"Clearly, they admire the work that's being done by SpaceX and are trying to replicate it."

The lawsuit also names io Products, the hardware company led by Jony Ive.

Apple has sued OpenAI, alleging that former employees that now work for the AI company have stolen Apple's trade secrets "for the benefit of OpenAI." In its complaint, Apple alleges that it has uncovered "a pattern of theft of Apple's trade secrets by OpenAI employees who were formerly at Apple," and it names IO Products […]

AI Agents: Memory Layers, Test Automation, and Workflow Orchestration Today's Highlights This week's highlights dive deep into critical aspects of AI agent development, from choosing the right memory layer for TypeScript agents to innovative applications in end-to-end testing and content automation. We explore practical frameworks and methodologies for building robust, intelligent workflows. Mem0 vs TurboMem: which memory layer actually fits your TypeScript agent (Dev.to Top) Source: https://dev.to/arneesh/mem0-vs-turbomem-which-memory-layer-actually-fits-your-typescript-agent-54pc This article offers a direct comparison between two popular memory management solutions for TypeScript-based AI agents: Mem0 and TurboMem. Mem0 is presented as a widely recognized choice, often implemented as a separate service. In contrast, TurboMem advocates for an embedded memory model, integrating directly into the agent's process. The core of the discussion likely revolves around the architectural trade-offs of these two approaches, particularly for AI agent orchestration. The piece would detail the implications of operating memory as a distinct service versus embedding it. Key considerations for developers would include performance overheads, operational complexity (e.g., managing another service for Mem0), and data consistency. It provides practical guidance on selecting the appropriate memory layer based on an agent's specific requirements, such as real-time responsiveness, scalability needs, and deployment environment. Understanding these differences is crucial for optimizing agent performance and ensuring efficient state management in complex AI applications. Comment: As someone building TypeScript agents, understanding the pros and cons of embedded versus service-based memory like Mem0 and TurboMem is essential for architecture and performance. This comparison helps me make informed decisions on how to manage my agents' state. Slack Introduces Agent Driven End-to-End Testing to

Rewriting a Database Giant: Meet pgrust PostgreSQL is the bedrock of modern application development. It is incredibly stable and feature-rich, but it is built on a C codebase that started in the 1980s. In systems programming, legacy C architectures carry memory-safety risks and make core changes difficult. pgrust is an experimental open-source project that aims to rewrite the entire PostgreSQL database engine from scratch in Rust. The project recently hit a historic milestone: it now passes 100% of the official PostgreSQL 18.3 regression test suite (over 46,000 test queries). What is pgrust? pgrust is a native reimplementation of the Postgres query execution and storage layers. Unlike other projects that wrap Postgres or write extensions, pgrust is a complete rewrite of the database core itself. Crucially, it is disk-compatible with PostgreSQL 18.3, meaning it can boot up and read from an existing Postgres database directory on your machine. Key Technical Improvements By shifting from C to Rust, pgrust introduces several modern engineering improvements: 1. Memory Safety Rust’s strict compiler guarantees eliminate major classes of security vulnerabilities (like buffer overflows and dangling pointers) that frequently patch legacy C databases. 2. Thread-Per-Connection Model Standard PostgreSQL uses a "process-per-connection" architecture, which consumes a lot of system memory. pgrust changes this to a "thread-per-connection" model, drastically reducing the overhead of open connections. 3. Massively Improved Performance Because of its optimized query engine and thread-based architecture, early benchmarks show: 50% faster execution on standard transaction workloads. Up to 300x faster execution on analytical workloads. Built with an "AI Agent Factory" Rewriting a database with millions of lines of code is a monumental task. The authors of pgrust accomplished this by setting up an automated pipeline of concurrent AI coding agents. The agents were tasked with explaining leg

What I built I wanted a workout timer that doesn't just beep at me. So this weekend I built one that writes the workout AND talks me through it, out loud, in a voice that actually sounds like it's yelling at you when things get hard. You give it a callsign, how long you've got, what you want to work, and how brutal you want it. It hands that to Gemini, which breaks the whole thing into 30-90 second intervals with a coaching line for each one. Then every one of those lines gets turned into real audio by ElevenLabs before it ever hits your browser. Nothing is pre-recorded, nothing is a fixed track. Ask for a different workout, get a completely different script and a completely different set of audio clips, generated on the spot. Demo Unedited screen recording, straight off my machine hitting the real APIs, sound included. Compose a routine, it comes back in a couple seconds, pacing curve draws itself as an SVG line, then hitting Start walks through each interval with the active one highlighted in red as it counts down and you actually hear it. The Maximum-intensity segments sound noticeably more unhinged because I turn the ElevenLabs stability knob way down for those specifically. Code https://github.com/marwankous/sonic-kinetic How I built it Go backend, one endpoint. It takes your workout params, sends a prompt to gemini-3.1-flash-lite with a JSON schema locked down tight enough that I don't have to think about parsing garbage back out of it, and gets back a full timeline plus a heart-rate pacing curve. The part I actually enjoyed was the audio pipeline. Every coaching line in the timeline gets fired off to ElevenLabs at the same time, one goroutine each behind a sync.WaitGroup , so a routine with a dozen segments doesn't take a dozen times longer than one with a single segment. Whatever comes back gets base64'd straight onto its segment. I also tie the eleven_flash_v2_5 stability setting to the segment's energy level, dropping it to 0.30 for anything marked Maximum

