The best Hacker News stories from Show from the past day
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Show HN: AI-town, run your own custom AI world SIM with JavaScript
Hi HN community! We want to share AI-town, a deployable starter kit for building and customizing your own version of AI simulation - a virtual town where AI characters live, chat and socialize.<p>Inspired by great work from the Stanford Generative Agent paper (<a href="https://arxiv.org/abs/2304.03442" rel="nofollow noreferrer">https://arxiv.org/abs/2304.03442</a>).<p>A few features:
- Includes a convex.dev backed server-side game engine that handles global state
- Multiplayer ready. Deployment ready
- 100% Typescript
- Easily customizable. You can fork it, change character memories, add new sprites/tiles and you have a custom AI simulation<p>The goal is to democratize building your own simulation environment with AI agents. Would love to see the community build more complex interactions on top of this. Let us know what you think!<p>Demo: <a href="https://www.convex.dev/ai-town" rel="nofollow noreferrer">https://www.convex.dev/ai-town</a><p>I made a world Cat Town to demonstrate how to customize AI town. Using C(h)atGPT :)<p>Demo: <a href="https://cat-town.fly.dev/" rel="nofollow noreferrer">https://cat-town.fly.dev/</a>
Code: <a href="https://github.com/ykhli/cat-town">https://github.com/ykhli/cat-town</a>
Show HN: User Persona Generator
I hate talking to people.<p>So I made ChatGPT do it for me.
Show HN: Epsilla – Open-source vector database with low query latency
Hey HN! We are building Epsilla (<a href="https://github.com/epsilla-cloud/vectordb">https://github.com/epsilla-cloud/vectordb</a>), an open-source, self-hostable vector database for semantic similarity search that specializes in low query latency.<p>When do we need a vector database? For example, GPT-3.5 has a 16k context window limit. If we want to let it answer a question about a 300 page book, we cannot put the whole book content into the context. We have to choose the sections of the book that are most relevant to the question. Vector database is specialized at ranking and picking the most relevant content from a large pool of documents based on their semantic similarity.<p>Most vector databases utilize hierarchical navigational small world (HNSW) for indexing the vectors for high precision vector search, and its latency significantly degrades when the precision target is higher than 95%.<p>At a previous company, we worked on building the parallel graph traversal engine. We realized that the bottleneck of HNSW performance is because there are too many sequential traversal steps that don't fully leverage multi-core CPU computation resources. After some research, we found that there are algorithms such as SpeedANN that are targeting this problem, which is not leveraged by industry yet. So we built the Epsilla vector database to turn the research into a production system.<p>With Epsilla, we shoot for 10x lower vector search latency compared to HNSW based vector databases. We did an initial benchmark against the top open source vector databases: <a href="https://medium.com/@richard_50832/benchmarking-epsilla-with-some-of-the-top-vector-databases-543e2b7708e5" rel="nofollow noreferrer">https://medium.com/@richard_50832/benchmarking-epsilla-with-...</a><p>We provide a Docker image for you to install Epsilla backend locally, and provide a Python client and a JavaScript client to connect and interact with it.<p>Quickstart:<p><pre><code> docker pull epsilla/vectordb
docker run --pull=always -d -p 8888:8888 epsilla/vectordb
pip install pyepsilla
git clone https://github.com/epsilla-cloud/epsilla-python-client.git
cd examples
python hello_epsilla.py
</code></pre>
We just started a month ago. We'd love to hear what you think, and more importantly, what you wish to see in the future. We are thinking about a serverless vector database on cloud with a consumption based pricing model, and we are eager to get your feedback.
