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Trickle – Let GPT-4 Understand Your Screenshots

Hacker News

Trickle – Let GPT-4 Understand Your Screenshots

Hey there HN! It took nearly 5 years for my team and me to truly find right direction. So, after introducing our work, I'm keen to share the story behind it. The following content is divided into two parts. If you're not interested in the backstory of the product, feel free to skip the content after the divider. > The Problem We're Solving: During a casual afternoon, while brainstorming what to do next on a WeWork sofa, we realized that almost everyone present had a habit of saving information via screenshots. When I opened my photo gallery, I was astonished to see that more than half were such screenshots. Given that traditional OCR and gallery apps hadn't really addressed our screenshot chaos, we decided to build something to solve our own problem. > How it works: At first glance, you might think Trickle is a manual screenshot version of Rewind. But in reality, they're vastly different. All you need to do is send your screenshots, and let Trickle handle the rest: [1] Trickle doesn't constantly record the entire desktop, so it won't consume all your Mac's storage or affect its performance. Moreover, it won't give you the unsettling feeling of being constantly watched. [2] Although we have a Mac screenshot tool, and a browser extension is on its way, you don't actually need to install them. You can easily upload your screenshots via a web page. This makes it platform-agnostic; you can browse, search, and ask about your historical screenshots at any time via a browser. Of course, it doesn't occupy any of your local storage. Last but not least, it's Windows-user friendly. [3] User-controlled screenshots mean that the embedded chunks are semantically more precise. Beyond the advanced reasoning capabilities of GPT-4, we've integrated some tricks of our own, allowing Trickle to truly comprehend your screenshots, rather than just summarizing the text. Sometimes you might be surprised when Trickle even reasons out essential information that's not present in the image. This also ensures a better experience when you try to recall information. ------------The Story Behind the Product:------------ In 2018, I left my consulting role and dove head-first into the startup world with two co-founders. Our initial venture involved creating a visual recognition model for a vending machine company, marking our first income. Yet, custom builds weren’t sustainable, prompting our first pivot. By 2019, we were deep into retail tech, winning a demo day and launching a product to automate in-store promotions. The climax seemed to be our partnership with a large multinational, but 2020 and the pandemic shifted landscapes. We then explored the realm of asynchronous video, building an alternative to Loom. By 2021, we hit 10k users, but profitability remained elusive. As workspaces evolved and people returned to offices, we sensed another opportunity. Our solution? An in-house social platform for teams, named "Trickle". In 2022, after a launch on Product Hunt, even though we garnered attention from people like Ryan Hoover and former Microsoft VP Lu Qi, we faced a stark realization: Daily team updates might not be as 'cool' as anticipated. Our anxiety steered us off-path, and soon, Trickle became a bulky hybrid, attempting to replace tools like Notion and Slack. Looking back, this detour was a misstep. The turning point came mid-2023. A series of tepid Product Hunt launches forced introspection. We stripped Trickle down, retaining only its name and began anew. Today, with the original team intact, we're addressing an everyday issue that resonated with all of us: the chaos of managing screenshots. Reflecting on our journey, it's clear that the essence of the startup spirit lies in adapting, evolving, and pursuing that 'Eureka' moment, no matter how winding the path.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, slack · Missing: agents, macos, agent
98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
98%98% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, friendly, user friendly · Missing: plus, intuitive, reviews
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, video, users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: profit · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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