Hannah Cooper @hanper_320

I’m with Chad

Chad Hurley @Chad_Hurley

11 months ago

The full stack wave is coming! $GOOG is going to be the most valuable company on earth within 2 years.

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Alcarez … what a treat to watch

I won @howardlindzon's giveaway 10 years ago, and attending Stocktoberfest was a major accelerant for my brand and career. Excited to now work at Stocktwits, providing the opportunity for others. Listen to my @Stocktwits story and then go create yours! youtube.com/shorts/a2KdL9F…

Stocktwits @Stocktwits

11 months ago

🚨Stocktoberfest Giveaway 🚨 We’re covering airfare, hotel, and ticket for one lucky Stocktwits Community Member to join us at Stocktoberfest 2025 in Coronado, Oct 20-22. Want in? Pitch us on why YOU should be there in the comments or submit a short video here:

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two old farts talking markets and fintech fun to see my pal @JanvanEck3

Drop in for today's trade set-ups with @TheVoz4Real 👇

Stocktwits @Stocktwits

11 months ago

🟡 DAILY RIP LIVE - HOSTED BY KATIE PERRY WITH GUESTS OLIVIA VOZNENKO & EVAN PONDEL x.com/i/broadcasts/1…

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Every once in a while something big happens. Something that in hindsight, changes EVERYTHING. This is our time. Together we will Make $CRO Great Again!

Two ways to win in the @howardlindzon Degenerate Economy. 1. Become a dopamine-fuelled world-class momentum investor. This is difficult. Really difficult. 2. Become a lazy, slow-twitch, world-class Wyckoff Rotation investor. This is easier. Much easier. @BruniCharting @katieeperry

truth

Jake Wujastyk @Jake__Wujastyk

11 months ago

Some people will never understand that you can make money without trading options. The addiction to options is honestly a pandemic in the market.

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My abs hurt from laughing 😆 Today's ep. w/ @Jake__Wujastyk, his future $ONDS ass tat, and @newsomenuggets ⬇ ⇢ 1:55 Never trust a man w/ a J name except these 2 ⇢ 2:44 Lisa Cook: 'You can't fire me b/c I don't quit' ⇢ 3:05 $CBRL stands by new logo, let's look at the charts

Stocktwits @Stocktwits

11 months ago

🟡 DAILY RIP LIVE - HOSTED BY KATIE PERRY WITH JERREMY NEWSOME & JAKE WUJASTYK x.com/i/broadcasts/1…

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As @rabois said, "retail is a feature, not a bug." Radical Portfolio Theory 🤝 Degenerate Economy Listen below!

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57 years ago

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This starts at the top of the hour! And ends at the top of the following hour! In between it will pass through the bottom the hour!

God @TheTweetOfGod

a year ago

Today on 'Droll Call' - John Hodgman! open.substack.com/pub/theskeetof…

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The story of the week was $FIG IPO 📈 Listen to what @MichaelNaussCMT and @acethekid think about the buzz and the stock

Stocktwits @Stocktwits

12 months ago

🟡 DAILY RIP LIVE - WITH JORDAN LEE, MICHAEL NAUSS, AND LIA THE TRADER x.com/i/broadcasts/1…

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Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by DeepLearning.AI and taught by @zainhas, experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here: coursera.org/learn/retrieva…

