FlashMyPico – an on-line firmware distribution tool

Hacker News

I've made a tool that could help you develop and distribute your RP2040/RP2350 firmware [1, 2, 3]. While programming these chips can be as simple as copying a file onto a removable drive, I believe this process can be further improved with on-line hosting, verification and versioning features, the ability to update firmware on the go with your smartphone, build system integration, and more. You can also use this tool to learn about electronics engineering topics by exploring existing firmware (you don't need to sign up to do this). [1] https://www.raspberrypi.com/products/rp2040/ [2] https://www.raspberrypi.com/products/rp2350/ [3] https://www.raspberrypi.com/products/raspberry-pi-pico-2/

Visit product View on Hacker NewsDec 24, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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