标签：#real

Eric Wu, who built and ran Opendoor before stepping away in 2022, has had his new company, NavigateAI, out of stealth since May — building AI copilots that give construction workers real-time, hands-free guidance through smartphones and Meta's AI glasses, backed by $25 million from Elad Gil, Khosla Ventures, and Lennar to tackle a labor shortage severe enough that data center projects alone now need 4,000 to 5,000 workers apiece.

I Have a Job — I Just Need to Do It A few months ago, I left my last job as a remote Unity developer. Before leaving, I had already started working on a freelance project to build a multi-tenant security and workforce management system . It became a fairly large system involving web applications, mobile apps, real-time tracking, scheduling, reporting, GPS, notifications, and more. The project is now mostly completed, but the client wants to continue adding maintenance, business logic changes, UI modifications, and new development under the same maintenance fee. That doesn't work for me. Maintenance and development are two different things, and when the amount of new development keeps growing while the price stays the same, eventually it stops being sustainable. So I started thinking about what I would do next. The Fear of Not Having a Job For the last few days, I was genuinely worried. I have more than 250,000 BDT in savings , so I'm not in an immediate financial crisis. But money slowly disappears when there is no income. And freelancing isn't exactly comforting right now either. I've been using Upwork, but the experience has become increasingly frustrating. You apply for jobs and often hear nothing. Some clients post a job and never hire anyone. Some jobs get dozens of proposals and disappear quickly. Some invites arrive, but someone else gets hired almost immediately. And every application costs money. After a while, it starts feeling like you're continuously putting money into a machine that promises a job somewhere in the future. You keep applying. You keep waiting. You keep hoping. And eventually, I realized something. What I Was Actually Missing I wasn't missing money. I was missing a job . And there is an important difference. I already have the skills. I already know how to build software. I already have ideas. I already have projects I want to work on. I was simply thinking that a "job" had to come from someone else. Then I thought: I can create my own job

Author: Ignacio Paz An AI agent is only as useful as what it can remember - and how well it can connect the things it remembers. Most teams hand their agent a memory by reaching for a vector store: embed everything, retrieve by similarity, hope the relevant context comes back. It works, until you notice the agent keeps surfacing things that are near the question but not actually connected to it. Cobrainer , a skills-intelligence company based in Munich, took a different route. They gave their AI agent a memory that lives in the database as a graph, where the agent builds the relationships between nodes as it goes. They did it without adding a graph database, a vector engine, or a search engine to their stack. It all runs on SurrealDB, alongside a Rust-native agentic graph RAG built on the same store. Here's how, and why a single engine made the difference. The problem with flat memory Cobrainer runs a skills-intelligence platform - the kind of system that reasons about how people, roles, skills, and capabilities relate to one another. That's an inherently graph-shaped problem. But their first retrieval setup wasn't graph-shaped at all. It pulled context through flat vector retrieval over an S3-and-OpenSearch pipeline, which carried two recurring costs: Accuracy . Flat vector matches returned context that was loosely related - semantically near, but not necessarily connected in any meaningful way. The team wanted the agent to follow real relationships between entities, so its answers were grounded rather than approximate. Tokens . Broad vector matches meant stuffing a lot of marginally relevant context into every prompt - expensive, and more so with every call. The team wanted to fetch only the context that mattered. The obvious fix - adding a graph database on top of the vector and search systems they already ran - would have meant more infrastructure to operate. For a startup moving fast, that fragmentation was the thing to avoid, not embrace. What they wanted inst

The grid has been straining under the weight of new AI data centers, and that has utilities courting fusion startups.

Short answer: make the event identity durable, deduplicate at the consumer boundary, and resume from a server-issued cursor; a client-side set alone cannot make a marketplace chat room survive reconnects or an incident-response burst. The constraint is trust. A browser reconnects after a laptop sleeps, a mobile radio changes networks, or a tab is restored from the back-forward cache. It may replay its last request, lose an acknowledgement, or present an event twice. In an incident response dashboard, the same mechanics become dangerous at scale: an alert that appears twice can page two people, while a missing alert can hide the incident. I design the storage boundary first, because a pretty WebSocket demo does not answer either question. Start with an event identity that can outlive a connection Every published event needs an immutable identity scoped to the stream, not to a socket. For a marketplace chat room, I use (room_id, sequence) as the primary key and keep a globally unique event_id for tracing. The sequence is allocated by the room writer, so two reconnecting clients can compare progress without trusting wall-clock timestamps. The payload is deliberately boring. It includes the room, sequence, event ID, type, and data. A client can verify that an event belongs to the room it requested; it cannot mint a higher sequence or widen its token scope. That last rule matters more than transport choice. from dataclasses import dataclass from typing import Any @dataclass ( frozen = True ) class ChatEvent : room_id : str sequence : int event_id : str event_type : str data : dict [ str , Any ] def identity ( event : ChatEvent ) -> tuple [ str , int ]: """ The room sequence is the replay-safe identity. """ return event . room_id , event . sequence Do not use a payload hash as the only key. Two legitimate messages can have identical text, and a producer retry can produce different JSON ordering. Persist the identity and the payload together, with a uniqueness constraint,

