In the last seven days alone, forty-seven new AI video generation tools launched into the wild. If you feel like you are drowning in a sea of identical looking browser tabs and landing pages, you are not alone. It has reached a point where finding the tools is actually harder than using them.
It is a massive discovery bottleneck. We have shifted from a shortage of intelligence to an absolute surplus of applications, and that makes curation the most valuable skill in the ecosystem right now. By the way, today's episode of My Weird Prompts is powered by Google Gemini three Flash. Today's prompt from Daniel is about the best platforms for discovering these tools, specifically looking at how we filter the signal from the noise when thousands of products are dropping every single month.
I love that Daniel sent this over because I am personally exhausted. My bookmarks folder looks like a graveyard of "Product of the Day" winners that I used once and never opened again. It feels like 2026 is the year of the "Great Thinning," where we have to stop being tourists and start being settlers in our AI stacks.
Herman Poppleberry here, and I have actually been digging into the data on this. As of March twenty twenty-six, There is An AI For That is indexing over fifteen thousand tools. If you spent just sixty seconds looking at each one, you wouldn't finish for ten days, and that is assuming you don't sleep or eat. The volume is just unprecedented.
That is a terrifying statistic, Herman. It is the ultimate paradox of choice. You want to find a tool to help you write a legal brief or edit a podcast, and by the time you have finished researching the "best" tool, you could have just done the work manually. It’s like standing in the cereal aisle of a grocery store that’s five miles long. You just end up grabbing the first box you recognize because your brain literally can’t process the alternatives.
It’s exactly like that. The cognitive load of evaluating software is now higher than the cognitive load of performing the task the software was designed to automate. So, let's get into the heavy hitters. If someone is just starting to build their discovery workflow, where are they actually going first?
You have to start with the "Big Three." These are the gatekeepers. First, you have Product Hunt. Now, Product Hunt isn't AI-exclusive, but in twenty twenty-six, AI tools are essentially the only thing reaching the top of the leaderboard. It is the "Launchpad." If a founder wants a big splash, they go there.
Right, but Product Hunt has a bit of a reputation problem, doesn't it? I mean, I see things with two thousand upvotes, and it turns out to be a landing page with a "Join Waitlist" button and zero actual functionality. It feels more like a popularity contest for marketing teams than a utility check.
That is a very fair critique. The signal on Product Hunt is "hype." High upvotes tell you that the team has a strong UI, a good marketing budget, and they know how to mobilize a community. It is a great place to see where the venture capital interest is flowing. If you want to see the "next big thing" in terms of consumer trends, you look at the Product of the Day. But you're right, it doesn't guarantee the tool actually works under the hood.
How do you even distinguish between a real launch and a "vaporware" launch on there anymore? It feels like half the comments are just other founders saying "Great launch, congrats on the hunt!" without even clicking the link.
That’s the "engagement pod" effect. You have to look for the negative comments or the specific technical questions. If a user asks "How does this handle multi-track audio?" and the founder gives a vague answer like "We are working on that for V2," you know it’s a shell. It’s the shiny object filter. If I want to find something that actually solves a niche problem, like "I need an AI that specifically understands Swedish tax law," Product Hunt is probably going to fail me.
For that, you go to There is An AI For That, or TAAFT. This is the "Search Engine" of the AI world. Their natural language search is actually very impressive now. You can describe a specific workflow, like the one you just mentioned, and it pulls from that database of fifteen thousand plus tools. What I find fascinating about TAAFT is their timeline view. You can actually see the evolution of categories. You can see when "AI Video" went from three tools to three hundred tools over a six-month span.
I’ve used TAAFT, and the UI is almost intentionally overwhelming. It feels like a stock ticker for software. But I will give them credit; their tagging system is superior. If I'm looking for "AI tools for architectural rendering that work with Rhino," they usually have a category for that. It’s less about the "vibe" and more about the "index."
It’s a database, not a magazine. And then the third of the big three is Futurepedia. They position themselves more as a "Curated Library." They have about three million monthly visitors now, and they focus heavily on categorization and verified reviews. They have a "Verified" tag which is crucial because it helps filter out what we call "wrappers."
Oh, the dreaded wrappers. We should probably define that for people who aren't deep in the weeds. A wrapper is basically just a pretty skin on top of OpenAI's API, right? It’s someone charging you twenty dollars a month for something you could do for free in the ChatGPT sidebar if you knew how to write a three-sentence prompt.
