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Streaming services democratized entertainment consumption, the creator economy democratized distribution, and AI could do the same for high-quality production. Rohan Nayak, Co-Founder and CEO of Pocket FM, gave us the scoop on the company’s thesis behind their AI-driven audio drama platform. Pocket FM doesn’t rely on general-purpose LLMs alone.... Rohan explains that Sherpa, its multi-agent fiction-writing system built on custom post-trained models, focuses on long-horizon narrative memory, emotionally convincing prose, and long-form planning. Rohan also gets into the harder problem that comes with cheaper creation. More content means Pocket has to get better at identifying the stories people actually want to keep listening to. Pocket’s fiction models are trained on billions of minutes of engagement data, while listener behavior helps determine which stories reach larger audiences. The team is now driving their newest product, Pocket Saga, an app that converts the best audio dramas into AI-animated vertical video series. Rohan sees AI as a tool that creators can use to execute on their ideas. He believes: “For the first time, literally anyone in the world can create studio-quality entertainment at their home.” Watch the full episode with Rohan on Lightwork at the link in the comments.show more

Lightspeed

132,939 subscribers

14,177 views • 25 days ago •via X (Twitter)

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Harley Lewis Foote25 days ago

@RohanNayak2 @PocketFM_App Does better production actually win if discovery stays broken? Feels like the bottleneck moved upstream.

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166,804 views • 2 months ago

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Base Labs

244,827 views • 1 month ago

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Stability AI

68,301 views • 1 year ago

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.

paul.nft

32,465 views • 1 year ago

Today Sam Asante and I are releasing Typeahead 2.0 on Product Hunt. Sam and I started this company because we think local AI software is going to become a much bigger part of how people use their computers. Typeahead is the first product from that thesis. The first version made the idea clear. AI autocomplete should work in the apps where writing already happens. Since then, we have been shipping toward the product that idea deserves. The app feels faster and more natural. Suggestions are easier to control. The experience is more native to the Mac. It's getting better at staying out of the way until the exact moment it can help. That matters because writing is personal. The best AI writing help keeps you in the sentence. You stay in flow and keep your voice. The machine helps without taking over. Typeahead works across your Mac, runs locally, works offline, and is built to feel like software you own. We’re live on Product Hunt today.

Today Sam Asante and I are releasing Typeahead 2.0 on Product Hunt. Sam and I started this company because we think local AI software is going to become a much bigger part of how people use their computers. Typeahead is the first product from that thesis. The first version made the idea clear. AI autocomplete should work in the apps where writing already happens. Since then, we have been shipping toward the product that idea deserves. The app feels faster and more natural. Suggestions are easier to control. The experience is more native to the Mac. It's getting better at staying out of the way until the exact moment it can help. That matters because writing is personal. The best AI writing help keeps you in the sentence. You stay in flow and keep your voice. The machine helps without taking over. Typeahead works across your Mac, runs locally, works offline, and is built to feel like software you own. We’re live on Product Hunt today.

Hiten Shah

28,205 views • 3 months ago

Generative AI is a Psy-op to Keep the Poor Dumb The growing mass reliance on Artificial Intelligence (AI) is not accidental. It is a deliberate effort driven by a few wealthy Silicon Valley capitalists to commoditise “intelligence” and convince people to adopt it in exchange for their real-life problem-solving abilities, critical thinking and cultural authenticity. The ultimate goal as always, is to enrich this already super-wealthy tech elite at the expense of everyone else. Tellingly, while these billionaire tech oligarchs spend billions to convince consumers to adopt and become dependent on “AI” solutions, they are also doubling down on the primacy of human intelligence in their elite bubbles. This was illustrated by luxury car brand Porsche, which recently released an advert whose messaging conspicuously signalled that it used exclusively human-created content. This is a clear sign of a sharp divide between the wealthy and everyday people on the question of AI adoption. While the working classes are heavily influenced to buy into the idea that generative AI platforms like ChatGPT, Suno, and VEO-3 represent the future of work, research, and art, luxury brands meant for the elite are concurrently reassuring their market that human craftsmanship, critical thinking, and genuine creativity remains central to their vision. “AI for thee, not for me” appears to be the message. Across Africa, multiple Western state and NGO actors are pushing for this so-called “AI revolution” to take a central place in African educational systems. Even in some parts of the continent where basic access to electricity remains a challenge, governments are being feverishly lobbied to adopt “AI strategies” for their under-resourced educational systems. At the very same time, it has been reported that Elon Musk, Mark Zuckerberg, and other Silicon Valley billionaires who are pushing AI adoption, not only enroll their children in Montessori schools but also restrict their exposure and access to the very same technology that their lobbyists are trying to push into African classrooms. The obvious danger in opening African education systems up to the so-called “AI Revolution” is that the next generation of Africans could end up devoid of the exact reading, writing, critical reasoning and creative skills that Africa needs to fully take its place in the world - instead trained from an early age to be reliant on ChatGPT, Grok, Suno, Nano Banana, and VEO-3 to do their thinking and expression for them. At a time when high-level human thinking is needed more than ever on the continent, it is no accident that Western lobbyists are heavily pushing the normalisation of generative AI as a core pillar of African education. If Africa is to be maintained as a colonial resource plantation and a market for excess overseas production, young Africans must be made to read, write and think less, and consume more. In Africa and elsewhere, the constant global dynamic is that the poor and underprivileged are encouraged to outsource their intellectual processes to AI in order to “stay competitive," while the wealthy quietly protect the disciplines that actually sharpen the mind: reading, writing, artistry, and critical thinking. Africans must see the “AI Revolution” for what it is. Far from just benign or neutral technological advancement, it is yet another manifestation of power consolidation by Western racial-capitalists. This class of people understands very well that literacy, philosophy, and art produce power, while delegation of thought only produces ignorance and compliance. Despite whatever message they put out, the reality remains that thinking for yourself will in fact, never be “disrupted.”