This weekend, I strapped on an Apple Vision Pro to watch a baseball game in immersive virtual reality for the first time. It was technically impressive, visually pretty remarkable, and also didn't make that much sense. Apple and Major League Baseball chose a classic match-up of the Boston Red Sox and the New York Yankees […]

For realtime release compatibility in a delivery tracking map, scale event delivery with a durable, ordered log per delivery and treat every browser connection as a disposable projection of that log. Presence can guide fan-out and capacity planning, but it must never decide whether a location update exists. Short answer: release compatibility comes from versioned envelopes, resume cursors, and an explicit resync path; scaling comes from partitioning by delivery ID and coalescing map updates at the edge, not from trusting a long-lived connection to carry every event exactly once. This decision targets an e-commerce tracking experience in which a shopper may open a map, lose connectivity in a tunnel, return on another network, and also join a delivery-specific support chat room. The deciding constraint is presence accuracy: an online indicator is useful only when its expiry rules are understood, while the delivery state must remain correct even when that indicator is late. A green dot isn't a commit log. How should realtime release compatibility scale event delivery in a delivery tracking map? Separate the system into three contracts: durable delivery state, transient room presence, and the connection used to move updates. The first contract owns truth. The second answers a narrower question: which sessions have renewed a lease recently enough to be considered reachable? The third may disappear at any point and should be replaceable without changing either of the other two. For each delivery, append an event with a monotonically increasing sequence within that delivery's partition. The client persists the last applied sequence and includes it when reconnecting. If retained events cover the gap, the server replays them in order; if they don't, the server returns a fresh snapshot plus its sequence. This is at-least-once delivery with idempotent application, which means duplicates are ordinary and gaps are detectable. It does not promise global order across unrelated del

Most WhatsApp AI projects start with a simple goal: Receive a message → send an AI-generated reply. For real estate, I think that's only the beginning. A useful real-estate AI system should do more than generate text. It should understand the buyer's intent, capture important information, qualify the lead, preserve conversation context, organize that information in a CRM, and know when a human salesperson should take over. That's the system I'm currently building with Vaxyro . The problem: a WhatsApp conversation is not a lead record A typical real-estate enquiry might look like this: "Hi, is the 3 BHK available?" Then: "What's the price?" Then: "Is there anything around 80L in Gurgaon?" Then: "I can visit this weekend." The messages themselves are simple. The difficult part is turning the conversation into structured information that a sales team can actually use. The system should be able to understand something like: Property type: 3 BHK Location: Gurgaon Budget: ₹80 lakh Timeline: This weekend Intent: High Next action: Site visit discussion Instead of leaving all of that information buried inside a WhatsApp conversation. What a WhatsApp AI lead qualification system should do I think the workflow can be broken into six stages: WhatsApp message ↓ Message understanding ↓ Intent detection ↓ Lead qualification ↓ Structured CRM data ↓ Follow-up ↓ Human handoff The important part is that the AI is not only generating a reply. It is also producing structured sales information. That distinction changes the architecture. 1. Message understanding The first step is understanding what the buyer is actually asking. For example: "Looking for a 3 BHK in Gurgaon under 80L" could produce structured information such as: { "property_type": "3 BHK", "location": "Gurgaon", "budget": "8000000", "intent": "property_search" } This gives the rest of the system something useful to work with. The goal is not to perfectly understand every sentence. The goal is to extract the information tha

