Show HN: Unlock Your Productivity Potential https://ift.tt/aSkdMyE

Show HN: Unlock Your Productivity Potential Leave Feedback https://ift.tt/5nIs1Ag November 2, 2023 at 11:41PM

Show HN: EdgeDB Cloud and 4.0 with FTS and Auth https://ift.tt/aLT8RkV

Show HN: EdgeDB Cloud and 4.0 with FTS and Auth Hi there! This is Yury, a co-founder. We've been busy for a while building EdgeDB Cloud and now it's finally here. EdgeDB Cloud is pretty cool and takes full advantage of our database. You get declarative schema, strict typing throughout, great performance, a query language that's best of GraphQL & SQL, and many many other perks. The Cloud itself is quite unique because of EdgeDB. Our query language gives you composability - so you can fetch everything for your page or API endpoint in one network request. And our protocol and client libraries give you auto recovery on network errors, automatic transaction retry, built-in transparent connection pooling, and many other benefits. Check out the blog post where we explain it all. And watch the currently ongoing YouTube Premiere of the launch! https://ift.tt/AQjEcLo November 1, 2023 at 10:57PM

Show HN: Stream vinyl (any RCA) to your Sonos system using RPi and open-source https://ift.tt/NwFcOu8

Show HN: Stream vinyl (any RCA) to your Sonos system using RPi and open-source https://ift.tt/2jzwDH7 November 1, 2023 at 11:11PM

Show HN: JS Local-only AI Apps starter kit: cost $0 to run and test locally https://ift.tt/lG3Dvob

Show HN: JS Local-only AI Apps starter kit: cost $0 to run and test locally Hi HN! Here's a local-only stack I built over the weekend - hope it can be useful for you! I have been building a lot of AI apps - https://ift.tt/spq6Dh4 https://ift.tt/tu6nTSc ... And there were definitely times I spent way too much $$ before deploying the app to production. So I was looking for a "local only" stack and found a few tools that worked well together. I used the following set of tools but may add more options later: - Inference: Ollama - VectorDB: Supabase pg-vector - LLM orchestration: langchain - Framework: nextjs My learning from building ai-town was also that many people don't have the luxury of swiping a credit card to test an app with a lot of AI workloads -- not surprisingly, many models on Ollama are already very good - I used llama2-7b for majority of my dev work. Hope this can lower the barrier to entry for building AI apps. :) https://ift.tt/bmacsy0 October 31, 2023 at 10:12PM

Show HN: An instantly usable To-Do List desktop Web App, no sign-up/log-in https://ift.tt/HqVx1iN

Show HN: An instantly usable To-Do List desktop Web App, no sign-up/log-in ... by saving data in the URL, base64 encoded. About a year ago I wanted a fast, simple, throwaway, no sign-up/log-in To-Do List at my fingertips at work, I couldn't find one so I built one myself, in a quick and dirty way. Recently I decided to release to public, so I rewrote, productise it a bit, added markdown support, added dark mode, added extension for chromium browsers etc, and this is it. May you find this small utility website useful if you happen to need something like this as well. Some tech & non-tech bullets - Pure static website - Data goes up to URL on updating, base64 encoded, non-hashed spa style - Rewrote with React & Redux - ChatGPT assistance for markdown parsing, regex and its unit testing - Playwright for testing - CDN with AWS CloudFront Any feedback and comments are appreciated. ---- A note on saving data in the URL, To-Do lists usually wouldn't be too long so saving its data in URL seems a natural fit, plus my initial sketchy research showed ff max URL is 64k and chrome can go MBs. But actually in real world, if you take server and CDNs into consideration, 8k seems to be a de facto limit ( https://ift.tt/emxs5SF ), still enough for usual todo lists :) Previous discussions on HN about saving data in URL, https://ift.tt/I7UnWmu , https://ift.tt/i1NwxOR , https://ift.tt/7q3XFIp https://2doli.st/ November 1, 2023 at 03:10AM

Show HN: Undead – scary fast, bewitching web pages on the JVM with no JavaScript https://ift.tt/KkSIZQg

Show HN: Undead – scary fast, bewitching web pages on the JVM with no JavaScript Hey HN. I wrote a LiveView implementation for the JVM called Undead. (The name Undead is a play on the "Live" part of LiveViews.) I had been waiting for the new StringTemplates feature of Java 21 which made it scary-easy to get the right data structures to make things work. It's been enchanting to write Java after about ten years in Javascript and Go lands. Would love folks to try it out and lmk what you think. If you aren't too scared that is. ;) https://ift.tt/0nafZ7P October 31, 2023 at 10:46PM

Show HN: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context https://ift.tt/Fei9hLQ

Show HN: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context Hi HN, We’re excited to announce that Phind now defaults to our own model that matches and exceeds GPT-4’s coding abilities while running 5x faster. You can now get high quality answers for technical questions in 10 seconds instead of 50. The current 7th-generation Phind Model is built on top of our open-source CodeLlama-34B fine-tunes that were the first models to beat GPT-4’s score on HumanEval and are still the best open source coding models overall by a wide margin: https://ift.tt/lGLAp7N... . This new model has been fine-tuned on an additional 70B+ tokens of high quality code and reasoning problems and exhibits a HumanEval score of 74.7%. However, we’ve found that HumanEval is a poor indicator of real-world helpfulness. After deploying previous iterations of the Phind Model on our service, we’ve collected detailed feedback and noticed that our model matches or exceeds GPT-4’s helpfulness most of the time on real-world questions. Many in our Discord community have begun using Phind exclusively with the Phind Model despite also having unlimited access to GPT-4. One of the Phind Model’s key advantages is that it's very fast. We’ve been able to achieve a 5x speedup over GPT-4 by running our model on H100s using the new TensorRT-LLM library from NVIDIA. We can achieve up to 100 tokens per second single-stream while GPT-4 runs around 20 tokens per second at best. Another key advantage of the Phind Model is context – it supports up to 16k tokens. We currently allow inputs of up to 12k tokens on the website and reserve the remaining 4k for web results. There are still some rough edges with the Phind Model and we’ll continue improving it constantly. One area where it still suffers is consistency — on certain challenging questions where it is capable of getting the right answer, the Phind Model might take more generations to get to the right answer than GPT-4. We’d love to hear your feedback. Cheers, The Phind Team https://ift.tt/wWHQrqf October 31, 2023 at 11:10PM