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n8n is the open-source workflow automation tool that competes with Zapier and Make. I have been running it for saas.pet's content pipeline for 6 months. Here is my honest take on self-hosting n8n versus paying Zapier, and whether it is worth the hassle. What n8n does that Zapier cannot n8n is an open-source workflow automation platform with 400+ built-in integrations. You connect nodes on a visual canvas: when X happens in one app, do Y in another. The killer difference from Zapier: you control where it runs. Self-host on a $5/month VPS or run on n8n Cloud at $20/month. No per-task pricing, no 'you hit your zap limit' emails. For high-volume workflows, the cost difference is dramatic. I run n8n on the same $6/month HK server that hosts my proxy. 12 workflows handle saas.pet's entire content pipeline: daily data fetch from GitHub Trending API, transform JSON, write to data files, trigger build, push to git, notify me on Telegram. The same workflows on Zapier would cost $73.50/month (Professional plan with 2,000 tasks). On n8n, $6/month for the server plus $0 for the software. The self-hosting overhead is real—updates, SSL certs, monitoring—but for 12+ active workflows, the savings are $800+/year. If you only have 2-3 simple zaps, stay on Zapier's free tier. If you have 5+ workflows with volume, n8n pays for the hosting in month 1. My 6-month setup for saas.pet I run n8n in Docker on the HK server. The initial setup: install Docker, pull n8n image, configure nginx reverse proxy, set up SSL via Certbot. That took about 2 hours the first time. Now I can deploy n8n in 15 minutes on a fresh server. The 12 workflows: (1) Daily GitHub trending fetch via saas.pet/api/trending, (2) data transform to unified JSON, (3) write to data/YYYY-MM-DD.json, (4) trigger build-ci.mjs, (5) git add + commit + push, (6) Telegram notification with commit SHA, (7) weekly sitemap health check, (8) monthly backup of reviews/ JSONs to S3, (9) uptime ping every 15 minutes, (10) DNS health check,

Notes from bringing up a PCIe WiFi module on i.MX8MQ; symptoms and how to diagnose them. Phy link never came up — what does this mean? This message is typically seen in dmesg as shown below. [ 3.828121] imx6q-pcie 33800000.pcie: iATU: unroll T, 4 ob, 4 ib, align 64K, limit 4G [ 4.807241] imx6q-pcie 33c00000.pcie: Phy link never came up [ 4.841482] imx6q-pcie 33800000.pcie: Phy link never came up [ 5.821279] imx6q-pcie 33c00000.pcie: Phy link never came up [ 5.830481] imx6q-pcie 33c00000.pcie: PCI host bridge to bus 0001:00 [ 5.854997] imx6q-pcie 33800000.pcie: Phy link never came up [ 5.862205] imx6q-pcie 33800000.pcie: PCI host bridge to bus 0000:00 It means one of the following The PCIe peripheral is not powered up. PCIe reset is not deasserted, so the chip is in reset. This could be because the DTB is deasserting an incorrect GPIO. Reference clock is not enabled Using the incorrect PCIe controller in the device tree. As we can see that both the PCIe controllers can report this. So first determine which controller is the peripheral hooked to. More on this in the next section. Which PCIe controller is my device on? ( &pcie0 vs &pcie1 ) The rule here is to match by address and not by label/name. If the schematic calls out controllers as PCIE1 and PCIE2, and the device tree lists pcie0 and pcie1, understand the mapping. The DTS label is arbitrary - match by register base ( @address in the node name), which is the same in the DTS reg and the reference memory map. For definitive addresses look in the .dtsi , as sometimes the manuals are misleading. Given below is a mapping table for i.MX8MQ DTS. | ADDRESS (in .dtsi) | Silicon (RM) Label &pcie0 | 0x33800000 | PCIe1 &pcie1 | 0x33c00000 | PCIe2 The addresses are listed in the chip’s memory layout are from processor reference manual. Below is snapshot from the i.MX8MQ reference manual, where the layout for the core A-53 is listed. Start Address | End Address | Size | Description 3381_0000 | 3381_3FFF | 4MB | PCIe-2 << inco

You ship all the time. A fix here, a new setting there, a feature you spent a whole weekend on. And your users mostly don't notice. That gap is expensive. When people can't see a product moving, it feels abandoned, even when you're shipping every week. They churn a little faster, they email asking for things you built a month ago, and all the momentum you're actually creating stays invisible. The fix is boring and old: a changelog. But not a changelog rotting in a Notion doc nobody opens. One that shows up inside your app , where users already are. Here's the approach I settled on. The idea: a widget, not just a page A "what's new" widget is a small button or badge in your UI. Click it, and a panel slides out with your latest updates. Users see it in the flow of using your product, not on some /changelog page they'd never visit. You really want three things: An in-app widget users actually see. A public page and RSS feed you can link from emails and docs. A way to write updates in plain language and publish in a click. The one-tag version I ended up building a tool for this (honest disclosure below), but the integration is the part worth showing, because it's the pattern any changelog widget should follow: <!-- Paste before </body> --> <script src= "https://cdn.patchlog.io/widget.js" data-project= "your-project-id" data-position= "bottom-right" async ></script> One script tag. No SDK, no npm install, no framework coupling. It behaves the same in React, Vue, Rails, or a plain HTML page. Two implementation details matter, whether you build one of these yourself or evaluate an existing one: Render it in a Shadow DOM. A changelog widget should not inherit or leak styles. If it uses the host page's global CSS, it will look broken on half the sites it lands on. Shadow DOM isolates it completely. Fail silently. A marketing widget must never break the host app. If the network call fails, it should quietly do nothing. What to actually write in it The tool is the easy part. T

Reinforcement learning uses error information to adjust control algorithms.

