🛠️ All DevTools

Fast C++ logging library.

ADR secures enterprise AI agents through observability, security benchmarking, and threat detection. Deployed at Uber.

Launch HN: EdotEnv (YC S26) – Quant Trading RL Envs to Teach LLMs Research We are Rui and Michael and we’re building EdotEnv (<a href="https:&#x2F;&#x2F;edotenv.com" rel="nofollow">https:&#x2F;&#x2F;edotenv.com</a>): self-improving RL environments from Quant Trading workflows.<p>With all the benchmaxxing around, evals saturate and become meaningless for model comparison. Useful benchmarks should increase in difficulty as models advance. Back in our Quant jobs, Michael and I saw that the market has exactly this property: markets became more efficient as people profited from trading inefficiencies, making new profitable strategies harder to find and old ones decay over time.<p>This makes markets an ideal, continuously evolving benchmark for LLM training. The hard part is to turn professional quant workflows into reliable training envs, as this is a very niche expertise.<p>In our environments, we give LLMs a quant trading workflow and evaluate their performance on out-of-sample data: build predictive features&#x2F; models, design a portfolio, backtest strategies, adapt continuously to market regimes. Each step is a task with different self-built tools. For example, a predictive feature building task gives the agent cleaned market data of time period [0,T] to research ideas, a backtesting tool to test created features at time t on [0, t], an execution tool to trade strategies with the new features on [t+1, T] and a final evaluation. Our reward isolates the agent&#x27;s feature building skills and yet benefits from market properties.<p>From running SOTA models in our environments, we see that i) they seem to struggle with iterating deeply on research ideas, preferring broad shallow searches; ii) higher reasoning does not seem to increase performance and iii) agents do not understand trading, e.g. when losing money they stop trading instead of trading smarter. Check out our blogs for more details! <a href="https:&#x2F;&#x2F;edotenv.com&#x2F;?tab=blog" rel="nofollow">https:&#x2F;&#x2F;edotenv.com&#x2F;?tab=blog</a><p>Quant workflows are essentially applied ML research, long-horizon planning and continual learning. Through our envs, we teach these transferable research skills, rather than task specific answers. Our environments are closer to a realistic research workflow: we use real-world data instead of synthetic ones; our envs naturally contain noise and real trade-offs; our rewards are verifiable and immediate, with no need for an additional LLM judge or human expert.<p>We open sourced a sample task repository: <a href="https:&#x2F;&#x2F;github.com&#x2F;MMcollab-dotcom&#x2F;feature-engineering" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;MMcollab-dotcom&#x2F;feature-engineering</a>. We plan to sell continuously improving envs to AI labs&#x2F;researchers&#x2F;enterprises training their own agents, who are interested in ML modelling capabilities, continual learning, long horizon planning or Quant Research in general.<p>We&#x27;d love feedback from anyone trying out their own agents in our envs, for either eval or post training. And of course, we are always happy to discuss the future of trading with LLMs (and no, it should not be asking the LLM to read tea leaves and give you the stock to buy tomorrow). Looking forward to your comments!

Hop.earth – OpenStreetMap based car racing game

What's Behind the Sharp Drop in Labor Force Participation?

How Cops Are Trying to Hide Their Use of Flock

The Warp Agent CLI

The Knowledge Chipper: An Agentic Coding Story

MariaDB: Promote getting to 10k GitHub stars in server log and client prompt

Most countries provide between 20 and 40 paid days off

[Other] Deliver web apps with confidence 🚀

Apple says more ex-employees may have taken confidential data to OpenAI

Show HN: Simple algorithm and color space to generate diverse skin tones Hello HN!<p>I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy.<p>I&#x27;ve built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has.<p>The methodology might be a bit shaky, but hopefully the result is as helpful for others as I have found it! There&#x27;s lots of room for improvements (see the Future Work section), but I&#x27;m really happy with how it turned out.

[Other] Agent skills that bring team coding standards to Claude Code and Codex

[Build/Deploy] A bundler for javascript and friends. Packs many modules into a few bundled assets. Code Splitting allows for loading parts of the application on demand. Through "loaders", modules can be CommonJs, AMD, ES6 modules, CSS, Images, JSON, Coffeescript, LESS, ... and your custom stuff.

U.S. used 'virtually all' of its long-range precision missiles during Iran war

[Other] Learning-Rust.Github.io: Rust Programming Language Tutorials for Everyone

"Clean" Code, Horrible Performance (2023)

[API/SDK] The Billable Usage API: programmatic cost visibility for Cloudflare

[Other] Show HN: Latex.to – LaTeX to PDF via Full Tex Live in the Browser Hi HN, I implemented your feedback:<p>latex.to is an Overleaf alternative that now runs a full Tex Live distribution in the browser.<p>This is done via CheerpX (JIT wasm engine) running a 32bit alpine image with TeX Live.<p>Things I previously tried: - originally this project started with just KaTeX for LaTeX to image - v86 wasm engine: Too slow (at least 10 times slower than CheerpX) - various TeX Live binaries wasm ports: These usually don&#x27;t port Biber (because it&#x27;s a Perl app) or engines like ConTeXt.<p>What makes this work: - CheerpX only streams (via HTTP range requests) the data it needs. Compiling the base example only downloads 50 MB (the whole image is 5GB). - Cloudflare R2 can only cache files less than 512MB. Hence the 5GB image is split into 1 base image and 19 mount images. - LaTeX needs to bake (index) the fonts before compiling which can take a long time and can OOM CheerpX. Hence all fonts more than 0.7 MB are prebaked into the image file.<p>The first compile is a bit slower because it needs to download assets like fonts, but everything is cached and the second compile is much faster.<p>100% free, no login, no compile timeout, documents are compiled in the browser and NOT sent to a remote server.<p>Please let me know in case something doesn&#x27;t work &#x2F; you have any questions or feedback.