That is exactly what it is. And in twenty twenty-four and twenty twenty-five, the market was flooded with them. Futurepedia tries to move past that by looking for "proprietary moats." Does the tool have its own unique data? Does it have a specialized workflow that saves you fifty clicks? If it’s just a text box that sends a query to Claude or Gemini, they are less likely to feature it prominently.
I appreciate that human-in-the-loop element. Because let’s be honest, algorithms are great at indexing, but they are terrible at discerning "soul" or "utility." An algorithm sees a hundred tools that do "AI Headshots" and thinks they are all equally relevant. A human looks at them and says, "Ninety-eight of these make you look like a plastic mannequin, and these two are actually good."
That leads us perfectly into the curation specialists, the newsletters. This is where the real signal-to-noise filtering happens. Have you been keeping up with Ben's Bites?
I have. Ben's Bites is my "coffee and anxiety" read every morning. It's a daily newsletter, and what I like about it is the no-nonsense tone. Ben is very good at spotting tools that actually work for developers and creators. It’s not just "here is a list of ten things," it’s "here is why this one matters." He has about fifty thousand subscribers now, which is massive for a niche tech newsletter.
It’s the "practical AI" angle. He isn’t interested in the philosophical debates about AGI; he wants to know if there is a tool that can automate your Jira tickets today. Then you have the AI Tool Report, which is even bigger—over five hundred thousand subscribers. They are very structured. They give you these "five-minute reads" categorized by use case. If you are in marketing, you jump to the marketing section. It’s very B2B focused.
I find AI Tool Report a bit more corporate, but it’s great for the "Integration Scout" types we talked about in previous discussions. They focus heavily on how these tools play with others—does it have a Zapier integration? Does it work with Microsoft Teams? That is a huge filter for businesses. A tool can be brilliant, but if it’s a silo, it’s useless in an enterprise environment.
But don't you think those huge newsletters eventually face the same problem as Product Hunt? When you have half a million subscribers, every tool you mention gets an instant surge of ten thousand users. Does that "kingmaker" status actually hurt the quality?
It can. You start seeing "sponsored" spots that look a lot like editorial content. That’s why I always cross-reference. If I see a tool in AI Tool Report, I immediately go look for a YouTube demo or a Reddit thread to see if it’s actually functional or just a well-funded marketing push.
I also have to mention Matt Wolfe and his site, Future Tools. Matt is a YouTuber, and he brings a "trusted personality" filter. Every tool on his site is something he has personally vetted or reviewed on his channel. In an era of AI-generated content and AI-generated reviews, having a human face you trust say, "I used this, and it didn't break my computer," is a huge competitive advantage.
It’s the "influencer as a filter" model. It works because we are cognitively lazy—and I say that affectionately. We don't want to test ten video editors. We want Matt Wolfe to test ten video editors and tell us which one to buy. But Herman, I have to ask, isn't there a risk here? If we all follow the same five curators, aren't we just creating a new kind of information bubble? We are all using the same five "vetted" tools while some brilliant, obscure tool from a developer in Estonia gets ignored because it didn't make it into the newsletter.
That is a legitimate concern. It is the "centralization of discovery." If you only look at the top of Product Hunt or the featured section of Futurepedia, you are seeing a very narrow slice of the ecosystem. That is why I still spend time on the "raw" feeds. You have to go to Reddit—subreddits like r-slash-ArtificialIntelligence—and even X, though that is a chaotic mess.
X is just a "Thread Guy" graveyard at this point. "I found ten AI tools that will make you a millionaire by lunch! Thread below!" I have developed a physical twitch whenever I see that emoji of the little robot head.
It is exhausting, but if you look past the engagement bait, you find the "unsolicited screenshots." That is my favorite metric. I don't care about the marketing demo. I want to see a random user post a screenshot of a result they got and say, "I can't believe this worked." That is where the real discovery happens.
So, let’s talk about the "Wrapper Test" you mentioned earlier. How do you, as someone who reads the white papers and tracks the GitHub repos, actually decide if a tool is worth more than five minutes of your time? What are your criteria?
My first filter is the "Last Updated" metric. If you go to a site like Toolify-dot-ai, you can see traffic and revenue data for these apps. If a tool launched in twenty twenty-four and hasn't had a major update or a spike in traffic in three months, it’s likely "zombie-ware." The founder probably realized they couldn't compete with the big guys and moved on.