The Spearhead

85,901 views • 8 months ago

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.

Six Five Media

7,422,478 views • 6 months ago

It's time to Free Creators! My new company #Lumi is empowering anyone to be a storyteller, regardless of their skill set. Lumi enables you to create, publish, and monetize your stories all in one platform. How do we do that? By creating access to: 🤩 End to end story creation 🛠️ Simple AI tools 📚 Physical and digital publishing 👕 Built-in merchandising The best part is CREATORS OWN ALL OF THEIR WORK! It's everything a creator needs to build and scale their stories, with having to rely on personal connections into Hollywood or having to bankroll a full team. This is how we level the playing field. This is how we create access and opportunity for the world to tell their stories. Lumi is about to turn every storyteller into their own personal Disney. Sign up now to start creating ↓ #LumiStory

It's time to Free Creators! My new company #Lumi is empowering anyone to be a storyteller, regardless of their skill set. Lumi enables you to create, publish, and monetize your stories all in one platform. How do we do that? By creating access to: 🤩 End to end story creation 🛠️ Simple AI tools 📚 Physical and digital publishing 👕 Built-in merchandising The best part is CREATORS OWN ALL OF THEIR WORK! It's everything a creator needs to build and scale their stories, with having to rely on personal connections into Hollywood or having to bankroll a full team. This is how we level the playing field. This is how we create access and opportunity for the world to tell their stories. Lumi is about to turn every storyteller into their own personal Disney. Sign up now to start creating ↓ #LumiStory

Colin Kaepernick

438,200 views • 2 years ago

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

Matt Dancho (Business Science)

242,084 views • 1 year ago

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

Dan Shipper 📧

67,958 views • 6 months ago

this is huge news from Cursor, they've pulled of the impossible and turned their ai-wrapper into a in-destructible moat sam altman literally called it cursor's ai agent harness is available for anyone to build on, which means ai models are now a commodity > 1-time install and now anyone can run cursor's agent locally or via cloud. use any model (e.g. gpt 5.5) but with the added cursor harness that makes it 10X better. > its so good that 3 of cursors biggest competitors are embedding it into their products. > now cursor DOESN'T DEPEND on anthropic or openai. their own model (composer 2) competes directly! yesterday sam altman said the ai model and harness are one and the same and today cursor turned their harness into a self-owned moat fucking masterclass (coming from a former cursor / ai wrapper hater)

this is huge news from Cursor, they've pulled of the impossible and turned their ai-wrapper into a in-destructible moat sam altman literally called it cursor's ai agent harness is available for anyone to build on, which means ai models are now a commodity > 1-time install and now anyone can run cursor's agent locally or via cloud. use any model (e.g. gpt 5.5) but with the added cursor harness that makes it 10X better. > its so good that 3 of cursors biggest competitors are embedding it into their products. > now cursor DOESN'T DEPEND on anthropic or openai. their own model (composer 2) competes directly! yesterday sam altman said the ai model and harness are one and the same and today cursor turned their harness into a self-owned moat fucking masterclass (coming from a former cursor / ai wrapper hater)

Ejaaz

950,836 views • 5 months ago