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A post scheduled to publish at a specific time doesn't go live when expected. A plugin's recurring email notification never arrives. This tends to happen on low-traffic sites, and there's a specific reason for it. Note: WP-Cron is WordPress’s built-in scheduling system. It sounds like the OS-level cron daemon, but the underlying mechanism is quite different. WordPress’s WP-Cron doesn’t work like a real OS cron daemon. On every page load, WordPress checks whether any scheduled task is past its due time and, if so, runs it. This is what's known as "pseudo-cron" — and its weakness is that nothing runs without a page visit . Schedule a post to publish at 3am on a site with little overnight traffic, and the publish task can sit unexecuted until the next visitor happens to load a page. WP-CLI lets you look inside this otherwise invisible system and run exactly the task you need, right now. Listing what's scheduled wp cron event list hook next_run_gmt recurrence publish_future_post 2026-06-20 03:00:00 - wp_version_check 2026-06-20 06:12:00 12 hours wp_scheduled_delete 2026-06-21 00:00:00 daily hook is the task's identifier, next_run_gmt is the next scheduled run time in UTC, and recurrence is the repeat interval. If publish_future_post is still listed despite its time having already passed, that confirms the task is overdue simply because no page load has triggered it yet. Running a task right now To trigger a specific task immediately: # Run a specific hook right now wp cron event run publish_future_post # Run every overdue task at once wp cron event run --due-now --due-now finds every task whose scheduled time has passed but hasn't run yet, and executes all of them. Instead of waiting for a visitor to trigger the check, this one command runs the post publish, the email notification, or whatever else is pending. Confirming WP-Cron itself is working wp cron test This checks whether the WP-Cron scheduler is functioning at all. On sites where wp-config.php has define('DISABL

Every MCP server I've read that wraps a third-party REST API has the same shape: someone picked fifteen or twenty endpoints that seemed useful, hand-wrote a Zod schema for each, and shipped it. That works for about three months. Then the API adds a field, deprecates an enum value, renames a query parameter. The wrapper doesn't notice, because nothing in it is connected to the API's own description of itself. The schemas drift. The model starts getting rejected by the upstream API for reasons it can't see, and you get to debug an LLM guessing at a shape that stopped being true in February. The other failure is quieter. You need endpoint twenty-one — the one about persistent disks, or scaling, or environment groups — and the author skipped it. Now you're back to curl , except the agent is holding half the context and you're holding the other half. I wanted neither, so I built render-useful-mcp : an MCP server for Render where every API tool is generated from Render's own OpenAPI document. All 207 endpoints, no curation. The generating part took a weekend. Everything after that was the actual work. Make the generator refuse to guess The tempting way to write a spec-to-tools generator is to make it forgiving. Skip what you can't parse, fall back to { type: "object" } when a $ref gets hairy, log a warning and move on. You get 207 tools on the first run and feel great. You also get tools that lie. A parameter typed as a free-form object when the API actually wants one of four enum values is worse than no tool at all — the model will confidently produce garbage, and the failure surfaces three layers away from the cause. So the generator is fail-closed. It aborts the build, loudly, on: An operation whose tag doesn't map to a known toolset. Render added a resource category and I haven't classified it yet. That's my problem to solve, not something to paper over. A tool name collision. Two operations deriving the same name means my naming scheme is wrong. A cyclic $ref it can'

Microsoft pitched its own homegrown AI models, harnesses, and even a Mythos competitor on Wednesday, telling Wall Street it plans for continued growth.

