pncnmnp
Joined 9 years ago
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307
Member since
Dec 3
Parth Parikh: https://pncnmnp.github.io/
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Comment · 8 days ago
I have recently started doing this too - I prefer r/iTunesMovieDeals for discovering stuff on sale. What I have been trying to optimize for is a low cost per hour and a high critical rating.
For example, I recently got the entire bundle for HBO's Rome for $9.99. That is ~50 minutes per episode (22 episodes), or $0.54/hour, which is not a bad deal! For a casual viewer like me, who might spend <6 hours each week watching TV, this should last a little more than 3 weeks. Recently, The Sopranos went on sale for $29.99, or $0.38/hour.
For streaming vs. buying to work, you just have to be patient about nabbing ~1-2 purchases per month and not be tempted to buy every single day.
The best part is the reduced cognitive load - you don't have to spend time browsing for content every day, and there are no ads, too.
Comment · 29 days ago
Just a note: if anyone wants the old-school experience, https://github.com/dashboard-feed works fine for now.
Comment · 1 month ago
Apparently, there's a term for the general practice, it's called "link shimming" - https://www.usenix.org/conference/usenixsecurity20/presentat....
I had some fun in my grad days building a private information retrieval (PIR) scheme so the shim server can do its job without knowing which link you clicked: https://github.com/pncnmnp/shimmey
Comment · 4 months ago
I want to share why I am posting this paper and why I think this is an interesting read. From "Henry Faulds Proposes Fingerprints as a System of Identification":
https://historyofinformation.com/detail.php?id=525
> In a one page letter published in the London journal Nature on October 28, 1880, Henry Faulds was the first to propose the use of fingerprints as a system of identification, including the scientific identification of criminals.
From the paper itself:
> When bloody finger-marks or impressions on clay, glass, etc., exist, they may lead to the scientific identification of criminals. Already I have had experience in two such cases, and found useful evidence from these marks. In one case greasy finger-marks revealed who had been drinking some rectified spirit. The pattern was unique, and fortunately I had previously obtained a copy of it. They agreed with microscopic fidelity. In another case sooty finger-marks of a person climbing a white wall were of great use as negative evidence. .....
Comment · 6 months ago
The whole setup works on my M2 MacBook Pro with 16 GB RAM. I use Gemma 4B via LiteRT-LM.
I've found that LiteRT-LM has a much lower DRAM footprint than Ollama. I've also made tons of optimizations in the code - for eg, you can do quite a bit with a 16k context window for a voice assistant while managing a good footprint, so I keep track of the token usage and then perform an auto-compaction after a while. I use sub-agents and only do deep-think calls with them, so the context window is separated out. In a multi-turn conversation, if Gemma 4 directly processes audio input, the KV cache fills up within a few turns, so I channel it all via Whisper.
Also, by far the biggest optimization is: 3-stage producer-consumer architecture. The LiteRT-LM streams tokens and I split them into sentences. A synthesizer thread then converts each sentence to audio via Kokoro TTS - the main thread then plays audio chunks sequentially. There's a parallel barge-in monitor thread. https://github.com/pncnmnp/strawberry/blob/main/main.py#L446
I did not want to use openWakeWord or Picovoice because they had limitations on which wake word you could choose. Alternative was to train a model of my own. So I created my own wake word detection pipeline using Whisper Tiny - works surprisingly well: https://github.com/pncnmnp/strawberry/blob/main/main.py#L143...
Also, I have VAD going with smart turn v3 (like I mentioned above) + I use browser/websocket for AEC + Barge-in (https://github.com/pncnmnp/strawberry/blob/main/audio_ws.py).
I'm using the MacBook's built-in microphones for this, though, and I haven't fully tested it with other microphones. I've been ironing out the rough edges on a daily basis. I should write a quick blog on this too.
Comment · 6 months ago
I wish I had known about Pipecat a lot sooner. I found out about it a few weeks back, and since Gemma 4 launched, I've been building my own entirely local voice assistant using Gemma 4 + Kokoro TTS + Whisper from scratch - https://github.com/pncnmnp/strawberry.
Pipecat's smart turn model is really good for VAD - https://huggingface.co/pipecat-ai/smart-turn-v3
Comment · 6 months ago
I have come to a similar realization recently - its what I call "Take it home OSS" - i.e. fork freely, modify it to your liking using AI coding agents, and stop waiting for upstream permissions. We seem to be gravitating towards a future where there is not much need to submit PRs or issues, except for critical bugs or security fixes. It's as if OSS is raw material, and your fork is your product.
Comment · 6 months ago
A fun tangent - if someone wants to explore how Azure is performing RDMA over RoCEv2 - check this paper out - https://www.microsoft.com/en-us/research/wp-content/uploads/...
There is an interesting NSDI talk on the paper too - https://www.youtube.com/watch?v=kDJHA7TNtDk (2023)