标签：#features

Sure, maybe your clock has a backup battery, but mine has a deployment pipeline!

Automakers love connected vehicles, but support isn't open-ended.

How does AI make you feel? Are you excited to “vibe-code” your smart home? Or anxious about all the added pollution and billions of gallons of water used by data centers? Dig a little deeper and you’ll start to question the actual value of the GPUs that underpin all the leaps and promises of generative […]

Many will say Spa is the best racetrack on earth, but not for these F1 cars.

The pricing formulas in Motor, the estimating engine I built for a water feature shop, did not come from the manual. I pulled 32 of them out of the JavaScript behind Aquascape's contractor calculator, the tool contractors actually use to bid jobs. The manual was sitting right there, official and free. Ignoring it was the best design decision in the whole system. A vendor never ships a sloppy calculator Why trust the calculator over the manual? Because of what happens when each one is wrong. If the manual sizes a pump wrong, a reader shrugs and moves on. If the calculator sizes a pump wrong, a contractor bids a job at that number, wins it, and loses money on the install. Then the phone rings. So calculators get fixed and manuals drift. Give it ten years and the two quietly disagree, and everyone in the trade knows which one to trust without anyone saying so. A vendor will ship a sloppy PDF. They will never ship a sloppy calculator. Documentation is what a domain says about itself. The artifacts money flows through are what it actually believes. Once you see that split, you cannot stop seeing it. The other half was in old invoices Formulas only get you to cost. What a shop charges on top of cost is a belief about its market, and no vendor document holds that number. So I pulled 132 historical quotes out of the shop's CRM. Real quotes, sent to real customers, most of them paid. I calibrated Motor's markup against those, then checked its output against what the shop had actually charged. The result: Calibrated against 132 real quotes, Motor's estimates landed within 5 percent of what the shop actually charged, with no pricing rule taken from documentation. I could have just asked the owner what his markup was. But what an owner says and what his invoices show are rarely the same number, and the invoices are the ones customers paid. When the two disagree, believe the invoices. The same bug in a different industry I build and run systems in several industries, and the sur

Quick, comfortable, roomy, and agile for a large electric SUV.

"The ISS radiators are expensive and heavy. We're focused on making them cheap and light."

Experts explain how they work, what they can do, and what's still unsettled.

Learning how to add evals to an LLM feature is the difference between shipping a demo and shipping a reliable product. When you embed an LLM into a real feature — a chatbot, a voice agent, a document summarizer — you’re not just calling a model. You’re betting your user’s experience on a non‑deterministic system that can silently break with every prompt tweak, model update, or edge case. That’s why we instrument every LLM feature we build with a purpose‑built eval suite. Here’s how we did it for an outbound AI calling agent and how you can do the same. Why Evals Are Not Optional LLMs are non‑deterministic: give them the same input twice, and you’ll get two different responses. That means unit tests that check for exact string matches are useless. As Pragmatic Engineer notes , you need evals to verify that the solution works well enough — because there’s no guarantee it will. When you’re building a feature that speaks to real customers, like the AI Calling Agent dashboard we built, a regression in tone or missed booking intent can cost revenue immediately. Evals turn that uncertainty into signal. How to Add Evals to an LLM Feature: A 4‑Step Workflow We’ll walk through the exact process we followed, from defining success to automating checks in CI, using the DeepEval framework as an example. You can swap in Evidently AI or build your own, but the pattern is the same. Step 1: Define Success for Your Feature Takeaway: Before you pick a metric, write down the one thing that makes the feature “done” — usually a business outcome, not a technical measure. For the AI Calling Agent, the core feature was an outbound call that books a meeting. The success criterion wasn’t “the LLM replied politely.” It was “the agent scheduled a meeting with the right time and date.” This is a reference‑based evaluation: you compare the output to a known ground truth. Evidently AI’s guide calls this pattern out as essential for regression testing and experimentation. From that criterion, we der

Now it's an arms race between OEMs locking down chips and tuners trying to crack them.

"The Starship Pez dispenser demonstrates very smart industrial design and scale."

Top robotics researchers and founders explain how robot autonomy is evolving.

Top robotics researchers and founders explain how robot autonomy is evolving.

This hybrid V8 has organic-looking 3D-printed components and shatters lap records.

It's difficult to pinpoint the moment in my life where America started to lose the plot.

Your comments on a dangerous rule putting politicals in charge of science can matter.

The storm developed quickly over west central Illinois on April 17th, first as a single high-intensity system called a supercell, and then later that evening transforming into a long squall line of thunderstorms. Tucked inside a wall of wind were several smaller, quick-forming tornadoes, one of which landed directly on Rivian’s electric vehicle factory on […]

A year in, National Design Studio delays plan to update government web standards.

NASA’s quiet supersonic flight tests could eventually go on a national tour.

It has 205 miles of bare-bones range.