That’s a great point. The "Great Thinning" is real. I’ve seen so many "AI PDF Readers" that were amazing for two months, and then Adobe added AI to Acrobat, and those startups just vanished overnight. It’s like the "Sherlocking" effect Apple used to do to developers, but on a massive, industry-wide scale.
My second filter is the "Specialized Workflow." A good example is a tool called Harvey in the legal space. They aren't just a wrapper. They have integrated specific legal databases and designed a UI that mirrors how a lawyer actually works. They have a "moat" because they understand the domain better than a general-purpose AI does. If a tool is just "ChatGPT for X," and X is a generic task like "writing emails," it’s not worth a subscription.
How do you feel about tools that claim to be "Local First"? I’ve noticed a lot of discovery platforms adding a "Privacy" or "Local LLM" tag lately.
That is a massive growth category. People are getting tired of their data being used to train the next version of the model they are paying for. Tools like LM Studio or AnythingLLM are gaining traction because they allow you to run the intelligence on your own hardware. If a discovery platform doesn't have a "Private" filter in 2026, they aren't paying attention to the enterprise market.
I call that the "Feature vs. Product" problem. Most AI tools today are actually just features that haven't been added to Word or Google Docs yet. Once they are added, the standalone tool dies. So, if I'm a listener and I want to build a personal discovery workflow that doesn't consume my entire life, what does that look like?
I suggest a "Three-Tiered Approach." Tier one is your "Passive Feed." Pick two newsletters—I’d suggest Ben’s Bites for general tech and maybe AI Tool Report for business. Let those hit your inbox. Don't click every link, just scan the headlines once a day.
Tier two?
Tier two is "Active Search." When you actually have a problem to solve, don't go to Google. Go to There is An AI For That or Futurepedia. Use their filters. Look for "Verified" tools or tools with high community "Favorite" counts. Spend thirty minutes, pick the top two, and test them.
And Tier three must be the "Community Sanity Check."
Spot on. Before you put in your credit card, go to a community like Reddit or a specialized Discord and search for the tool’s name. See what actual people are saying. Look for the bugs, the billing issues, the "this didn't work like the demo" comments. If you do those three things, you will be ahead of ninety-nine percent of people.
I’d add a Tier four: the "Sloth Filter." If it takes more than three minutes to set up, I’m out. If an AI tool is supposed to save me time, but I have to watch a twenty-minute tutorial just to get the API key working, it has already failed its primary mission. I want "zero-friction" utility.
That is actually a very astute observation. In twenty twenty-six, the UI is the product. We are seeing a shift where the underlying models—the Geminis and the Claudes—are becoming commoditized. They are all very good. So the winner is the one who builds the most frictionless interface between that intelligence and the user’s specific task. Think about the difference between a command-line tool and a one-click mobile app. The intelligence is the same, but the value is vastly different.
It’s like the early days of the internet. Everyone had a browser, but the people who built the best portals—the Yahoos and the Googles—were the ones who won because they made the web usable. We are in the "Portal Phase" of AI.
And that brings up a really interesting trend for twenty twenty-six: GEO, or Generative Engine Optimization. Tool creators are no longer just trying to rank on Google. They are trying to optimize their documentation and their site structure so that when YOU ask ChatGPT, "What is the best tool for X?", the AI recommends THEM.
That is wild. We are optimizing for the robot's recommendation engine instead of the human's search query. It’s a complete inversion of how marketing has worked for the last twenty years. But wait—if the AI is doing the recommending, does it become biased toward the tools that gave it the most training data?
Almost certainly. It’s the "data-incest" problem. If an AI tool company provides a massive, high-quality dataset to OpenAI or Google, the LLM might naturally view that tool as the "authority" in its category. We're essentially moving from SEO to "LLM Lobbying."
It changes everything about discovery. If the AI is the one discovering the tools for us, then the "discovery platforms" we've been talking about might eventually become data sources for the AIs themselves. Futurepedia might just become a training set for a "Discovery Agent."
"Hey Gemini, find me an AI tool that can transcribe my grandmother's handwritten recipes and suggest wine pairings, but make sure it’s not a wrapper and has a high privacy rating." If the agent can do that, why would I ever visit Product Hunt again?
