Jacob Somer @jacob_somer_
AI Enthusiast & Software Engineer 💻 Building intelligent systems that make a difference. jacobsomer.com Falls Church, Virginia Joined August 2014-
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A "generally good" advanced RAG pipeline to start with 👇 1. Embed on smaller chunks: Current embedding models work best when matching on granular facts, like sentences or smaller paragraphs. If you clutter each chunk with too much text, relevant chunks are less likely to be…
A "generally good" advanced RAG pipeline to start with 👇 1. Embed on smaller chunks: Current embedding models work best when matching on granular facts, like sentences or smaller paragraphs. If you clutter each chunk with too much text, relevant chunks are less likely to be… https://t.co/HmipmoKZAX
programming at its best feels like the constant discovery and invention of new concepts, materialized into useful form
The @PyTorch team is developing a library for large model training called torchtitan 👀 They have scripts to train Llama-3 from scratch The library went public today on GitHub but it is still in pre-release state & active development Check it out → github.com/pytorch/torcht…
We just published the first beta for React 19! This beta release is for collecting feedback and preparing libraries for the next major version of React. To see what's included, check out the release post: react.dev/blog/2024/04/2…
.@WalkaboutMG Launches All-New Venice Course, Out Today meta.com/blog/quest/wal…
It's been exactly one week since we released Meta Llama 3, in that time the models have been downloaded over 1.2M times, we've seen 600+ derivative models on @huggingface and much more. More on the exciting impact we're already seeing with Llama 3 ➡️ go.fb.me/xsqzz8
Shut down the open air drug markets in San Francisco
Shut down the open air drug markets in San Francisco
Introducing Jamba, our groundbreaking SSM-Transformer open model! As the first production-grade model based on Mamba architecture, Jamba achieves an unprecedented 3X throughput and fits 140K context on a single GPU. 🥂Meet Jamba ai21.com/jamba 🔨Build on @huggingface
The bridge between a good product and a great one is often just good design. It's time to bring creativity back to the table. #Innovation #DesignFirst
Simplicity in model design often beats complexity, especially when you can do more with less. What's your approach to simplifying algorithms? #DeepLearning #Simplicity
In a world full of quick solutions, taking time to craft meaningful experiences is revolutionary. How are you ensuring your product delights at every touchpoint? #UserExperience
The key to advancing AI isn't just more data, but smarter ways to interpret it. Think less about quantity, more about quality. #MachineLearning #AI
Language Agent Tree Search 🤖🌲 As LLMs get faster, better, cheaper, developers will be able to compose agentic systems that are able to plan out an entire tree of possible futures, instead of just sequentially planning the next state (e.g. in ReAct). This is crucial for higher…
Phi-3 is the most capable ~4B model out there today - we're fast approaching a world where small models can reliably perform agentic reasoning ⚡️ This has the potential to make complex agent applications much more feasible in terms of cost and latency. There's still a slight…
Phi-3 is the most capable ~4B model out there today - we're fast approaching a world where small models can reliably perform agentic reasoning ⚡️ This has the potential to make complex agent applications much more feasible in terms of cost and latency. There's still a slight… https://t.co/WLwfGDBAPv
Are we witnessing an #AI Industrial revolution or just incremental innovation since the release of Apple's Siri?
We’ve introduced more enterprise-grade features for API customers, including enhanced security, administrative controls, new Assistants API capabilities, and tools to help better manage costs. openai.com/blog/more-ente…
A way to fix bad retrieval issues in RAG is to add a “reflection” layer on the retrieved context 💡 Corrective RAG (CRAG, Yan et al.) adds a retrieval eval module that categorizes gathered information into “Correct”, “Incorrect”, and “Ambiguous”. In the event that context is…
I'm open-sourcing ChatBoba under the new name Document Chat. Basically, it is a prototype that brings together language models and document interaction. Explore the code and contribute to the development here: github.com/jacobsomer/Doc… #LLM #ConversationalAI #OpenSource #NLP
Just discovered the power of @metaai's LLaMA 3! I've been really impressed with it's ability to code. Mind blown by the potential of #LLMs in coding. #MetaAI #LLaMA3 #FrontendEngineering Try it out here: huggingface.co/meta-llama/Met… Example of a projects page designed by LLaMA 3:
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teachyoutips @teachyoutips
746 Followers 823 Following Digital marketer in dreams, blogger by passion and engineer by profession. Creator of https://t.co/6SQXqAxUOq & https://t.co/pOfQX0KHVb
Gary Sousa @gary_sousa
7K Followers 6K Following NYC transplanted #Marketing Exec Limitless curiosity all topics, always seeking latest insights. #Digital #IoT #tech #science #AI #DX #photography #Japan #Tokyo
Dmitry Alimov @dmitryalimov
6K Followers 7K Following Tech VC and entrepreneur. Curious. Investing and building in AI. Built companies in media and tech. Founder @frontiervc. Learned things @harvard, @stanford
Queen Driskill @QDriski
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𝕏 Akash Muneshwar .. @Akashmuneshwar6
881 Followers 1K Following Python Maestro | GenrativeAI Artisan 🤖 | Prompt Engineer 🌐 | AI Linguist | Deep Learning | LLM | Machine Learning 🔍 | Let's connect and grow together 💬|
ip7h5smhvab6om @se3w2h4ezd2xz
3 Followers 372 Following The team is a company that provides short-term investment income in cryptocurrency. With a rigorous plan, you can make $500 to $5,000. Click the link to join
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546 Followers 2K Following Company deep-dives and market trends decoded. Here for knowledge, not to advise. 📈✖️Advice. #EquityExplorer
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341 Followers 483 Following Curious about knowledge graphs, theorem provers, increasing speed of (human) learning. founder of seed startup https://t.co/9ucLWVNELe. prev Stanford/Google
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I published my filtered and uncensored dataset for Dolphin-2.9 on @huggingface so if you wanna make your own spin on Dolphin, or just see how Dolphin is created, you can check it out. Thanks to all the upstream dataset creators for open source data! huggingface.co/datasets/cogni…
What is the most impactful ML systems work from academia published at a systems conference? For example, ASPLOS or OSDI?