Last year, we told you about Xtra, the company that lets DJI sneak its popular cameras into the US, and Skyrover, a brand seemingly selling DJI drones in disguise. They're just two of the many firms DJI is suspected of starting to skirt the United States' foreign drone ban. But it appears the FCC is […]

The café was crowded that evening, but to me, the world had fallen completely silent. An open...

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The incident that taught me to respect pinning looked like nothing. A service freshly migrated to virtual threads, a load test that plateaued at about 420 requests per second no matter how much traffic we threw at it, CPU sitting at 9%, zero errors, zero warnings, nothing in the logs. The machine had 8 cores, and the one downstream HTTP call in the hot path took about 19 ms. Do the arithmetic: 8 × (1000 / 19) ≈ 421. The service that was supposed to scale to millions of virtual threads was serving exactly one request per CPU core. Loom had quietly handed us back a bounded thread pool, and the code looked perfectly innocent. That failure mode has a name — pinning — and this is the field guide I wish I'd had that night: what it is, the two (and only two) things that cause it, what JDK 24 changed, and how to catch it before your throughput graph does. What pinning actually is A virtual thread doesn't own an OS thread. It runs on a small pool of platform threads called carrier threads — concretely, the workers of a dedicated ForkJoinPool living in a thread group named CarrierThreads , with default parallelism equal to Runtime.availableProcessors() . When a virtual thread blocks — on I/O, a lock, a queue — it normally unmounts : it saves its stack, steps off the carrier, and frees that carrier to run another virtual thread. That unmount is the entire trick that lets a handful of OS threads serve millions of virtual ones. Pinning is when the unmount can't happen. The virtual thread blocks but stays mounted, and its carrier sits there doing nothing useful for the whole duration. One pinned carrier is a rounding error. But the default carrier pool is only as big as your core count, so if a hot path pins routinely, you pin every carrier at once — and then no virtual thread anywhere makes progress. That's not a slowdown; it's scheduler starvation, and from the outside it looks a lot like a deadlock. You can raise the ceiling with -Djdk.virtualThreadScheduler.parallelism=N , bu

When building an AI agent, you need LLM calls. It can be done via a remote API or a local API, but either way you need to do it. Whether the agent is a simple conversational agent or a ReAct agent with a bunch of tools, whether it's using a complex graph or a simple RAG, it must be based on the concept of sending prompts to the language model. But, what if we replace the language model with... another agent? Let's say we already have a smart agent with a bunch of tools that can handle complex problems. Why not use it to build a new agent on top of it? This way we can focus on the specific custom functionality we want to achieve, while already having the common functionalities covered by the underlying agent. You might think this must be expensive. You get a better performance, so you have to pay for it, right? Well, not necessarily. The catch is that the coding assistants are actually surprisingly cheap when compared to API prices. They offer much more than raw LLM calls, they offer amazing agent functionality, but the cost is actually lower, and it's not a small difference. The cost of LLM calls per 1M tokens is usually between $2 and $7. For coding assistant subscriptions, it's a bit more tricky to calculate because you pay for monthly subscription, but it can be still converted to per 1M tokens cost, and from what LLM just told me it is around $0.08 to $2. That's a huge difference! And the complex agents are cheaper than raw LLM's! according to ChatGPT: Service Cost per 1M tokens Codex / ChatGPT coding plan ~$0.08 Cursor Pro ~$0.08–0.25 GitHub Copilot Pro ~$0.10–0.30 Claude Pro / Claude Code ~$0.74 GPT-5 API ~$2.1 Claude Sonnet API ~$4.2 Claude Opus API ~$7.0 according to Claude: Service Cost per 1M tokens Haiku 4.5 (API) $1.80 Sonnet 5 (API, intro thru Aug 31 '26) $3.60 Sonnet 5 (API, standard) $5.40 Opus 4.8 (API) $9.00 Fable 5 (API) $18.00 Claude Code — Pro ~$1.10–$2.15 (est.) Claude Code — Max 5x ~$2.10–$4.15 (est.) Claude Code — Max 20x ~$2–$4 (est.) So, why

"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 […]

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

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

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?

Apple alleges the misconduct was directed by OpenAi's senior leadership, including a long-time former employee.

The iPhone maker claims OpenAI encouraged poached employees to bring over confidential presentations, secret prototypes, and key supplier details.

The Quest shipwreck is in worse shape than expected, but it has turned into a thriving marine ecosystem.

When Niantic dropped the first Pokémon Go trailer in 2015, it was hard to grasp how a bunch of players could work together to catch a pokémon like Mewtwo. But this week at the game's 10th anniversary event in New York City, Pokémon Go showed the world how it's done. Almost 2,000 players (many of […]