With all of the primary mechanics sufficiently developed to allow playtesting, the next step in the project was to develop levels to teach players how to use them as well as a single level containing puzzles for players to solve using the knowledge provided in the tutorial levels. The Tutorial The game tutorial is split into five short levels. Each level imparts knowledge of one or more of the previously developed game mechanics. The number of tutorial levels was decided by listing out all the required mechanics and dividing them up across a number of levels that gave each mechanic the desired amount of attention. The goal of this was to ensure that the player was not overwhelmed with too much new information at any one time. Here is how the mechanics were divided across the five levels: Level 1: Player movement, looking around, jumping, and interacting with objects Level 2: Command generation system, Activate command, and signal blocking and range limitations Level 3: Controlling robot NPCs, target destinations, Follow command, Move To command, Cancel command, NPC-locked doors, and pressure plates Level 4: Using the Attack command against NPCs and destinations Level 5: Reflective and absorptive surfaces, signal reflection, low-frequency signals, signal penetration limits, and secret areas Tell and Show The tutorial levels all follow a pattern of tell then show. Every mechanic is explained via text on walls in the levels, and immediately followed by an opportunity or obligation to use that mechanic. For example, the first tutorial level starts with the player facing a wall displaying the controls to look around. In order to progress into the next area, the player must use those controls to turn around in order to see the way out of the starting room as well as the text explaining how to move. Similarly, at the beginning of the second tutorial level, the controls for generating an Activate command are displayed on the wall in front of the player with a door to their

Are XREAL's smart glasses the way of the future for home entertainment?

Async-first teams have correctly diagnosed the disease of meeting overload — and then prescribed a treatment that quietly kills something they didn't know they valued. Picture this: a senior engineer, somewhere in a distributed engineering org, opens their laptop on a Monday morning to find three Slack threads, two auto-generated recaps from the previous week's planning sessions, and a Confluence page summarizing a strategy discussion they weren't invited to. They read all of it in eleven minutes. They feel fully informed. They feel efficient. And they have absolutely no idea that the head of product spent the last forty minutes of that strategy call sounding resigned — not convinced — about the new roadmap direction. That engineer will build the wrong thing. Not because the summary was inaccurate. Because it was accurate. This is the efficiency trap: the belief that capturing the output of a conversation is equivalent to having been present for it. Teams increasingly act on this assumption, and the organizational cost is not yet showing up in any dashboard anyone is measuring. How We Got Here The backlash against meetings has genuine merit behind it. A 2012 work efficiency survey of 3,200 employees found that 47% of participants identified corporate meetings as the single leading time-wasting factor at work. About 15% of an average organization's time budget is spent in meetings, with middle management spending up to 35% of their time there and upper management approaching 50%. Those numbers are hard to defend when the actual decision could have lived in a three-paragraph document. Researchers have been calling for more intentional use of meetings and a reduction of unnecessary ones, partly in response to documented issues like Zoom fatigue and meeting overload. That's a reasonable prescription. The problem is that "fewer bad meetings" got operationally translated into "fewer meetings," full stop, with async tools — Slack, Loom, Notion, automated recap systems — ab

Zillow and Redfin reached a settlement with the FTC, which requires Redfin to reenter the rental advertising business.

Async-first culture is making teams faster and lonelier — but the real damage isn't loneliness. It's that people stopped owning decisions they were never in the room to make. Picture a mid-sized software company, somewhere between Series B and exhaustion, that has gone fully async. No standups. No sprint reviews that anyone actually attends. A bot joins every Zoom, spits out a bulleted summary, drops it into Confluence, and everyone reads it — or doesn't — in their own time. The calendar is clean. The focus time is protected. And six months later, a major architectural decision made in a Loom video that half the engineering team never watched has quietly become the source of a simmering, passive-aggressive standoff between backend and platform. Nobody was in the room. Nobody feels they own it. This is the efficiency trap, and it's subtler than its critics usually admit. The problem isn't that async communication is bad. Much of what passes for a meeting in most organizations is theater: 77% of workers attend meetings that end in a decision to schedule yet another meeting, and 62% regularly sit through meetings that didn't even state a goal in the invite. Cutting that noise is not just reasonable — it's overdue. The real issue is what happens when the pendulum swings past optimization into avoidance, and teams start treating the absence of synchronous contact as a metric of operational maturity. Presence Isn't the Point — Participation Is There's a distinction the async evangelism wave has systematically glossed over: the difference between being informed and being part of something . A well-structured meeting summary tells you what was decided. It does not tell you that the decision was almost different, that someone pushed back and was overruled, or that the VP's sudden hedge on a key tradeoff means the whole thing is about to be relitigated in three weeks. Those signals live in tone, in the slight pause before someone agrees, in who did and didn't speak. In face-t

Renters say hidden Zillow listings make it harder to afford living in New York City.

