Most requests go thought in a couple 100ms, but some take up to 10 seconds. I suspect this is due to Apple's internal network throttling of background service. Can anything be done? I thought at least VPN apps get special treatment but this is still abysmal performance.
Važno je le, da je server v Sloveniji in ima dobro povezavo, da bo hendlal par sočasnih uporabnikov brez problema in upočasnjevanja. CPU/RAM je lahko minimalen.
Naj ima root access ali pa že inštaliran WireGuard.
I spent a few days testing out existing apps and none are suitable so far. I am beginning to think the reason for that is Apple's limitations.
Is there a way to make an app that connects to a VPN, keeps it persistent, doesn't proxy device traffic through it but also starts a persistent local socks server on the private interface of the VPN?
First or second part alone is easy but there is no app that can do both. Closest I got was an app that connected to a VPN and could pipe that through a socks upstream connection but I need a socks server on the device, not connecting to a socks server somewhere else. Thanks.
Je en skok ali 2 čez mejo na voljo kak mobilni ISP, ki bi ponujal neomejene pakete ali pakete s par 100 GB podatkov za podobno ceno kot pri nas Bob? Hrvaška, Avstrija, Italija, Madžarska, Nemčija recimo. Srbija bi tudi bila zanimiva opcija. 4G je več kot dovolj.
I spent a few days testing out existing apps and none are suitable so far. I am beginning to think the reason for that is Apple's limitations.
Is there a way to make an app that connects to a VPN, keeps it persistent, doesn't proxy device traffic through it but also starts a persistent local socks server listening on the private interface of the VPN?
First or second part alone is easy but there is no app that can do both. Closest I got was an app that connected to a VPN and could pipe that through a socks upstream connection but I need a socks server on the device, not connecting to a socks server somewhere else. Thanks.
I spent a few days testing out existing apps and none are suitable so far. I am beginning to think the reason for that is Apple's limitations.
Is there a way to make an app that connects to a VPN, keeps it persistent, doesn't proxy device traffic through it but also starts a persistent local socks server on the private interface of the VPN?
First or second part alone is easy but there is no app that can do both. Closest I got was an app that connected to a VPN and could pipe that through a socks upstream connection but I need a socks server on the device, not connecting to a socks server somewhere else. Thanks.
I spent a few days testing out existing apps and none are suitable so far. I am beginning to think the reason for that is Apple's limitations.
Is there a way to make an app that connects to a VPN, keeps it persistent, doesn't proxy device traffic through it but also starts a persistent local socks server on the private interface of the VPN?
First or second part alone is easy but there is no app that can do both. Closest I got was an app that connected to a VPN and could pipe that through a socks upstream connection but I need a socks server on the device, not connecting to a socks server somewhere else. Thanks.
Anyone using shadowrocket on an apple phone to connect to their wireguard VPN?
Could use some advice with troubleshooting. The wireguard connecting part works, but I got issues with getting a local socks proxy server set up by extending the .conf file.
It seems the app allows me to either connect to the VPN or connect outbound via socks, but I want to connect to VPN and have a socks inbound listener like microsocks at the same time and on the same private network.
Hi! I am looking for new challenges as I learn about various way to scrape and reverse engineer web APIs. Post some ideas, preferably APIs, that you had the most challenges getting the data from.
Looking forward giving it a try.
My data set is a list of images, each equipped with a a couple sentences of text.
A user would search primarily with text only. My default approach is using BM25, but how would I facilitate searching with a vector DB and a model that embeds vectors in a multimodal combined space?
Here is my dilemma:
Do I embed text part and image part as 2 separate individual vectors or do I combine them into 1 vector?
If a typical search happens with text only, that would immediately deprioritize all image-only embeddings and only good text matches would float up. This is why I am now considering embedding text and images together but would prefer to hear more opinions on this. Thanks.
My data set is a list of images, each equipped with a a couple sentences of text.
A user would search primarily with text only. My default approach is using BM25, but how would I facilitate searching with a vector DB and a model that embeds vectors in a multimodal combined space?
Here is my dilemma:
Do I embed text part and image part as 2 separate individual vectors or do I combine them into 1 vector?
If a typical search happens with text only, that would immediately deprioritize all image-only embeddings and only good text matches would float up. This is why I am now considering embedding text and images together but would prefer to hear more opinions on this. Thanks.
My data set is a list of images, each equipped with a a couple sentences of text.
