Meet your AI Executive Assistant(shortwave.com)
shortwave.com
Meet your AI Executive Assistant
https://www.shortwave.com/blog/meet-your-ai-email-executive-assistant/
25 comments
We're actually exploring that with our app, if you've a GPU large enough you can run a llama2 based model locally at almost chatgpt speeds... If you're interested in trying drop me a message at rafael (at) curiosity.ai
Thank you!
How much would you pay for an at home appliance? His responsive would it need to be?
I would pay between 5 and 10k USD depending on how close it was to Iron Man's Jarvis. I'd even pay an annual subscription for updates.
Would need to be about as fast as ChatGPT seems to be for the conversation piece and perhaps utilize some kind of optimistic response while it does things asynchronously
Would need to be about as fast as ChatGPT seems to be for the conversation piece and perhaps utilize some kind of optimistic response while it does things asynchronously
1. Not enough to make it profitable in 2023
2. Consumers want 0 latency. Every millisecond counts. Ask google about how they optimize search result latency
2. Consumers want 0 latency. Every millisecond counts. Ask google about how they optimize search result latency
I was ready to give you my money, but you're Gmail only, sorry, I migrated out so will wait for IMAP support.
It really hurts my head to even think about how that works. Does google offer some proprietary email api that isn't IMAP? Why would such a thing exist? Why would you make an email app without IMAP?
They're outsourcing identity verification to gmail to replace the "email verification flow". This way they don't have to configure any email infra like sendgrid, ses, etc, deal with bounces and so on. It's also possible gmail has better API support to process a lot of email, but I have seen this in products that have nothing to do with email. Just plain we only serve gmail customers. It's likely also another stricter variation of we only serve US customers.
I was also was ready to add a credit card but have also moved off gmail.
The concept sounds great though, I have to imagine there is another product out there, or will be soon, that does not require gmail.
The concept sounds great though, I have to imagine there is another product out there, or will be soon, that does not require gmail.
Maybe I'm crazy but I switched a few months ago from Zoho which had I mapped to Gsuite so I could try shortwave lol
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I was in the early beta and judging from how it's worked in my tests, there's definitely more going on under the hood then the most straightforward approach of "embed all your emails, find nearest neighbors, then ask the LLM to answer".
Kudos on innovating around applying LLMs to real-world problems and going beyond the bog-standard approach. It'd be interesting to see a more detailed blog on the technical approach you took!
Kudos on innovating around applying LLMs to real-world problems and going beyond the bog-standard approach. It'd be interesting to see a more detailed blog on the technical approach you took!
[Co-founder of Shortwave here] I know a lot of folks are launching “AI Assistants” right now – but ours isn’t just a “chat with your PDF” thin shim on GPT4. We’ve got some serious infrastructure behind this.
Here are some notes on our architecture:
- We use LLMs at multiple places to choose what data to pull at each step. We use an additive approach rather than a chaining approach to avoid error propagation. We use GPT3.5-turbo with a bunch of hand-rolled prompts for most of this.
- We’re using InstructorXL + Pinecone running on GCP for vector-based search. We combine this with more traditional search methods backed by Postgres & Elasticsearch, to give the assistant the ability to fast searches of multiple types. We use a x-encoding model trained on open source Q&A data from Bing for scoring & reranking to allow us to combine multiple data sources and determine what makes the most sense to feed into the final prompt.
- We hand-rolled a bunch of rule-based algorithms and heuristics on top of the LLMs to deal with email-specific corner cases and other issues we couldn’t resolve reliably in prompts
- Our user-facing output is generated with GPT4.
This enables a bunch of capabilities that other AI assistants can’t match:
- Way better search — Ask a question and get a succinct direct answer, including finding emails that would be tough for you to find through traditional search (ie. you can’t remember a keyword to use).
- Scheduling – Since we can dynamically pull in multiple types of data, we can access calendar data at the right time to help you schedule meetings.
- Analyze across multiple emails & types of data - The assistant can synthesize answers across multiple emails, your calendar, setting, etc to give you an answer (eg. “What are the top 5 issues that customers emails support about last week”, “what are some meeting times that work for me and the other people on this thread”),
- Write in your voice — the assistant can automatically learn your style and tone based on your sent emails. This means it actually sounds like you and, while it still requires some tweaking occasionally, it’ll save you a lot of time.
- Summarize & translate – it can dynamically access the data you have on your screen right now if you reference it, so it can help you with whatever you’re reading.
A note on privacy: We take privacy very seriously. We’re running everything above on our own GPUs + using OpenAI for final outputs. We aren’t training any models on user data.
We’ve put a lot of thought and effort into this one – I hope you like it – either way, let me know what you think in the comments below!
-Andrew
Here are some notes on our architecture:
- We use LLMs at multiple places to choose what data to pull at each step. We use an additive approach rather than a chaining approach to avoid error propagation. We use GPT3.5-turbo with a bunch of hand-rolled prompts for most of this.
- We’re using InstructorXL + Pinecone running on GCP for vector-based search. We combine this with more traditional search methods backed by Postgres & Elasticsearch, to give the assistant the ability to fast searches of multiple types. We use a x-encoding model trained on open source Q&A data from Bing for scoring & reranking to allow us to combine multiple data sources and determine what makes the most sense to feed into the final prompt.