Apple is laying off staff on the Siri and the Vision Pro teams, according to Bloomberg. The cuts include "largely shutting down" a Vision Pro gaming team and "reducing the size" of the team that makes Vision Pro immersive content, the publication says. More than 200 jobs were cut. Apple said in a statement to […]

Mark Zuckerberg just bought a cozy abode somewhat close to Meta’s international headquarters in Ireland.

Sonic Fire Protection raised its new funding to help get its sound-powered fire protection system into everything from commercial kitchens to apartment buildings.

This burgeoning wearable tech lets you talk to an AI assistant, listen to music, or check out a display screen from the comfort of your very own face.

Why Build a Custom RTOS in 2026? Why build a custom RTOS when there are already tons of battle-tested ones like FreeRTOS or Zephyr available? It’s a fair question, but there's much more to it than just reinventing the wheel. Building a kernel from scratch forces you to understand low-level hardware interactions, assembly context switching, and memory layout. It fundamentally transforms you into a better embedded firmware engineer, sharpens your low-level debugging skills (hello, hard fault handlers!), and gives you complete freedom to architect a system tailored to your exact specifications. How to Get Started If you've decided to embark on building your own RTOS, here are the three critical decisions you need to make first: 1. Target Instruction Set Architecture (ISA) You need to choose a target architecture—common choices include ARM Cortex-M, RISC-V, MIPS, or x86. I chose the ARM Cortex-M4 architecture. It provides dedicated features tailored for OS design—such as the NVIC (Nested Vectored Interrupt Controller) , SysTick Timer , and the PendSV interrupt for safe context switching—along with an incredible community and ecosystem for developers. 2. Hardware vs. Simulation Select a development board featuring your target architecture (e.g., STM32). Alternatively, you can use QEMU to simulate the hardware environment before flashing physical silicon. In fact, many professional RTOS teams rely heavily on QEMU for automated testing and rapid prototyping. 3. Toolchain & Build Setup If you aren't using a Hardware Abstraction Layer (HAL) and want to write bare-metal code, you'll need a cross-compiler toolchain. Because I'm writing it in C and assembly for ARM, I am using the arm-none-eabi-gcc toolchain alongside GNU Make/CMake. What’s Next? In the next post, we’ll dive into startup scripts, linker scripts, and setting up the vector table . (Note: Throughout this series, I’ll be moving forward with the ARM Cortex-M4 setup, but the core operating system concepts will apply

Evidence checked on July 25, 2026. This comparison separates vendor claims, general coding evidence, and native Unreal Engine delivery. Those are not the same thing. Kimi K3, Claude Opus 5, and Qwen3.8-Max-Preview all arrived with unusually strong claims around coding, visual iteration, long-running agents, or 3D creation. That makes one question inevitable for game developers: Which AI model is actually best for building an Unreal Engine 5 game? The short answer is Claude Opus 5 currently has the strongest public evidence for reliable agentic engineering and 3D reconstruction; Kimi K3 has the clearest first-party claim around playable 3D games and vision-in-the-loop iteration; Qwen3.8-Max-Preview is promising for large, multimodal engineering tasks but remains a preview with no official Unreal delivery proof. The more important answer is that none of these model announcements, by itself, proves that the model can deliver a valid native Unreal project, compile Blueprint or C++, cook assets, package a build, and reproduce the result. For Unreal work, the execution environment often matters more than a small difference in model intelligence. TL;DR: the Unreal-specific verdict Model Strongest relevant evidence Unreal-specific gap Best current role Claude Opus 5 Strong agentic coding, verification, computer use, a successful 3D FreeCAD reconstruction case, and early-user reports of better games and 3D output No official native Unreal project or packaging benchmark Lead engineering agent for difficult implementation, debugging, and review Kimi K3 First-party claim for playable multiplayer and 3D games, native vision, 1M context, long-horizon tool use, and screenshot-driven iteration Showcases do not establish .uproject , Blueprint, C++, cook, or package success Long-context, visually iterative game prototyping and tool-driven workflows Qwen3.8-Max-Preview 2.4T multimodal preview positioned for repository-scale coding, long tasks, image/video/document understanding, and a