NotesVectorVFS is a lightweight Python package that transforms your Linux filesystem into a vector database by leveraging the native VFS (Virtual File System) extended attributes. Rather than maintaining a separate index or external database, VectorVFS stores vector embeddings directly alongside each file—turning your existing directory structure into an efficient and semantically searchable embedding store.Unfurl
NotesDiane, it’s Thursday and I’ve been figuring out how transcription fits into my everyday work. I had to make up a character to make it make sense, as I’ll say.FeedUnfurl
NotesPerhaps what we need is a new understanding of where happiness can exist in this AI-augmented world. Maybe the joy doesn’t have to disappear completely — it just shifts. Instead of finding delight in writing the perfect algorithm, perhaps we’ll discover satisfaction in the higher-level thinking about system design, in the creative process of describing exactly what we want to build, or in the human aspects of software development that AI can’t touch.FeedEmbedUnfurl
NotesNew data reveals the hidden network of African workers powering AI, as they push for transparency from the global companies that employ them indirectly.Unfurl
NotesThis is my journey. It is largely the path I took. I think you could speed run it if you were compelled. I don’t think you need to follow every step, but I do think every step is additive.FeedUnfurl
NotesLarge Language Models (LLMs) have transformed natural language processing, but face significant challenges in widespread deployment due to their high runtime cost. In this paper, we introduce SeedLM, a novel post-training compression method that uses seeds of a pseudo-random generator to encode and compress model weights. FeedUnfurl
NotesBlindspots in LLMs I’ve noticed while AI coding. Sonnet family emphasis. Maybe I will eventually suggest Cursor rules for these problems.FeedUnfurl
NotesOK, so AI doesn’t think the same way that people do. I’m fine with that. What’s important to me is that it can do some work for me, work that could also be done by people thinking. Cars (“horseless carriages”) do work that used to be done by horses running. No one now complains that cars work differently than horses.FeedUnfurl
NotesBy emphasizing documentation as the primary source of truth, establishing explicit linkages between development artifacts, and employing iterative, human-guided prompting, the whole team can harness the power of LLMs while mitigating their current limitations.Unfurl
NotesRight now, I’m not overly excited by MCP over “standard” tool calling. I much prefer agents.json and the concepts around endpoint discovery, which feel much more natural if you are working with APIs.FeedUnfurl
Notes"Vibe Coding" might get you 80% the way to a functioning concept. But to produce something reliable, secure, and worth spending money on, you’ll need experienced humans to do the hard work not possible with today’s models.Unfurl
NotesOne consistent pattern I’ve observed in the past year, since I published "The death of the junior developer", is that junior developers have actually been far more eager to adopt AI than senior devs. It’s not always true; a few folks have told us that their juniors are scared to use it because they think, somewhat irrationally, that it will take their jobs. (See: Behavioral regret theory. Thanks for the pointer Dr. Daniel Rock!)FeedUnfurl
NotesUsing LLMs to write code is difficult and unintuitive. It takes significant effort to figure out the sharp and soft edges of using them in this way, and there’s precious little guidance to help people figure out how best to apply them.
If someone tells you that coding with LLMs is easy they are (probably unintentionally) misleading you. They may well have stumbled on to patterns that work, but those patterns do not come naturally to everyone.
I’ve been getting great results out of LLMs for code for over two years now. Here’s my attempt at transferring some of that experience and intution to you.FeedUnfurl
NotesA native macOS app that allows users to chat with a local LLM that can respond with information from files, folders and websites on your Mac without installing any other software. Powered by llama.cpp. Unfurl
NotesThe first company to get this will own the next phase of AI development tools. They’ll build tools for real software instead of toys. They’ll make everything available today look like primitive experiments.FeedUnfurl
NotesWelcome to Boomer Prompts—an affectionate trip down memory lane of the elaborate, quirky, and sometimes overkill techniques used to guide earlier Large Language Models.
These “relics” showcase just how much LLMs have evolved—where once you needed to triple your instructions and role-play as a wise oracle, now simpler, more direct prompts suffice. Read on for a chuckle, and discover how far we’ve come!Unfurl
NotesConcluding, I think this is a very interesting way of working with AI inference in the browser. The obvious downside is that you need to convince your users to download an extension, but the obvious upside is that you possibly can save them from having to download a model they may already have downloaded and stored on their disk. FeedUnfurl
NotesTimeline algorithms should be useful for people, not for companies. Their quality should not be evaluated in terms of how much more time people spend on a platform, but rather in terms of how well they serve their users’ purposesFeedUnfurl
NotesBy focusing on the answer correctness as a key success metric, and designing our datasets and metrics carefully, we’ve managed to build a reliable evaluation process which has helped us increase confidence in our system’s quality.FeedEmbedUnfurl
NotesTransformer Lab is a free, open-source LLM workspace that you can run on your own computer. It is designed to go beyond what most modern open LLM applications allow. Using Transformer Lab you can easily finetune, evaluate, export and test LLMs across different inference engines and platforms.FeedUnfurl
Notesllm-mlx is a brand new plugin for my LLM Python Library and CLI utility which builds on top of Apple’s excellent MLX array framework library and mlx-lm package. If you’re a terminal user or Python developer with a Mac this may be the new easiest way to start exploring local Large Language Models.FeedUnfurl
NotesTo me, all signs point towards software engineering changing radically as a profession to be much more oriented around the what and why of software, and much less around the how. This will cause disruption at a massive scale in the long run. But in the short run, it's just a lot of fun to play with these tools and see what they can do.Unfurl
NotesIt is surprisingly straightforward to increase the VRAM of your Mac (Apple Silicone M1/M2/M3 chips) computer and use it to load large language models. Here’s the rundown of my experiments. ... I found a way to bypass this limitation. To allocate more of your Mac’s system RAM to VRAM – in this case, up to 28 GB – the following command can be used in the terminal window:
sudo sysctl iogpu.wired_limit_mb=27536FeedEmbedUnfurl
Notesmy attempt to build such a capable AI computer without spending too much. I ended up with a workstation with 48GB of VRAM that cost me around 1700 eurosFeedUnfurl
NotesYou wrote an interesting comment about getting your work into the LLM training corpus: "there has never been a more vital hinge-y time to write."
