标签：#m

The One TDD Habit That Saved My Sanity (and My Codebase) Quick context (why you're writing this) Here's the thing: I used to think I was doing TDD right. I’d write a test, watch it fail, then write just enough code to make it green. Rinse and repeat. Sounds textbook, right? But a few months ago I spent an entire afternoon chasing a bug that only showed up after I refactored a service class. The tests were all passing, yet the app was throwing NullReferenceExceptions in production. I was shocked. How could everything be green and still be broken? Turns out I was testing the inside of my code instead of what it actually did for the outside world. That realization hit me like a truck, and it completely changed how I approach TDD. The Insight Test behavior, not implementation. If your test is coupled to private fields, internal data structures, or the exact way a method accomplishes its goal, you’re not testing what matters—you’re testing how you happen to do it today. When you later refactor to improve performance, swap out a dependency, or even just rename a variable, those tests start failing for no good reason. You end up spending more time fixing tests than delivering value, and you lose confidence in the suite because it feels fragile. The payoff? A test suite that gives you confidence when you change code, not anxiety. You can refactor fearlessly because the tests only care about the contract: given these inputs, the system should produce these outputs or side‑effects . How (with code) Let’s look at a tiny but realistic example: a PasswordValidator service that checks whether a user‑chosen password meets our policy. ❌ The mistake: testing implementation details // PasswordValidator.cs public class PasswordValidator { private readonly IRegexProvider _regex ; // injected for testability public PasswordValidator ( IRegexProvider regex ) { _regex = regex ; } public bool IsValid ( string password ) { // implementation we might want to change later return _regex . IsMa

This week, the first offline-first PWA went live on WebsitePublisher.ai . A travel blog that works without internet. Write posts on a plane, attach photos, and everything syncs the moment you reconnect. Service Worker, IndexedDB, sync queue — the full stack. The twist: it was built entirely through conversation with an AI assistant. No IDE, no terminal, no deploy pipeline. How that works WebsitePublisher.ai exposes 92 integrations as building blocks via MCP (Model Context Protocol). Any AI assistant — ChatGPT, Claude, Cursor, Windsurf, Copilot, Gemini, Grok, Mistral — connects to the same runtime and assembles these blocks into working applications. The offline-first PWA is one of those blocks. The AI doesn't generate a Service Worker from scratch. It activates a proven, tested building block and configures it for the use case. We call this wave coding — one deliberate wave of proven pieces, instead of 15 fragile vibe-coding attempts. What shipped recently Offline-first PWA building block — push/pull sync, conflict handling, IndexedDB storage, works on iOS 92 integrations (up from 78) — 45 built-in, 47 bring-your-own-key Integration stacks — pre-composed combinations: e-commerce (13 integrations), lead generation, B2B prospecting, booking, content/blog 9 AI platforms supported — all via MCP, no vendor lock-in 416 API endpoints across the platform ## The architecture in short AI assistant (any) → MCP → WebsitePublisher runtime ├── PAPI (pages + assets) ├── MAPI (structured data) ├── SAPI (forms + auth + sessions) ├── IAPI (integration proxy) ├── VAPI (encrypted vault) └── AAPI (scheduled AI agents) Credentials never touch the AI. They're stored AES-256-GCM encrypted in the vault and injected server-side during execution. The positioning We're not competing with Lovable or Bolt on the chat interface. We're the Supabase + Vercel + n8n underneath — reachable via whichever AI you already use. The platform your AI builds on. websitepublisher.ai

Hey all, I'm a robotics engineer by training turned ML/AI engineer because of passion right after school. I want to start combining these skills together and I think a competition is the best way of doing it. Here's an example of a challenge I'm talking about to set expectations : https://www.intrinsic.ai/events/ai-for-industry-challenge Anyone up for this? submitted by /u/Due_Pickle1627 [link] [留言]

Former DOGE members and Elon Musk allies are backing a startup aimed at using AI to apply "learnings" from DOGE to the private sector.

Microsoft’s AI products aren’t selling and Github’s been plagued with troubles. WIRED spoke with VP Scott Hanselman about whether the company is in catch-up mode.

