"Forward Deployed Engineer" went from a Palantir-specific title to one of the hottest roles in AI in about eighteen months. But nobody had actually counted the market, so I did. I pulled every open FDE role I could find from public ATS job boards (Greenhouse, Lever, Ashby) across 11 companies and analyzed all 292 of them. Here is what the data says. Who is hiring Three companies account for 250 of the 292 openings: Palantir: 95 (they coined the title, and still call many of these roles "Deployment Strategist") Databricks: 85 OpenAI: 70 Then a long tail: Cohere and Scale AI (13 each), Sierra, Writer, Modal, Baseten, Ramp, and Sardine. What it pays Of the 40 roles that disclosed a US pay band, the median ran $197K to $294K , topping out at $390K plus equity at OpenAI and Sierra, with a floor around $137K. That is senior-software-engineer money for a role a lot of engineers have never heard of. International and most Palantir roles did not publish bands, so the true market is likely even broader. Three things that surprised me 1. 98% of these roles are customer-facing. This is the defining trait. It is not a backend role with occasional meetings. It is an engineer who lives in the customer's world, and if that sounds terrible to you, this is not a role you would enjoy occasionally. It is the whole job. 2. The title is chaos. The same role goes by at least four names: Forward Deployed Engineer (152), Forward Deployed Software Engineer (58), AI or Deployment Engineer (43), and Deployment Strategist (36). If you only search one term, you miss most of the market. 3. The job descriptions undersell the technical bar. JDs emphasize customer-facing work, cloud (AWS/GCP/Azure), Python, and integrations. But SQL and algorithms show up in only about a third of them, even though every FDE loop I have seen tests live coding and SQL under time pressure. The description sells the breadth. The interview tests the depth. The other details Geography: about 48% USA, but genuinely global

If you're using AI coding assistants like ChatGPT, Claude, Cursor, or Lovable, you've probably accumulated dozens of JSX components. Generating them is incredibly fast. Previewing them? Not so much. The Typical Workflow Every time I received a JSX component, I found myself repeating the same process. Create a React project (or open an existing one) Copy the JSX file Install dependencies Fix missing imports Run the development server Wait for everything to compile All of that... just to see one component. It felt like unnecessary overhead. There Had to Be a Better Way I asked myself a simple question: Why can't I just double-click a JSX file and preview it? We can instantly open images, PDFs, videos, and text files. Why should JSX files require an entire development environment? That's what inspired me to build PreviewKit . What is PreviewKit ? PreviewKit is a lightweight Windows application that lets you preview frontend components instantly. Supported file types include: ✅ JSX ✅ Vue ✅ HTML No project setup. No dependency installation. No terminal commands. Just open the file and see the result. Why I Built It AI has dramatically changed frontend development. We're no longer spending most of our time writing components—we're reviewing, comparing, and refining them. That means fast visual feedback is more important than ever. I wanted a tool that removed the repetitive setup process so I could focus on building better interfaces instead of preparing a preview environment. Who Is It For? PreviewKit is useful if you: Build React applications Work with Vue components Test standalone HTML files Generate UI with AI tools Review components from teammates Prototype interfaces quickly If opening frontend files feels slower than it should, PreviewKit was built for you. The Goal Isn't to Replace Your Framework You'll still use React. You'll still use Vue. You'll still use Vite or Next.js. PreviewKit isn't trying to replace your existing workflow. It simply removes one frustrat

The most dangerous moment in an AI-built app project is not when the code breaks. It is earlier. It is the moment where your idea is still blurry, the AI coding tool is sitting there politely, and you type: Build me an app that... That sentence feels productive. It also gives the tool permission to make a pile of decisions you have not made yet. Who is the app for? What is version one? Which workflow matters first? What data has to exist? What should not be built yet? What would make the first version successful? If those answers are missing, AI has to guess. And AI guessing at product shape is how beginners end up with a login system, dashboard, profile editor, notifications panel, admin area, billing flow, and settings page before one real user problem has been solved. That is not momentum. That is software confetti. I like AI coding tools. I use them heavily in real app work. But the tool gets much better when the project has boundaries before code starts changing. So before you ask AI to code your first app, run the idea through a checklist. Not a giant business plan. Not a pitch deck. Not a 47-tab spreadsheet that makes you feel like you joined a corporate strategy retreat by accident. A practical beginner checklist. The goal is simple: turn a rough app idea into something AI can help you build without inventing the whole product for you. 1. Can You Name The Person? Do not start with "users." Start with one person you can picture. Bad: This app is for people who want to be more productive. Better: This app is for freelance designers who need one place to track client feedback, revision status, and final file delivery. Bad: This app is for musicians. Better: This app is for guitarists who want to capture riff ideas quickly on their phone without opening a full mobile studio app. Bad: This app is for students. Better: This app is for college students who want to scan textbook chapters and turn them into study notes before an exam. When you name the person, the ap