Show HN: Epsilla – Open-source vector database with low query latency
Hey HN! We are building Epsilla (<a href="https://github.com/epsilla-cloud/vectordb">https://github.com/epsilla-cloud/vectordb</a>), an open-source, self-hostable vector database for semantic similarity search that specializes in low query latency.<p>When do we need a vector database? For example, GPT-3.5 has a 16k context window limit. If we want to let it answer a question about a 300 page book, we cannot put the whole book content into the context. We have to choose the sections of the book that are most relevant to the question. Vector database is specialized at ranking and picking the most relevant content from a large pool of documents based on their semantic similarity.<p>Most vector databases utilize hierarchical navigational small world (HNSW) for indexing the vectors for high precision vector search, and its latency significantly degrades when the precision target is higher than 95%.<p>At a previous company, we worked on building the parallel graph traversal engine. We realized that the bottleneck of HNSW performance is because there are too many sequential traversal steps that don't fully leverage multi-core CPU computation resources. After some research, we found that there are algorithms such as SpeedANN that are targeting this problem, which is not leveraged by industry yet. So we built the Epsilla vector database to turn the research into a production system.<p>With Epsilla, we shoot for 10x lower vector search latency compared to HNSW based vector databases. We did an initial benchmark against the top open source vector databases: <a href="https://medium.com/@richard_50832/benchmarking-epsilla-with-some-of-the-top-vector-databases-543e2b7708e5" rel="nofollow noreferrer">https://medium.com/@richard_50832/benchmarking-epsilla-with-...</a><p>We provide a Docker image for you to install Epsilla backend locally, and provide a Python client and a JavaScript client to connect and interact with it.<p>Quickstart:<p><pre><code> docker pull epsilla/vectordb
docker run --pull=always -d -p 8888:8888 epsilla/vectordb
pip install pyepsilla
git clone https://github.com/epsilla-cloud/epsilla-python-client.git
cd examples
python hello_epsilla.py
</code></pre>
We just started a month ago. We'd love to hear what you think, and more importantly, what you wish to see in the future. We are thinking about a serverless vector database on cloud with a consumption based pricing model, and we are eager to get your feedback.
Show HN: Epsilla – Open-source vector database with low query latency
Hey HN! We are building Epsilla (<a href="https://github.com/epsilla-cloud/vectordb">https://github.com/epsilla-cloud/vectordb</a>), an open-source, self-hostable vector database for semantic similarity search that specializes in low query latency.<p>When do we need a vector database? For example, GPT-3.5 has a 16k context window limit. If we want to let it answer a question about a 300 page book, we cannot put the whole book content into the context. We have to choose the sections of the book that are most relevant to the question. Vector database is specialized at ranking and picking the most relevant content from a large pool of documents based on their semantic similarity.<p>Most vector databases utilize hierarchical navigational small world (HNSW) for indexing the vectors for high precision vector search, and its latency significantly degrades when the precision target is higher than 95%.<p>At a previous company, we worked on building the parallel graph traversal engine. We realized that the bottleneck of HNSW performance is because there are too many sequential traversal steps that don't fully leverage multi-core CPU computation resources. After some research, we found that there are algorithms such as SpeedANN that are targeting this problem, which is not leveraged by industry yet. So we built the Epsilla vector database to turn the research into a production system.<p>With Epsilla, we shoot for 10x lower vector search latency compared to HNSW based vector databases. We did an initial benchmark against the top open source vector databases: <a href="https://medium.com/@richard_50832/benchmarking-epsilla-with-some-of-the-top-vector-databases-543e2b7708e5" rel="nofollow noreferrer">https://medium.com/@richard_50832/benchmarking-epsilla-with-...</a><p>We provide a Docker image for you to install Epsilla backend locally, and provide a Python client and a JavaScript client to connect and interact with it.<p>Quickstart:<p><pre><code> docker pull epsilla/vectordb
docker run --pull=always -d -p 8888:8888 epsilla/vectordb
pip install pyepsilla
git clone https://github.com/epsilla-cloud/epsilla-python-client.git
cd examples
python hello_epsilla.py
</code></pre>
We just started a month ago. We'd love to hear what you think, and more importantly, what you wish to see in the future. We are thinking about a serverless vector database on cloud with a consumption based pricing model, and we are eager to get your feedback.
