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The bar we set We approved a plan on 2026-07-10 with an acceptance test we weren't sure was reachable. Six drafted analyst-memo queries against Nigerian economic data, scored 0-10 across five dimensions — named-entity density, citation quality, sector-specific detail, honest-gap acknowledgement, decision-usefulness. The strict pass criterion: every query's mean score across three temperature=0.2 runs must be ≥8/10, with no query below 6 in any single run. At approval time the aggregate was somewhere around 30/60 across the six queries — a system that produced grounded but generic answers, and refused competently but not always. The gap to the bar was real. We gave it 4-5 weeks. What we shipped Phase 1 — retrieval breadth. Kind-diversity enforcement across the top-K result set so a "start a fintech" query stopped collapsing into 12 CBN circulars and started pulling BOI, NEXIM, PayStack, Flutterwave, and the World Bank agribusiness chapters in the same context window. Named-entity boost when the query mentions "factory", "startup", "invest", "loan". Deduplication so a briefing about the same fact doesn't crowd out its own primary source. Phase 2 — a six-class rule-based intent classifier and memo templates. Sub-millisecond routing on regex patterns: venture\_feasibility\ , strategic\_forecasting\ , credit\_risk\ , regulatory\_analysis\ , market\_sizing\ , general\_qa\ . Each intent gets a memo template — a section-headed scaffold with a named-entity mandate, an honest-gaps section, and a 1000-1500 word target. The general\_qa\ template stays empty (no memo shape) so genuinely-general questions don't get forced into a memo they don't need. Phase 3 — composition quality. Two changes did most of the work here: 1. A CITATION PREFERENCE: PRIMARY OVER BRIEFING\ block in the system prompt. Primary sources — CBN circulars, NAICOM regulations, NBS reports, textbook chapters, IMF Article IV, press coverage of specific events — get cited over daily briefings when both are presen

The new strategy that Amazon gaming exec Jeff Gattis talked to The Verge about last month is getting clearer now that Prime Video has added a new Games tab on Fire TV devices. With Prime Gaming, Amazon Game Studios, and Luna now combined into one organization, it's aiming at the more casual audience of people […]

Amazon is hoping the addition of games can turn Prime Video into a one-stop entertainment destination, borrowing a strategy from Netflix, which has increasingly embraced party games over the past several years.

Luna will be first arrive in the Prime Video app on Fire TV devices in the US and UK.

A configuration change in AWS's bill computation system showed customers estimated bills in the billions and trillions of dollars for over 24 hours. AWS's own alarms detected the anomalies but failed to halt bill generation or page engineers; customer escalations alerted the company 4.5 hours later. Budget and cost anomaly alerts were disabled platform-wide during mitigation. By Steef-Jan Wiggers

Amazon greenlit a Robocop pilot. Now it has committed to a full TV show.

It's easy to see who's at the door without grabbing your phone.

Amazon's Adaptive Display accessibility feature for Fire TVs is officially rolling out today

Several Chinese businesses associated with the ebike brand Aipas settled a lawsuit with Amazon and the safety certification company UL in July.

Hi, I'm Cesar, a Senior Software Engineer with 10+ years of experience. In 2023, I got sick and...

An error with the cloud computing giant’s billing operation caused some customers’ monthly bills to rise from a few cents to billions of dollars.

Apple is blaming an Apple Music price hike on "rising licensing costs."

"The displayed billing estimates do not reflect actual usage and charges," Amazon said.

Some Amazon customers logged on Friday to a surprise bill estimate claiming that they owed the tech and cloud giant billions in fees.

New report says our ability to tie weather damages to climate change is improving.

Apple will reportedly refresh the iPad mini this fall and the rest of its iPad lineup in the first half of 2027.

You're paying $10 to $20 a month for Copilot. You don't have to. A 2024-era laptop can run a coding model good enough for autocomplete, refactors, and "explain this function" entirely offline. No API key, no telemetry, no per-token bill. Here's the exact 2026 setup I run on a 16GB machine. Why local in 2026 Two years ago, local coding models were a toy. The autocomplete was slow and the suggestions were noise. That changed. qwen2.5-coder and deepseek-coder-v2 are genuinely useful now, and the tooling caught up: Ollama serves them, Continue.dev wires them into your editor, and the whole thing runs on hardware you already own. The pitch is simple: Free. No subscription, no usage caps. Private. Your proprietary code never leaves the machine. This matters if you work on smart contracts or anything under NDA. Offline. Works on a plane, in a basement, behind a corporate firewall. The tradeoff is quality and latency. We'll be honest about both. Pick a model (and match it to your RAM) This is the decision that makes or breaks the experience. Pick a model your machine can actually hold in memory, or it spills to disk and crawls. # Fast, fits anywhere (8GB+) ollama pull qwen2.5-coder:1.5b # ~1.0GB ollama pull qwen2.5-coder:3b # ~1.9GB # The sweet spot for most laptops (16GB) ollama pull qwen2.5-coder:7b # ~4.7GB # Quality tier, needs headroom (32GB+ comfortable) ollama pull deepseek-coder-v2 # ~8.9GB (16b MoE) ollama pull qwen2.5-coder:14b # ~9.0GB ollama pull qwen2.5-coder:32b # ~20GB Rough rule: the model file size is the floor, then add a few GB for context and the OS. A 4.7GB model on a 16GB machine is comfortable. A 20GB model on the same machine is not. Model Size RAM I'd want Use it for qwen2.5-coder:1.5b 1.0GB 8GB Autocomplete, fast iteration qwen2.5-coder:7b 4.7GB 16GB Daily driver: chat, refactors, explain deepseek-coder-v2 8.9GB 32GB Harder reasoning, multi-file context qwen2.5-coder:32b 20GB 64GB Near-cloud quality, if you have the RAM deepseek-coder-v2 is a 16b m

Don't use Amazon Music? Too bad, Samsung is installing it for you anyway.

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