If you run a pentest, allowing rooted devices will almost certainly show up as a vulnerability. It'll be marked "low risk", but you'll also be told that you don't want to "accept risk" for too many "low risk" vulnerabilities.
So somebody then needs to say that this is not something they worry about rather than doing the easy thing and remediating it.
ScribbleVet (https://scribblevet.com) | Full-stack, former technical founders | Full-time | Remote (must be able to work US timezones)
ScribbleVet is an AI-powered scribe for veterinarians. We save veterinarians two hours every day, reduce veterinary burnout and help animals get better care.
* We're generating real ($XM/yr) revenue
* Our users love the product
* We're fully remote but not asynchronous, so we have a lot of autonomy but it's easy to collaborate with someone when needed.
We're looking for:
* Senior engineers (former founders or early startup experience strongly preferred) who can work across the stack.
* Engineers with a product mindset. Customer-centricity or design skills are a plus.
We’ve found that the engineers who are happy and successful at Scribble really enjoy technology (learning new frameworks, experimenting with cutting-edge platforms) and are very product-oriented.
ScribbleVet (https://scribblevet.com) | Designer (marketing & product) | Full-time | Remote (must be able to work US timezones)
ScribbleVet is an AI-powered scribe for veterinarians. We save veterinarians two hours every day, reduce veterinary burnout and help animals get better care.
- We're generating real ($XM/yr) revenue
- Our users love the product
- We're fully remote but not asynchronous, so we have a lot of autonomy but it's easy to collaborate with someone when needed.
We're looking for:
- A designer to join our team (full-time) to work across product design (UX) and marketing assets (landing pages, trade show materials etc)
- Strong product skills and founding, early employee, or building something from scratch experience required
If you're interested and think you might be a fit, please contact me at rohan (at) scribblevet.com!
You'll probably find this talk [1] interesting. They control all the training data for small LLMs and then perform experiments (including reasoning experiments).
ScribbleVet (https://scribblevet.com) | Full-stack (frontend emphasis), former technical founders, AI engineers | Full-time | Remote
(must be able to work US timezones)
ScribbleVet is an AI-powered scribe for veterinarians. We save veterinarians two hours every day, reduce veterinary burnout and help animals get better care.
- We're generating real ($XM/yr) revenue
- Our users love the product
- We're fully remote but not asynchronous, so we have a lot of autonomy but it's easy to collaborate with someone when needed.
We're looking for:
- Senior engineers (former founders or early startup experience preferred) who can work across the stack and are AI curious
- Engineers with a product mindset. Customer-centricity or design skills are a plus.
Is Modal a good solution for running fine-tuned LLMs and Whisper models? If the cold-start time is low we're more than willing to modify our code to use Modal's infra.
Happy to follow up via email but didn't see one in your profile.
It requires API access, but once you have access you can easily play around with it in the openai playground.
Setting temperature to 0 makes the output deterministic, though in my experiments it's still highly sensitive to the inputs. What I mean by that is while yes, for the exact same input you get the exact same output, it's also true that you can change one or two words (that may not change the meaning in any way) and get a different output.
I think you can go far quite cheaply. Get your code working on smaller/toy models, and then when you want to test it on larger ones you can ship it over to a machine at one of the cheaper providers (vast.ai/jarvislabs etc) to give it a run before pausing/killing the machine.
I've been porting Stable Diffusion (which isn't a small model) over to Elixir and as part of doing that have been starting/stopping my jarvislabs machine when I start/stop building. I've been spending about $1/day without trying to be efficient.
Also, fast.ai is a great resource for learning ML, I highly recommend it.
I've trained models using Jupyter and Livebook (though only smaller toy models [1]) so I can deposit my 2 cents here. Small disclaimer that I started with Jupyter, so in some sense my mental model was biased towards Jupyter.
I think the biggest difference that'll trip you up coming from Jupyter is that Livebook enforces linear execution. You can't arbitrarily run cells in any order like you can in Jupyter - if you change an earlier cell all the subsequent cells have to be run in order. The only deviation from this is branches which allow you to capture the state at a certain point and create a new flow from there on. There's a section in [1] that explains how branching works and how you can use it when training models.
The other difference is that if you do something that crashes in a cell, you'll lose the state of the entire branch and have to rerun from the beginning of the branch. Iirc if you stop a long running cell, that forces a rerun as well. That can also be painful when running training loops that run for a while, but there are some pretty neat workarounds you can do using Kino. Using those workarounds does break the reproducibility guarantees though.
Personally while building NN models I find that I prefer the Jupyter execution model because for NNs, rerunning cells can be really time-consuming. Being able to quickly change some variables and run a cell out of order helps while I'm exploring/experimenting.
Two things I love about Livebook though are 1) the file format makes version control super easy and 2) Kino allows for real interactivity in the notebook in a way that's much harder to do in Jupyter. So in Livebook you can easily create live updating charts, images etc that show training progress or have other kinds of interactivity.
If you're interested to see what my model training workflow looks like with Livebook (and I have no idea if it's the best workflow!), check out the examples below [1][2]. Overall I'd say it definitely works well, you just have to shift your mental model a bit if you're coming from Jupyter. If I were doing something where rerunning cells wasn't expensive I would probably prefer the Livebook model.
I was wondering the same and this video [1] helped me better understand how the prediction is used. The original paper isn't super clear about this either.
The diffusion process predicts the total noise that was added to the image. But that prediction isn't great and applying it immediately wouldn't result in a good output. So instead, the noise is multiplied by a small epsilon and then subtracted from the noisy image. That process is iterated to get to the final result.
Hi, we’re Hello World! We’re a new non-profit on a mission to build a community that helps discover and develop the potential in teens. We are building a global, mobile-first platform to help notice teens and connect them with opportunities (fellowships, scholarships, etc).
Our product is live on iOS and Android and growing quickly. In the eight months since we've been live, we've already placed over 11,000 teens from around the world with opportunities, and plan to place several thousand more by the end of 2021.
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Our tech stack uses Typescript + React Native on the frontend with a light Rails backend running on App Engine. We’re still early so there’s lots of room to craft and improve our stack.
We’re looking for generalists with a backend focus who can own the backend and dive into frontend as needed. At our current stage we’re light on process, so you should enjoy working with a high-level of autonomy and decision making authority. If you’re looking to use your stellar tech skills and expertise to meaningfully impact the lives of talented teens around the world, this might be the place for you!
So somebody then needs to say that this is not something they worry about rather than doing the easy thing and remediating it.