Medbill AI is hiring Founding Software Engineers! We’re building the most trusted service for reducing the time, money, and stress associated with medical bills and health insurance.
The Problem:
40% of medical bills in the U.S. contain errors, contributing to over $195B in medical debt annually. Navigating health insurance and resolving billing disputes is a nightmare for millions.
About Us:
Backed by Top Investors: $17M seed round led by Forerunner, with participation from the founders of HuggingFace, Oscar Health, RocketMoney, and Tim Ferriss.
Our Team: We’re a small but mighty group of 6 engineers, including 3 former Staff Engineers from Oscar Health.
What We’re Looking For:
-4+ years of experience engineering software as a product/full-stack engineer or back-end engineer.
-Excited to solve complex problems in health tech and shape the technical foundation of a fast-growing startup.
Medbill AI | Founding Senior Engineer + Founding Designer | Full Time | Remote - US timezones
We are building an AI assistant that helps people with medical billing and health insurance issues. Thanks to AI and new healthcare regulations around patient data access and price transparency, it is now possible for everyone, including you, to have their own assistant who works around the clock to save them time and money on medical bills and health insurance issues.
Reach out if you’re interested in the role or want to learn more about what we’re up to!
Medbill AI | Founding Senior Engineer + Founding Designer | Full Time | Remote - US timezones
We are building an AI assistant that helps people with medical billing and health insurance issues. Thanks to AI and new healthcare regulations around patient data access and price transparency, it is now possible for everyone, including you, to have their own assistant who works around the clock to save them money on medical bills and navigate the disjointed US healthcare system.
Reach out if you’re interested in the role or want to learn more about what we’re up to!
What about the comments on commits? Are those all displayed in the Github PR when it's merged, and attached to all the correct lines, even the ones that were made on branches that were rebased?
Seems interesting! I've been very unhappy with Github workflow and UI for PRs. It's insane that each time you rebase a PR, you lose all your existing comments on that PR (i.e. they are no longer attached to lines in the UI).
Does anyone know if you need Graphite to see a PR once it's been merged in the Github repo? I'm curious about how locked you in when you start using Graphite.
“High-temperature properties such as the volumetric storage density, viscosity and transparency are similar to water at room temperature. The major advantages of molten salts are low costs, non-toxicity, non-flammability, high thermal stabilities and low vapor pressures. The low vapor pressure results in storage designs without pressurized tanks (Fig. 1). Molten salts are suitable both as heat storage medium and heat transfer fluid (HTF). In general, there is experience with molten salts in a number of industrial applications related to heat treatment, electrochemical treatment and heat transfer for decades.”
I don’t know anything about this but it does seem that things are not as clear cut as your comment made it seem.
“ One of the most commonly used molten salts in nuclear reactors is a mixture of lithium fluoride (LiF) and beryllium fluoride (BeF2), commonly referred to as FLiBe. FLiBe is used as both a coolant and a neutron moderator in some types of nuclear reactors, such as molten salt reactors (MSRs) and some advanced small modular reactors (SMRs).
FLiBe has several advantages as a coolant in nuclear reactors, including its good heat transfer properties and its ability to operate at high temperatures without evaporating. Additionally, FLiBe is not highly corrosive to many materials commonly used in reactor components, which can help reduce maintenance and replacement costs.
However, FLiBe does have some potential disadvantages, such as its relatively high viscosity, which can make it more difficult to pump and circulate, and its high melting point, which can increase startup times for reactor systems. Additionally, FLiBe can be corrosive to some materials, such as aluminum and some types of steels, so care must be taken in selecting materials that are compatible with FLiBe.”
Looks great. I've waiting for a service like that ever since Microsoft released their paper on speech synthesis using voice samples. Feature requests:
- make the voice generation available via API so devs can embed that in their app
- expose a streaming API like Polly so we can feed it text in real time and get the voice as an audio stream
- make it Hipaa compliant and have a plans that offer signing a BAA
I'll be your first customer if you do this! You can get in touch with me at @juliennakache
The main ideas it that theoretically the demand should shift to greener alternative. However, as far as I understand the issue is that currently it shifts the demand to another producer. There is this concept of additionality in carbon offsets but it's intractable to measure. IMHO the only way to really prevent it would be to pay most or all polluters so they stop. One day we might get there when we've offset most emission sources of a given type (ie say land forest). But in the meantime, those offsets are not actually moving the needle. And also, does that mean we should start paying via carbon offsets every single owner of an oil pit so they don't get oil off the ground?
Does anyone have a sense of how the Shopify integration work under the hood? Specifically, how were they able to have Chat-GPT makes a recommendation from their product database? Given the model cannot be fine-tuned and there is a 4000 token context limit, I'm guessing the only thing really possible is to have chat gpt extract a few keyword and generate a query in their internal product search and return the first one? Is there anything else that might going under the hood?
I liked very much the idea of dynamic masking. However, I wonder how good it works in practice. I was actually assessing one of your competitor (www.satoricyber.com) and found an easy to workaround the masking - I was able to essentially access any mask data using not-so-advanced SQL functions. Do you guys have a publicly available test suite against your proxy that people and security researcher can review? Also, do you have a bug bounty program and / or a clear disclosure policy when a vulnerability is found?
Is there a way to toggle / collapse bullet list? I find that it’s one of the most useful features when organizing my thought as it allows me to explore one nested branch very deeply while still keeping the doc easy to deal with. You can hide the unnecessary details when you want to keep think a higher level.
Probably the darkest UX pattern I've seen in a while... It's not spam per say so I'm not sure if this can be reported to the FTC, but it sure is a dirty move. Whoever designed is either evil or had was forced by an evil organization who would do anything to make their stock go up after a rough SPAC introduction.
Does anyone know if this can be reported to a public agency?
Looking forward to trying this out. I've always felt that PagerDuty was absurdly expensive for the feature set they were offering. It costs something at least $250 per user for organization larger than 5 person - even if you're not an engineer who is ever directly on call. At my previous company, IT had to regularly send surveys to employees to assess if they really needed to have a PagerDuty account. Alerts are a key information in an organization that runs software in production and you shouldn't have to pay $250 / month just to be able to have some visibility into it. I'm hoping Grafana OnCall is able to fully replace PagerDuty.
Because as far as I know you can't easily access your password outside of Chrome. For example, it's not practical to use on my iOS devices. If you want to log into an app, I believe you can't easily use passwords stored in Chrome.
I haven't looked into the Django ORM recently but last time I checked I think it would have been a lot more challenging to implement something similar. SQLAlchemy is a lot more flexible, very well designed and actively maintained. My recommendation would be to try using SQLAlchemy with Django. It should work without any problems but you might lose the ability to use some of the Django plugins that assume the Django ORM.