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Orbital founder Euwyn Poon built 250,000 scooters at Spin. Now he wants to launch 10,000 space data centers.

A decade ago, commuter buses attracted big protests in San Francisco. Years later, the city is still feeling the repercussions.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

** How I instrument ASR, LLM, TTS, and the client with OpenTelemetry, and which number in each layer I actually look at ** TL;DR. A voice agent is four moving parts stuck together: speech to text, the model that writes the reply, text to speech, and the client that plays the audio back. End to end latency hides which of those four is slow on any given turn, so I stopped tracking it as one number and started tracing each stage as its own OTel span with a shared session id. The number I watch hardest is barge-in: when the user starts talking over the agent, how many milliseconds until the agent actually stops sending audio. In our setup we want that under 200ms, and when p95 barge-in creeps past that, the agent feels like it is talking at you instead of with you. Everything below is how I wire the spans, what attributes go on each one, and the p95 I page on per layer. The thing I keep saying, and the thing that keeps being true: voice agents fail in production not because of raw latency but because nobody simulated the audio and LLM pipeline together. You can have a fast ASR, a fast model, a fast TTS, and a voice agent that still feels broken, because the failure lives in the seams between them and in the parts (barge-in, jitter) that no single-stage benchmark touches. Tracing is how I get the seams to show up. A note before the layers. This is just the setup we run, the spans we emit, and the mistakes that made us add each attribute. Some of it is probably specific to our stack and will not transfer. I will flag that where I can. The shape of a turn, and why one span is not enough One turn is: user says a thing, agent says a thing back. Underneath that is roughly: audio frames come in, ASR turns them into text (streaming partials as it goes); the text plus history goes to the LLM, which streams tokens back; as text comes out, TTS turns it into audio, also streaming; the client receives audio frames and plays them, with some buffering to smooth out jitter. If you wrap

The startup is one of many entering the RV space, but it's banking on a hybrid power system that can go far beyond campsites.

TL;DR As an international student in the United States, I joined tech communities hoping to find...

Something I keep noticing across teams and orgs that are actually getting value from AI versus the ones that aren't. The difference rarely comes down to the model or the algorithm. Most organizations are already drowning in data. Logs, metrics, alerts, reports, dashboards, tickets. The information exists. The bottleneck is what happens after the data shows up. How long does it take to interpret what the signal means? Who decides what to prioritize when three things need attention at once? How fast can the right people coordinate a response once a decision is made? That's where AI actually earns its keep. Not by replacing the human in the loop but by compressing the time between something happening and someone doing something useful about it. Signal to understanding to action. That's the chain that matters. Think about it in terms you deal with every day. A vulnerability gets disclosed. The CVE exists, the advisory is public, your scanner picked it up. None of that is the bottleneck. The bottleneck is figuring out which of your services are affected, who owns them, how bad the exposure actually is in your specific context, and getting a patch scheduled before someone exploits it. AI that helps you answer those questions in minutes instead of days is genuinely valuable. AI that and adds another dashboard to look at isn't. This applies across the board. Incident response, infrastructure management, risk assessment, customer systems, operational workflows. The teams getting real value aren't the ones with the fanciest models. They're the ones who figured out where their decision bottlenecks actually are and pointed AI at those specific gaps. The strategic advantage is rarely in the algorithm. It's in organizational responsiveness. How fast can you go from "something happened" to "we're handling it". The AI is just the thing that compresses that timeline. Where's the biggest decision bottleneck in your current workflow?

A tweet landed on my timeline from Peter Steinberger — OpenClaw founder, now at OpenAI: "Here's...

👋 Hey there, Tech Enthusiasts! I'm Sarvar, a Cloud Architect who loves turning complex tech problems...

Writing a prompt isn't engineering. It's typing. You type what you want. The AI figures out the rest...