▶ Try it live (in your browser): https://umbraaeternaa.github.io/loom/play.html Built solo, in the open, from Ukraine 🇺🇦. The problem nobody can scale their way out of AI now writes a large and growing share of the code that runs in the world. The uncomfortable part isn't that the code is often wrong — it's that the same model frequently writes both the code and the tests that check it. When one intelligence authors the solution and the criteria, "it passed" quietly stops meaning "it's safe." The gate becomes foolable. You can make the model bigger, but a bigger model that grades its own homework is still grading its own homework. The honest answer isn't "trust a smarter model." It's: trust only what can be independently proven — and make that proof mechanical, not a matter of hope. That is the whole idea behind LOOM. What LOOM is LOOM is a small, open-source, effect-typed language that acts as a machine-checked trust layer for AI-written code. It doesn't just run code — it proves, at a gate, exactly what the code is allowed to do, before a single line executes. If the code lies about what it does, the compiler refuses it. The slogan is: AI proposes, the compiler disposes. Today it is a research kernel with 385 self-verifying checks, all green — every feature added only with an adversarial test, so the language can only ever get greener. There's a live browser playground where a stranger can paste a program and watch the checker accept or reject it in under a minute. What it can actually do Effect honesty. Every function declares its effects — Pure, IO, Net, Alloc, FFI, Rand. Declared effects must cover what the code actually does; the lie is caught transitively through calls, branches, recursion — not just straight-line code. Capabilities, not ambient power. A foreign call has no ambient authority — un-wrapped, it's refused. A seam is the only thing that grants authority, so (seam (Pure) (ffi untrusted)) makes that code's I/O physically impossible. Reinterpreting h

TL;DR— Most production AI teams build safety layers using the content-moderation mental model: classify input, classify output, block or pass. But the incidents that actually take down AI systems in production look like distributed-systems failures— retries amplifying bad state, cascading errors across agent steps, silent drift with no rollback path. Guardrails need to borrow from SRE, not from trust-and-safety. Ask a team how they handle AI safety in production and you'll get the same answer almost every time: an input classifier, an output classifier, maybe a moderation API bolted on the side. This is the content-moderation mental model— filter bad stuff in, filter bad stuff out. It's borrowed wholesale from trust-and-safety teams who spent a decade building spam filters and abuse detectors. It's also the wrong model for most of what actually breaks AI systems in production. The incidents that page you at 2am rarely look like a jailbreak slipping past a classifier. They look like distributed-systems failures: a retry loop that amplifies a bad tool call, a hallucinated intermediate result that poisons every downstream step, a silent shift in output distribution that nobody notices until a customer complains three weeks later. These are not content problems. They're systems problems, and they need systems solutions. The Cascade, Not the Jailbreak Consider a typical agent pipeline: retrieve context, call a model to plan, call tools, call a model again to synthesize, maybe loop if a tool fails. Each step has some non-zero error rate. In a single-call chatbot, that error rate is the whole risk surface. In a five-step agent chain, errors compound, and worse, they compound non-linearly because failed steps often trigger retries, and retries on a stateful action are not free. A model that hallucinates a tool argument doesn't just produce one bad output— it produces a bad state that the next step reasons over as if it were true. If that next step is another LLM call, it wi

Wearables are best at noticing breaks from your body's usual patterns. Those outliers can hint that something warrants further investigation with your doctor.

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For the past two years, most of us integrated AI into our workflow using a "ping-pong" model: we write a prompt, get some code, copy-paste it, hit a bug, and paste the error back. But in 2026, the tech stack is shifting from simple chat interfaces to Autonomous AI Agents . We aren't just talking about smarter chatbots. We are talking about production-ready systems that can plan, use specialized tools, debug themselves, and interact with our local development environments. The Core Blueprint of an AI Agent Unlike a standard LLM call that finishes after a single response, an AI Agent operates in an Evaluate-Act-Learn loop. To actually build or interact with one, you need to understand its three core pillars: State & Memory: Maintaining context across complex, multi-step tasks (both short-term session state and long-term vector-based memory). Planning & Reflection: The ability to break down a high-level goal (e.g., "Scrape this e-commerce site and update our DB schema" ) into a sequence of executable tasks, and pivot if a step fails. Tools (The Game Changer): Giving the model execution capabilities via APIs, sandboxed code execution environments, and file system access. Enter MCP: The Architecture Connecting It All The biggest catalyst for this shift right now is the adoption of the Model Context Protocol (MCP) . Think of MCP as an open standard that acts like a universal adapter. Instead of writing custom, brittle glue-code for every single tool you want an AI to use, MCP provides a secure, structured way for LLMs to safely read and write to local repositories, query databases, or trigger deployment pipelines. [ AI Agent ] ──( MCP Protocol )──► [ MCP Server ] ──► [ Local Files / DB / API ] When an agent is plugged into your workspace via MCP, it doesn't just guess what your code looks like. It can scan an entire TypeScript repository, map out your Tailwind components, identify type mismatches, and apply a refactor across multiple files simultaneously. From Dev to Arch

