No, it’s the other way around. Comment above is confused about the relationship.
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> I used to reach out and tell them I didn't sign up for their service. But honestly, after doing it for a few years I gave up.
Same here. It's surprising that most of the services don't use double-opt in before sending emails.
Some day, I want to use an LLM to identify those emails and label them.
A lower all cash offer (say $975K) is likely a better offer for the seller because it reduces the risk for them and closes the transaction much quicker than a mortgage transaction.
I have been a buyer in two transactions where my offer was slightly lower than the highest bidder, but with better terms.
I’m not sure about revolution, but e-mail can certainly be evolved.
20 years ago, not as many emails were sent, especially transactional emails. User behavior has evolved since then.
For example, storing email receipt of a random Amazon order from 2 years ago doesn’t make much sense these days.
Hey has addressed some of these changes, but there is a lot of room for improvement.
https://www.cnet.com/tech/services-and-software/gmail-accoun...
Google later changed it to “unlimited” which increased daily by an MB or so. At some point settled at 15 GB.
How do you keep the GPT updated so that it knows about the final decision made for a specific problem. Like if api schema changes or the db is moved from SQLite to Postgres.
Assuming some of these challenges are solved, integrations will follow.
Imagine the web before CSS, JS, AJAX. That’s where we are with Gen AI.
I’ve seen Gmail put legit update emails coming from Google itself in spam.
~13B models should work well with plenty of room for other applications. Lately, I’ve heard good things about Solar10B, but new models come in a dozen by day, so it might have already been changed.
LM Studio is another option.
Based on this assumption, I have a couple of questions:
1. Is chunking the most significant factor in RAG quality?
2. If there are no limitations, would humans that are experts in that dataset, be the best people to create chunks?
Your comment prompted me to search for “Govee LAN” and found HomeAssistant LAN integration. Time to dig deeper!
Wouldn’t this segment have an incentive to not digitize their records as they’re evading tax?
This was the case several years ago, not sure if still relevant anymore. People evading tax were only interested in non-cloud solutions for their records.
Ingest Client Data - You will have to find customers who ingest dynamic data schemas. The example in the video shows more of a standard schema, which can be mapped once (using Lume or otherwise). No need to add an overhead or extra cost to run that data pipeline.
Normalize Data - One of the challenges will be to establish quality metrics for these mappings. Based on the demo, the quality score is supposed to be 100% all the time, but that’s far from the truth. Real data is messy. Validations will catch a lot of issues, but there will still be cases where incorrect mappings slip through. Ability to provide metrics around this will be very helpful in adoption.
Response Time - I’m not sure if you’re using OAI or your own models in the background. Even small latency in the pipeline for 100s of millions of records adds up to hours/days delay.
All the best!
One possibility is to capture Thanksgiving gatherings as a growth hack where people demo this to their families/friends and increase app downloads for OAI.
I’ve received people’s tax returns (including their SSN), airline tickets and hotel reservations, matrimonial proposals, photographs, etc.
I often used to email back and let people know about the mistake, but rarely do it now unless there is sensitive/urgent information.
To deal with some of the repeated unwanted emails, I have rules created to label those emails with a specific label. I use Google scripts to auto-delete all emails from that label which are older than 365 days. It cleans up repeated transaction emails from Indian banks/credit cards, newsletters, etc. but also gives me a year to retrieve the email if it was actually meant for me.
Not sure about the speed, but the garbage output might be due to the model instead of the library. I’ve always got garbage (using other tools) when I tried 256x256.
This is a good solution for all the use cases I’ve dealt with.
Are there widespread use cases where, knowing a deformity exists is required than just fixing the deformities?
There is a finite number of public APIs (100K?) which keeps the problem manageable. IMO, adding support for custom/private APIs (something like OpenAI functions) will make this a very powerful tool.
[1] https://www.amazon.com/Never-Split-Difference-Negotiating-De...