The invention of modern writing instruments like the typewriter made writing easier, but they also led to the rise of writer’s block, where deciding what to write became the bottleneck. Similarly, the invention of agentic coding assistants has led to a new builder’s block, where the holdup is deciding what to build. I call this the Product Management Bottleneck. Product management is the art and science of deciding what to build. Because highly agentic coding accelerates the writing of software to a given product specification, deciding what to build is the new bottleneck, especially in early-stage projects. As the teams I work with take advantage of agentic coders, I increasingly value product managers (PMs) who have very high user empathy and can make product decisions quickly, so the speed of product decision-making matches the speed of coding. PMs with high user empathy can make decisions by gut and get them right a lot of the time. As new information comes in, they can keep refining their mental models of what users like or do not like — and thereby refine their gut — and keep making fast decisions of increasing quality. Many tactics are available to get user feedback and other forms of data that shape our beliefs about users. They include conversations with a handful of users, focus groups, surveys, and A/B tests on scaled products. But to drive progress at GenAI speed, I find that synthesizing all these sources of data in a PM's gut helps us move faster. Let me illustrate with an example. Recently, my team debated which of 4 features users would prefer. I had my instincts, but none of us were sure, so we surveyed about 1,000 users. The results contradicted my initial beliefs — I was wrong! So what was the right thing to do at this point? - Option 1: Go by the survey and build what users told us clearly they prefer. - Option 2: Examine the survey data in detail to see how it changes my beliefs about what users want. That is, refine my mental model of users. Then use my revised mental model to decide what to do. Even though some would consider Option 1 the “data-driven” way to make decisions, I consider this an inferior approach for most projects. Surveys may be flawed. Further, taking time to run a survey before making a decision results in slow decision-making. In contrast, using Option 2, the survey results give much more generalizable information that can help me shape not just this decision, but many others as well. And it lets me process this one piece of data alongside all the user conversations, surveys, market reports, and observations of user behavior when they’re engaging with our product to form a much fuller view on how to serve users. Ultimately, that mental model drives my product decisions. Of course, this technique does not always scale. For example, with programmatic online advertising in which AI might try to optimize the number of clicks on ads shown, an automated system conducts far more experiments in parallel and gathers data on what users do and do not click on, to filter through a PM's mental model of users. When a system needs to make a huge number of decisions, such as what ads to show (or products to recommend) on a huge number of pages, PM review and human intuition do not scale. But in products where a team is making a small number of critical decisions such as what key features to prioritize, I find that data — used to help build a good mental model of the user, which is then applied to make decisions very quickly — is still the best way to drive rapid progress and relieve the Product Management Bottleneck. [Original text: deeplearning.ai/the-batch/issu… ]

Announcing our new event - Buildathon: The Rapid Engineering Competition. See the video for details, and please apply to participate!

Stocktoberfest is BACK - October 20-22 on Coronado Now that @Stocktwits launched @CryptotwitsHQ we will also have some incredible founders and investors from the sector joining to fireside chat including @joemccann and @EmanAbi of SUI and our AI whisperer @MParekh

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Last week, the United States Congress passed President Trump’s “Big Beautiful Bill.” I’m disappointed it didn’t include a proposed moratorium on U.S. state-level AI regulation. While there is a role for AI regulation, it is when the technology is new and poorly understood that lobbyists are most likely to succeed at pushing through anti-competitive regulations that hamper open-source and other beneficial AI efforts. A moratorium would have bought more time for regulators to figure out the realistic risks and rewards of AI and thereby avoid bad regulatory proposals. Many jurisdictions loosely follow this trajectory: - When new AI technology is still poorly understood, companies can make grandiose statements about its benefits or dangers, and both traditional and social media are ineffective at fact-checking them and tend to parrot what they say. During this initial period, businesses can get away with saying almost anything. - This opens opportunities for hype as well as fear mongering based on exaggerated claims about AI dangers. Some businesses exploit this opportunity to try to get regulators to pass anti-competitive laws that impede open-source and other competitors. - But eventually, smart regulators learn enough about AI to understand its realistic benefits and risks. For example, the U.S. Senate’s bipartisan Insight Forum on AI, which I participated in, heard from many stakeholders and came to support innovation and dismiss ill-founded fears of “AI takeover” and the like. Indeed, the European Union went through this trajectory as well. After the EU AI Act was passed, many regulators realized many of its “protections” are not actually helpful. They relaxed some of the law’s provisions to make it less stifling of innovation than many observers initially had feared. There are AI regulations that would limit harmful applications appropriately, for example, banning non-consensual deepfake porn and preventing misleading marketing. However, many states, which have less resources than the federal government to deeply understand AI, have proposed harmful regulations, especially those that aim to regulate the technology rather than the applications. For example: - California’s SB 1047 purported to impose safety requirements on frontier AI systems, but it placed ambiguous and/or technically infeasible requirements on model creators to prevent harmful downstream uses. This is akin to holding the maker of a hammer liable if someone uses it for harmful purposes. Fortunately, Governor Gavin Newsom quashed SB 1047 with a veto. - New York’s Responsible AI Safety and Education Act, which passed the state legislature in June and awaits Governor Kathy Hochul’s signature or veto, also places ambiguous and unreasonable requirements on model builders, purportedly to guard against theoretical “critical harms.” It would hamper open source without making anyone meaningfully safer. - The Texas Responsible AI Governance Act initially included many of the problematic elements of SB 1047. It would have created unreasonable requirements that model providers would have had a hard time complying with, and compliance would have amounted to safety theater that was unlikely to actually make people safer. Fortunately, as Texas regulators came to understand AI better, they significantly scaled back the law, and Governor Greg Abbott signed it into law in late June. The final law focuses on specific application areas, establishes an advisory council and regulatory sandbox, and places more burden on government agencies than private companies. Sadly, I see the net impact of the regulations proposed so far as negative. Many would severely hamper innovation despite some lesser positive benefits. This is why a moratorium on state-level regulation would have been a net benefit to AI and to society. Shutting down bad regulations for a limited period would have given regulators time to figure out AI technology and ignore irresponsible fear mongering. In addition, it would have helped them avoid creating a patchwork of state-level regulations that businesses large and small have a hard time complying with. Perhaps a 10-year blanket moratorium was a step too far. A more modest, say, 2-year moratorium, and one that covered only the most problematic regulatory proposals, might have had a better chance of passing. Even though a moratorium did not make it into Trump’s bill, I hope that efforts continue in the U.S. and other nations to give regulators time to understand the real risks and benefits of AI, and not pass stifling regulations during that initial period when the technology is new and the power of fear mongering is strongest. [Original text: deeplearning.ai/the-batch/issu… ]

