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A homelab box that never sleeps runs 8,760 hours a year. So the spec that decides what it costs you is not the one on the box. It is the one nobody prints: how many watts it pulls sitting at the login prompt doing nothing. I kept hitting this while shopping for a Proxmox node, so I put the measured numbers in one place. More on that at the end. First, why the spec sheet lies to you. TDP is a thermal budget, not a power reading TDP is the heat the cooler has to handle at full tilt. It is a design target for the heatsink, not a measurement of what the chip draws, and it says almost nothing about idle. Your homelab box spends 95%+ of its life idle, so the number that runs up the meter is idle wall power, and that number is never on the product page. The arithmetic is unforgiving. One watt running continuously is 8.76 kWh a year. So the gap between a 7 W box and a 35 W box is not 28 watts, it is about 245 kWh a year, every year, for as long as the box is on. Plug in your own rate to get the dollars; the point is the gap compounds. Where TDP actively misleads you A few measured results from the dataset I'll link below, all from third-party wall-meter readings, not vendor claims: The new N100 wave is genuinely low. A Minisforum UN100C measures 5 to 7 W at idle. Beelink, GMKtec and Trigkey N100 boxes land in the 6 to 10 W range. For a Pi-hole, a few containers and some light VMs, this tier is hard to beat on running cost. AMD mini PCs idle far higher than their marketing suggests. A Minisforum UM790 Pro measures 25 to 45 W at idle. A Beelink SER6 Pro lands at 20 to 35 W. These are fast little machines, but if you picked one expecting "small box, small draw," the meter disagrees, and over a year that delta is real money. Newer and higher-TDP is not lower-idle. A Dell OptiPlex 7060 Micro idles just over 18 W on its 65 W-TDP desktop chip. The older 7070 with a six-core part sits around 13 W, and the low-power "T" SKUs lower still. The CPU's TDP class predicted idle better tha

submitted by /u/BlondieCoder [link] [留言]

I don't identify as a gamer. I occasionally participate in board games or an amateur football match,...

In a post on X, Blue Origin CEO Dave Limp provided an update on the investigation into the New Glenn launchpad explosion.

submitted by /u/patrixxxx [link] [留言]

The dominant production failure mode for LLM agents in 2026 isn't bad reasoning — it's capacity. Here's what the data shows, why nobody demos it, and the capacity-engineering patterns that actually keep agents alive under load.

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submitted by /u/mttd [link] [留言]

From the motivation-pattern-log — a public, dated, falsifiable prediction log for AI-era cybersecurity attack patterns grounded in motivation analysis. Predictions are scored quarterly against stated falsifiers. PREDICTION-20260601-0008 Created: 2026-06-01 Pattern: boredom-with-asymmetric-leverage Substrate: Open-source package registries (npm, PyPI, Crates.io, Packagist) and GitHub Actions CI/CD workflow injection Leading indicator observed: Four distinct, concurrent, cross-registry supply chain campaigns (TrapDoor: 34 packages across npm/PyPI/Crates.io; Megalodon: 5,718 automated commits to 5,561 GitHub repos in six hours; Packagist compromise of 8 packages; Laravel-Lang PHP credential stealer) appeared within a 72-hour window in 2026-W22, all exhibiting automation signatures — throwaway publisher accounts, wave publishing, base64-encoded shell payloads, off-the-shelf delivery via GitHub Releases — consistent with toolkit operation rather than bespoke tradecraft. npm's reactive rollout of 2FA-gated publishing signals registry operators recognizing volume pressure. Predicted window: 2026-Q3 through 2027-Q1 Predicted shape: Automated, low-sophistication credential-stealing and backdoor-planting campaigns against npm, PyPI, Crates.io, and Packagist will continue to increase in incident volume while average per-campaign novelty declines. The dominant operational signature will be scripted account creation, automated package publication across multiple registries simultaneously, and CI/CD workflow injection via forged or compromised GitHub bot identities — all executable with commodity toolkits requiring no original exploit development. At least two registry operators beyond npm will announce reactive publishing controls (mandatory 2FA, namespace-squatting detection, automated malware scanning with publication holds) within the window in direct response to volume pressure. Security vendors will report a measurable increase in "unsophisticated supply chain" incidents re

