Gorilla
Find real users already asking for what you're building
Gorilla launched on May 1, 2026 with 7 votes, #1,574 of 20,538 launches that month and more than 90% of that year's launches.
What it does
Gorilla finds people already asking for what you're building. One-shot search across Reddit, X, YouTube and TikTok. From $0.99 per run. Built for solo founders.
Gorilla is the growth agent that understands your product and surfaces the conversations worth joining, ranked by match and linked to the source.
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- IUI unlocked reaching 10.000 real potential customers as low cost as $502022 · ▲37
As a founder without an audience, building great products is no longer enough. It is difficult to find first paying users, grow, and discover where and how to reach them. You have to find the right people through the noise. As a founder who likes to break the rules, I discovered a method. I unlocked reaching 10.000+ real potential customers as low cost as $50 with 90% successful targeting. Solution: Twitter custom audience lists. This is the Holy Grail. you can turn it into a lead generating machine by using custom audience list. Twitter lets you use lists to target. But there is more…


I built a 1‑dollar feedback tool as a Sunday side projectDec 2025 · onedollarfeedback.com · ▲21I’ve always found it funny how simple feedback widgets end up as $20–$30/month products. The tech is dead simple, infra is cheap, and most of us here could rebuild one in a weekend. So as a “principle experiment” I built my own today as a side project and priced it at 1 dollar. Just because if something is cheap to run and easy to replicate, it should be priced accordingly, and it’s also fun marketing. 1$ feedback tool. Shipped today, got the first users/moneys today, writing this post today. Side Sunday project, then back to the main product tomorrow.
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Source: Product Hunt launch ↗
Launched alongside, May 2026
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Parallel agents, diff reviewer, and multi-model comparisons
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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