Show HN: Mastra – Open-source JS agent framework, by the creators of Gatsby https://ift.tt/VDq84R7

Show HN: Mastra – Open-source JS agent framework, by the creators of Gatsby Hi HN, we’re Sam, Shane, and Abhi, and we’re building Mastra ( https://mastra.ai ), an open-source JavaScript SDK for building agents on top of Vercel’s AI SDK. You can start a Mastra project with `npm create mastra` and create workflow graphs that can suspend/resume, build a RAG pipeline and write evals, give agents memory, create multi-agent workflows, and view it all in a local playground. Previously, we built Gatsby, the open-source React web framework. Later, we worked on an AI-powered CRM but it felt like we were having to roll all the AI bits (agentic workflows, evals, RAG) ourselves. We also noticed our friends building AI applications suffering from long iteration cycles: they were getting stuck debugging prompts, figuring out why their agents called (or didn’t call) tools, and writing lots of custom memory retrieval logic. At some point we just looked at each other and were like, why aren't we trying to make this part easier, and decided to work on Mastra. Demo video: https://www.youtube.com/watch?v=8o_Ejbcw5s8 One thing we heard from folks is that seeing input/output of every step, of every run of every workflow, is very useful. So we took XState and built a workflow graph primitive on top with OTel tracing. We wrote the APIs to make control flow explicit: `.step()` for branching, `.then()` for chaining, and `.after()` for merging. We also added .`.suspend()/.resume()` for human-in-the-loop. We abstracted the main RAG verbs like `.chunk()`, `embed()`, `.upsert(),’ `.query()`, and `rerank()` across document types and vector DBs. We shipped an eval runner with evals like completeness and relevance, plus the ability to write your own. Then we read the MemGPT paper and implemented agent memory on top of AI SDK with a `lastMessages` key, `topK` retrieval, and a `messageRange` for surrounding context (think `grep -C`). But we still weren’t sure whether our agents were behaving as expected, so we built a local dev playground that lets you curl agents/workflows, chat with agents, view evals and traces across runs, and iterate on prompts with an assistant. The playground uses a local storage layer powered by libsql (thanks Turso team!) and runs on localhost with `npm run dev` (no Docker). Mastra agents originally ran inside a Next.js app. But we noticed that AI teams’ development was increasingly decoupled from the rest of their organization, so we built Mastra so that you can also run it as a standalone endpoint or service. Some things people have been building so far: one user automates support for an iOS app he owns with tens of thousands of paying users. Another bundled Mastra inside an Electron app that ingests aerospace PDFs and outputs CAD diagrams. Another is building WhatsApp bots that let you chat with objects like your house. We did (for now) adopt an Elastic v2 license. The agent space is pretty new, and we wanted to let users do whatever they want with Mastra but prevent, eg, AWS from grabbing it. If you want to get started: - On npm: npm create mastra@latest - Github repo: https://ift.tt/1FwHl89 - Demo video: https://www.youtube.com/watch?v=8o_Ejbcw5s8 - Our website homepage: https://mastra.ai (includes some nice diagrams and code samples on agents, RAG, and links to examples) - And our docs: https://mastra.ai/docs Excited to share Mastra with everyone here – let us know what you think! https://ift.tt/1FwHl89 February 19, 2025 at 05:25AM

Show HN: The One File I Created for Every Programming Language (10+ Years) https://ift.tt/IqJ0Ogh

Show HN: The One File I Created for Every Programming Language (10+ Years) Throughout my career, while exploring different programming languages, I realized that having a quick reference was essential for refreshing my memory on the fly. So I developed a habit: for every new language I learned, I’d build a reference file (like python_reference.py) that documented everything from basic syntax to advanced features. Now, a decade later, these files have become my go-to resource. They’ve helped me study for interviews, revisit old projects after years, and now, in the AI era where generating code in unfamiliar languages is more common than ever, I think they’re more relevant than ever. A concise, straight to the point reference can be the difference between "it works" and "I actually understand why it works." I believe these references will be especially helpful for developers looking to pick up a new language quickly, without having to go through extensive courses or tutorials. I've updated most of the references, added some new ones, and made them open source. I've also put together a website where you can find them all: https://ift.tt/dl1XKpR Github Repo: https://ift.tt/ugpxETm I’d love to hear your thoughts, and feel free to contribute if you have ideas or improvements! <3 https://referencecollection.com/ February 19, 2025 at 04:13AM

Show HN: I analyzed 1500+ job ads to find the most wanted skills by recruiters https://ift.tt/OzRNd6F

Show HN: I analyzed 1500+ job ads to find the most wanted skills by recruiters https://skillsets.tech/ February 19, 2025 at 02:22AM

Show HN: EVMole for Analysing EVM Bytecode https://ift.tt/HPfzirG

Show HN: EVMole for Analysing EVM Bytecode Hi HN, I'm excited to share the latest release of EVMole, a smart contract analysis tool for Ethereum (works directly with bytecode - verified contracts are not required). In version 0.7.1, I've added an advanced Control Flow Graph Reconstruction feature that, even though benchmarks are still coming, already outperforms every existing solution in both accuracy and speed. In addition to the new CFG capabilities, EVMole continues to offer function selector, argument, state mutability, and storage layout analysis. Open-source libraries are available in Rust, Python, and JavaScript. Online Demo: https://evmole.xyz Source Code: https://ift.tt/GAQrpa9 I'm looking forward to your feedback and thoughts! https://evmole.xyz February 18, 2025 at 04:39AM

Show HN: Welcome, Cot: the Rust web framework for lazy developers https://ift.tt/oRwYLsp

Show HN: Welcome, Cot: the Rust web framework for lazy developers https://ift.tt/1Vus2Jm February 18, 2025 at 03:53AM

Show HN: oParse.com – Track what different AI models "think" about your brand https://ift.tt/5FMGLiV

Show HN: oParse.com – Track what different AI models "think" about your brand https://oparse.com February 17, 2025 at 02:34AM

Show HN: Send and Receive Voice Messages to ChatGPT on WhatsApp and Telegram https://ift.tt/WilbLNq

Show HN: Send and Receive Voice Messages to ChatGPT on WhatsApp and Telegram https://ift.tt/yjlwEXP February 16, 2025 at 09:59PM