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So you end up having in the context thousands of lines of old compiler dumps and duplicate file reads sitting there rent free destroying the agent's attention.Having an LLM write a summary of your conversation and blowing away your KV-cache makes no engineering sense imho especially on local models where prompt prefill latency is critical (and on cloud models where you lose prompt-cache discounts).So I built a piece of software called *contextWatcher* (or *`cw`* for friends and family).It’s
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hi everyone! I wanted to share what I've been working on these past 6~ years.<p>TL;DR: Rust is great, but somewhere along the way, I learned that I was too dumb to read and write Rust. As I worked on larger and larger Rust projects, I also became frustrated with the developer experience of working in Rust (slow compile time), so I wanted a better way.<p>Vu's language changed a bit over time but the core principles remained the same:<p>1. It should be easy to read and write 2. It should be memory safe 3. It should be fast to run 4. It should be fast to compile<p>Here's how I approached each:<p>1. Vu is syntactically very close to Python. The ownership rules are the ones Rust has, but I personally find it a lot easier to parse and understand. I've tested this with various LLMs and found it can read and write Vu out the box because of the similarity to Python.<p>2. Vu uses the same principles of Rust. Every value has one owner, borrows can't escape or outlive what they borrow, and nothing changes while it's borrowed.<p>3. Vu compiles down to C at the end of the day, so everything runs approximately at C speeds.<p>4. vuc check on the compiler’s own source (about 21,500 lines of Vu), takes about 0.4~ s. This check covers parsing, type and borrow checking. cargo check on a Rust project that size usually takes several seconds from scratch. Then there are other opinionated language decisions. Vu has no macros, Vu's borrow checker is lighter weight than Rust's with simpler interfaces and generics.<p>However, in the past few years, as Vu's language evolved and AI began to take over, I expanded Vu's principles to include a couple additional principles:<p>5. It should be secure 6. It should be efficient for LLMs to write<p>5. I want to pen test Vu in public. In <a href="https://github.com/vu0tran/vu/tree/main/tests" rel="nofollow">https://github.com/vu0tran/vu/tree/main/tests</a>, you'll see a battery of tests where I'm trying to basically pwn Vu. There are explicit language decisions in there as well that tries to prevent a host of security flaws that exist in other languages, e.g, integer overflow stops the program instead of quietly wrapping around.<p>6. The syntax of Vu is designed to be tokenizer efficient. In addition, when Vu checks throws errors, the errors are designed to be token efficient and written in a way that guides LLMs to more easily fix bugs, reducing turns overall.<p>So, in summary, Vu is still experimental. The compiler is 21,500 lines of Vu and compiles itself. The standard library also has TLS, HTTP/2, WebSockets and a PostgreSQL client, all tested against Python’s implementations and hostile input. I think most of the pieces are there. It does also compile down also to WebAssembly so you can play around w/ the checker on the website!<p>Feel free to try to break it!<p>What's next: 1. Figure out how to get Vu to perform faster than Rust 2. Rewrite some OSS libraries in Vu to further prove the language
Developer of 6 years here, been using AI (increasingly) over the last year for most of my work. A few weeks back I started to look for new jobs as the current startup I work for is falling into pieces. "Do I need to do leetcode again?" - I thought. So I picked a few challenges and tried to solve them without an assistant.<p>This felt impossible. What?! Is this the brainrot everyone is talking about? I mean I still read code every day, I just don't write it anymore. I could read and review the solution easily, but writing it myself felt like speaking a language I only understand, but cannot speak.<p>Being the data hoarder I am, I immediately wanted numbers, so I built Muscle Memory to help me feed them.<p>It's like leetcode but better. Challenges are more realistic, and I'd like to think the UX is more straightforward. It covers 12 languages and your code runs in a network-denied sandbox in your browser. No account needed.<p>I want it to be a place to keep your hands sharp: short tasks, clear prompts, and difficulty that comes from solving the problem rather than figuring out what the question means.<p>About the data (that I'm curious about): With your consent, Muscle Memory records your practice as raw events, including keystrokes, runs and submissions. Hoping to build an open dataset of how we write code in the AI era. That could help us understand how we approach problems, where we get stuck, and how we well do we retain our craft during this transition.<p>Your individual results stay private unless you share them. “Stealth” mode keeps your data out of published findings, and you can export or delete everything.<p>The data will be published as a report/notebook on the site itself, and be available raw under MIT license (parquet) once we reach a critical mass of sorts, so it makes sense to share.<p>The code is also MIT-licensed on GitHub (https://github.com/ekkuleivonen/muscle-memory). I wrote more about why I built it in the manifesto here: https://musclememory.run/manifesto.<p>I'd especially like to hear what surprised you about your first attempt.<p>Try it out here: https://musclememory.run
Some whales kind of do that by singing across the ocean.This inspired a thought process for a conceptual social network - whalesong - where an idea, thought or a conversationally valuable statement could be matched on.In contrast with the wide-broadcast social network visibility (restricted on groups or connections), I’d love to explore the idea of context-based networking.This experiment is showcasing the technical feasibility and potential value in whalesong.The goal: uncover conversational co
I’ve open sourced a library from my learnings, along the same principles of the Matt Pocock skills.Principles:* Claude / Codex / Cursor etc are great data agents - you just need context to enable them to answer business questions correctly.* Much of the important context lives in analysts heads'* Context must be maintainable (short, human readable - markdown and yaml)* Added context should be load bearing, not repeat of information that is available in another source of truth - an
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