I've spent enough time in the trenches of software engineering to know that there is nothing more soul-crushing than writing 'glue code.' You know exactly what I mean—the thousands of lines of boilerplate, error handling, and webhook listeners required just to make two services talk to each other. When Bland AI first arrived on the scene, it was essentially another API you had to integrate. You'd write a Node script, handle the async nature of outbound calls, manage your credentials in environment variables, and then spend weeks building a dashboard just so you could see what happened during a call. It worked, but it wasn't intelligent. The shift we are seeing right now with the Model Context Protocol (MCP) changes the fundamental architecture of integration. We are moving from 'integration as an engineering task' to 'integration as a capability.' Instead of writing code to bridge Bland AI and your application, you provide an MCP server that gives your LLM—whether it's Claude or Cursor—direct access to those telephony tools. I recently started using the Bland AI MCP server via Vinkius, and the difference in how I can orchestrate workflows is night and day. This isn't about just 'making a call.' It's about giving an agentic loop control over a communication channel. The Architecture of Voice Orchestration When you look at traditional API integrations for something like Bland AI, you focus on the request/response cycle. You send a payload to trigger a call, and then you wait for a webhook to notify your backend that the call is finished. With this MCP server, the mental model shifts. You aren't managing webhooks; you are managing tools. The toolset provided here—including send_phone_call , create_voice_agent , and list_recent_calls —allows an LLM to act as a telephony engineer. Here is what happens when you actually use it in Cursor or Claude: You don't just say "Make a call." You can instruct the agent, "Look at my recent calls from yesterday, find any where the tran

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry. You know those...

When Microsoft reported killer fourth-quarter earnings for its fiscal 2026 year (which ended June 30), it tucked in an interesting little tidbit about how its investments in the two biggest, and competing, AI labs are doing.

On the company’s second-quarter earnings call Wednesday, CEO Mark Zuckerberg said Meta sees a “large enterprise opportunity” spanning AI agents, APIs, compute, and internal software.

In my previous article , I built a fully local RAG assistant Ollama, ChromaDB, LangChain, all running in Docker. It answered technical support questions by searching through documentation and citing sources. It worked. But after using it for a while, I noticed something uncomfortable: it treated every question the same way . Ask it "how to close monthly payroll?" it searches the docs. Fine. Ask it "the server crashes at startup" it also searches the docs. Less fine. Ask it something completely outside the documentation it searches the docs. Useless. A real support technician doesn't do that. They first assess the situation, then decide what to do: look it up, run a diagnosis, or escalate to a human. My RAG had no such judgment. That's what this article is about how I evolved the system into an Agentic AI architecture using LangGraph, where the assistant first decides which strategy to use , then acts accordingly. The Core Limitation of Classic RAG Classic RAG is a linear pipeline. Every query follows the exact same path: Question → Embed → Retrieve → Prompt → LLM → Answer No branching. No decision-making. No memory between steps. This works perfectly for procedural questions where the answer lives in the docs. But technical support involves at least three distinct scenarios: Scenario Example Best strategy Procedural question "How do I create an account?" Search documentation Known error code "ERR-COMP-001 appears" Lookup error database Unknown incident "Server crashes, no idea why" Diagnose + escalate if needed A single RAG pipeline handles the first case well and the other two poorly. The solution is to add a layer of reasoning before retrieval. What Agentic AI Adds The shift from RAG to Agentic AI comes down to one thing: the system plans before it acts . Instead of one fixed pipeline, you have: Question ↓ Classifier (what kind of question is this?) ↓ ├── Procedural → RAG Agent (search docs) ├── Error code → Diagnostic Agent (lookup + LLM analysis) └── Complex → D

RAMageddon won't be the only reason your next phone costs more - Qualcomm is about to raise prices on all its processors, as well. Qualcomm CEO Cristiano Amon said on Wednesday that "prices are going to go up" on the company's products starting on September 1st, CNBC reports. The price hikes were rumored last week […]

Open-source voice dictation for Mac, fully on-device Discussion | Link

Everyone evaluating AI avatar platforms focuses on voice quality. The bigger UX killer is almost always latency — and it's a harder problem than picking a good TTS provider. Where the delay actually comes from: User speaks/types → STT (if voice input) → LLM generates response (streaming helps, but first-token latency matters) → TTS converts text to audio → Audio playback + lip-sync rendering Each hop adds latency. A naive implementation that waits for the full LLM response before starting TTS can easily hit 2-4 seconds of dead air — long enough for a user to assume the bot is broken. How production systems actually solve this: Token streaming into TTS — start synthesizing audio on partial LLM output (sentence-by-sentence chunks) instead of waiting for the full response Speculative rendering — start lip-sync animation slightly ahead of audio using predicted phoneme timing WebSocket/SSE persistent connections — avoid the overhead of repeated HTTP round-trips per turn Regional API routing — TTS/LLM provider latency varies a lot by user geography; this matters more than most benchmarks show A practical note: platforms that advertise "real-time" avatars but load all logic behind a single request/response cycle will feel noticeably worse than ones built around streaming pipelines, even if they use the identical LLM and TTS providers underneath. If you're evaluating a platform (or building one), test with realistic network conditions, not office wifi — that's where the architecture differences actually show up. Bottom line: the voice provider matters less than people think. The orchestration around it — how aggressively you stream and pipeline each stage — is what separates a "wow" demo from a production-ready conversational agent.

