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Super exciting news tomorrow. Stay tuned!

AI Terminology #17: Gradient Descent ↳ An optimization algorithm used to minimize a model’s error by iteratively adjusting its parameters in the direction that reduces loss. https://lnkd.in/g3pB7bDJ

AI is the best tool for builders. We are moving past writing every line of code by hand. Using LLMs for the heavy lifting lets you focus on the big ideas and solve harder problems faster. It has never been easier to create. What are you shipping this week?

What are you working on?

AI coding agents are moving past suggestion engines into full execution loops. They parse the repository, construct control-flow and dependency graphs, and reason about state changes before generating a single diff. • Static analysis to map symbols and side effects • Multi-file transactional edits with rollback safety • Test harness execution with failure classification • Iterative refinement against compiler and runtime signals This is not assisted coding. This is autonomous code synthesis integrated with the build system.

AI use case: Code explanations AI instantly breaks down complex code into clear, understandable logic. It eliminates guesswork, accelerates onboarding, and gives you full confidence in any codebase.

AI Terminology #16: Multi-Head Attention ↳ A mechanism within transformer models that allows the system to attend to different parts of the input simultaneously, capturing multiple relationships at once. https://lnkd.in/gJajrBgX

AI that can operate directly on a production codebase. Serious coding agents don’t autocomplete. They build a symbol graph, resolve cross-file references, track call chains, and plan edits before touching a file. • AST-level transformations instead of regex edits • Dependency-aware refactors across modules • Deterministic patch generation with scoped diffs • Test execution, stack trace parsing, and failure-driven retries This is automated change management wired into the compiler, the test runner, and the repo itself. Software is becoming executable infrastructure for agents.

Today, we’re launching a new course in collaboration with Scrimba It's designed for beginners to learn concepts like context engineering, agents, rag for building production-ready AI agents powered by Memory. You’ll start by learning the core concepts of context engineering Next, you’ll create memory with the Langbase SDK, upload docs, and perform RAG retrieval. By the end, you’ll know how to: 🔹 Context Engineering with AI primitives 🔹 Build memory agents 🔹Deploy serverless AI agents without frameworks You can watch it in ~1 hour, 100% free! 👉 https://lnkd.in/gA_stJUN