Ever received a WebSocket tick stream for US stocks and wondered why your indicators behave oddly outside regular hours? The raw data doesn’t tell you which session a trade belongs to, but identifying the session is crucial for signal quality. Here’s a clean, no-dependency-heavy way to do it in Python. Quick Session Reference Session US Eastern Time Data Characteristics Pre-market 04:00-09:30 Sparse trades, choppy moves Regular hours 09:30-16:00 Dense liquidity, smooth price action After-hours 16:00-20:00 Volatility often triggered by news Method 1: Timestamp Conversion Almost every API sends a UTC timestamp. Convert it to US/Eastern and classify. from datetime import datetime import pytz # US Eastern timezone et = pytz . timezone ( ' US/Eastern ' ) def get_session ( ts ): t = datetime . fromtimestamp ( ts , et ) # Check pre-market window if t . hour < 9 or ( t . hour == 9 and t . minute < 30 ): return " pre " # Regular session if t . hour < 16 : return " regular " # After-hours return " after " Method 2: Use a Session Status Field If your provider sends a field like sessionType , you can skip the timezone math. Just make sure to test edge cases at session boundaries. Live Integration Example Using a WebSocket feed (like AllTick’s market data stream) that includes a timestamp, I label ticks on the fly. import websocket import json from datetime import datetime import pytz # US Eastern timezone et = pytz . timezone ( ' US/Eastern ' ) def session ( ts ): t = datetime . fromtimestamp ( ts , et ) if t . hour < 9 or ( t . hour == 9 and t . minute < 30 ): return " pre " elif t . hour < 16 : return " regular " else : return " after " def on_message ( ws , message ): data = json . loads ( message ) s = session ( data [ " timestamp " ]) print ( f " { data [ ' symbol ' ] } | { s } | { data [ ' price ' ] } | { data [ ' volume ' ] } " ) # Open WebSocket connection ws = websocket . WebSocketApp ( " wss://ws.alltick.co/stock " , on_message = on_message ) ws . run_forever () Effic

When we started experimenting with AI translations, we assumed the biggest challenge would be accuracy. We were wrong. The harder problem was preference. Give two AI models the same sentence, and both translations can be technically correct. Yet people almost always have a favorite. One sounds more natural. One feels more human. One is the version they'd actually use. That observation eventually led us to build Parley , a simple game where players compare two translations and choose the better one. What happened next surprised us. People became highly engaged with a task that looked almost trivial. They started debating word choices, discussing tone, and noticing subtle differences between translations. Some users spent far longer interacting with translation examples than they ever would reading documentation or language-learning materials. It highlighted something interesting about AI products: evaluation can be more engaging than generation. Most AI interfaces focus on creating content. But humans are often much better at judging quality than producing it from scratch. Asking someone to choose between two outputs requires less effort while still training their intuition. The experiment also changed how I think about language learning. Traditional language apps often rely on memorization and repetition. But comparing alternatives forces you to think about meaning, context, and natural expression. You're not just learning vocabulary, you're developing taste. And in a world where AI can generate endless content, taste might become one of the most valuable skills we can build. Have you seen similar patterns in AI products where evaluation turns out to be more engaging than creation?

submitted by /u/Successful_Bowl2564 [link] [留言]

Last week I built a little dashboard with Claude. Took maybe ten minutes. Then I spent the next hour trying to get it online. ssh in, install docker, write a Dockerfile, set up nginx, run certbot, certbot fails, read the log, oh the DNS hasn't propagated, wait, run it again, open port 443, realize ufw was blocking it the whole time. By the time it was live I'd forgotten what the app even did. I've done that maybe a few hundred times by now. I'm a backend guy, I'm fast at it. But fast at something boring still means doing the boring thing. So at some point I just thought: the AI already wrote the app. Why does it stop right when the annoying part starts? Why doesn't it just deploy the thing itself? The reason is it has no hands. The model can write you a perfect docker-compose file. It can't ssh into your box and run it. No connection to your server, nowhere to hold your key. So I gave it hands. It's an MCP server, vibe-deploy. You hook it up once to a VPS you own, and then you just say "deploy this to notes.mydomain.com" and the agent containerizes it, ships it over ssh, sets up nginx, gets a real Let's Encrypt cert. Node, Python, Go, plain static. It figures out the stack and writes the Dockerfile. No PaaS, no per-seat pricing, no free tier you'll outgrow. A $5 box runs a dozen of my projects and I own the whole thing. The "you gave an AI root on your server??" reaction is fair, so: it runs locally, your key never leaves your laptop. I used a separate ssh key scoped to deploys, not my real one, and you should too. It checks the server host key before connecting and validates everything you pass it, because a deploy tool that pastes your input straight into a shell is a horror story waiting to happen. I had someone audit the security before I put it out. They found two real bugs. I fixed them. It's free and MIT, on GitHub and npm as @cgnguyen/vibe-deploy . I built it because I wanted it. If you live in the same gap between "it works on localhost" and "it's online",