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Interactive RISC-V CPU Visualizer (Sequential and Pipelined)

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Interactive RISC-V CPU Visualizer (Sequential and Pipelined)

I built an interactive RISC-V CPU visualizer for a course that lets you explore how instructions move through both a sequential and a 5-stage pipelined processor. You can step through execution, watch data hazards resolve, and see how branching and forwarding work in real time. The goal is to make CPU architecture learning more intuitive for students (and anyone who likes poking around pipelines). The whole verilog code and implementation details are available on the project report! Right now, the demo supports basic arithmetic, memory, and branch instructions, and includes two pre-programmed examples (a basic ALU demo and a Fibonacci pipeline program). Would love feedback on how to handle branching better, I remember crushing it due to deadline and it is not handled properly, but haven't looked at the code in-depth after that :p. I also want to do something similar for OS and write from scratch, been seeing a lot of post so any recommendations are appreciated :) Visualizers: https://mostlykiguess.github.io/RISC-V-Processor-Implementat... https://mostlykiguess.github.io/RISC-V-Processor-Implementat... Code: https://github.com/MostlyKIGuess/RISC-V-Processor-Implementa...

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, code · Missing: mac, agents, macos
76%76% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: intuitive · Missing: plus, platform, reviews
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: visualize · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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