Show HN: Little Rat – Chrome extension monitors network calls of all extensions
Hi HN<p>I needed a way to monitor network calls made by chrome extensions so I made a small extension.<p>You can install it by dropping the zip or crx into the extensions page. It'll be on the chrome store whenever/if it gets through the review.<p>Hopefully it's useful to others.<p><a href="https://github.com/dnakov/little-rat">https://github.com/dnakov/little-rat</a><p><a href="https://twitter.com/dnak0v" rel="nofollow noreferrer">https://twitter.com/dnak0v</a>
Show HN: Little Rat – Chrome extension monitors network calls of all extensions
Hi HN<p>I needed a way to monitor network calls made by chrome extensions so I made a small extension.<p>You can install it by dropping the zip or crx into the extensions page. It'll be on the chrome store whenever/if it gets through the review.<p>Hopefully it's useful to others.<p><a href="https://github.com/dnakov/little-rat">https://github.com/dnakov/little-rat</a><p><a href="https://twitter.com/dnak0v" rel="nofollow noreferrer">https://twitter.com/dnak0v</a>
Show HN: Little Rat – Chrome extension monitors network calls of all extensions
Hi HN<p>I needed a way to monitor network calls made by chrome extensions so I made a small extension.<p>You can install it by dropping the zip or crx into the extensions page. It'll be on the chrome store whenever/if it gets through the review.<p>Hopefully it's useful to others.<p><a href="https://github.com/dnakov/little-rat">https://github.com/dnakov/little-rat</a><p><a href="https://twitter.com/dnak0v" rel="nofollow noreferrer">https://twitter.com/dnak0v</a>
Show HN: Little Rat – Chrome extension monitors network calls of all extensions
Hi HN<p>I needed a way to monitor network calls made by chrome extensions so I made a small extension.<p>You can install it by dropping the zip or crx into the extensions page. It'll be on the chrome store whenever/if it gets through the review.<p>Hopefully it's useful to others.<p><a href="https://github.com/dnakov/little-rat">https://github.com/dnakov/little-rat</a><p><a href="https://twitter.com/dnak0v" rel="nofollow noreferrer">https://twitter.com/dnak0v</a>
Show HN: LLMs can generate valid JSON 100% of the time
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming.<p>Recently we came up with a fast way to generate text that matches a regex (<a href="https://blog.normalcomputing.ai/posts/2023-07-27-regex-guided-generation/regex-guided-generation.html" rel="nofollow noreferrer">https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...</a>). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We can transform this DFA into a generative model: in each state we get a list of symbols which correspond to completions that partially match the regular expression. We mask the other symbols in the logits returned by a large language model, sample a new symbol and move to the next state. The subtelty is that language models work with tokens, not symbols, so we derive a new FSM whose alphabet is the model's vocabulary. We can do this in only one pass over the vocabulary.<p>Generating the token masks thus only requires a dictionary lookup at each state. Our method blows other libraries like Microsoft's guidance out of the water.<p>From there it was only a small leap to be able to generate text that follows a JSON schema (<a href="https://json-schema.org/" rel="nofollow noreferrer">https://json-schema.org/</a>), or is parseable into a Pydantic model (<a href="https://docs.pydantic.dev/latest/usage/models/" rel="nofollow noreferrer">https://docs.pydantic.dev/latest/usage/models/</a>). The method works with union types, optional types, nested schemas, arrays, everything. It is guaranteed that the output is parseable.<p>I think it's cool, and I've spent a lot of time watching even tiny models output valid JSON over the weekend. Hope you will too.<p>I look forward to feedback, bug reports, feature requests and discussions!<p>Edit: Link to our pre-print explaining the method and how this can be extended to generate text that follows a Context-Free Grammar <a href="https://arxiv.org/abs/2307.09702" rel="nofollow noreferrer">https://arxiv.org/abs/2307.09702</a>
Show HN: LLMs can generate valid JSON 100% of the time
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming.<p>Recently we came up with a fast way to generate text that matches a regex (<a href="https://blog.normalcomputing.ai/posts/2023-07-27-regex-guided-generation/regex-guided-generation.html" rel="nofollow noreferrer">https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...</a>). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We can transform this DFA into a generative model: in each state we get a list of symbols which correspond to completions that partially match the regular expression. We mask the other symbols in the logits returned by a large language model, sample a new symbol and move to the next state. The subtelty is that language models work with tokens, not symbols, so we derive a new FSM whose alphabet is the model's vocabulary. We can do this in only one pass over the vocabulary.<p>Generating the token masks thus only requires a dictionary lookup at each state. Our method blows other libraries like Microsoft's guidance out of the water.<p>From there it was only a small leap to be able to generate text that follows a JSON schema (<a href="https://json-schema.org/" rel="nofollow noreferrer">https://json-schema.org/</a>), or is parseable into a Pydantic model (<a href="https://docs.pydantic.dev/latest/usage/models/" rel="nofollow noreferrer">https://docs.pydantic.dev/latest/usage/models/</a>). The method works with union types, optional types, nested schemas, arrays, everything. It is guaranteed that the output is parseable.<p>I think it's cool, and I've spent a lot of time watching even tiny models output valid JSON over the weekend. Hope you will too.<p>I look forward to feedback, bug reports, feature requests and discussions!<p>Edit: Link to our pre-print explaining the method and how this can be extended to generate text that follows a Context-Free Grammar <a href="https://arxiv.org/abs/2307.09702" rel="nofollow noreferrer">https://arxiv.org/abs/2307.09702</a>
