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Icepi Zero – The FPGA Raspberry Pi Zero Equivalent

Hacker News

Icepi Zero – The FPGA Raspberry Pi Zero Equivalent

I've been hacking away lately, and I'm now proud to show off my newest project - The Icepi Zero! In case you don't know what an FPGA is, this phrase summarizes it perfectly: "FPGAs work like this. You don't tell them what to do, you tell them what to BE." You don't program them, but you rewrite the circuits they contain! So I've made a PCB that carries an ECP5 FPGA, and has a raspberry pi zero footprint. It also has a few improvements! Notably the 2 USB b ports are replaced with 3 USB C ports, and it has multiple LEDs. This board can output HDMI, read from a uSD, use a SDRAM and much more. I'm very proud the product of multiple weeks of work. (Thanks for the pcb reviews on r/PrintedCircuitBoard ) (All the sources on github under an open source license :D) PS. See some more pics on reddit https://www.reddit.com/r/FPGA/comments/1kwxvk8/ive_made_my_f...

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233points
51comments
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: open source · Missing: https docs, excited, just released
84%84% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
24%24% 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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