How I Built a Zero-Cost Facebook Auto-Poster Using Node.js and GitHub Actions Automating social media management can save hours of manual work. In this guide, I will show you how to build a fully automated, production-ready system that posts daily motivational quotes with images to a Facebook Page— completely for free , running on autopilot via GitHub Actions. We will also tackle a major pain point: resolving Meta's strict token expiration and permission structures by dynamically fetching a Page Access Token using a Meta Business System User, the officially recommended way for secure automation. 🛠️ Prerequisites Before diving into the code, make sure you have: A Facebook Page A Meta Developer Account A Meta Business Suite (Business Portfolio) A GitHub Account Basic knowledge of Node.js 🎯 Step 1: Configuring Meta Architecture for Secure Automation Meta has deprecated direct publish_actions for user tokens, making automated image uploads tricky. The professional way to solve this is by using a System User bound to a Business Portfolio . 1. Create a Meta App Go to the Meta for Developers dashboard. Create a new app, choose Business and pages as the category, and give it a clean name. 2. Link your Facebook Page Inside your App Dashboard, navigate to App Settings -> Advanced . Scroll down to the App Page section and select your target Facebook Page to link it. 3. Setup a System User Go to your Meta Business Settings ( business.facebook.com/settings ). Under Users , click on System Users and create an Admin System User (e.g., Ttp-penguin ). Click Assign Assets , select your Facebook Page, and turn on the Full Control (Everything) toggle. 4. Generate the Permanent Token Click Generate Token for that System User and select your app. Explicitly check these 3 essential scopes : pages_manage_posts pages_read_engagement pages_show_list Copy the generated token ( EAak2B... ). Save this safely —this token acts as our master key! 💻 Step 2: Writing the Automation Script We will wri

Most of us treat design systems as a functional problem: consistent colors, consistent spacing, consistent components. That part's solved for most teams now. The part nobody writes down is tone. How should this loading state feel? Should this error feel scary or manageable? Is this confirmation message robotic or human? Here's what I've learned paying attention to that layer. Four moments that carry the emotional weight In any app, four states do most of the emotional work: Loading Error Empty Success Get these four right and the whole product feels better, even if nothing about the actual functionality changed. ** Loading: ambiguity feels worse than the wait itself** jsx // Vague, slightly anxious < Spinner /> // Specific, calmer < div className = "loading-state" > < Spinner /> < p > Fetching your latest data... </ p > </ div > A spinner with no context makes people wonder if something's frozen. A spinner with a short label tells them exactly what's happening. Same wait time, different feeling. Errors: same bug, different emotional outcome jsx // Robotic " Error: Request failed with status 500 " // Human " Something went wrong on our end. Your changes weren't lost, try again in a moment. " The second version does three things the first doesn't: it's plain language, it removes blame from the user, and it tells them what to do next. That's the difference between an error that frustrates and one that reassures. Success: robotic vs genuine jsx // Robotic " Action completed successfully. " // Human " Done! Your changes are saved. " This message shows up constantly across a typical app. If it reads like a system log every time, the product feels cold. A small rewrite makes it feel like a person is on the other end. Micro-interactions: timing is part of tone too `jsx// No feedback during the wait, feels broken <button onClick={handleSave}>Save</button> // Immediate feedback, feels responsive <button onClick={handleSave}> {isSaving ? "Saving..." : "Save"} </button>` A butt

I got tired of SSHing into the box every time I shipped something, just to watch the logs come up. So I wanted a page in the admin panel where the lines scroll past as they happen, no dashboard, no Grafana, just the raw tail. The surprising part was how little I had to build for it, most of the pieces were already sitting on the server waiting for me to connect them. Here's the whole idea. The app writes JSON to stdout, systemd grabs that stdout and drops every line into the journal, and journalctl can follow the journal and hand the lines back live. All three of those already exist on an Ubuntu box. So the "live log viewer" is really just me spawning journalctl on the server and piping its output to the browser over an EventSource , which is a lot less code than it sounds like. journald is boring and well understood. SSE is boring and well understood (it's been in browsers since about 2011). Nobody gets excited about either one on its own. But snap the two together and you get a real-time log tail with no agent, no log shipper, no vendor, and nothing new to keep alive. Two defaults meeting each other and pretending to be a feature. Systemd thing People have opinions about systemd. Some of them are that you should avoid every part of it that you can, run your own supervisor, ship logs somewhere with your own daemon, and treat journald as a thing to route around. That's a fine hobby if you have the time for it. I don't. The box boots, systemd starts my unit, and when the unit writes to stdout the line ends up in the journal without me configuring anything. Being a purist here costs real hours and buys me a philosophy. Being pragmatic costs nothing and buys me a log tail. So this is the pragmatic path. If you're on a distro where journald is the default (Ubuntu, Debian, Fedora, most of them now) the setup below is basically free. Getting logs into the journal Here is the part most people overcomplicate. You do not "set up journald". You do not open a socket to it or p

