Chris Fregly

LLMs look impressive in demos — but become inconsistent once they're embedded in real workflows. BASE Studio helps teams fine-tune models from SFT to RLHF using data built for real-world use, not toy examples: 🔵 Multi-turn conversations that preserve context 🔵 Clear, followable instructions models can reliably execute 🔵 Code data that explains *why* decisions are made — not just the final answer 🔵 Reasoning data that strengthens step-by-step thinking For multimodal models, we also align prompts with visual inputs to reduce inconsistency in text-to-image and text-to-video generation. The result? Models that behave more consistently — even after deployment. Learn more 👉 https://boden.ai #EmbodiedAI #Robotics #AITraining #TrainingData #MachineLearning #DataPipeline #RobotLearning #MultimodalAI #Automation #ComputerVision #AIInfrastructure #Annotation

1

Claude just released a memory import tool: https://lnkd.in/eM8c7Gf5 You paste a prompt into ChatGPT (or whatever you're using), it spits out your context — preferences, projects, how you work — and you drop it into Claude's settings. That's it. Claude picks it up from there. The switching cost has always been the real barrier. Not the AI itself, just the fact that you've spent months building context somewhere else. This is Anthropic admitting that out loud and actually doing something about it. Quiet release. Worth knowing about.

2

📈 More LLMs, higher quality!! 💰 Gemini, Cursor (Composer) & GPT-5 reviewed Claude plans & implementation on 27 PRs Only 35% of issues were found by multiple LLMs. The rest of the findings? Completely unique to a single tool. Claude caught a CSRF bypass and WebSocket auth gaps that nobody else touched. GPT-5 flagged flow-breaking type errors. Cursor caught race conditions and a few bugs. Dropping any one tool would leave blind spots. Claude is the strongest reviewer, however, in Claude Code it has the most context as well. Subjectively, Cursor Bugbot delivers best insights & actual bugs on Github - Composer run on local changes is not as productive. My next steps are 1) Quantify quality of Copilot & Bugbot reviews on Github 2) try claude-octopus (link in comments) & acpx for stability of the setup

14

3 Comments

🚀 From Vector Search to Database Agents - Weaviate’s Journey to Powering Production AI 💡 Don't miss this deep dive with Weaviate CEO & Co-Founder, Bob van Luijt, on the evolution of vector databases into AI-Native Databases! 🧠 We discuss Weaviate's journey to powering production AI, covering: - Vector Search and RAG (Retrieval-Augmented Generation) use cases. - The exciting new wave of Database Agents (query & transformation). - Practical engineering topics like multi-tenancy and storage optimization. If you're building AI in production, this is a must-watch! Bob offers clear explanations, a short demo walkthrough, and key lessons from scaling an open-source database. 👉 https://lnkd.in/d5hvWfYD #AI #VectorDatabase #Weaviate #RAG #OpenSource #LLMs #Engineering #Database

4

1 Comment

💥Mentorship and collaboration opportunity on MidcurveLLM is now open. 💥 ➡️ This project focuses on fine-tuning LLMs for geometric midcurve generation. ➡️ You will work on non-NLP, geometry-centric datasets using text-based formats. ➡️ The goal is to generate midcurves from 2D closed shapes with high precision. ➡️ Learn why fine-tuning works better than RAG for geometry problems. ➡️ Get hands-on with LLM fine-tuning, transformers, and benchmarking. ➡️ Ideal for practitioners with LLM fine-tuning and Python experience. ➡️ This is a voluntary, research-focused learning opportunity. Read the full post here: https://lnkd.in/dbXNgYfu #spatialai #AI #LLM #FineTuning #GeometryAI #Research #OpenSource

29

Great article by Ashpreet B. on adaptive AI agents and continuous learning AI. Highly recommended reading for agentic engineering. https://lnkd.in/gZQknWrn

16