We’ve all been there: every year, you get a physical, receive a thick PDF full of blood markers, glance at the "normal range" checkmarks, and toss it into a digital folder titled "Health Stuff" to be forgotten. But what if I told you that those isolated data points are actually a time-series story of your biological aging? In this tutorial, we are going to build a Longevity Knowledge Graph . We will leverage GraphRAG (Graph-based Retrieval-Augmented Generation) , Neo4j , and Unstructured.io to transform a decade of messy medical PDFs into a structured intelligence layer. By the end of this post, you'll be able to query your health history with context that standard vector search simply can't grasp—like "How has my fasting glucose trended relative to my BMI over the last five years?" If you're interested in advanced data engineering patterns or looking for more production-ready AI health architectures, I highly recommend checking out the deep dives over at WellAlly Blog , which served as a major inspiration for this build. Why GraphRAG? (The Problem with Vector Search) Standard RAG (Retrieval-Augmented Generation) is great at finding a specific needle in a haystack. But if you ask, "What is the relationship between my Vitamin D levels and my bone density over time?", a vector database might just pull three separate paragraphs. GraphRAG allows us to: Connect Entities : Link a Blood_Metric (e.g., LDL) to a specific Time_Point . Traverse Relationships : Follow the path from User -> Report -> Marker -> Trend . Global Reasoning : Summarize high-level health trajectories across multiple years of data. The Architecture 🏗️ Here is how the data flows from a messy PDF to a queryable graph: graph TD A[Medical PDF Reports] -->|Unstructured.io| B(Clean JSON/Elements) B -->|Entity Extraction| C{LLM Processing} C -->|Nodes & Edges| D[Neo4j Graph Database] D -->|GraphRAG Query| E[Longevity Insights] F[User Query: 'Is my HbA1c rising?'] --> E subgraph Storage D end Prerequisites To f

Mystery of GPS interference across Europe raises questions about Russian motives.

The ChatGPT maker announced it has filed paperwork to go public, just a week after rival Anthropic took the same step.

Social media posts questioning the integrity of LA’s mayoral election were labeled “paid partnerships.” Then Kalshi and Polymarket told creators to delete them.

Waymo has acquired a massive 5,500-acre proving ground in Arizona owned by Route 14 Investment Partners LLC, a Delaware shell company associated with Apple, according to documents filed with Maricopa County.

WhatsApp disrupted spear phishing attempts, asks court to hold NSO in contempt.

The British government thinks a state-backed infrastructure initiative will help supercharge homegrown chip startups.

The code WIRED identified is gone from the latest version of Meta AI, the companion app for the company’s smart glasses. Meta won’t say why or whether it’s coming back.

We take the Falcon 9 rocket for granted. But we probably shouldn't.

Field journal of Dr. E. Rempel, Department of Minority Neurological Studies, University of New Carthage (A work of fiction. "Allism" is a real term used by some autistic people to describe the neurological profile of the non-autistic majority.) March 3, 2089 I have now spent three months embedded with an allistic community in the outer provinces. Allism, for those unfamiliar, is a rare neurological variant affecting approximately 1% of our population. My colleagues at the University have long debated its origins and persistence. After direct observation, I am no more certain of the answers, but I have accumulated a remarkable set of field notes. The allistic subjects I have observed appear, on the surface, entirely functional. They hold jobs, maintain relationships, raise children. And yet their neurological profile diverges from the norm in ways that are at once fascinating and bewildering. March 11, 2089 The most immediately striking feature of the allistic profile is their relationship with information. Where a typical individual experiences the sharing of useful knowledge as a basic social reflex, the allistic subject appears to require an elaborate ritual before any information exchange can occur. Approach an allistic subject directly with a piece of useful data and observe what happens. Rather than receiving it, they freeze. A threat-assessment process appears to engage, entirely pre-consciously, before the content of the communication can be evaluated at all. One subject described it to me as feeling "strange" when a stranger approached with unsolicited information, though she could not articulate why. I have learned to preface all information exchanges with what my translator calls "the preamble ritual" — a sequence of social signals that appears to deactivate the threat response and allow communication to proceed. The exact form varies, but typically involves eye contact, a softening of posture, and verbal acknowledgment that one is about to speak. Only the