I love that the boom loop has become a mainstream meme End the doom loop Spark the boom loop Detroit, San Francisco, and everywhere
My gift to the world. Train llama-3-8b on any dataset with 1,500 lines or less (about) with free google colab tier (all code provided in model card. Using (Unsloth + Galore + Qlora) Qalore if you will 🤷♂️ huggingface.co/rombodawg/test…
FAIR researchers (@AIatMeta) presented SegmentAnything and our robotics work at the White House correspondents’ weekend. Llama3 + Sim2Real skills (trained with @ai_habitat) = a robot assistant
Washingtonians delved into the world of artificial intelligence (AI) at the Washington AI Network’s inaugural weekend TGAIFriday Lunch for White House correspondents. trib.al/FwHF9Um
A "generally good" advanced RAG pipeline to start with 👇 1. Embed on smaller chunks: Current embedding models work best when matching on granular facts, like sentences or smaller paragraphs. If you clutter each chunk with too much text, relevant chunks are less likely to be…
Multi-Stage Advanced RAG with @llama_index and @cohere reranking Making retrieval a multi-hop process 💡 instead of single-shot can ensure better LLM context and reduce hallucinations. This article by Michael R. (@kxsystems) highlights an example of this multi-stage…
Better late than never 😅
Original #MsDOS source code! Was published by @Microsoft on @github github.com/microsoft/MS-D…
My tirade against AI doomers in Davos. youtu.be/YdaRd_vitLw?si…
🚀 Perplexica - An AI-powered search engine 🔎 Perplexica is an open-source AI-powered searching tool or an AI-powered search engine that goes deep into the internet to find answers. Built with LangChain, this is a great resource for getting started! github.com/ItzCrazyKns/Pe…
As long as AI systems are trained to reproduce human-generated data (e.g. text) and have no search/planning/reasoning capability, performance will saturate below or around human level. Furthermore, the amount of trials needed to reach that level will be far larger than the…
Interesting how in all these domains AI is asymptoting at roughly human performance - where's the AI zooming past us to superintelligence that Kurzweil etc. predicted/feared?
improvise, adapt, adapt, adapt, adapt, adapt, adapt, adapt, adapt, overcome
programming at its best feels like the constant discovery and invention of new concepts, materialized into useful form
🤖AutoHyDE: Making HyDE Better for Advanced LLM RAG A fun article exploring new and creative RAG techniques Builds upon HyDE to add in keyword generation, clustering, etc Implements a LangChain Embedder which makes it easy to try out in your pipeline towardsdatascience.com/autohyde-makin…
Mission: make Google the best home for developers building with AI.
I got #1 Product of the Day on Product Hunt and it grew my YouTube too → youtube.com/@_taishi As of the 13th this month, I had 151 subscribers. Now it's 192. I haven't posted a new vid. This is very fast growth for my channel. It's v easy to spot when I launched my app…
A 9-part series on RAG from Prototype to Production ⭐️ RAG in a notebook is easy, RAG serving live production users is hard. This tutorial series by Marco Bertelli is the perfect step-by-step resource to outline all the architectural components you need to productionize a full…
💬"LangChain Tool Calling feature just changed everything" We recently added a common interface for tool calling across model providers This makes it easy to build agents that work across models Watch @EdenEmarco177 explain why this is a big deal youtube.com/watch?v=dj8Yqi…
Launching Signals: analyze thousands of calls with insane accuracy in seconds. Filtering through it like quantitative data can save hours answering questions about user research, sales, onboarding, and support calls. It feels like magic.
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