No such thing as exactly-once delivery
10 by todsacerdoti | 2 comments on Hacker News.
Monday, September 30, 2024
Sunday, September 29, 2024
Saturday, September 28, 2024
New top story on Hacker News: Show HN: Bringing multithreading to Python's async event loop
Show HN: Bringing multithreading to Python's async event loop
11 by nbsande | 1 comments on Hacker News.
This project explores the integration of multithreading into the asyncio event loop in Python. While this was initially built with enhancing CPU utilization for FastAPI servers in mind, the approach can be used with more general async programs too. If you’re interested in diving deeper into the details, I’ve written a blog post about it here: https://ift.tt/BGzafih
11 by nbsande | 1 comments on Hacker News.
This project explores the integration of multithreading into the asyncio event loop in Python. While this was initially built with enhancing CPU utilization for FastAPI servers in mind, the approach can be used with more general async programs too. If you’re interested in diving deeper into the details, I’ve written a blog post about it here: https://ift.tt/BGzafih
New top story on Hacker News: Show HN: Modern Benchmarking Tooling for JavaScript
Show HN: Modern Benchmarking Tooling for JavaScript
7 by evnwashere | 2 comments on Hacker News.
I always had a sweet tooth for how easy it is to use google/benchmark, but when working with js, current libraries didn't feel right and some were not even accurate enough, so I decided to create my own library to make JavaScript benchmarking tooling better. With more free time, I finally implemented all features I wished for in 1.0.0 and made a lightweight C++ single-header version for moments when google/benchmark is too much. Hope this library helps you as much as it does me.
7 by evnwashere | 2 comments on Hacker News.
I always had a sweet tooth for how easy it is to use google/benchmark, but when working with js, current libraries didn't feel right and some were not even accurate enough, so I decided to create my own library to make JavaScript benchmarking tooling better. With more free time, I finally implemented all features I wished for in 1.0.0 and made a lightweight C++ single-header version for moments when google/benchmark is too much. Hope this library helps you as much as it does me.
Friday, September 27, 2024
Thursday, September 26, 2024
Wednesday, September 25, 2024
Tuesday, September 24, 2024
Monday, September 23, 2024
New top story on Hacker News: Launch HN: Panora (YC S24) – Data Integration API for LLMs
Launch HN: Panora (YC S24) – Data Integration API for LLMs
7 by nael_ob | 0 comments on Hacker News.
Hey HN! We're Nael and Rachid, and we're building Panora ( https://ift.tt/IucP84n ), an open-source API that connects various data sources to LLMs, from 3rd party integrations to embeddings and chunking generation. Here's a demo: https://www.youtube.com/watch?v=45QaN8mzAfg , and you can check our docs here: https://ift.tt/XumNG7j Our GitHub repo is at https://ift.tt/IucP84n . Building integrations by hand is tedious and time-consuming. You must adapt to API documentation quirks, manage request retries, OAuth/API key authorization, refresh tokens, rate limits, and data sync freshness. Moreover, you have to keep up with the constant rise of embedding models and chunking capabilities. On the other hand, with the rise of AI-powered apps, you have to handle embedding and chunking of all the unstructured data. The dominant player in this space is Merge.dev, but it has several drawbacks: 1. It's a black box for most developers, lacking transparency on data handling. 2. Strong vendor lock-in: once an end-user connects their software, it's challenging to access authorization tokens if you want to perform requests on their behalf after leaving Merge. 3. Long time-to-deploy for the long tail of integrations, leading to lost opportunities as integrations become the backbone of LLM-based applications. 4. Unrealistic prices per connection (action of one end-user connecting their tool). 5. Not positioned to serve LLM-based products that need RAG-ready data to power their use cases. That's how Panora was born. We set out to build a solution that addresses these pain points head-on, creating something that is both developer-friendly and open-source. Our goal was to simplify the complex world of integrations and data preparation for LLMs, allowing developers to focus on building great products rather than wrestling with integration headaches. Panora is 100% open-source under the Apache 2.0 license and you can either use our cloud version or self-host the product. We provide two ways for your end-users to connect their software seamlessly. 1. A frontend SDK (React) where you can embed the integrations catalog within your app. 2. A magic link that you can share with anyone allowing them to connect their software. You can either use your own OAuth clients or our managed ones. You receive a connection token per user and per provider connected, which you must use to retrieve/insert data using our universal API. We have different categories of software such as CRMs or File storage. Every category is divided into entities (e.g: File Storage has File, Folder, Drive, Group & User) following a standard data model. You even have access to remote data (non-transformed data from the provider) within each response, so you can build custom & complex integrations on your end. If the remote data isn't enough beyond the standard data model, you can create custom fields either via API or our dashboard to map your remote fields to our model. We're more than just integrations—we provide ready data for your RAG applications with auto-generation of embeddings and chunks for all your synced documents. You have the option to select your own vector database and embedding model in the dashboard. We then sync your documents and store the chunks/embeddings to the specified vector DB. We make sure to maintain up-to-date data that we send through webhooks, and you can set custom sync frequency (1hr, once a day, etc.) depending on your use case. Developers use our API to access fragmented data across various software such as File storage systems (Google Drive, OneDrive, SharePoint) and retrieve the embeddings of their documents using a single API. Our backend SDK is available for Python, TypeScript, Ruby, and Go. Your honest feedback, suggestions, and wishes would be very helpful. We'd love to hear about your integration stories, challenges you've faced with data integration for LLMs, and any thoughts on our approach. Thanks, HN!