That is the open question. As AI tools become more commoditized, curation platforms have to evolve. They have to offer something the AI can't—which is subjective, human taste. They have to be the "tastemakers." It’s like the difference between a Spotify algorithm and a high-end DJ. One gives you what you’re likely to like; the other gives you what you should like.
I think there is a huge opportunity for "Vertical Curation" too. I don't want a generic AI directory. I want "The AI Directory for Woodworkers" or "The AI Directory for Oncology Researchers." Those niche communities are where the high-value, high-complexity tools are hiding.
We are already seeing that. There are specialized platforms popping up for healthcare AI and legal tech. They understand the regulatory requirements, the data privacy needs, and the specific jargon that a general directory misses. If I'm a surgeon, I don't care about a tool that can write a catchy tweet; I care about a tool that can segment a 3D medical scan with 99.9% accuracy.
The more specialized the task, the less useful the "Big Three" become. You need curators who actually understand the domain. It’s the difference between a general practitioner and a specialist.
So, to recap for the folks listening who are feeling overwhelmed: step one, breathe. You don't need to know all fifteen thousand tools. You probably only need four. Step-two, outsource your scouting to a few trusted voices like Ben’s Bites or Matt Wolfe. And step three, look for the "moat." If it’s just a skin on a chatbot, move on.
And remember the "Minimum Viable Stack." Your goal shouldn't be to find the "coolest" tools; it should be to find the three or four tools that actually change your daily workflow. For me, it’s a coding assistant, a research summarizer, and a meeting transcriber. Everything else is just entertainment.
I’m still looking for the AI that can explain to my wife why I need another mechanical keyboard, but I haven't found that on Futurepedia yet.
You might need a very specialized model for that one, Corn. Maybe a negotiation-specific LLM trained on marriage counseling data. Or just a tool that generates convincing reasons why "this one has tactile switches that improve my word count by 12%."
I’ll check There is An AI For That after the show. But seriously, this explosion of tools is a good problem to have. It means the "cost of creation" is dropping to near zero. We are seeing a level of innovation that used to take decades happening in weeks.
It is the most exciting time to be in tech, but it requires a new kind of "information hygiene." You have to be intentional about what you let into your brain. If you spend all day "discovering" tools, you never spend any time "using" them. It’s the "App Store" effect—you spend an hour downloading games and then five minutes actually playing them.
That is the ultimate trap. "Productivity Porn" is real, and AI tools are the new drug. You feel productive because you found a new tool that could save you an hour, but you spent two hours finding it.
Guilty as charged. I have spent many Saturday nights digging through GitHub repos for a tool that I ultimately never installed. But that research is what allows us to have these conversations, I suppose. It’s a dirty job, but someone has to wade through the fifteen thousand wrappers to find the one diamond.
Well, thank you for doing the heavy lifting so I don't have to, Herman Poppleberry. I think we've given people a solid map of the landscape. Start with the Big Three for breadth, move to newsletters for signal, and use a "Sloth Filter" for sanity.
That is a perfect summary. And honestly, it’s about finding a rhythm. Check the feeds once a week, not once an hour. The "next big thing" will still be there on Friday. Most of these tools are ephemeral anyway. If they are still around in three months, that’s when they are worth your attention.
Wise words from a donkey. This has been a great deep dive. I feel slightly less overwhelmed, or at least I have better places to go when the next forty-seven video tools launch next week.
They probably launched five more just while we were talking. I can hear the Product Hunt notifications dinging in my head.
Don't tell me that. I’m closing my tabs. Alright, I think that’s a wrap on our discovery guide. We covered the platforms, the filters, and the future of how we’re going to find this stuff.
It’s a moving target, but the principles of good curation remain the same: look for utility, look for community, and look for a human in the loop.
Big thanks to our producer, Hilbert Flumingtop, for keeping the gears turning behind the scenes. And a huge thank you to Modal for providing the GPU credits that power this show. Their serverless infrastructure is actually one of those "utility" tools that doesn't need a wrapper—it just works.
This has been My Weird Prompts. If you found this useful, we’d love it if you could leave us a review on Apple Podcasts or Spotify. It’s the best way to help other people discover the show—ironically, a bit of human curation in action.
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Thanks for listening, everyone. See you in the next one.
Stay curious, and maybe close a few tabs today. Bye for now.