A user would search primarily with text only. My default approach is using BM25, but how would I facilitate searching with a vector DB and a model that embeds vectors in a multimodal combined space?
Here is my dilemma:
Do I embed text part and image part as 2 separate individual vectors or do I combine them into 1 vector?
If a typical search happens with text only, that would immediately deprioritize all image-only embeddings and only good text matches would float up. This is why I am now considering embedding text and images together but would prefer to hear more opinions on this. Thanks.
My data set is a list of images, each equipped with a a couple sentences of text.
A user would search primarily with text only. My default approach is using BM25, but how would I facilitate searching with a vector DB and a model that embeds vectors in a multimodal combined space?
Here is my dilemma:
Do I embed text part and image part as 2 separate individual vectors or do I combine them into 1 vector?
If a typical search happens with text only, that would immediately deprioritize all image-only embeddings and only good text matches would float up. This is why I am now considering embedding text and images together but would prefer to hear more opinions on this. Thanks.
My data set is a list of images, each equipped with a a couple sentences of text.
A user would search primarily with text only. My default approach is using BM25, but how would I facilitate searching with a vector DB and a model that embeds vectors in a multimodal combined space?
Here is my dilemma:
Do I embed text part and image part as 2 separate individual vectors or do I combine them into 1 vector?
If a typical search happens with text only, that would immediately deprioritize all image-only embeddings and only good text matches would float up. This is why I am now considering embedding text and images together but would prefer to hear more opinions on this. Thanks.
Torej 1 Gbps dol in gor.
Preseda mi skrivanje cenikov za poslovne uporabnike, plezanje po petornih podmenijih, "contact us" bullshit.
Če kdo to doma ima, naj prosim napiše, koliko plačuje in ime ponudnika. Tudi 500/500 Mbps bi me zanimal.
Hvala vsem.
What is a good practice for storing time series data with the following requirements.
Couple dozen million documents.
Several fields with daily data (one point per day).
I am thinking about having a multiValue string field, read it back in my application and append new value if todays date has no entry yet.
The value would be a string with a simple delimiter and would store date and an integer.
I would then precompute changes of this value weekly to update a 2nd variable that would facilitate search by the strength of change in the value. The list of strings would only be used to display data and would not be searchable. I also have a 3rd field that stores most recent data point as integers and allows for range queries.
I considered the dual list approach but I don't like the idea of only index number linking the 2 data pieces together in otherwise completely seemingly unrelated fields. On the other hand the more daily data types I would track, the more I would save by having only 1 list of dates and then other lists for data entries.
I am already updating several other fields daily or every few days so the reindexing is not something I can avoid anyway.
I also avoided using child documents so far, using entirely flat structure for simpler queries.
Is this already a point where I should consider other external DBs and only store precomputed data in collection? It feels not worth adding that much more maintenance yet.
How does that sound? What are best practices in the industry? Thanks for help.
What is a good practice for storing time series data with the following requirements.
Couple dozen million documents.
Several fields with daily data (one point per day).
I am thinking about having a multiValue string field, read it back in my application and append new value if todays date has no entry yet.
The value would be a string with a simple delimiter and would store date and an integer.
I would then precompute changes of this value weekly to update a 2nd variable that would facilitate search by the strength of change in the value. The list of strings would only be used to display data and would not be searchable. I also have a 3rd field that stores most recent data point as integers and allows for range queries.
I considered the dual list approach but I don't like the idea of only index number linking the 2 data pieces together in otherwise completely seemingly unrelated fields. On the other hand the more daily data types I would track, the more I would save by having only 1 list of dates and then other lists for data entries.
How does that sound? What are best practices in the industry? Thanks for help.
What is the current state of this area? Are there any tools available that could compete with a native speaker mentoring someone trying to improve their pronunciation and reduce accent?
Perhaps something that visualizes accuracy of spoken word so that the student can adjust on their own without a third party actively correcting them?
My goal is to adjust pronunciation to be closer to a native English dialect. Any kind will do.
What is the current state of this area? Are there any tools available that could compete with a native speaker mentoring someone trying to improve their pronunciation and reduce accent?
Perhaps something that visualizes accuracy of spoken word so that the student can adjust on their own without a third party actively correcting them?
Asking for English.
What is the current state of this area? Are there any tools available that could compete with a native speaker mentoring someone trying to improve their pronunciation and reduce accent?
Perhaps something that visualizes accuracy of spoken word so that the student can adjust on their own without a third party actively correcting them?
Asking for English (TL). Thanks.