This project packages four principles—Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution—into a Claude Code plugin or CLAUDE.md file to curb LLM code pitfalls like overengineering and hidden assumptions. It enforces explicit reasoning, minimal edits, and test-driven success criteria to produce cleaner, more accurate AI-generated code.

PrismML’s Bonsai 8B trains a large language model with 1-bit weights from scratch, squeezing 8.2 billion parameters into just 1.15 GB. In benchmarks it ties or outperforms FP16 models like Llama 3.1 and runs at real-time speeds on phones, shifting the size-performance trade-off.

The article compares LLMs’ frozen knowledge to the amnesiac in Memento, showing how they rely on context prompts, retrieval systems, and external memory instead of updating their own weights. It reviews in-context learning and state-space memory layers, then argues that only continual learning—letting models compress new information into their parameters after deployment—can bridge the gap to genuine, scalable understanding.

The author connects a 16 GB Mac Mini to a 64 GB MacBook Pro using LM Studio Link’s encrypted mesh VPN, offloading heavy model inference to the more powerful machine without exposing ports or tweaking firewalls. This setup lets you run large LLMs on low-RAM devices as if they were local, with no cloud or API key hassles.

OpenAI trained a new LLM, GPT-Rosalind, on 50 common biological workflows and major public databases to help researchers navigate massive genomic and protein datasets. The model links genotype to phenotype, suggests biological pathways, and prioritizes potential drug targets by leveraging mechanistic understanding.

Yelp explains how it turned a two-week prototype into a scalable, production-ready AI assistant for business pages. They built near-real-time indices for reviews, photos, and structured data in an EAV schema, combined keyword-first retrieval with LLM prompts, and added query classification and trust-and-safety filters. The system streams answers with citations, logs metrics, and balances freshness, performance, and reliability.

This article breaks down the core concepts behind LLMs—from next-token prediction training to tokens, vectors and attention layers—to show how they generate text. It also covers context windows, parameters and why model scale affects performance.

This article reruns a 2023 benchmark with the latest LLMs, comparing direct SQL generation against querying through a structured dbt Semantic Layer. It finds that while text-to-SQL accuracy has jumped, a modeled Semantic Layer still delivers near-perfect, deterministic results for covered queries, making it ideal for complex or critical use cases.

This article explores how advancements in software design, particularly through LLMs, shift the focus from using standard libraries to generating custom code. It highlights the implications for dependency management and emphasizes the need to understand the problem being solved rather than just the mechanics of coding. The author compares this shift to the evolution of 3D printing in manufacturing.

In a podcast discussion, predictions for the tech industry in 2026 are shared, highlighting the undeniable improvement of LLMs in writing code, advancements in coding agent security, and the potential obsolescence of manual coding. Other predictions include a successful breeding season for Kākāpō parrots and the implications of AI-assisted programming on software engineering careers.

The article analyzes the unit economics of large language models (LLMs), focusing on the compute costs associated with training and inference. It discusses how companies like OpenAI and Anthropic manage their financial projections and cash flow, emphasizing the need for revenue growth or reduced training costs to achieve profitability.