标签：#ama

I've been building AI agents for a while now, and streaming responses to a UI has always been the painful part. In previous projects I tried API Gateway streaming, Lambda response streaming, and even AppSync Events via an agent tool call to notify the UI. I also looked at adding my own WebSocket API through API Gateway, which requires managing $connect , $disconnect , and $default routes, storing connection IDs in DynamoDB, and posting messages back through @connections . All of these approaches felt like too much ceremony for what should be simple. That's when I found that AgentCore has built-in WebSocket support. The browser can just connect directly to the agent. No middleman. I built WearCast to demonstrate this functionality. This is an AI agent that helps you pick out what to where based on the weather. I've found this very helpfu when packing for a trip. The code for this application is public and you can find the full implementation on this GitHub repo . The problem with the traditional approach We usually build app applications as we do REST APIs User → REST API → Lambda → AI Service → Lambda → REST API → User Every message makes a round trip through multiple intermediaries. The response waits until the entire generation is complete, and then it all comes back at once. For a chat interface, this feels sluggish. Users stare at a spinner while the model generates hundreds of tokens they could already be reading. Even if you add Server-Sent Events or long-polling, you're still stitching together a real-time experience on top of infrastructure that seemed like overkill. I wanted something better. The architecture Here's what I ended up with: ┌─────────────┐ ┌──────────────────┐ │ React UI │─── JWT ─▶│ API Gateway + │──▶ Lambda (presigned URL) │ (Cognito) │ │ Cognito Auth │ └──────┬──────┘ └──────────────────┘ │ │ WebSocket (SigV4 presigned URL) │ ← No middleman! Direct connection → ▼ ┌──────────────────────────────────┐ ┌────────────────────┐ │ AgentCore Runtim

For about 3 months I've been running an autonomous agent — one that thinks up and writes its own social media posts and comments — unattended, 4 sessions daily, on a 16GB M1 Mac with small models in the 9B / E4B class. I'm about to publish what that operation taught me about hardening, as a series of 4 technical articles. Before that, there's one thing I want to write down first: why small models . I've been to the purchase page for a Mac Studio or a new MacBook Pro more than once or twice. Backing the agent with a large cloud model (Opus or the GPT family) has always been an option in the code. And yet I haven't bought, and I haven't switched. The 16GB M1 is not an economic constraint — it's a constraint I chose . From the outside, building on small models looks like a cheap compromise. This article explains why it isn't, and states where I stand. It also serves as the hub for the 4-article series. A model's intelligence hides the roughness of your design Large models absorb sloppy prompts, ambiguous instructions, and missing guards with sheer intelligence. If all you want is to ship a product, that's a virtue. But if you want to become someone who can build things , it becomes a defect. Because inside the thing that worked, you can no longer tell where your design ends and the model's intelligence begins. "It worked" and "I built it" are different things. Something you bludgeoned into working with model capability counts as a thing that ran — it doesn't become the ability to build. Small models have no absorption capacity. So every design flaw comes to the surface. In my operation, all of the following surfaced: The context window being silently truncated Outputs cut off midway A runaway caused by one missing sampling parameter In cloud or large-model environments, these rarely bother you. The environment has cushioning built in. Context windows are in the 200K–1M token class, so truncation itself rarely happens. And when you do exceed the limit, you get an explic

In an interesting twist that takes advantage of the company's core product, users can chat with these shows' characters, ask them questions, and even roleplay different storylines.

TL;DR Short version: no. I dropped a much older GPU ( Quadro P2000, 5GB, Pascal, 2016 ) next to an RTX 3090 (24GB, Ampere) on the same box, ran the same context-length ladder (8K→128K) through Ollama and vLLM on qwen3-coder:30B-A3B , and got zero extra usable context in either engine — and a 74% decode-speed hit for the trouble. Ollama hits the identical Chunk too big wall at ctx=65536 whether the P2000 is there or not. vLLM refuses tensor-parallel across the two cards entirely — not a VRAM problem, a flat compute-capability rejection ( Minimum capability: 75. Current capability: 61. ) that fails in 40 seconds, before any memory profiling. And the one real, measured effect of adding the P2000 to Ollama: decode speed goes from 76 → 19.5 tok/s at ctx=49152 once the P2000 gets pulled in as an actual compute device. Full narrative version — the two-stage collapse, the prompt-cache validation bug caught mid-sweep, the CUDA13-silently-drops-Pascal finding — is on Medium .## The setup ardi (dual Xeon E5-2680 v4, 128GB RAM, openSUSE Leap) has a Quadro P2000 sitting in a second slot next to the RTX 3090 this whole series has run on so far. Same model as phase 1 ( qwen3-coder:30B-A3B ), same box, four legs: {Ollama, vLLM} × {3090 only, 3090+P2000 tandem}, priced through HomeLab Monitor against real GPU power draw. Ollama: same wall, extra tax ctx 3090 only decode tok/s tandem decode tok/s P2000 VRAM (tandem) 8,192 124.3 122.0 6 MB / 0% 24,576 108.2 70.0 62 MB / 0% 32,768 99.4 61.0 62 MB / 0% 49,152 75.7 19.5 3,580 MB / 55% 65,536 fatal: Chunk too big fatal: identical Chunk too big — Two separate costs, not one: decode already falls behind at ctx=24576 while the P2000 is still basically idle (62MB, 0% util) — some scheduling overhead just from having a second visible device. Then the real collapse hits at ctx=49152, when the P2000 actually gets pulled into the compute path (3.58GB, 55% util) and decode craters to 19.5 tok/s . Same context ceiling either way, worse speed the wh