- We hand-rolled a bunch of rule-based algorithms and heuristics on top of the LLMs to deal with email-specific corner cases and other issues we couldn’t resolve reliably in prompts
- Our user-facing output is generated with GPT4.
This enables a bunch of capabilities that other AI assistants can’t match:
- Way better search — Ask a question and get a succinct direct answer, including finding emails that would be tough for you to find through traditional search (ie. you can’t remember a keyword to use).
- Scheduling – Since we can dynamically pull in multiple types of data, we can access calendar data at the right time to help you schedule meetings.
- Analyze across multiple emails & types of data - The assistant can synthesize answers across multiple emails, your calendar, setting, etc to give you an answer (eg. “What are the top 5 issues that customers emails support about last week”, “what are some meeting times that work for me and the other people on this thread”),
- Write in your voice — the assistant can automatically learn your style and tone based on your sent emails. This means it actually sounds like you and, while it still requires some tweaking occasionally, it’ll save you a lot of time.
- Summarize & translate – it can dynamically access the data you have on your screen right now if you reference it, so it can help you with whatever you’re reading.
A note on privacy: We take privacy very seriously. We’re running everything above on our own GPUs + using OpenAI for final outputs. We aren’t training any models on user data.
We’ve put a lot of thought and effort into this one – I hope you like it – either way, let me know what you think in the comments below!
-Andrew
I honestly think tightly integrating language models with email will be one of the most impactful use cases for LLMs in the short-term. Email, as a medium, is pretty much nothing BUT text, and it's something that I (and probably the average HN reader) spend tens of hours on each week.
In trying "write it for me" AI tools, the biggest hurdle is always matching my own tone and style - I'm pretty particular about my writing, and I kind of hate the default tone that ChatGPT and Bard use. It seems like you've put a ton of hard work into making sure that isn't the case here.
And the analysis is really a cherry on top - I've been waiting for a tool that I can ask "what are the 3 most important messages that are unread in my inbox?" Excited to try this out!
In trying "write it for me" AI tools, the biggest hurdle is always matching my own tone and style - I'm pretty particular about my writing, and I kind of hate the default tone that ChatGPT and Bard use. It seems like you've put a ton of hard work into making sure that isn't the case here.
And the analysis is really a cherry on top - I've been waiting for a tool that I can ask "what are the 3 most important messages that are unread in my inbox?" Excited to try this out!
Boom! Glad you like it - and thank you for the kind words :)
Curious about the “Write in your voice” feature. Is it mostly derived from LLM stylometry, or do you blend statistical approaches like those used by spaCy? Do you capture all of lexical, syntactical, rhetorical, and semantic cues? I’d love to chat about your approach, if you’re open to any opportunities for comparing notes!
Right now we use an LLM to extract a textual description of your writing style from past emails, and then we use that in the prompt.
The nice thing about this is that it's editable, so the user can customize the style to be the style they want, not necessarily exactly the style they have.
We're also investigating doing per-user model training for more refined voice... not launched yet though (and comes with tradeoffs).
Happy to swap notes -- email me: [email protected]
The nice thing about this is that it's editable, so the user can customize the style to be the style they want, not necessarily exactly the style they have.
We're also investigating doing per-user model training for more refined voice... not launched yet though (and comes with tradeoffs).
Happy to swap notes -- email me: [email protected]
The pitch could use a bit of work. I had to muster a lot of willpower to read all those paragraphs to figure out what the product even does. It would greatly benefit from more straightforward writing.
When I clicked, I hoped that it could manage the tedium of dealing with emails:
- Negotiating meeting dates and adding events to my calendar.
- Combining pointless corporate emails into an executive summary, or deleting them as they come.
- Surfacing metadata when appropriate: the phone number of the person I should call, directions to a mentioned place, profiles of mentioned people.
A good executive assistant gets stuff out of the way. They reduce friction and let you focus on work that matters. "You have to be in Frankfurt? Here are your tickets. You are checked in. A taxi will pick you up in the afternoon. Your hotel is booked. I sent the address to your phone". There is so much low-level tedium
To me, this tool feels like ChatGPT with access to my emails. You still have to converse with it, ask it specifically for what you want. Instead of magically sorting the things I don't want to deal with, it handles the thing I should be dealing with: working with other humans.
When I clicked, I hoped that it could manage the tedium of dealing with emails:
- Negotiating meeting dates and adding events to my calendar.
- Combining pointless corporate emails into an executive summary, or deleting them as they come.
- Surfacing metadata when appropriate: the phone number of the person I should call, directions to a mentioned place, profiles of mentioned people.
A good executive assistant gets stuff out of the way. They reduce friction and let you focus on work that matters. "You have to be in Frankfurt? Here are your tickets. You are checked in. A taxi will pick you up in the afternoon. Your hotel is booked. I sent the address to your phone". There is so much low-level tedium
To me, this tool feels like ChatGPT with access to my emails. You still have to converse with it, ask it specifically for what you want. Instead of magically sorting the things I don't want to deal with, it handles the thing I should be dealing with: working with other humans.
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There's definitely more we want to do here - including prompting you proactively in helpful ways. This is our v1 -- stay tuned for v2 :)
The pricing page claims that the free tier allows searching of email history up to 90 days ago, but when using it, the AI assistant says that searching of email history requires upgrading to the Standard plan.
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Sending all my data off to two external services might be a bridge too far for me... Then again, I don't trust Microsoft or Google either.