Dejan Strbad @dstrbad

I like the smell of napalm and coffee in the morning! | co-founder & CTO of @ascalia_io where real #AI meets #industry40 | https://t.co/S6zY3mVF9I ascalia.io Joined May 2012

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Dejan Strbad @dstrbad

6 days ago

Erik Satie - Gymnopédies & Gnossiennes youtu.be/wnacdOIoTBQ?si… via @YouTube

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Dejan Strbad @dstrbad

a week ago

@steeve overheating not an issue?

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Dejan Strbad @dstrbad

2 weeks ago

Woke Up This Morning (Detroit Mix) youtu.be/fO6ddzILEXE?si… via @YouTube

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Dejan Strbad @dstrbad

2 weeks ago

@Ekaeoq So you're telling me you're 5 years late 🥲

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Dejan Strbad @dstrbad

2 weeks ago

NoLoRa turns a microcontroller’s SPI/I2S clock harmonics into real LoRa chirps that COTS 433 MHz receivers decode. Your MCU’s EMI is now a feature, crazy!

Owen Brake @OwenBrakes

2 weeks ago

NoLoRa: Team from Edinburgh demonstrated LoRa transmission on MCUs with no radio chips, by utilizing the 27th harmonic of the SPI peripheral

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Dejan Strbad @dstrbad

3 weeks ago

@Ekaeoq @hthieblot game level, above insane

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Dejan Strbad @dstrbad

3 weeks ago

Mark Knopfler - Brothers In Arms HD - Royal Albert Hall 2019 SBD youtu.be/2R2Qtf3UeL0?si… via @YouTube

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I think AI coding hype follows roughly four stages: 1. Amazement You try it and can’t believe how much code it generates from a few prompts. 2. Expansion You start more and more projects because shipping suddenly feels cheap and fast. This is also the phase where people start convincing everyone around them: - coworkers - management - friends in other companies because nobody wants to “fall behind” in 6–12 months. That creates a massive snowball/FOMO effect. 3. The grind phase You realize the generated code has architectural issues, sloppy mistakes, weird abstractions, duplicated logic, broken edge cases, etc. So you start: - re-prompting - switching models - increasing reasoning effort - reviewing fixes - generating fixes for previous fixes And suddenly you spend your days reviewing AI-generated pull requests instead of building software. 4. Realization You realize AI coding increases output much faster than it increases certainty. The code still needs: - review - testing - ownership - architectural understanding - long-term maintenance Usually by expensive senior engineers. And the interesting thing is: this whole cycle can take many months or even more than a year because people become socially and professionally invested in the narrative themselves. Once teams, managers, and entire companies have been convinced that this is the future, it becomes psychologically and politically very hard to later say: “Actually, the ROI is much lower than we expected.”

Aiswarya Sankar @Aiswarya_Sankar

3 weeks ago

This is what we've been seeing with every company we work with. Try justifying spending 100k on token spend when only 18k even makes it to a stable prod feature. In the rush to maximize AI token spend, companies are wasting over 44% on bug fixes