标签：#ann

On this episode of Uncanny Valley, we dive into accusations that China’s Moonshot AI stole from Anthropic, and how the US Army needs to cut back on AI use.

The AI “startups” worth billions of dollars are raising alarm bells about Chinese AI. The smaller players have a totally different take.

Last month, more than a hundred Stanford students left their own graduation to protest Google’s military contracts and deals with ICE. Two organizers, Amanda Campos and Eva Jones, tell us why.

David Risher insists that Lyft has lowered its prices and that customers who only check his competitor’s app are “leaving money on the table.”

On today’s Uncanny Valley, we unpack OpenAI’s ongoing drama, both legal and reputational, and whether these developments could further hurt the company—particularly in its fight against Anthropic.

Colocar los muebles de una habitación es un problema de optimización con muchas restricciones: la cama no va delante de la puerta, el escritorio quiere luz natural, hay que poder circular. Hay un número enorme de disposiciones posibles. RoomCraft AI las explora automáticamente a partir de una descripción en lenguaje natural. El pipeline de tres etapas Parser con LLM: el usuario describe su habitación en texto libre ("un dormitorio de 4x3 con la puerta al norte y una ventana al este"). Un LLM ( Llama 3.1 vía Groq ) lo convierte en una estructura de datos validada con Pydantic : dimensiones, aberturas, muebles deseados. Latencia: <1s. Optimizador con Simulated Annealing: aquí está el corazón del proyecto. Visualización y export: los layouts se renderizan en 3D en el navegador con Three.js y se exportan como plano técnico en PDF con ReportLab . Por qué Simulated Annealing El espacio de disposiciones posibles es combinatorio y lleno de óptimos locales. Una búsqueda voraz se queda atascada en la primera solución "decente". El Simulated Annealing imita el enfriamiento de un metal: al principio acepta movimientos malos con cierta probabilidad (alta "temperatura"), lo que le permite escapar de óptimos locales; según baja la temperatura, se vuelve cada vez más exigente y converge. Es una metaheurística ideal cuando el espacio de soluciones es irregular y no tienes gradiente. La función objetivo puntúa cada disposición de 0 a 100 según ergonomía: espacio de circulación, relaciones entre muebles, acceso a luz y aberturas. El sistema devuelve el top 5 de layouts, no solo el mejor, para dar opciones. Rendimiento Parse: <1s . Optimización: 2–5s . Export PDF: <1s . Footprint en reposo: ~100 MB de RAM. Qué aprendí Que combinar un LLM (para entender lenguaje) con una metaheurística clásica (para optimizar de verdad) es un patrón potentísimo: el LLM traduce el problema humano a uno formal, y un algoritmo determinista y barato lo resuelve mejor —y de forma más explicable— que pedirle

"I pretty much, at that point in time, gave up on being an astronaut."

On this week’s episode of The Big Interview podcast, WIRED’s senior culture editor Manisha Krishnan talks to Gowanus about eschewing Big Tech, going outside, and rejection in the age of dating apps.

Yes, Sarah Wynn-Williams violated her deal to stay silent. But the power disparity bolsters the view that Meta is a heartless bully.

Today on Uncanny Valley, we unpack the political debacle unfolding in Maine surrounding the campaign of Democratic candidate Graham Platner.

Every system that does "semantic" anything — RAG pipelines, recommendation engines, image search, dedup — boils down to one operation: given this vector, find the closest ones out of millions. The vectors are embeddings, a few hundred to a couple thousand numbers each, and "closest" means closest in meaning. You'd assume the database either scans all of them (slow but correct) or uses some clever tree to jump straight to the answer. It does neither. Instead it deliberately settles for the approximately closest vectors — and that compromise is the entire reason vector search is fast enough to exist. Two algorithms do almost all the heavy lifting in practice, in pgvector, Qdrant, FAISS, and the rest: IVF and HNSW . Here's what they're actually doing under the hood, and how to choose between them. Why "exact" is off the table The natural objection is: why approximate? Just find the real nearest neighbor. In two or three dimensions you could — a k-d tree or similar structure prunes away big regions of space and finds the true closest point quickly. The trouble is that embeddings live in hundreds of dimensions, and high-dimensional space is deeply weird. It's called the curse of dimensionality . As dimensions grow, the distance to your nearest point and the distance to your farthest point drift toward being almost the same. Formally, the contrast (d_max − d_min) / d_min shrinks toward zero. When everything is roughly equidistant from everything else, a tree can't confidently say "skip this whole branch, it's too far" — the bounding regions all overlap, every branch looks plausible, and the search degrades into checking nearly everything. Exact indexes quietly collapse back into brute force. So we change the question. Instead of "prove you found the nearest," we ask "quickly find something very probably among the nearest." That's approximate nearest neighbor (ANN) search, and it swaps a guarantee for speed. The quality knob becomes recall : of the true top-k neighbors, wh

Manna is launching a U.S. operations and manufacturing facility in Tulsa, Oklahoma, that will eventually employ 1,000 people.