"The whole conversation shifted from tokenmaxxing and 'go fast' to 'we need guardrails, how do we control this?'"

The Huracan gave us the R8s, now the Temerario lends itself to a new Audi.

OpenAI details Codex Windows sandbox architecture, showing how SIDs, ACLs, restricted tokens, and dedicated sandbox accounts enable safe execution of autonomous coding tasks. The design balances isolation with real developer workflows and shows how OS security primitives must be composed for AI agents on local development environments. By Leela Kumili

The U.S. eavesdropping agency is reportedly preparing Anthropic's Mythos for use in cyberattacks, despite a federal ban on using the AI model maker.

From talking about sex with JFK Jr. to a surprise show in Times Square, the queen of pop is leveraging Grindr’s “gayborhood” to sell her new album.

NASA expects to begin stacking the SLS rocket this summer for next year's Artemis III launch.

About 10 years ago, I got into the basics of ML (like regression, KNN's, LVQ's) and read a few papers before taking a break a few years back. It feels like now, there's a lot of researchers in AI. How do you identify the ones who are actually solid vs those who (forgive my phrasing) are more researchers for appearance/status (i.e don't actually know what they're talking about)? Is the core filter h-index or where they work? How would you identify them? submitted by /u/roguejedi1 [link] [留言]

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. A few days ago, I walked into the basement of a midtown gym. Smoothies and healthy snacks were passed out. […]

When Quilty hit the industry trades earlier this year, the AI startup promised that its tool could accurately predict a film's success just by reading the script. When people actually got a chance to experiment with Quilty's product, though, they were left skeptical. Even with all the available data in the world, it predicted the […]

submitted by /u/Expurple [link] [留言]

This week on the Engaadget Podcast, we dive into NVIDIA's RTX Spark chip and the many ways it could reshape the world of Windows PCs (or not).

Where's the Trump phone? We're going to keep talking about it every week. We've reached out, as usual, to ask about the Trump phone's whereabouts. This week, I'm investigating where it might have been built - and why it definitely wasn't the US. Almost a year after its announcement, the Trump phone has "launched." A […]

A closer look at how undersea cables connect the world.

Researchers have prototyped an AI-powered internet worm . The coolest thing about the prototype is that it carries its own LLM with it, and runs it on computers that have been broken into. This is the closest to John Brunner’s original 1975 conception of a computer worm that I’ve seen.

Sharing a small CPU inference benchmark for nvidia/parakeet-tdt-0.6b-v3 that turned up a result I didn't expect going in. Setup: 2 x86-64 vCPUs (AVX2/FMA), 7.7GB RAM, no GPU. Test audio: 16.78s Harvard sentences at 16kHz mono. Results: Inference path RTF Peak Memory CPU utilization HF Transformers bfloat16 0.519 ~430MB delta — ONNX Runtime FP32 (onnx-asr) 0.328 2,667MB 49.9% GGUF Q6_K (parakeet.cpp) 0.708 928MB 99.8% ONNX Runtime is 37% faster than HF Transformers bfloat16 on this hardware. The gap comes from operator fusion and AVX2-optimized execution providers in ONNX Runtime that the PyTorch CPU path doesn't exploit as aggressively. Memory cost is the tradeoff — FP32 weights load at ~2.7GB peak. GGUF Q6_K trades throughput for memory efficiency. 928MB peak vs 2.7GB, but RTF doubles and CPU utilization hits 99.8%. For memory-constrained deployments it's the right call. For sustained throughput on a box with headroom, ONNX wins. One methodological note worth flagging for anyone doing ASR benchmarking with synthetic audio: espeak-ng inflated WER to 20.9% on a sentence set where gTTS got 4.65%. Both runtimes got identical WER within each run, confirming it's the TTS distribution mismatch rather than model or quantization quality. NVIDIA reports 1.93% on LibriSpeech — the gTTS number is a much more honest CPU-only proxy. Github repo with code, raw results, and evaluation scripts in comments below. Disclosure: benchmark was run using Neo, a local AI engineering agent inside Claude Code using its MCP. Mentioning because the runtime and audio choices came from its research phase, not prior knowledge on my end. submitted by /u/gvij [link] [留言]