LLM scrapers are taking down FOSS projects' infrastructure, and it's getting worse.

Reviewing the capabilities and limitations of LLM agents in software development and their impact on skilled and less skilled developers.

Online discussions about using Large Language Models to help write code inevitably produce comments from developers who’s experiences have been disappointing. They often ask what they’re doing wrong—how come some …

Here comes the AI autumn, a season of change that will usher in a new chapter for these incredible technologies.

[Simon Willison] has put together a list of how, exactly, one goes about using a large language models (LLM) to help write code. If you have wondered just what the workflow and techniques look like…

Exploration of using mechanisms provided by Elixir/OTP to build LLM powered applications

Unstract is an open-source, no-code platform purpose-built for extracting data from unstructured documents using LLMs, with high accuracy. Easily deploy API and ETL pipelines for your unstructured data.

It's been just over two years and two months since ChatGPT launched, and in that time we've seen Large Language Models (LLMs) blossom from a novel concept into one of the most craven cons of the 21st century — a cynical bubble inflated by OpenAI CEO Sam Altman built to sell

Elixir, together with the Membrane framework, is great for building pipelines that enable seamless communication between users and LLMs.

DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a series of open source Large Language Models (LLMs) from DeepSeek, an AI firm funded solely by Chinese hedge fund High-Flyer based in Hangzhou.

The release of DeepSeek R1 in January 2025 has created significant disruption in the LLM landscape, particularly in the realm of reasoning models. Here's a...

Quixotic is a nonsense generator designed to help static website operators confuse and confound bots and content-stealing LLM scrapers.

A reverse-proxy that serves diassociated-press style reimaginings of your upstream pages, poisoning any LLMs that scrape your content.

konterfAI is a model-poisoner for LLM (Large Language Models) to generate nonsense("bullshit") content suitable to degenerate these models.

Building Reliable LLM-powered Sofware in an Agentic World

Today I added an infinite-nonsense honeypot to my web site just to fuck with LLM scrapers, based on a "spicy autocomplete" program I wrote about 30 years ago. Well-behaved web crawlers will ignore it, but those "AI" people.... well, you know how they are. I'm intentionally not linking to the honeypot from here, for reasons, but I'll bet you can find it pretty easily (and without guessing ...

Local LLMs can evaluate 100s of result pairs a minute in a Macbook, enabling a new age of rapid search relevance improvements

I thought this was a fascinating post by Simon Willison: Things We Learned About LLMs in 2024 This increase in efficiency and reduction in price is my single favourite trend from 2024. I want the utility of LLMs at a fraction of the energy cost and it looks like that’

A LOT of LLMs

A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past …

In How should you adopt LLMs?, we explore how a theoretical ride sharing company, Theoretical Ride Sharing, should adopt Large Language Models (LLMs). Part of that strategy’s diagnosis depends on understanding the expected evolution of the LLM ecosystem, which we’ve build a Wardley map to better explore. This map of the LLM space is interested in how product companies should address the proliferation of model providers such as Anthropic, Google and OpenAI, as well as the proliferation of LLM product patterns like agentic workflows, Retrieval Augmented Generation (RAG), and running evals to maintain performance as models change.

A look at why the batch paradigm is a relic of the past, how it hinders AI apps and why the future of AI demands a real-time event-streaming platform.

The Netscapes of AI

Small language models and local LLMs are increasingly popular with devs. We list the best models and provide tips for evaluation.

Vector databases are quite popular right now, especially for building recommendation systems, adding context to chatbots and LLMs, or comparing content based on similarity. In this guide, I'll explain what vector databases are, how they work, and when to use them.

No.333 — With AI, the future of Augmented Reality is in your ears ⊗ Why every company needs a futurist-in-residence ⊗ AI isn’t about unleashing our imaginations ⊗ Bringing life to L.A.’s infrastructure

How should software development respond to how LLMs operate? We need truly explainable Artificial Intelligence with testable components.

Got a modern Nvidia or AMD graphics card? Custom Llamas are only a few commands and a little data prep away

Best-in-class embeddings, rerankers, LLM-reader, web scraper, classifiers. The best search AI for multilingual and multimodal data.

No.329 — It feels like 2004 again ⊗ Three Future Frames ⊗ What is futures literacy ⊗ Cracks in LLMs’ “reasoning” capabilities ⊗ Trees and land absorbed almost no CO2 last year