Fully local deep research assistant with Gemma3 Google just released Gemma3, several open source highly performant models that can be run locally. We overview Gemma3 and test it w/ local deep research using @ollama
. : https://
youtu.be/XsJxF_MDfyI : https://
github.com/langchain-ai/o
llama-deep-researcher
…
LLMS
-
Gemma3 Local Deep Research Assistant with Ollama
By
–
-
Bolt Revolutionizes Browser-Based Development with AI
By
–
Some key takeaways: 1. Bolt succeeded by doing what was thought impossible—running full dev environments in a browser. 2. Bolt operated on minimal resources and near-zero runway, mostly just staying alive long enough to keep trying new things. When Claude 3.5 Sonnet launched,
-
Google Upgrades Deep Research with Gemini 2.0
By
–
BREAKING : Google is upgrading its Deep Research feature to be powered by Gemini 2.0 Flash Thinking. The mention of Gemini 1.5 Pro has been removed from the description recently.
-
Generative AI Learning Roadmap Guide
By
–
Generative #AI Learning Roadmaphttps://t.co/blf14GwXBT#GenAI @HaroldSinnott @mvollmer1 @Khulood_Almani @FrRonconi @CurieuxExplorer @BetaMoroney @sallyeaves @Nicochan33 @sonu_monika @FmFrancoise @anijov @AkwyZ @Damien_CABADI @Hana_ElSayyed @Eli_Krumova @enilev @YvesMulkers pic.twitter.com/k6YXfiQZjV
— Marcus Borba (@marcusborba) 13 mars 2025Generative #AI Learning Roadmap https://
bit.ly/41JGX1L #GenAI @HaroldSinnott @mvollmer1 @Khulood_Almani @FrRonconi @CurieuxExplorer @BetaMoroney @sallyeaves @Nicochan33 @sonu_monika @FmFrancoise @anijov @AkwyZ @Damien_CABADI @Hana_ElSayyed @Eli_Krumova @enilev @YvesMulkers -
Reasoning systems for idea clustering and proposition grouping
By
–
Now, onto the cool part: after extracting propositions, we need a system that can reason about each one and decide whether it fits into an existing chunk or if it should start a new one. This grouping process mimics how we naturally group ideas when reading or taking notes.
-
Agentic Chunking: LLMs Merging Related Content Intelligently
By
–
Unlike traditional chunking that often isolates scattered information, agentic chunking leverages LLMs to merge related content, maintaining the natural connections between chapters, paragraphs, and clauses.
-
Semantic Chunking for Optimized RAG Retrieval and Context
By
–
Instead of simply splitting text at every sentence break, this approach groups ideas much like we naturally do when reading or taking notes. It preserves context and helps organize your LLM content when retrieving the RAG chunks.
-
Command A Expands Multilingual Support to 23 Languages
By
–
Command A is even better at multilingual tasks, offering expanded support for 23 languages to serve global enterprises.
-
Command A: 256K Context LLM for Enterprise RAG
By
–
We designed Command A with business needs in mind. Its 256k context length (2x most models) can handle longer enterprise documents for enhanced RAG with in-line citations.
-
Command A Model Achieves Scalability with Minimal GPU Requirements
By
–
Command A is scalable, efficient, and fast. With a serving footprint of just two GPUs, it requires far less compute than other models–making it great for private deployments.