Tiny LLMs
Powerful yet Compact: Browser-based AI models for a wide array of tasks, designed for optimal efficiency and user-friendliness, ensuring your privacy.
Building a Research Assistant from Scratch
In this video, we will walk through the steps of building a research assistant from scratch with LangChain and LangSmith. We will cover prompting strategies,...
Khoj: An Open-Source AI Copilot for your Second Brain
LLM: A CLI utility and Python library for interacting with Large Language Models
thiggle
Deterministic output to generate text completions with LLMs that match regex patterns, categories, or anything else.
KeyMate.AI Search Plugin For ChatGPT Home Page
ChainForge Documentation
Teach your LLM to always answer with facts not fiction | MyScale | Blog
Setup - LLM
My LLM CLI tool now supports self-hosted language models via plugins
LLM
A command-line utility for interacting with Large Language Models, such as OpenAI’s GPT series.
All the Hard Stuff Nobody Talks About when Building Products with LLMs | Honeycomb
Commercial LLMs like gpt-3.5-turbo and Claude are the best models to use for us right now. Nothing in the open source world comes close. However, this only means they’re the best of available options. They can take many seconds to produce a valid Honeycomb query, with latency ranging from two to 15+ seconds depending on the model, natural language input, size of the schema, makeup of the schema, and instructions in the prompt. As of this writing, although we have access to gpt-4’s API, it’s far too slow to work for our use case.
TP#18 The AI Trust Paradox
Plus: High Impact Prompt Injection through ChatGPT Plugins
Stability-AI/StableStudio: Community interface for generative AI
Community interface for generative AI. Contribute to Stability-AI/StableStudio development by creating an account on GitHub.
Pinbot
chathub/README.md at main · chathub-dev/chathub · GitHub
Chrome and Edge plugin for chatting with multiple models in the browser
Bardeen | Automate your repetitive tasks with one click
Bardeen is an automation app to replace your repetitive tasks with a single shortcut and control your web apps from anywhere. Explore our integrations with your favorite apps and hundreds of pre-built playbooks that help you stay in the flow.
ClippyGPT - How I Built Supabase’s OpenAI Doc Search (Embeddings)
Supabase hired me to build ClippyGPT - their next generation doc search. We can ask our old friend Clippy anything you want about Supabase, and it will answer it using natural language. Powered by OpenAI + prompt engineering.
In this video I will be showing you exactly how I did this, and how you can do the same in your projects. We'll be covering:
- Prompt engineering and best practices
- Working with a custom knowledge base via context injection + OpenAI embeddings
- How to store embeddings in Postgres using pgvector
Supabase blog post:
https://supabase.com/blog/chatgpt-supabase-docs
pgvector extension:
https://github.com/pgvector/pgvector
Generate embeddings implementation:
https://github.com/supabase/supabase/blob/54d39d4958575e5b58aa1d5d2a02db863ab4673c/apps/docs/scripts/generate-embeddings.ts
Clippy edge function implementation:
https://github.com/supabase/supabase/blob/54d39d4958575e5b58aa1d5d2a02db863ab4673c/supabase/functions/clippy-search/index.ts
Clippy frontend implementation:
https://github.com/supabase/supabase/blob/54d39d4958575e5b58aa1d5d2a02db863ab4673c/packages/ui/src/components/Command/AiCommand.tsx
Prompt engineering:
https://prmpts.ai/blog/what-is-prompt-engineering
00:00 Why?
01:40 Let's get started
03:15 Custom knowledge base
04:49 Context injection
06:13 Pre-process MDX files
13:40 Embeddings
15:40 Storing in Postgres + pgvector
22:21 API endpoint (edge function)
23:44 Calculating similarity in pgvector
27:55 Prompt engineering
33:15 Prompt best practices
38:37 Demo time!
41:32 Thanks for watching!
Phind: AI search engine
Get instant answers, explanations, and examples for all of your technical questions.
Ask Your PDF
Your gateway to dynamic, interactive, and intelligent conversations with any PDF document
The Nightmare of AI-Powered Gmail Has Arrived
It’ll write your emails for you and read them, too. What could go wrong?
In the context of Gmail and collaborative documents, we see suggestions of automation processes at war with one another, feeding problems that must be solved with more automation as Google manufactures demand for its own mitigating products. It’s an arms race in every inbox! It’s textual hyperinflation in every office! It’s a hundred meetings a day scheduled and attended and summarized by bots! Before Google productized this vision, OpenAI’s Sam Altman joked about how ChatGPT users had discovered it themselves.