first, compare model behavior side by side, then decide whether API access is actually worth it. This matters because model choice is becoming part of the workflow itself. The best setup is not blindly paying for every subscription. It is testing the same task across multiple models, finding which one performs best for your use case, and only then deciding what is worth paying for.

Predict to win $2,000,000? Must be a bear market thingy 🤣

.@predictdotfun just turned the World Cup into a $2M acquisition machine. This is not just another sports betting campaign. It looks more like a full fantasy-style prediction market built around the biggest sporting event in the world. The idea is simple: you pick up to 5 teams, make predictions across their matches, earn World Cup reward points based on accuracy, and compete for a share of the prize pool at every stage of the tournament. The full reward pool is $2,000,000, split across the World Cup structure: 1. Group Stage Each group has a $70,000 prize pool, with 12 groups creating $840,000 in total rewards. 2. Round of 32 Each winning matchup carries $25,000 in rewards, with 16 winners and $400,000 distributed in total. 3. Round of 16 Each matchup has $30,000 in rewards, with 8 winners and $240,000 distributed. 4. Quarterfinals Each matchup has $40,000 in rewards, with 4 winners and $160,000 in total. 5. Semifinals Each semifinal matchup has $50,000 in rewards, with $100,000 distributed across the two winners. 6. Final The World Cup final has the biggest single prize pool, with $260,000 allocated to the final stage. The interesting part: Predict fun is not just asking users to place one prediction and leave. It is turning the tournament into a multi-week game where every match, every team choice and every prediction can affect your leaderboard position. That is a much stronger retention loop than a normal one-off betting market. World Cup attention brings the users in, fantasy-style mechanics keep them active, and real rewards give them a reason to come back through every stage. Prediction markets need more than liquidity. They need games people actually want to keep playing. Predict might have found one of the cleanest formats for that.

Get your boots on.

.@predictdotfun just crossed an important line. It's no longer just building inside the @binance ecosystem. It's becoming part of it. The integration of Predict Points (PP) and leaderboards directly into the Binance App means users can now earn rewards through: trading placing orders providing liquidity holding positions Even more notable: 3.3M+ PP was distributed retroactively to Binance Prediction Markets participants from the past 8 weeks. That sends a clear signal: activity inside Binance is now feeding directly into Predict's incentive system. For a prediction market, distribution is everything. And Binance has plenty of it.

How did he create Linux without OPUS 4.8 ??

If you want to trade the Polymarket IPO narrative, doing it on @predictdotfun is actually pretty interesting. They already have a Polymarket IPO market live, and this is exactly the kind of long-dated market that fits their PP system well. So instead of just betting on the outcome, you’re also farming PP while expressing the view. And for longer markets, open positions can earn yield while you wait.

GM There is some news