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This is f*cking insane. Anthropic CEO Dario Amodei was asked what a kid finishing high school should do now. His answer: "AI can do entry-level law work. It can do entry-level finance. It can do entry-level consulting We can't deny that it will also eliminate jobs, and probably a large number of them" In a few minutes he names which jobs go first and the one skill that keeps you in the game His own prediction: 50 percent of entry-level white-collar jobs disrupted in the next 1 to 5 years Prompts → Agents → AI Coworkers → Entry-Level Work Done By AI Farming to factories took generations. This time AI hits every step of a career at once His advice fits in one line: "Of course, learn to use AI" The people who start this year won't be competing with AI, they'll be the ones running it Everything below is free. Start today: Anthropic - Prompt Engineering Interactive Tutorial: the fastest way to learn to direct a model Anthropic Courses: from your first prompt to running agents Anthropic Cookbook: real task recipes you can copy today Microsoft - Generative AI for Beginners: 21 lessons, no degree required Microsoft - AI Agents for Beginners: build agents that do the entry-level work for you Hugging Face - Agents Course: free and certified, start to finish Google Cloud - Generative AI: Google's own hands-on notebooks OpenAI Cookbook: code for every common task - Prompt Engineering Guide: the reference everyone links to Andrej Karpathy - Neural Networks Zero to Hero: understand what is under the hood CrewAI: run a small team of AI workers Microsoft - AutoGen: put several agents on one job Awesome LLM Apps: hundreds of real builds to learn from - Short Courses: free 1-hour courses from the people who teach this Total cost: $0 You don't need another year of tuition to learn this. You need to start telling AI what to do 95% will graduate and compete with AI for the same entry-level job The 5% who learn to run it this year will manage the agents doing that job Save this and send it to someone graduating this year

Zambia has started connecting 751 health facilities across all 10 provinces with Starlink, and the first 90 kits went to Western Province. There's something poetic about where they started. Every year at the end of the rainy season the upper Zambezi floods the Barotse plain, and for centuries the Lozi king has left his compound at Lealui by royal barge and moved to higher ground at Limulunga. The ceremony is called Kuomboka. In Lozi it means "to get out of water." Now picture running fiber or building towers across land that turns into a lake for part of every year. A Starlink dish only needs a clear view of the sky, so the water below stops being the problem. In March 2020, months before Starlink had its first beta users, Elon Musk said it would serve the 3 or 4 percent hardest-to-reach customers, people with no connectivity or really bad connectivity. Six years later, with funding from the Global Fund through Zambia's Ministry of Health, clinics on one of Africa's great floodplains are being brought into the country's electronic health systems from orbit.

not all crypto rewards are created equal. when a platform advertises yield, i think the first thing to check is what holders actually receive and where that value comes from. @RebateLaunch has an interesting approach to this. instead of paying holder rewards in newly issued $rebate, eligible holders receive payouts in the pair asset. on robinhood chain, that's usdg. the platform reports: → $28.5k distributed to holders. → 120,403 individual payouts. → rewards sent several times throughout the day. → no staking or manual claiming required. but the reward distribution is only one part of the model. rebate pad also uses a declining launch tax to address early sniping. → 99% during the initial stage. → 66% in the next stage. → 33% in the following stage. these penalties go into a locked treasury that supports the buyer bonus mechanism. separately, the 0.7% platform fee is used to buy $rebate from the market and burn it. the reported cumulative burn has reached 35.3m tokens, representing 3.53% of the original 1 billion supply. what makes this model worth examining is how several mechanisms connect: ✦ early launch activity contributes to the treasury. ✦ the treasury supports buyer bonuses. ✦ trading fees fund holder payouts and the $rebate buyback-and-burn mechanism. my perspective is that paying rewards in the pair asset is an interesting distinction because holders aren't simply receiving more of the same token. however, attractive payout numbers alone don't prove sustainable yield. trading volume, fee generation, treasury health and long-term user activity still matter. i'd rather evaluate the source of rewards than focus only on the advertised percentage. also, round 3 of the creator campaign closes november 17 at 22:00 utc. the real question is whether rebate pad can maintain this economic cycle as adoption grows. $rebate