If this is too long, tldr : Google Conway’s Law wath yt video and think There is a popular rule in software development called Conway's Law. It says that organizations design systems that mirror the way people inside those organizations communicate. In simpler terms: Your architecture will eventually look like your team structure. Big company with separate frontend, backend, data, DevOps, and platform teams? You will probably end up with separate services, separate processes, separate ownership, and a lot of API calls between people who sit in different Slack channels. But what happens when the entire company is just two people? That is where things get interesting. At bundle.social, we are running a unified social media API that handles a lot of edge cases. And there are two of us. There is no dedicated platform team No analytics department No infrastructure group. No product manager translating customer feedback into Jira tickets. Just two people are trying to keep a fairly large system moving without turning it into a pile of slop services nobody fully understands. You would think Conway's Law does not really apply to such a small team. It absolutely does. It just shows up differently. How Conway’s Law Works in a 2-Person Team When you have 50 developers split across departments, Conway's Law creates microservices and cross-team dependency hell. When you have two developers, Conway's Law forces your system into one of two extremes: The "Two Halves of a Brain" Split: Service A belongs entirely to Person A, and Service B belongs entirely to Person B. Because human communication between two people has practically zero friction, it's extremely tempting to drift into the lazy version of Conway's Law: ignoring technical boundaries altogether because "we can just talk about it on Slack." Why write explicit API documentation when you sit next to the person who wrote the endpoint? Why enforce strict domain boundaries when you can just export a helper function across modul

Hey everyone, For the past few months I've been working on a side project called MeshDrop. The idea started because I wanted a simple way to send files and folders directly between my own devices (and with friends) without uploading everything to cloud storage or relying on third-party servers. MeshDrop is built on the Holepunch ecosystem using Pear Runtime, Bare JS, and Hyperswarm. It supports direct transfers over LAN and can also connect over the internet using DHT hole punching with end-to-end encryption. I've also been experimenting with a few extra features like short 8-character pairing codes, cross-device clipboard sharing, and a remote drive feature that's still a work in progress. I'm still learning as I build this project, and I've been using AI coding tools alongside documentation, testing, and a lot of trial and error to help me move faster. I'm trying to understand the code and improve with every feature instead of just generating code and hoping it works. This is very early beta, so please expect bugs, rough edges, missing features, and probably a few questionable UX decisions. I'm sharing it now because I'd rather get feedback early than spend months building something people don't actually enjoy using. If you decide to give it a try, I'd really love honest feedback on things like: Does the overall workflow feel simple or confusing? Is the UI easy to understand? Did you run into any bugs or connection issues? Are there features you'd expect from a P2P file sharing app that are missing? Is there anything that feels unnecessary or poorly designed? Please don't hold back. Constructive criticism is exactly what I'm looking for. If something feels wrong, confusing, or badly designed, I'd much rather hear about it now so I can improve it. GitHub: https://github.com/aamirali51/MeshDrop Latest Beta: https://github.com/aamirali51/MeshDrop/releases/tag/v1.0.0-beta.1 Thanks for taking the time to read this. Whether you try it, report a bug, suggest a feature, o

At TechCrunch Disrupt 2026, the AI Stage is back to dig into the single hottest topic in the community for the past few years, presented by Google for Startups.