Mem0 is the name everyone hears first. If your agent runs in TypeScript, TurboMem bets on a different model i.e embedded memory in your process, not another service to operate. Here is an honest comparison based on hard facts. If you are building an AI agent that needs to remember things across sessions, you have probably run into Mem0 already. It is one of the most talked about memory layers in the space, well funded and framework agnostic. But if your stack is TypeScript, there is a newer option worth a serious look: TurboMem . It takes a different architectural bet, and for a lot of TS focused companies, that bet pays off. The core difference: embedded vs server based This is really the whole story, and it is worth understanding before anything else. Mem0 is built around a separate memory service. Even in its open source form, the typical setup wires up a Postgres instance with pgvector, or Qdrant, plus optionally Neo4j for graph memory, then talks to that stack either through the Python Memory class or over an HTTP API. Every memory read or write crosses a process boundary. TurboMem skips that boundary entirely. It runs inside your Node, Bun, or browser process as a native TypeScript library. There is no sidecar, no separate memory server, and no network hop for a local memory call. You call memory.add() or memory.search() and it executes in process, backed by PGlite (a WASM build of Postgres) by default. If you are shipping a TypeScript product and want memory to behave like any other library you import, this is a meaningfully simpler model. Getting started With TurboMem, setup is about as light as it gets: npm install turbomem PGlite ships as a dependency, so the default stack (OpenAI embeddings plus PGlite storage) works right after install, no database to provision. With Mem0, self hosting means standing up actual infrastructure. The typical Docker Compose deployment involves a Postgres container with the pgvector extension, optionally a Neo4j container for

I’m VikingRob—Full-Stack Dev, SaaS Builder, and Solo Survivor. Hello I Just wanted to introduce myself. I’m Robert, but most people know me as VikingRob (thanks to a long red beard and a habit of grinding through hard Jobs with a foul mouth. Down to earth guy I'm a No B.S Person. I’ve been surviving in the trenches of solo entrepreneurship and freelancing for a while now. Lately, the market feels incredibly flooded, and landing solid, consistent work has become a massive mountain to climb. I’ve managed to keep things moving with some passive income from selling front-end and back-end sites I've built, but as anyone with a family knows, "passive" rarely means "enough" when consistency drops. I’m supporting a family of five—including a wife dealing with severe mental health challenges—so the pressure to secure steady, reliable income is incredibly real right now. To adapt, I am shifting my core focus toward offering full-scale services: Custom Website Architecture (End-to-end development) Front-End & Advanced Back-End Integration SaaS Product Development A lot of my heaviest back-end work is locked away under strict NDAs, which makes traditional portfolio-sharing tough, and I don't maintain standard social media accounts. But I know how to build clean, functional, scalable software that drives results. If you're looking to collaborate, need an engineering heavy-lifter for a SaaS project, or just want to swap freelance survival stories, let’s connect! What is everyone else doing to beat the market noise right now?

Every camera-equipped connected device you build today, from a smart doorbell to an ESP32-CAM streaming frames over Wi-Fi to a factory machine-vision rig, is a descendant of one clunky, toaster-sized prototype: the first digital camera , built at Eastman Kodak in December 1975. It weighed about 8 pounds, took 23 seconds to capture a single 0.01-megapixel black-and-white image, and recorded that image to a cassette tape. It looked like a science-fair project, but it proved a radical idea that underpins the entire IoT sensing industry: an image could be captured, digitized, and stored as data with no film at all. An engineer, a side project, and a CCD The camera was built by a 24-year-old Kodak engineer named Steven Sasson . His manager had handed him a loose assignment: could the newly invented charge-coupled device (CCD) image sensor be used to build a camera with no moving film? The CCD, developed at Bell Labs in 1969, converts light falling on an array of tiny capacitors into electrical charge, pixel by pixel. Sasson took a Fairchild 100-by-100-pixel CCD, bolted it to a lens from a Super 8 movie camera, added a digitizer, and wired the output to a portable cassette recorder. The result captured just 0.01 megapixels, a grid of 10,000 pixels. To view a photo, Sasson's team built a custom playback rig that read the tape and painted the image onto a television screen. That first image, a Kodak lab technician, took 23 seconds to write to tape and several more to display. Crude, yes, but it was the first fully electronic, filmless photograph. Why Kodak shelved the future Here is the twist that every embedded engineer should remember. Kodak owned the patent on the first digital camera, but the company made its money selling film, chemicals, and photo paper. Executives saw a filmless camera as a threat to that business, so the project was quietly set aside. Kodak did file the patent in 1978 and collected licensing revenue for decades, but it never led the digital transiti

Article URL: https://www.ycombinator.com/companies/moss/jobs/52LnqLQ-software-engineer-sdk Comments URL: https://news.ycombinator.com/item?id=48865332 Points: 0 # Comments: 0

The headless iOS app publishing pipeline for AI agents. Discussion | Link

In a rare combined cybersecurity/squid post, a twenty-nine-year-old squid proxy bug can leak HTTP requests. As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.