Jensen Huang is effectively OUT of NVIDIA🚨 Man His wife is 65 years 🇺🇸 They have been married for 41 years. As early microchip designers, she reportedly earned more money She totally left engineering work to raise their two children while Jensen Huang built Nvidia She was one of only three women in a class of 250 electrical-engineering students at Oregon State University when she and Jensen met. Jensen was 17 years when he walked up to the 19 year old Lori (his wife) and asked "Do you want to see my homework?" He promised that if they studied together every Sunday she would get straight A's, turning homework sessions into weekly dates. He also told her he would be a CEO by age 30. A promise he kept when he co-founded Nvidia in 1993. Jensen has repeatedly called meeting her "the most important, single event of Oregon university, and of my life" They married in 1985, the same year she graduated with her electrical-engineering degree. In 2022 the couple gave $50 million to Oregon State University (the campus where they met) to create a major innovation complex named after both of them. Jensen has said he does "zero percent" of his own shopping and that Lori (with their daughter) chooses his clothes, including the signature black leather jacket. He has publicly credited her with being "part of Nvidia every step of the way" and with handling the personal and financial details of their life. It's quite sad what is happening currently and how shattered Lori Huang must be.

The Philadelphia Eagles have problems, but people need to STOP BLAMING JALEN HURTS AND NICK SIRIANNI. You should be mad at First-year offensive coordinator Sean Mannion. Four games in the Eagles 2nd half offense is: Last in the NFL in points Last in Yards Last in Yards per play Last in EPA Scoring 5 points a game after halftime 12 second-half first downs all year Last in 3rd Quarter 1st downs (3) Last in Play action percentage (14.5%) 28th in under-center percentage at 31 percent. And 0-for-12 on third down while failing on 17 consecutive third-down attempts the last 2 weeks. Zero adjustments are being made. Sean Mannion has to be better. #outtapocket# #RG3# #FlyEaglesFly#

I literally hooked up ALL my finance accounts to Grok Bot. I really mean all of them: 1/ Chase 2/ Charles Schwab 3/ E*TRADE 4/ Fidelity 5/ Robinhood 6/ Wealthfront 7/ Apple Card 8/ 𝕏 Money FYI, this is a total of 30 accounts... Chase alone is 13 accounts between checking, savings, brokerage, credit cards, a 401(k), trusts, a stock plan, and 529s for the kids. I was already honestly the best with my $ money and I’ve NEVER made a late payment in my life. But before Grok Bot, I was wasting so much time bc every answer lived in different apps. Like my cash was in one place, cards in another, brokerage accounts somewhere else, and had to compile everything in a spreadsheet, if I even got around to putting it together. Now I just text my Grok Bot CFO. I can ask, “What’s my cash right now?” and it pulls everything together and separates personal from business. I can ask which credit cards are coming due and whether I have enough cash to pay them all in full. Green means I’m covered, red means I’m short, and it tells me exactly how much I need. I actually tested this... going red on purpose. Like I moved $ money around so I would come up short on one of the upcoming bills, and Grok caught it. I can even ask, “Where did my $ money go?” and instead of opening a bunch of apps, downloading transactions, cleaning up a spreadsheet, and making a chart myself, it pulls everything together and gives me the breakdown. Today, I also asked it for a pie chart of all my stock investments with the amounts and percentages. 1/ TSLA is 84.9% 2/ SPCX is 10.4% 3/ NVDA is 4.7% That’s across $ money sitting in different brokerage accounts, and it's awesome that I didn’t have to think about where any of it was. I literally just asked it one question... you can see for yourself. If you’re serious about your finances, you already ask the same questions all the time. • Like, can I cover everything that’s due? • Where did my $ money go? • How much $ cash do I really have? • What investments do I actually own? But honestly, the annoying part is that your financial life is usually scattered across 10+ different apps/platforms and a bunch of accounts, so getting a real answer takes a lot of work and your time. And honestly, most people probably don’t even look until something feels off or if they get a notification that a payment was missed or something. Now, with all my finance accounts linked, I just ask a normal question, Grok Bot checks everything it has connected, and gives me the answer. Then I can ask the same thing tomorrow, and again whenever, and get the updated version without rebuilding anything. The fact that you are able to talk to your entire financial life like you’re texting someone who already knows where everything is... this is a total game changer in the world of finance imo. If you really care about your $ money, hook yours up to Grok Bot. It's so worth it.

I know I didn’t make this account for shit like this and I know 99 percent of you do not care. but.. WE NEED TO CARE. We are killing ourselves, we are killing the planet and it’s happening SO fast. I feel like it’s constantly being swept underneath the rug and it breaks my heart. Our forestry is at stake, our wildlife is at stake, WE ARE AT STAKE. please if you are reading this, please have a open mind to fight for our world, for the things that make life.. life. Our animals, our trees. You can come on here and call me crazy, or whatever. I do not care. Just please try to open your heart to caring. I am begging.