On May 20, 2026, AWS ended support for DMS Fleet Advisor. Fleet Advisor answered a question every migration team asks first: what is actually in my database estate, and how hard will it be to move? It was free. It was fully managed. It was backed by the largest cloud provider on earth. It still lost. AWS's official notice says only: "After careful consideration, we decided to end support for AWS DMS Fleet Advisor." No reason given. But you don't need one — the documentation tells you. Here is what Fleet Advisor required before it would tell you a single thing about your databases: Install a standalone data collector in your local environment Create an Amazon S3 bucket Create IAM policies, roles, and users — via CloudFormation, which was the recommended path Create database users with the minimum required permissions on every source Establish network access from the collector to each database server Then you'd meet the ceilings: recommendations for up to 100 databases at a time, one-to-one target mapping only, no multitenant server support. Now picture running that gauntlet inside a bank. You are a Business Solution Architect. You have been asked to scope a migration. You do not yet have approval for the migration — that approval is what the assessment is for . And to produce the assessment, you must first request production database credentials, get an agent binary through software approval, provision an S3 bucket, and get an IAM stack past a security review. That is a six-week procurement conversation to answer a question you were hoping to answer this week. AWS's replacement recommendation is Migration Evaluator — a consulting-led engagement. Read that as the finding it is: AWS looked at self-serve migration assessment, and concluded that humans and services do it better than a product. I think they were half right. And the half they got wrong is the interesting part. The lesson: friction is a competitor, and it usually wins We talk about developer tools as if the

Why this combination keeps winning for IoT telemetry backends — not in a benchmark, but against flaky gateways, replayed data, and firmware that never quite agrees with itself. If you’ve ever built the backend for a fleet of IoT devices — sensors, gateways, industrial equipment reporting temperature, humidity, GPS, battery, signal strength — you’ve faced the same fork in the road early on: what do you build the ingestion layer with, and what do you store the data in? After building a telemetry backend from scratch for a real fleet of LoRa/BLE sensors and gateways — handling dual ingestion paths, binary and JSON payload formats, automatic recovery of lost data, and a growing set of operational dashboards — I keep coming back to the same combination: Python (FastAPI + asyncio) for the API, MQTT for device transport, and PostgreSQL for storage. Here’s why that combination holds up so well for this specific problem, not just “in general.” Full Article: https://medium.com/@jackpelorus/python-postgresql-and-mqtt-the-boring-stack-that-actually-survives-a-real-device-fleet-9297146cbe8d?sharedUserId=jackpelorus

Weng previously served as the VP of AI Safety Research at OpenAI.

xAI is suing Minnesota Attorney General Keith Ellison over a law passed back in May that broadly targets "nudification" apps, claiming that the statute's punitive provisions leave the company with "no practical choice but to restrict Grok Imagine's image-editing features in various ways." The law, the company argues, violates the First Amendment. Back in January, […]

Building an AI Operating Layer Episode 1 Why I Didn't Start Sooner Most engineering projects begin with an idea. This one began with a question. For months I found myself watching the explosion of AI tools, frameworks, models, and agent platforms. Every week there seemed to be another breakthrough, another library, and another opinion about where everything was headed. I could have started building immediately. Part of me thought I should have. But I realized something, and it kept bothering me. I wasn't afraid of writing code. I was afraid of solving the wrong problem. When a new technology appears, it's easy to jump straight into implementation. Pick a framework. Choose a model. Build something. Ship it. I didn't want to start there because I had a feeling there was a much bigger picture that I wasn't seeing yet. So I waited. I spent my time reading, experimenting, asking questions, and trying to understand how all of these pieces connected. The more I learned, the more I realized I wasn't actually interested in building another AI application. What fascinated me was the system behind the systems. What happens when you stop looking at models, memory, orchestration, tools, policies, and execution as separate ideas and start seeing them as parts of a much larger ecosystem? That question became the beginning of this project. This isn't a story about predicting the future. It's a story about trying to understand it. I'm sure some of my assumptions will be wrong. I'm sure parts of this architecture will change. If they do, you'll see that too. I don't want this journal to only show the polished results. I want it to capture the discoveries, the wrong turns, the redesigns, and the moments where a better idea replaces an old one. At the center of this journey is a project I'm calling the AI Operating Layer. Today it's mostly architecture, documentation, research, prototypes, and a growing collection of ideas. Maybe that's exactly where projects like this should begin. I'

Over the last few years, CD Projekt Red put a ton of work into fixing Cyberpunk 2077, squashing bugs, polishing content, and even launching new DLC in 2023, Phantom Liberty. As a result, this game has gone from a source for funny, glitch-fueled videos to a world I’ve returned to a number of times to […]