Do you mean that in the sense that you will be this drop in the bucket that’s steering the Shoggoth one way or the other? Or do you mean it in the sense of making sure your values and persona persist somewhere in latent space?FeedEmbedUnfurl
NotesPersonally, I feel like I get a lot of value from AI. I think many of the people who don’t feel this way are “holding it wrong”: i.e. they’re not using language models in the most helpful ways. In this post, I’m going to list a bunch of ways I regularly use AI in my day-to-day as a staff engineer.FeedUnfurl
NotesUndoubtedly, the sloppification of the internet will likely get worse over the next few years. And as such, the returns to curating quality sources of content will only increase. My advice? Use an RSS feed reader, read Twitter lists instead of feeds, and find spaces where real discussion still happens (e.g. LessWrong and Lobsters still both seem slop-free).Unfurl
NotesAt the same time, there should be some humility about the fact that earlier iterations of the chip ban seem to have directly led to DeepSeek’s innovations. Those innovations, moreover, would extend to not just smuggled Nvidia chips or nerfed ones like the H800, but to Huawei’s Ascend chips as well. Indeed, you can very much make the case that the primary outcome of the chip ban is today’s crash in Nvidia’s stock price.Unfurl
NotesPeople with less knowledge about AI are actually more open to using the technology. We call this difference in adoption propensity the “lower literacy-higher receptivity” link.Unfurl
NotesI feel like half of my social media feed is composed of AI grifters saying software developers are not going to make it. Combine that sentiment with some economic headwinds and it's easy to feel like we're all screwed. I think that's bullshit. The best days of our industry lie ahead.Unfurl
NotesJust as Midas discovered that turning everything to gold wasn't always helpful, we'll see that blindly applying cosine similarity to vectors can lead us astray. While embeddings do capture similarities, they often reflect the wrong kind - matching questions to questions rather than questions to answers, or getting distracted by superficial patterns like writing style and typos rather than meaning. This post shows you how to be more intentional about similarity and get better results.
NotesThis document is a summary of my personal experiences using generative models while programming over the past year. It has not been a passive process. I have intentionally sought ways to use LLMs while programming to learn about them. The result has been that I now regularly use LLMs while working and I consider their benefits net-positive on my productivity. (My attempts to go back to programming without them are unpleasant.)FeedUnfurl
NotesWelcome to OpenHands (formerly OpenDevin), a platform for software development agents powered by AI.
OpenHands agents can do anything a human developer can: modify code, run commands, browse the web, call APIs, and yes—even copy code snippets from StackOverflow.Unfurl
NotesPaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.Unfurl
VectorVFS: Your Filesystem as a Vector Database
Diane, I wrote a lecture by talking about it (Interconnected)
Teaching LLMs how to solid model
The Hidden Cost of AI Coding – Terrible Software
Vibe Coding is not an excuse for low-quality work
How Big Tech’s AI labor supply chain relies on hidden African workers - Rest of World
An LLM Codegen Hero's Journey | Harper Reed's Blog
SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators - Apple Machine Learning Research
Chatbots are AI anti-patterns! | Jakob Pörschmann
Horseless intelligence | Ned Batchelder
Literate Development: AI-Enhanced Software Engineering
Notes on MCP - Tao of Mac
Revenge of the junior developer | Sourcegraph Blog
Verifiability is the Limit
Here’s how I use LLMs to help me write code
What if AGI is Free? | Drew Breunig
Depth is all you need: how Antithesis crushes Gradius
FOSDEM 2025 - Build your own timeline algorithm
Evaluating Retrieval Augmented Generation for large-scale codebases
Getting Started | Transformer Lab
My LLM codegen workflow atm | Harper Reed's Blog
Run LLMs on macOS using llm-mlx and Apple’s MLX framework
How to Increase the VRAM of Your Mac with Apple Silicone for LLMs? | Hardware Corner
ewintr.nl - building a personal, private ai computer on a budget
Gwern Branwen - How an Anonymous Researcher Predicted AI's Trajectory
AI Slop, Suspicion, and Writing Back | Ben Congdon
DeepSeek FAQ – Stratechery by Ben Thompson
Knowing less about AI makes people more open to having it in their lives – new research
Ignore the Grifters - AI Isn't Going to Kill the Software Industry — Dustin Ewers
Home - PaddleOCR Documentation