Software engineer publishes final part of LLM-from-scratch series covering inference and decoding
A software engineer published the fourth and final part of a blog series explaining LLMs from the ground up, focusing on token-by-token generation, KV cache, and decoding strategies (temperature, top-k, top-p). The series aims to help other software engineers understand the internals of LLMs.
Entities
Related
LLM hardware recipe database with filters and community usage tracking
A community-driven database that lists which LLM models run on which hardware, with performance details. Users can filter by hardware, submit new recipes, and mark which recipes they actively use to show popularity. It solves the problem of finding compatible model-hardware combinations for LLM deployment.
Interactive app teaching LLM pipeline from pattern matching to transformer training
An interactive web app that walks through every stage of the LLM pipeline, from basic pattern matching to training a transformer from scratch. It includes working code that users can run locally, making it an educational tool for developers and learners.
Article explains how LLMs trigger real-world actions despite being next-token predictors
An article published on July 11, 2026, explains how large language models, which are fundamentally next-token predictors, can trigger real-world actions like fetching weather, running calculators, or searching the web. It addresses the common confusion about how a model trained only to predict the next word can perform tasks it has no direct ability to do.
Article explains how LLMs use tools and iterate to complete tasks
A technical article titled 'The Agent Loop: How AI Learns to Think, Act, and Get Things Done' describes how LLMs use tools, make decisions, learn from results, and iterate until tasks are complete. The piece provides a conceptual overview of agentic AI workflows.
CTOs share playbooks for governing LLM cost and usage in production
Engineering leaders discuss strategies for managing LLM costs and usage as AI features scale from prototype to production. A key challenge is that user-facing workflows often trigger multiple LLM calls, making costs non-obvious during MVP stages.