7 by nael_ob | 0 comments on Hacker News.
Hey HN! We're Nael and Rachid, and we're building Panora ( https://ift.tt/IucP84n ), an open-source API that connects various data sources to LLMs, from 3rd party integrations to embeddings and chunking generation. Here's a demo: https://www.youtube.com/watch?v=45QaN8mzAfg , and you can check our docs here: https://ift.tt/XumNG7j Our GitHub repo is at https://ift.tt/IucP84n . Building integrations by hand is tedious and time-consuming. You must adapt to API documentation quirks, manage request retries, OAuth/API key authorization, refresh tokens, rate limits, and data sync freshness. Moreover, you have to keep up with the constant rise of embedding models and chunking capabilities. On the other hand, with the rise of AI-powered apps, you have to handle embedding and chunking of all the unstructured data. The dominant player in this space is Merge.dev, but it has several drawbacks: 1. It's a black box for most developers, lacking transparency on data handling. 2. Strong vendor lock-in: once an end-user connects their software, it's challenging to access authorization tokens if you want to perform requests on their behalf after leaving Merge. 3. Long time-to-deploy for the long tail of integrations, leading to lost opportunities as integrations become the backbone of LLM-based applications. 4. Unrealistic prices per connection (action of one end-user connecting their tool). 5. Not positioned to serve LLM-based products that need RAG-ready data to power their use cases. That's how Panora was born. We set out to build a solution that addresses these pain points head-on, creating something that is both developer-friendly and open-source. Our goal was to simplify the complex world of integrations and data preparation for LLMs, allowing developers to focus on building great products rather than wrestling with integration headaches. Panora is 100% open-source under the Apache 2.0 license and you can either use our cloud version or self-host the product. We provide two ways for your end-users to connect their software seamlessly. 1. A frontend SDK (React) where you can embed the integrations catalog within your app. 2. A magic link that you can share with anyone allowing them to connect their software. You can either use your own OAuth clients or our managed ones. You receive a connection token per user and per provider connected, which you must use to retrieve/insert data using our universal API. We have different categories of software such as CRMs or File storage. Every category is divided into entities (e.g: File Storage has File, Folder, Drive, Group & User) following a standard data model. You even have access to remote data (non-transformed data from the provider) within each response, so you can build custom & complex integrations on your end. If the remote data isn't enough beyond the standard data model, you can create custom fields either via API or our dashboard to map your remote fields to our model. We're more than just integrations—we provide ready data for your RAG applications with auto-generation of embeddings and chunks for all your synced documents. You have the option to select your own vector database and embedding model in the dashboard. We then sync your documents and store the chunks/embeddings to the specified vector DB. We make sure to maintain up-to-date data that we send through webhooks, and you can set custom sync frequency (1hr, once a day, etc.) depending on your use case. Developers use our API to access fragmented data across various software such as File storage systems (Google Drive, OneDrive, SharePoint) and retrieve the embeddings of their documents using a single API. Our backend SDK is available for Python, TypeScript, Ruby, and Go. Your honest feedback, suggestions, and wishes would be very helpful. We'd love to hear about your integration stories, challenges you've faced with data integration for LLMs, and any thoughts on our approach. Thanks, HN!
Sunday, September 22, 2024
Saturday, September 21, 2024
Friday, September 20, 2024
New top story on Hacker News: Show HN: EloqKV – Scalable distributed ACID key-value database with Redis API
Show HN: EloqKV – Scalable distributed ACID key-value database with Redis API
11 by hubertzhang | 19 comments on Hacker News.
We're thrilled to unveil EloqKV, a lightning-fast distributed key-value store with a Redis-compatible API. Built on a new database architecture called the Data Substrate, EloqKV brings significant innovations to database design. Here’s the unique features that makes it stand out: - Flexible Deployment: Run it as a single-node in-memory KV cache, a larger-than-memory database or scale to a highly available, distributed transactional database with ease. - High Performance: Achieves performance levels comparable to top in-memory databases like Redis and DragonflyDB, while significantly outperforming durable KV stores like KVRocks. - Full ACID Transactions: Ensures complete transactional integrity, even in distributed environments. - Independent Resource Scaling: Scale CPU, memory, storage, and logging resources independently to meet your needs. We’d love to hear your thoughts and feedback!