My talk at YC Startup School on how to build AI startups. I share tips from @AI_Fund on how to use AI to build fast. Let me know what you think!

Y Combinator @ycombinator

a year ago

Andrew Ng (@AndrewYNg) on how startups can build faster with AI. At AI Startup School in San Francisco. 00:31 - The Importance of Speed in Startups 01:13 - Opportunities in the AI Stack 02:06 - The Rise of Agent AI 04:52 - Concrete Ideas for Faster Execution 08:56 - Rapid

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A lot of people have been telling me that @Stocktwits has been 'surprising' them lately. (In a good way!) Here's a peek at @howardlindzon's strategy. Pumped to be a small part of the story as a part-time creator 🙏 Stocktwits Everywhere: howardlindzon.com/p/having-one-a…

On Monday, a United States District Court ruled that training LLMs on copyrighted books constitutes fair use. A number of authors had filed suit against Anthropic for training its models on their books without permission. Just as we allow people to read books and learn from them to become better writers, but not to regurgitate copyrighted text verbatim, the judge concluded that it is fair use for AI models to do so as well. Indeed, Judge Alsup wrote that the authors’ lawsuit is “no different than it would be if they complained that training schoolchildren to write well would result in an explosion of competing works.” While it remains to be seen whether the decision will be appealed, this ruling is reasonable and will be good for AI progress. (Usual caveat: I am not a lawyer and am not giving legal advice.) AI has massive momentum, but a few things could put progress at risk: - Regulatory capture that stifles innovation, including especially open source - Loss of access to cutting-edge semiconductor chips (the most likely cause would be war breaking out in Taiwan) - Regulations that severely impede access to data for training AI systems Access to high-quality data is important. Even though the mass media tends to talk about the importance of building large data centers and scaling up models, when I speak with friends at companies that train foundation models, many describe a very large amount of their daily challenges as data preparation. Specifically, a significant fraction of their day-to-day work follows the usual Data Centric AI practices of identifying high-quality data (books are one important source), cleaning data (the ruling describes Anthropic taking steps like removing book pages' headers, footers, and page numbers), carrying out error analyses to figure out what types of data to acquire more of, and inventing new ways to generate synthetic data. I am glad that a major risk to data access just decreased. Appropriately, the ruling further said that Anthropic’s conversion of books from paper format to digital — a step that’s needed to enable training — also was fair use. However, in a loss for Anthropic, the judge indicated that, while training on data that was acquired legitimately is fine, using pirated materials (such as texts downloaded from pirate websites) is not fair use. Thus, Anthropic still may be liable on this point. Other LLM providers, too, will now likely have to revisit their practices if they use datasets that may contain pirated works. Overall, the ruling is positive for AI progress. Perhaps the biggest benefit is that it reduces ambiguity with respect to AI training and copyright and (if it stands up to appeals) makes the roadmap for compliance clearer. This decision indicates it is okay to train on legitimately acquired data to build models that generate transformational outputs, and to convert printed books to digital format for this purpose. However, downloading from pirate sites (as well as permanently building a “general purpose” library of texts, stored indefinitely for purposes to be determined, without permission from the relevant copyright holders) are not considered fair use. I am very sympathetic with the many writers who are worried about their livelihoods being affected by AI. I don‘t know the right solution for that. Society is better off with free access to more data; but if a subset of people is significantly negatively affected, I hope we can figure out an arrangement that compensates them fairly. [Original text: deeplearning.ai/the-batch/issu… ]