If you've ever managed a large collection of images, you've probably experienced this. Editing the images is only half the job. After exporting them, you still need to add: Titles Descriptions Alt text Keywords IPTC/XMP metadata For a handful of images, that's manageable. For hundreds of images, it becomes one of the most repetitive tasks in the entire workflow. The Problem I searched for a Windows application that could: Generate image metadata with AI Write IPTC and XMP metadata directly into image files Process multiple images in bulk Still allow full manual editing I found tools that handled parts of the workflow. Some could edit metadata. Some could generate AI text. But I couldn't find one focused on Image SEO from start to finish. So I decided to build it myself. Building Image SEO AI The project eventually became Image SEO AI , a Windows desktop application built specifically for creators who need to optimize image metadata. Instead of replacing existing photo editors, the goal was to eliminate repetitive metadata work. Today, the application can: Generate image titles with AI Create SEO-friendly descriptions Generate alt text Suggest relevant keywords Write IPTC & XMP metadata Process up to 50 images in a single batch Support both AI-assisted and manual editing One Challenge I Didn't Expect The biggest challenge wasn't AI. It was designing a workflow that still felt familiar. Many users don't want AI to make every decision. Sometimes they just want a better starting point. That's why every AI-generated field can be edited before saving. The application is designed to speed up repetitive work—not remove user control. Lessons Learned Building this project taught me a few things. AI works best as an assistant, not a replacement. Small workflow improvements can save hours every week. Metadata management is still an underserved problem. Simplicity often matters more than adding more features. What's Next? I'm continuing to improve Image SEO AI based on user feed

I broke my own project on purpose, twice, before it worked. Here's the story. Round one: the debate club My first idea for this hackathon sounded great in my head. Six AI agents, each with a "role" — security, architecture, performance, whatever — and they'd debate each other across multiple rounds before agreeing on a final answer. Like a mini panel of experts arguing it out. I built it. I ran it against some vulnerable test code. It came back with 127 findings. I got excited for about four minutes. Then I actually read them. Maybe three were real. The other 124 were the agents politely agreeing with each other about problems that didn't exist, or restating the same bug five different ways because five different agents happened to notice it. Precision was somewhere around 2%. Worse than a single model working alone. That stung a little, not going to lie. I'd spent days on the debate logic. Round two: quieter, and better So I ripped it apart. No more debate rounds. No more six agents shouting over each other. I went down to four, gave each one exactly one job, and — this is the part that actually fixed things — made them depend on each other in order instead of all firing at once. One agent maps out the code first. Two others use that map to look at security and quality separately. A last one compares what they found, throws out duplicates, and — importantly — actually checks the line numbers against the real file instead of trusting the AI's word for it. Same test file. This time: real vulnerabilities, correctly flagged, nothing made up. Point it at clean code afterward and it correctly said nothing was wrong, which honestly felt like a bigger win than finding the bugs did. The annoying lesson I wanted this project to feel impressive. More agents, more debate, more "look how sophisticated this is." What actually worked was the boring answer: fewer agents, clear roles, one checking the other's work instead of everyone talking at once. I named the final version Synod

