quickthoughts

Ha just wrote a post[1] about a sort of 4th option - max out cheap compute to create more tangible things that can be used/run locally.

1: https://news.ycombinator.com/item?id=48519181

what does this actually do? claude output -> drop -> app that you can't really change?

Are most corporations preformative? (yes)

I use small models like Gemma to improve transcriptions from ASR models amongst other micro-tasks. I actually built out a fine-tuning whisper pipeline with all local (smaller) models meaning no cloud/big-tech co is able to train/sell my (private) data.

Repo is https://github.com/Rebreda/listenr - mainly geared toward Whisper fine-tuning, AMD hardware and local inference

I don't think this will be the case, image and video processing typically is more computation hungry, especially in realtime contexts. Apple already pegged Google as their cloud-provider of choice when it comes to LLMs/AI compute.

https://www.business-standard.com/technology/tech-news/apple...

It's everyone. Especially google, but all the big tech companies play in the same pool. Amazon, Google, Apple, Meta etc make money selling ads, which ultimate enables the tools that result data harvesting from everyone across the internet. I wrote a little data investigation [1] (mostly finished) that show cases how every major news organization across the globe I scanned had some level of data collection integrated. This is just one industry, but its important (as it connects back to the incentives these media organizations have, which is to make money by selling ads at any cost). The eff also released an angle in how the bidding process to buy ads is itself a massive privacy nightmare[2]

[1] https://quickthoughts.ca/autotracko/ [2] https://www.eff.org/deeplinks/2026/03/targeted-advertising-g...

I've been working on the flip side of this with ASR models, but the problem space is the same, conversational/real-world data is needed. Whisper often mistook actual words I say and hallucinate all the time when speaking technical jargon. The solution is to fine-tuning whisper with my own data. Hardest part imo was getting the actual data, which in turn got me to build listenr (https://github.com/rebreda/listenr).It's an always-on VAD-based audio dataset builder. Could be used for building conversational/real-world voice datasets for TTS models too?

After getting it working i was get motivation to actually able to build out the full fine-tuning pipeline. I wrote a little post about it all https://quickthoughts.ca/posts/listenr-asr-training-data-pro...

Plus one for the IKEA pour over. Its cheap and functional, perfect for travel too.

VSCode with auto complete and Gemini 2.5 Pro in a standalone chat (pick any interface that works for you, eg librechat, vertex etc). The agents-in-a-IDE experience is hella slow in my opinion.

Plus its less about the actual code generation and more about how to use it effectively. I wrote a simple piece on how I use it to automate the boring parts of dev work to great effect https://quickthoughts.ca/posts/automate-smarter-maximizing-r...)