AI Drug Discovery Is Becoming a Bottleneck Trade I get excited when an industry starts going through a real regime change. AI drug discovery (“AIDD”) increasingly looks like one of those moments. Two things are happening: 1/ Upstream: the frontier AI labs are piling in. 2/ Downstream: The supply chain is clearly moving. Supply-chain checks suggest the upstream picks-and-shovels of discovery and early preclinical R&D are starting to feel the increase in experimental volume. DNA → protein → assays → sequencing → automation → preclinical testing Names across that stack include $TWST , @GenScript , $ILMN , $TXG , lab-automation vendors and CROs. $TWST expects triple-digit percentage growth in AI-enabled drug-discovery orders in FY26, and another year of triple-digit order growth in FY27. @GenScript's AIDD business doubled YoY in 1H26. Its current platform advertises industrial-scale validation of 4,000+ designs/day, with integrated sequence-to-data workflows. Our channel checks suggest the ramp is moving even faster: roughly 8,000 designs/day currently, with a path toward ~16,000/day by YE26. --- Why does AI drive more wet-lab demand? 1/ AI makes hypothesis generation almost free → way more shots on goal. The bottleneck is moving from expert-driven design to biological validation. 2/ AI models need continuous experimental feedback — and both good and bad data are useful. Traditionally, only the highest-conviction A+ candidates might get pushed into expensive validation. With AI, even the B/C candidates can be valuable because failed experiments generate training data. @GenScript has said its sequence-to-binding workflow can return data in 4–7 days, and that faster cycle times matter because AI models depend on continuous experimental feedback. @Anthropic is a clean example. @claudeai designed 1,320 protein binders. @adaptyvbio converted those digital sequences into DNA, expressed the proteins and tested binding. Only 354 actually bound. And the 966 failures are not wasted. They are useful negative labels: what does not express, what does not bind, what has poor affinity. Those results help train the next model iteration. 3/ Wet labs are no longer just making drugs. They are making training data. $TWST / @GenScript increasingly look like biological data foundries. $TWST explicitly talks about generating model-ready data from AI-designed sequences. In some workflows, the customer may care less about receiving the physical protein than about getting structured experimental results back into the model. Traditional drug discovery asks: “Does candidate X work?” AI drug discovery also asks: “What can this experiment teach the model?” --- TAM of AIDD If AI is simply a better R&D tool, the relevant spending pool is the $300–400B of annual global pharma R&D. If AI meaningfully increases the number of viable drug programs, the opportunity is larger because it expands downstream demand for DNA synthesis, protein production, assays, and preclinical work. Near term, we can also size demand from AI-company spending. If Anthropic reaches $80B of ARR in 2026 and spends just 1% on AIDD, that alone would imply ~$800M of annual investment. --- Trade setup This is a trade that could have long legs. It’s hard to really stop working until PhaseI/II results (2028+) It smells a lot like the bottleneck trade we just saw in semis: GPUs → HBM → networking → power/cooling. In biology, It basically follows the drug discovery process downstream: AI models → designs → DNA/protein → assays → preclinical capacity. After the upstream picks-and-shovels, animal testing could become the next bottleneck. Monkey prices are already near prior highs and CRO capacity is tight. AIDD pushing more candidates into preclinical development would only add demand. ?? But clinical trials are still the bottleneck? This is the biggest pushback I keep coming back to. No matter how fast discovery becomes, drugs still need to go through preclinical → Phase I → Phase II → Phase III → approval. You still need patients, time and capital. But that doesn’t mean the bottleneck trade won’t work. More viable candidates — especially with higher success rates — still means more demand throughout the development process. And who knows: clinical trials themselves may eventually be optimized by AI. ?? What breaks the trade? Near term, the picks-and-shovels trade breaks if experimental budgets stop growing, AI-generated designs don’t translate into useful wet-lab hits, or capacity catches up too quickly. Longer term, the thesis breaks if AI drugs look great in discovery / Phase I but fail at normal rates in Phase II/III. That is why Phase II matters so much. ?? Milestones Late 2026–2027: first Isomorphic-designed drugs enter human trials; more AI-native programs move into IND-enabling work / tox. 2028–2030: clinical trial results start telling us whether AI-designed drugs actually perform better than conventional drugs. Calling all the "bottleneck bros". :) @jukan05 @zephyr_z9 @aleabitoreddit @ParadisLabs +++ More comprehensive analysis:

25 percent of San Francisco’s convicted pedophiles, rapists, and sex offenders live in government-funded housing. You’re paying for predators to live in the most expensive city in America.