PREMARKET OPTIONS TRADING IS COMING
Cboe plans to let traders buy and sell options on select major stocks before the regular market opens.
Once live:
7:30–9:25 a.m. ET: Pre-market options trading
9:30 a.m.–4:00 p.m. ET: Regular session
4:00–4:15 p.m. ET: Extended “curb” session
Initial stocks include $AAPL, $AMD, $AMZN, $AVGO, $BABA, $BAC, $GOOG, $GOOGL, $HOOD, $INTC, $META, $MSFT, $MU, $NFLX, $NOK, $NVDA, $ORCL, $PFE, $PLTR, $TSLA and $TSM.
Orders can begin queuing at 7:15 a.m. ET.
Launch date is still TBD.
THIS IS HOW YOU RETIRE IN THE NEXT 5 YEARS.
I’ll be following where capital flows.
This is how I believe the AI Supercycle evolves:
2026–2027: AI Infrastructure
AI demand accelerates.
Capital pours into the companies building the foundation.
• AI Compute: $NVDA $AMD $AVGO $MRVL
• Memory: $MU $SNDK $WDC
• AI Infrastructure: $VRT $SMCI $NBIS $IREN
2028–2030: Power & Grid
AI becomes an energy problem.
Capital shifts toward:
• Power Infrastructure: $VRT $ETN $PWR $HUBB
• Battery Materials: $ALB $SQM
• Copper & Grid: $FCX $TECK $SCCO
• Rare Earths: $MP $CRML $USAR
• Nuclear: $CCJ $UUUU $SMR $OKLO
2030+: Physical AI
Intelligence moves into the real world.
Capital expands into:
• Robotics: $TSLA $SYM
• Autonomous Mobility: $ACHR $JOBY
• Defense: $LMT $NOC $KTOS $AVAV
• Space: $RKLB $ASTS $LUNR $PL
Bookmark this.
The biggest winners usually come from following capital before everyone else.
R.I.P. GOOGLE FLIGHTS IN 2026.
R.I.P. BOOKING COM IN 2026.
R.I.P. SKYSCANNER IN 2026.
$1,190 flight. I paid $159.
Use these 7 prompts before booking your next trip :
Wow, this tweet went very viral!
I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it for your specific needs.
So here's the idea in a gist format: https://t.co/NlAfEJjtJV
You can give this to your agent and it can build you your own LLM wiki and guide you on how to use it etc. It's intentionally kept a little bit abstract/vague because there are so many directions to take this in. And ofc, people can adjust the idea or contribute their own in the Discussion which is cool.
Claude Cowork is a literal CHEAT CODE for SEO.
I could take ANY local service business to $100k/month in 90 days using just Claude Cowork.
Here’s exactly how I'd do it:
BREAKING: AI can now build you a complete website in 2 hours (for free).
Here are 9 insane Claude Opus 4.6 + Figma Make prompts that create $5,000 websites in 2 hours:
(Save this before your competitors do)
Key Events This Week:
1. US Stock Market and Oil Futures Open - 6 PM ET TODAY
2. February Existing Home Sales data - Tuesday
3. February CPI Inflation data - Wednesday
4. US Q4 2025 GDP Data - Friday
5. January PCE Inflation data - Friday
6. January JOLTS Job Openings data - Friday
All eyes are on oil prices tonight.
Key Events This Week:
1. US Futures React to Iran Situation - TODAY 6 PM ET
2. February ISM Manufacturing PMI data - Monday
3. February ADP Employment data - Wednesday
4. Initial Jobless Claims data - Thursday
5. January Retail Sales data - Friday
6. February Jobs Report - Friday
Buckle up for an incredibly volatile futures open.
@prasannalara Machi, the SA-WI game is before Ind-Zim, so whether we'll be under pressure or not, we'll know then. Chill, Ind will Semis in Wankhade against England.
If you're interviewing for AI PM roles at OpenAI, Anthropic, Google, or Meta, the ability to define eval dimensions, build a test dataset, write eval criteria, and set blocking thresholds separates you from every other candidate who can only talk about evals conceptually.
In this episode, Ankur walked through the complete end-to-end process. Start with success criteria and expected behavior. Transform those into measurable metrics. Build a dataset from four sources (production data, research, synthetic generation, domain experts). Create your base product. Run the dataset through it. Get expert analysis on the outputs. Use failures to define eval criteria. Set thresholds that block bad releases. Run offline evals before every deploy. Run online evals on production traffic. Close the loop.
That process is the operating system for any AI PM shipping a product where the LLM output matters.
The five reasons AI prototypes fail to scale are data drift, cost explosion, engineering limitations, missing guardrails, and collaboration failure. Evals directly address four of those five. They catch drift. They let you validate cheaper models. They enforce guardrails. They force collaboration with domain experts.
95% of AI initiatives fail according to research from MIT. The major driver is the "learning gap," meaning teams either solve the wrong problems or build systems that don't evolve with user needs. A strong eval practice is the direct antidote. It forces you to define what good looks like before you build, and it forces the product to keep evolving after launch.
The PMs who learn this skill now are going to be the ones building the AI products that actually make it to scale. This episode is the best walkthrough I've seen of how to do it.