Livestreaming the channel through its app now starts at $5 per month.

Station F, the Paris-based startup hub founded by French billionaire Xavier Niel in 2017, is gearing up for a new edition of its selective acceleration F/ai program that will cement its role as a stepping stone for AI startups.

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

GitHub had 20,000+ secret scanning alerts across 15,000 repositories. Here's how we separated signal from noise, built remediation workflows, and reached inbox zero in nine months. The post How GitHub used secret scanning to reach inbox zero appeared first on The GitHub Blog .

A timely tale about a 50-year-old robotic arm...

We're excited to launch PaperQuire — a desktop app that turns plain Markdown into professional, print-ready PDFs. No cloud uploads, no accounts, no subscriptions required for personal use. Why We Built PaperQuire If you write in Markdown, you've probably hit this wall: your content looks great in your editor, but the moment you need to share it as a polished document — a proposal, a report, a spec — you're stuck copy-pasting into Word or fighting with LaTeX. We wanted something simpler. Write in Markdown, click Export, and get a document that looks like a designer made it. No extra steps, no cloud dependency, no learning curve. What PaperQuire Does Live preview — See your formatted document side-by-side as you type. What you see is what you'll get in the PDF. Professional templates — Choose from templates designed for technical docs, proposals, reports, and more. Every template supports custom branding: your logo, your colors, your fonts. Offline-first — Your documents never leave your machine. PaperQuire runs entirely on your desktop — macOS, Windows, and Linux. Plugin system — Extend PaperQuire with plugins for diagrams (Mermaid), math (KaTeX), syntax highlighting, and more. AI Assist — Bring your own API key and get writing suggestions, grammar fixes, and content generation right inside the editor. How It Works Open PaperQuire and start writing Markdown — or open an existing .md file Choose a template and customize your branding (logo, colors, fonts) Click Export to generate a polished PDF instantly Share your document with confidence The entire process takes seconds, not minutes. Free for Personal Use PaperQuire is free for personal use with no restrictions on the core features. The Pro plan adds advanced exports (DOCX, HTML), batch processing, and priority support for teams that need more. Get Started Download PaperQuire for your platform: macOS (Apple Silicon) Windows (x64) Linux (x86_64) Check out the documentation for a quick walkthrough, or just start writi

For decades, the senator has argued that concentrated wealth threatened American democracy. Now he’s betting that frustration with Big Tech, billionaires, and unchecked AI is reaching a tipping point.

Have you noticed how every demo of "an AI agent" looks impressive in the video and falls apart the moment you ask a sharper question? The agent confidently does the wrong thing. It forgets what it just decided. It tries to call a tool that doesn't exist. It loops forever rewriting the same file. It calmly tells you the deployment succeeded when it didn't. These aren't failures of the model. They're failures of the workflow around the model. Because that's all an agent really is: a software workflow where a language model can pick the next step and call tools. The "intelligence" sits in the prompt and the orchestration around it, not in some secret agent-flavoured fairy dust. Strip the word "agent" away and you've got five pieces of plumbing: planning, tool use, memory, constraints, verification. Every production-grade agent stands or falls on those five. This is a long walk through each one. Not the marketing version. The kind of detail you actually need before you ship something that talks to your database. The Loop You're Actually Building Before we touch any pillar individually, hold the whole loop in your head. A useful agent does roughly this on every turn: Read the goal (and whatever memory is relevant to it). Decide the next action: answer directly, call a tool, ask a clarifying question, or stop. If it called a tool, observe the tool's result and feed it back in. Update memory if anything is worth remembering. Check constraints: are we over budget, out of iterations, touching something off-limits? Verify the output before declaring success. Loop until done or stopped. That's it. Every framework (LangGraph, OpenAI Agents SDK, Claude Agent SDK, smolagents, whatever ships next month) is a different shape of the same loop with different defaults. agent-loop.ts async function runAgent ( goal : string , ctx : AgentContext ) { const state = ctx . startState ( goal ); for ( let step = 0 ; step < ctx . maxSteps ; step ++ ) { const decision = await ctx . model . decid