If you want remote job data, you do not need to scrape HTML or sign up for anything. Four of the bigger remote job boards publish keyless public feeds. The catch is that they all speak different dialects, so the real work is normalization. Here are the endpoints and the traps. The four feeds RemoteOK returns its whole current board as one JSON array: GET https://remoteok.com/api The first element is a legal notice, not a job: they ask for a link back with attribution as a condition of using the feed. Skip element zero, and honor the attribution if you republish. Jobs carry salary_min and salary_max as numbers, tags, and ISO dates. Remotive has the friendliest API of the four, including server side search: GET https://remotive.com/api/remote-jobs?search=python&limit=100 Salary here is free text ( "$120k - $160k" ), so do not expect numbers. Attribution with a link back is required here too. WeWorkRemotely publishes RSS: GET https://weworkremotely.com/remote-jobs.rss Two quirks: the company name is not a field, it is baked into the title as Company: Role , so split on the first colon. And useful data hides in nonstandard tags like <region> , <skills> , and <category> that generic RSS parsers drop on the floor. Himalayas has a proper paginated API with a surprisingly deep catalog (100k+ listings): GET https://himalayas.app/jobs/api?limit=100&offset=0 It gives structured minSalary / maxSalary with a currency and period, seniority arrays, location restrictions, and even timezone restrictions as UTC offsets. Dates are epoch seconds, not ISO strings. The normalization layer The row schema that survived contact with all four sources: { "source" : "Remotive" , "title" : "Senior Backend Engineer" , "company" : "Acme Corp" , "tags" : [ "python" , "aws" ], "salaryMin" : null , "salaryMax" : null , "salaryText" : "$120k - $160k" , "location" : "Worldwide" , "postedAt" : "2026-07-03T20:01:13.000Z" , "applyUrl" : "https://..." } Rules that mattered in practice: Keep both salary sh

Understanding the 550 SPF Check Failed Error The "550 SPF Check Failed" error indicates that a receiving mail server rejected an incoming email. This rejection occurs because the sender's domain failed its Sender Policy Framework (SPF) validation. SPF is an email authentication protocol defined in RFC 7208 . SPF helps prevent email spoofing. It allows domain owners to specify which mail servers are authorized to send email on behalf of their domain. Receiving mail servers perform an SPF check by querying the sender's DNS for an SPF TXT record. If the sending server's IP address is not listed in the domain's SPF record, the SPF check fails. The receiving server then rejects the email based on its configured policy, often resulting in a 550 error. This error protects recipients from unauthorized emails and enhances email security. Initial Diagnosis: Identifying the Root Cause Diagnosing an SPF failure requires examining the bounce message and the domain's DNS records. The bounce message often provides specific details about the SPF failure. Look for phrases like "SPF validation failed," "unauthorized sender," or "IP address not permitted." Common reasons for a 550 SPF Check Failed error include: Missing SPF Record: No SPF TXT record exists for the sending domain. Incorrect SPF Syntax: The SPF record contains errors, making it unreadable or invalid. Incomplete SPF Record: The SPF record does not list all legitimate sending IP addresses or hostnames. DNS Lookup Limit Exceeded: The SPF record requires more than 10 DNS lookups, violating RFC 7208. DMARC Policy Enforcement: A DMARC (Domain-based Message Authentication, Reporting, and Conformance) policy ( RFC 7489 ) with p=reject or p=quarantine is in place, enforcing strict SPF failure handling. To begin diagnosis, use our SPF checker to verify your domain's SPF record and its validity. This tool quickly identifies syntax errors and lookup issues. Step-by-Step Troubleshooting and Resolution Resolving SPF failures involves

Understanding DMARC and the 'p=none' Policy DMARC (Domain-based Message Authentication, Reporting, and Conformance), defined in RFC 7489, is an email authentication protocol. It builds upon SPF (Sender Policy Framework, RFC 7208) and DKIM (DomainKeys Identified Mail, RFC 6376) to provide domain owners with greater control. DMARC instructs recipient mail servers on how to handle emails that fail authentication and provides reporting on these failures. The p=none policy is often adopted as a preliminary step in DMARC implementation. It instructs recipient servers to take no specific action on emails failing DMARC alignment. Its primary function is to enable the collection of aggregate and forensic reports without impacting email deliverability. Many organizations view p=none as a safe, non-disruptive way to begin their DMARC journey. This initial perception, however, overlooks critical security implications. While it offers visibility, p=none provides no actual enforcement against malicious email. The Critical Security Vulnerability of p=none The fundamental flaw of DMARC p=none lies in its complete lack of enforcement. When a DMARC record is set to p=none , recipient mail servers will not block, quarantine, or reject messages that fail DMARC authentication. This includes emails that spoof your domain directly. Threat actors exploit this vulnerability to conduct phishing, business email compromise (BEC), and brand impersonation attacks. They can send emails appearing to originate from your legitimate domain, knowing that p=none offers no protective barrier. The recipient mail server simply delivers the fraudulent message. This policy effectively leaves your domain unprotected against direct domain spoofing. Despite having a DMARC record, your organization remains susceptible to advanced phishing techniques. The security posture of your email ecosystem is compromised. The Illusion of Insight: Data Without Action DMARC p=none does provide valuable data through its repor

As part of an ongoing legal dispute with three Hollywood studios, Midjourney is seeking to compel those studios to reveal how they use AI themselves.

The company's robotaxi roadmap mentions future expansions to Orlando and Tampa.