pbbakkum
Joined 14 years ago
Karma
190
Submissions
39
Member since
Feb 16
@pbbakkum
github.com/bakks
[email protected]
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Comment · 1 year ago
This is great, thank you for sharing. I work on these APIs at OpenAI, it's a surprise to me that it still works reasonably well at 2/3x speed, but on the other hand for phone channels we get 8khz audio that is upsampled to 24khz for the model and it still works well. Note there's probably a measurable decrease in transcription accuracy that worsens as you deviate from 1x speed. Also we really need to support bigger/longer file uploads :)
Comment · 1 year ago
Heyo, I work on the realtime api, this is a very cool app!
With transcription I would recommend trying out "gpt-4o-transcribe" or "gpt-4o-mini-transcribe" models, which will be more accurate than "whisper-1". On any model you can set the language parameter, see docs here: https://platform.openai.com/docs/api-reference/realtime-clie.... This doesn't guarantee ordering relative to the rest of the response, but the idea is to optimize for conversational-feeling latency. Hope this is helpful.
Comment · 3 years ago
If you're interested in GPT-powered shell autocomplete, check out https://butterfi.sh
This also enables shell-aware LLM prompting!
Comment · 3 years ago
Do you work from the command line? Butterfish is a project I wrote for myself to use AI prompting seamlessly directly from the shell. I hope it's useful to others, give it a try and send feedback!
Comment · 3 years ago
I've experimented with it, the reason I haven't yet added it is that I want deployment to be seamless, and it's not trivial to ship a binary that would (without extra fuss or configuration) efficiently support Metal and CUDA, plus download the models in a graceful way. This is of course possible, but still hard, and not clear if it's the right place to spend energy. I'm curious how you think about it - is your primary desire to work offline or avoid sending data to OpenAI? Or both?
Comment · 3 years ago
Plugging a project of mine: I've been working on a similar idea for the era of LLMs: https://butterfi.sh.
It's much more bare-bones than Fig but perhaps useful if you're looking for an alternative! Send me feedback!
Comment · 3 years ago
If you're looking for a lighter weight TUI for Top information check out a recent project of mine here: https://github.com/bakks/poptop
Comment · 3 years ago
Really like the design of these tools so that you can easily pipe between them, this a good way to make things composable. Also really cool to see all of the other CLI tools folks have posted here, lots that I wasn't aware of.
I've been experimenting with CLI/LLM tools and found my favorite approach is to make the LLM constantly accessible in my shell. The way I do this is to add a transparent wrapper around whatever your shell is (bash,zsh,etc), send commands that start with capital letters to ChatGPT, and manage a history of local commands and GPT responses. This means you can ask questions about a command's output and autocomplete based on ChatGPT suggestions, etc.
You can see this approach here, I hope it proves useful to other folks! https://github.com/bakks/butterfish
Comment · 9 years ago
A few notes here:
- An unmentioned alternative to this pricing is that GCP has a deal with Cloudflare that gives you a 50% discount to what is now called Premium pricing for traffic that egresses GCP through Cloudflare. This is cheaper for Google because GCP and Cloudflare have a peering arrangement. Of course, you also have to pay Cloudflare for bandwidth.
- This announcement is actually a small price cut compared to existing network egress prices for the 1-10 TiB/month and 150+ TiB/month buckets.
- The biggest advantage of using private networks is often client latency, since packets avoid points of congestion on the open internet. They don't really highlight this, instead showing a chart of throughput to a single client, which only matters for a subset of GCP customers. The throughput chart is also a little bit deceptive because of the y-axis they've chosen.
- Other important things to consider if you're optimizing a website for latency are CDN and where SSL negotiation takes place. For a single small HTTPS request doing SSL negotiation on the network edge can make a pretty big latency difference.
- Interesting number: Google capex (excluding other Alphabet capex) in both 2015 and 2016 was around $10B, at least part of that going to the networking tech discussed in the post. I expect they're continuing to invest in this space.
- A common trend with GCP products is moving away from flat-rate pricing models to models which incentivize users in ways that reflect underlying costs. For example, BigQuery users are priced per-query, which is uncommon for analytical databases. It's possible that network pricing could reflect that in the future. For example, there is probably more slack network capacity at 3am than 8am.