Show HN: LLMs can generate valid JSON 100% of the time
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming.<p>Recently we came up with a fast way to generate text that matches a regex (<a href="https://blog.normalcomputing.ai/posts/2023-07-27-regex-guided-generation/regex-guided-generation.html" rel="nofollow noreferrer">https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...</a>). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We can transform this DFA into a generative model: in each state we get a list of symbols which correspond to completions that partially match the regular expression. We mask the other symbols in the logits returned by a large language model, sample a new symbol and move to the next state. The subtelty is that language models work with tokens, not symbols, so we derive a new FSM whose alphabet is the model's vocabulary. We can do this in only one pass over the vocabulary.<p>Generating the token masks thus only requires a dictionary lookup at each state. Our method blows other libraries like Microsoft's guidance out of the water.<p>From there it was only a small leap to be able to generate text that follows a JSON schema (<a href="https://json-schema.org/" rel="nofollow noreferrer">https://json-schema.org/</a>), or is parseable into a Pydantic model (<a href="https://docs.pydantic.dev/latest/usage/models/" rel="nofollow noreferrer">https://docs.pydantic.dev/latest/usage/models/</a>). The method works with union types, optional types, nested schemas, arrays, everything. It is guaranteed that the output is parseable.<p>I think it's cool, and I've spent a lot of time watching even tiny models output valid JSON over the weekend. Hope you will too.<p>I look forward to feedback, bug reports, feature requests and discussions!<p>Edit: Link to our pre-print explaining the method and how this can be extended to generate text that follows a Context-Free Grammar <a href="https://arxiv.org/abs/2307.09702" rel="nofollow noreferrer">https://arxiv.org/abs/2307.09702</a>
Show HN: LLMs can generate valid JSON 100% of the time
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming.<p>Recently we came up with a fast way to generate text that matches a regex (<a href="https://blog.normalcomputing.ai/posts/2023-07-27-regex-guided-generation/regex-guided-generation.html" rel="nofollow noreferrer">https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...</a>). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We can transform this DFA into a generative model: in each state we get a list of symbols which correspond to completions that partially match the regular expression. We mask the other symbols in the logits returned by a large language model, sample a new symbol and move to the next state. The subtelty is that language models work with tokens, not symbols, so we derive a new FSM whose alphabet is the model's vocabulary. We can do this in only one pass over the vocabulary.<p>Generating the token masks thus only requires a dictionary lookup at each state. Our method blows other libraries like Microsoft's guidance out of the water.<p>From there it was only a small leap to be able to generate text that follows a JSON schema (<a href="https://json-schema.org/" rel="nofollow noreferrer">https://json-schema.org/</a>), or is parseable into a Pydantic model (<a href="https://docs.pydantic.dev/latest/usage/models/" rel="nofollow noreferrer">https://docs.pydantic.dev/latest/usage/models/</a>). The method works with union types, optional types, nested schemas, arrays, everything. It is guaranteed that the output is parseable.<p>I think it's cool, and I've spent a lot of time watching even tiny models output valid JSON over the weekend. Hope you will too.<p>I look forward to feedback, bug reports, feature requests and discussions!<p>Edit: Link to our pre-print explaining the method and how this can be extended to generate text that follows a Context-Free Grammar <a href="https://arxiv.org/abs/2307.09702" rel="nofollow noreferrer">https://arxiv.org/abs/2307.09702</a>
Show HN: Broken Bear, the AI teddy bear that loves your broken self
I made a GPT-based AI Chatbot based on Carl Roger's philosophy of radical self-acceptance. Broken Bear is designed to be a kind, comforting, and quietly encouraging friend.
Show HN: NotYetNews – AI-Generated News from the Future
Show HN: I wrote a RDBMS (SQLite clone) from scratch in pure Python
I wrote a relational database management system (RDBMS) (sqlite clone) from scratch in pure Python.
Show HN: I wrote a RDBMS (SQLite clone) from scratch in pure Python
I wrote a relational database management system (RDBMS) (sqlite clone) from scratch in pure Python.
Show HN: I wrote a RDBMS (SQLite clone) from scratch in pure Python
I wrote a relational database management system (RDBMS) (sqlite clone) from scratch in pure Python.
Show HN: Run LLaMa2 on the Browser with Ggml.js
You can now build serverless AI inference web application with ggml.js's LM backends.
Show HN: Openform – use Google Forms and Google Sheets as a simple database