SpaceX is currently ahead of last year's record-setting pace for Starlink satellite deployments. SpaceX launched 1,589 Starlink satellites into low-Earth orbit in the first half of 2026, according to launch data compiled by Jonathan McDowell's satellite tracker, compared to 1,489 satellites deployed at the same point in 2025. 2025 was already a record year for […]

The company's streaming devices aren't quite keeping up with competitors in several important categories.

Amazon is working on a project codenamed 'Moonraker' that would enable the AI assistant to handle multi-step tasks more effectively.

Amazon says it has enough satellites in orbit to begin initial broadband service at mid-latitudes later this year.

Its issues with current nuclear safety standards are termed semantic, not physical.

These may be the last days of Amazon’s Mechanical Turk.

The Fire HD 8 that launched in 2024 was the last new addition to Amazon's budget-minded tablet lineup, but the company has quietly updated the Fire HD 10 that debuted the year before. In 2023 it was offered with multiple storage configurations that each came with 3GB of RAM, but the 32GB version now ships […]

AI has made it a lot harder for tech companies like Amazon and Google to deliver on their net-zero pledges.

Amazon says it now has enough satellites operating in low-Earth orbit to light up its Starlink internet competitor. With last night's launch, Amazon Leo has 396 satellites deployed, which is "enough to support continuous service across initial latitudes," according to Chris Weber, VP heading up business and product for Amazon Leo. That puts the company […]

Yapay zeka mühendisi (AI Engineer) olmak, yalnızca ChatGPT’ye veya Claude'a akıllıca promptlar yazmaktan ibaret değildir. Yapay zeka modellerini kullanarak gerçek dünyadaki karmaşık problemleri çözen, sürdürülebilir, güvenli ve ölçeklenebilir yazılımlar inşa etmek ciddi bir mühendislik disiplini gerektirir. Eğer temel basamakları sağlam kurmadan doğrudan en üstteki otonom ajan (agentic) yapılarına atlamaya çalışırsanız, üretim ortamına çıktığınızda beklenmedik hatalarla (hallucination), kontrol edilemeyen maliyetlerle ve aşırı yüksek yanıt gecikmeleriyle (latency) karşılaşırsınız. Bu rehberde, sıfırdan başlayıp üretim seviyesine (Production-ready) kadar uzanan 17 katmanlı AI Engineer Yol Haritası'nı adım adım inceleyeceğiz. Phase 1: Foundation (Temel Katman) Binalar gibi, yapay zeka uygulamaları da güçlü temeller üzerine kurulur. Bu aşamada amacımız yazılım geliştirme ve temel model etkileşim mekanizmalarını kavramaktır. Python & Data Yolculuğun ilk ve en kritik basamağı Python programlama dili ve veri yönetimidir. Yapay zeka dünyasında veri okuma, temizleme, dönüştürme ve analiz etme süreçleri günlük işlerin büyük kısmını oluşturur. Python'ın liste, sözlük gibi yerleşik veri yapılarını, nesne tabanlı programlama (OOP) mantığını ve fonksiyonel yapısını kavramak şarttır. Ayrıca veri manipülasyonu için Pandas ve sayısal işlemler için NumPy gibi kütüphanelerde yetkinlik kazanılmalıdır. Detaylı bilgi için Python Resmi Dokümantasyonu incelenebilir. Software Engineering & APIs Kodunuzun lokal bilgisayarınızda sadece "çalışması" yeterli değildir. Üretim ortamında çalışacak kodun temiz, okunabilir, sürdürülebilir ve test edilebilir (Unit/Integration Tests) olması gerekir. Ayrıca modelleri uygulamalara entegre ederken REST API tasarımları, authentication (kimlik doğrulama) mekanizmaları, asenkron programlama, hata yönetimi (Error Handling) ve servis mimarileri hayati önem taşır. Git gibi versiyon kontrol sistemleri ve temel Docker bilgisi bu katmanın ayrılmaz parçasıdır. Pro

The retailer allegedly failed to meet requirements under the Fair Credit Reporting Act.

The Federal Trade Commission fined Amazon $2.25 million to settle claims that the company failed to help customers who fell victim to identity theft, as reported earlier by Bloomberg. In its complaint, the FTC accuses Amazon of refusing to provide customers with information about purchases made with fraudulent accounts, in violation of the Fair Credit […]

New Fire Stick OS helps Amazon block third-party homepage launchers, ad blockers.

Engineers on the new team will embed within companies to deploy purpose-built agents, focusing on fast deployments and customer self-sufficiency.

VCs remain thirsty to fund AI coding startups. This one, founded by investor Chamath Palihapitiya, is no exception.

The world's two largest memory chip companies vow to build more memory lab fabs as South Korea positions itself as an AI tech powerhouse country.