dharmesh

2023 was the year of chat UX powered by generative AI. That led to me hacking on ChatSpot and launching it last year. 2024 is the year of Agent AI, so that's what I've been hacking away late nights on. I'm obsessed with #AgentAI. Was up past 3am last night. I have a handful of simple (but useful) agents that I built for myself and one I built for my wife. Will start releasing them one at a time. I'm using a bunch of LLMs (GPT-4, Claude, Gemini) and a bunch of proprietary/paid data sources. Will be fun to see the bills rack up. :) Releasing it all for free. You can join the waitlist at agent.ai Thanks for your support.

Norway is a reminder that nations are not something to be embarrassed by. A people with a strong sense of history, a shared identity, and the confidence to celebrate their country without apology. Rather than weakening them, it gives them cohesion. We’ve been told that national pride is something be ashamed of. Yet the countries most admired around the world are often those most comfortable with who they are. Nations aren’t the problem. They’re one of the greatest achievements of civilisation. Norway is a lesson to us all.

The 80/20 rule is broken advice for founders. When everybody ships 80/20 work, average becomes the baseline. @mansourtarek_ shares why 100% of outsized results and viral resonance live in perfecting the last 10%. The difference between a feature that gets ignored and one that captures mainstream culture lies entirely in the final 10% of obsessive execution. As the founder, your personal standards and timing instincts are your ultimate competitive advantage. Link to full episode in the comments 👇

Congrats to Norway for their performance in the World Cup this year. It's particularly impressive given the population of Norway was the smallest of the 32 teams. p.s. Yes, I've been cheering for Norway. My wife is Norwegian.

It was a fabulous run. Proud of the Norwegian team! 🇳🇴

🚨🇳🇴 The Vikings have taken over Miami Beach 🚣

This is possibly the best example of guerrilla marketing by an airline all year - SAS - Scandinavian Airlines😍

Finding the balance between productive persistence and wasted effort is the whole game.

@nevmed @thesamparr Thanks, but I'm not as smart as people think I am.

@thesamparr @dharmesh is so smart!

On My First Million, Dharmesh Shah told me about the line that got him to $200k a year in the early 90s during his first company before HubSpot. His boss pushed back on the raises: "Do you really think you're going to find that many companies that will pay you $200,000 a year?" Dharmesh said: "I really don't need to find that many. I just need to find one. And I think there's one out there." He said the same math applies to dating, and to making one business work: "making one business work will do enough to change your life."

A prediction on what will happen in the next 20 years with ai. But first, some bro-history: - steam engine gets invented in 1712 in england by Thomas Newcomen. - its first job is boring: pumping water out of flooded coal mines. that's all it did for 60 years. - then James Watt makes it way more efficient in 1769, and now you can power factories with it. cotton mills everywhere. the modern factory is born. - 100s of of thousands of craftsmen who made fabrics by hand... were pissed. this damn machine's taking everything we've worked for. - so they start breaking the machines at night, in organized crews. one mill owner even gets murdered. england freaks out, makes machine-breaking a CAPITAL OFFENSE - the craftsmen signed all their threat letters "General Ned Ludd"...a completely made-up guy, so there was no leader to hang. - that's why we call them Luddites. - well, the Luddites lost. trade destroyed. their jobs never came back. - for the next 30-50 years, the economy boomed but wages stayed FLAT. - all the gains went to factory owners. this period is call "Engels' Pause." - named after Friedrich Engels, the son of a rich mill owner. Engel managed family's cotton factory. he looked around Manchester and wrote a whole book saying this system is grinding people to death. "a dogs life", he said. - then he meets Karl Marx (1844), hands him all the factory data, and funds him for DECADES off the mill profits. communism was literally bankrolled by cotton dividends. - meanwhile the railroads (steam engine on wheels) gets invented and get huge fast. entire new jobs that never existed before come about. 600,000 railway workers. new cities. the world transforms. - around 1850, wages finally take off. New cities form. The world changes for the better. The takeaway: When new technology comes, thins may be bad at first. But over time, things typically get far far better. If the goal is to decrease time in the pain cave for the average joe (and also make money): 1. pick which industries will explode (hard). for example: the coal indsutry blew up with steam engine. as did railroads (which wasn't even an industry at first) 2. compress the time for a new tech to become ubiquitous. took steam engine 50 years because factories and railroads are slow to make, internet 15 years, will ai be faster? 2. speed up the ability to reorganize companies and society around this new tech. hoe fast can you invent tangent products and industries? My opinion on all this is: the fear around ai is the same fear other eras had around new tech. And there will be pain - but there's a playbook on how to make it all a net positive.

This sentiment struck a chord. We all need to appreciate our culture.