11 by hubertzhang | 19 comments on Hacker News.
We're thrilled to unveil EloqKV, a lightning-fast distributed key-value store with a Redis-compatible API. Built on a new database architecture called the Data Substrate, EloqKV brings significant innovations to database design. Here’s the unique features that makes it stand out: - Flexible Deployment: Run it as a single-node in-memory KV cache, a larger-than-memory database or scale to a highly available, distributed transactional database with ease. - High Performance: Achieves performance levels comparable to top in-memory databases like Redis and DragonflyDB, while significantly outperforming durable KV stores like KVRocks. - Full ACID Transactions: Ensures complete transactional integrity, even in distributed environments. - Independent Resource Scaling: Scale CPU, memory, storage, and logging resources independently to meet your needs. We’d love to hear your thoughts and feedback!
Thursday, September 19, 2024
New top story on Hacker News: Ask HN: What email service(s) do you use for your side projects?
Ask HN: What email service(s) do you use for your side projects?
13 by jtap | 19 comments on Hacker News.
I have a couple side projects that I use for my friends, family, and myself. I'd like to have both an email such as team@mysite.com to send and receive emails that I might want to type out. I'd also like to be able to send transactional emails, password reset ... I would think that I'm not the only one with this problem. What do you all use to achieve this?
13 by jtap | 19 comments on Hacker News.
I have a couple side projects that I use for my friends, family, and myself. I'd like to have both an email such as team@mysite.com to send and receive emails that I might want to type out. I'd also like to be able to send transactional emails, password reset ... I would think that I'm not the only one with this problem. What do you all use to achieve this?
Wednesday, September 18, 2024
Tuesday, September 17, 2024
Monday, September 16, 2024
New top story on Hacker News: Ask HN: What runs L4-related microkernels/hypervisors these days?
Ask HN: What runs L4-related microkernels/hypervisors these days?
19 by AlexWandell | 5 comments on Hacker News.
I've been learning about the L4 microkernel, and am thinking about doing something related to it for a research project. I'm especially curious about more recent examples of specific devices that run L4 variants (seL4, PikeOS, OKL4, etc.). I already found a few that use seL4, but to take OKL4 as an example, most of the specific devices I could find are from more than a decade ago, and I'm trying to find things from at least the last 5 or 6 years. I'm even more curious to find devices that use a form of L4 as a hypervisor. Has anyone here worked on a device that used an L4-related kernel or hypervisor? I know one major area they're used in is defense and full of NDAs, but hopefully some of the other industries they're used in (medical devices, automotive, IoT) are a little less restrictive. Thanks in advance!
19 by AlexWandell | 5 comments on Hacker News.
I've been learning about the L4 microkernel, and am thinking about doing something related to it for a research project. I'm especially curious about more recent examples of specific devices that run L4 variants (seL4, PikeOS, OKL4, etc.). I already found a few that use seL4, but to take OKL4 as an example, most of the specific devices I could find are from more than a decade ago, and I'm trying to find things from at least the last 5 or 6 years. I'm even more curious to find devices that use a form of L4 as a hypervisor. Has anyone here worked on a device that used an L4-related kernel or hypervisor? I know one major area they're used in is defense and full of NDAs, but hopefully some of the other industries they're used in (medical devices, automotive, IoT) are a little less restrictive. Thanks in advance!
Sunday, September 15, 2024
Saturday, September 14, 2024
Friday, September 13, 2024
Thursday, September 12, 2024
Wednesday, September 11, 2024
Tuesday, September 10, 2024
Monday, September 9, 2024
New top story on Hacker News: Ask HN: How do you manage your prompts in ChatGPT?
Ask HN: How do you manage your prompts in ChatGPT?
10 by nabi_nafio | 5 comments on Hacker News.
I use ChatGPT regularly for a lot of different tasks. For example, coding, health Q&A, and summarizing docs. The different prompts stack up in the sidebar which becomes very difficult to manage. For example, I frequently have to refer back to a prompt that I wrote previously. But I usually give up looking for it because of the tedious scroll and search process. I was wondering if there is an easier way. How do you manage your prompts in ChatGPT?
10 by nabi_nafio | 5 comments on Hacker News.
I use ChatGPT regularly for a lot of different tasks. For example, coding, health Q&A, and summarizing docs. The different prompts stack up in the sidebar which becomes very difficult to manage. For example, I frequently have to refer back to a prompt that I wrote previously. But I usually give up looking for it because of the tedious scroll and search process. I was wondering if there is an easier way. How do you manage your prompts in ChatGPT?