Just coming off of meetings with a couple dozen enterprise IT leaders discussing AI agents. Here are a few of the common themes that stand out: * Lots of conversation that you have to solve an operating model challenge to get the full benefits of AI. Most companies have orgs that have always operated in siloes; but agents are most effectively when they are tied to a process, which often cuts across these siloes. So the big question is how do you start to deploy centrally managed agents that can work across organizational boundaries. Who manages these agents? How do they get deployed and adopted? * Data fragmentation remains a major issue for most organizations. As long as data remains highly fragmented and not in standard formats, or data is not available to the right people and agents, enterprises are dealing with issues around being able to get answers from agents that are accurate or that conform to their business practices. This cuts across both systems with structured data (product metrics or revenue figures) and unstructured data (product roadmap or customer contracts). * Clear sense that companies need to figure out what their core data moats are going to be in the future. If everyone has access to roughly the same superintelligence from the various models, then the context that you feed the models becomes proprietary value in the future. Capturing this data and getting it into a format that agents can use becomes very important. * Everyone is trying to figure out the right metrics to manage to for AI adoption. General consensus that tokens are not the right metric per se, and people leaning more toward business outcomes (in an ideal world). For business outcomes (like more revenue or more shipped product), though, you have to get close to each individual workflow to figure out if it was successfully transformed with AI so it’s harder to manage top down. * Growing view that enterprises are going to live in a multi-model world. Lots of interest (though early in actual adoption) in layers that can route workloads to different models (frontside or open weights) for cost or performance reasons. Also enterprises are trying to figure out what things do you give to the models directly vs. what do you separate as horizontal systems and context so you can swap any system in and out. * Talent for driving AI adoption and implementation still remains a major issue and topic. Many view it as something you necessarily have to train for internally due to a shortage of talent being trained on this in the outside. As an aside, this feels like it remains a huge opportunity for those that get very good at deploying and management agents in an enterprise since most companies are looking for these skills. * The best use-cases for AI tend to be those that fundamentally change the work being done instead of just replacing an existing process and doing it more efficiently. Companies are working through their versions of this individually because it’s different per industry, but this often remains both the most exciting and higher upside uses of AI. Many more topics discussed recently, but overall it’s clear that there’s a ton of change going on with much more to come.

“The lion moves in silence and strikes with purpose.”

Norway knocked out Brazil, then the army posted a Viking celebration video.

The Fourth of July celebrates the most radical idea in American history: that ordinary people can speak, argue, dissent, and govern themselves. Free speech has a remarkable power to defend itself. Let people talk, and bad ideas can be answered, dogmas can be challenged, and truth has a fighting chance. But the culture that makes free speech possible needs defenders. Join us at Soapbox to help keep it alive: soapbox.fire.org/soapbox

This video is so uplifting. Sports have never really been my thing, but seeing people from around the world experiencing what the United States has to offer and seeing Americans warmly welcoming visitors from every corner of the globe - is genuinely heartwarming. It's also a reminder that the United States is often far more welcoming, joyful, and inspiring than the constant stream of negative headlines would lead you to believe.

The battle in AI is shaping up to be a battle for context. Everything in AI is about making sure that agents are effective as possible. That effectiveness comes down to whether the agent has the right domain expertise, access to the right context and tools to work with, and are involved in workflow in a way that users can easily interact with, review its work, and incorporate it into the rest of the process. As a consequence, the platforms that are able to capture and leverage the best and most context within their agents —and be able to pick the right models for the task- will be the place where agents do their best work. You can just look at coding agents, legal agents, or support agents as examples of what this looks like at scale. This is why the applied AI layer has a lot more value than just being an LLM wrapper. The ability to organize the critical knowledge for the work being done, and maintain this knowledge in a governed way where only the right people and agents have access, and the ability to improve the context for agents more and more over time, is critical. Over time, this layer will be able to route work between a variety of models, leveraging frontier intelligence for planning and orchestration and review, and a mix of lower cost models (open or closed) for the large volume of work between these tasks. The applied layer is also in a good position to train and develop its own models as well that are purpose built for their domains. Never good to bet against the bitter lesson, but equally taking a near frontier base model and post training it for just one type of domain work can -in many cases- lower costs or deliver better performance for certain tasks. Finally, this applied layer is also where most of the change management of the workflow will need to occur. This is why FDEs are so important at the applied layer, because this is the point where the customer needs to have specific business problem solved by a particular vendor. Whichever companies can solve that completely in an end-to-end fashion will have the greatest moats. As each day goes on, we’re learning more about what the likely long term market dynamics will look like in AI.

Love this re-imagining of America’s founding using Docs, Gmail, Calendar and more from @GoogleWorkspace. Really puts the history in version history :)

Welcome to The Land of the Free! 🇺🇸🌏 God Bless America. 🙏

Absolutely love this. Norway and the Viking row after beating Ivory Coast at the World Cup. 🇳🇴 x.com/BHUYAN___007/s…