Article archive
Published news and blog articles, organized by category. Browse older coverage by month or search for a topic. Undated blog guides appear after dated news.
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Quantum computing explained: why qubits defy every intuition
A classical bit is a coin lying on a table; a qubit is a coin spinning in the air. Superposition, entanglement and decoherence, explained without the hype — and why useful quantum computers are still hard.
The GPU bet: why parallel silicon became the engine of AI
A chip designed to push pixels turned out to be the ideal machine for matrix multiplication. The architectural story of how GPUs became the engine of deep learning — and why CUDA still matters.
Apple's M6 Arrives: What the New Mac Mini and Mac Studio Say About the AI Chip Race
Apple refreshed the Mac mini and Mac Studio with its M6-generation silicon this week. Beyond the spec sheet, the launch is a statement about where consumer AI compute is heading.
Claude Fable 5: Inside Anthropic's Newest Model Release and What It Signals
Anthropic's Claude Fable 5 launch video has passed 800,000 views, a reminder that frontier-lab model releases are now media events with their own economics and their own failure modes.
Benchmarks vs Reality: Why LLM Leaderboards Keep Failing to Predict Real Agents
IBM Technology's new explainer on why benchmark-topping models still break in production lands amid a broader credibility crisis in AI evaluation.
Pixel 11 After One Week: Tensor G6, On-Device AI, and the Case for Google Silicon
9to5Google's one-week review of the Pixel 11 lands at a moment when Google's in-house chip program is under its sharpest scrutiny yet, and the verdict is more strategic than spec-sheet.
Dimensity 9500 vs Snapdragon 8 Elite Gen 5: the silicon duel powering 2026's best phones
MediaTek's flagship arrives as a genuine Qualcomm peer. N43 compares the architectures, the gaming evidence, and the NPU layer where the duel is actually decided.
A new era of intelligence: what Gemini 3 really says about the 2026 AI race
Google DeepMind's Gemini 3 launch reframes the model race as a platform war. N43 examines the lineage, the competition, and the limits the launch video leaves out.
What's on my Phone 2026: the home screen has become an AI dashboard
MKBHD's annual app teardown shows how far the smartphone has pivoted: assistants and agents now dominate the grid, and the NPU made it free. N43 reads the signal.
Which AI models are actually worth using in 2026: a field guide to the three-tier market
Flagship APIs, mid-tier subscriptions, or open-weight locals: the 2026 model market rewards matching the tier to the task. N43 breaks down the developer's field guide.
AI Agents, Clearly Explained: Inside the Agentic Loop Reshaping Work in 2026
The chatbot gave everyone a taste of conversational AI. The agent is what happens when the same models get tools, memory, and a loop: software that plans a task, acts on real systems, observes results, and corrects course without a human…
The Fastest Phone in the World: What 2026 Speed Tests Actually Measure
Every year a robot arm taps two phones side by side through the same app course, and the internet crowns a winner. The PhoneBuff speed test above has roughly 800,000 views, and behind the spectacle sits a genuinely useful question: in…
How Large Language Models Actually Work: The 2026 Explainer
Large language models are the engine of the 2026 AI economy, yet the mechanism inside them is strangely simple: predict the next token. A 3Blue1Brown explainer with more than 7 million views walks through the intuition, and the…
Nvidia vs Custom Silicon: Inside the AI Chip Battle of 2026
Nearly every large AI model is trained on silicon designed by one company, and the biggest buyers are now designing their own. The CNBC explainer above, with more than two million views, maps the contest between Nvidia and the custom chips…
The AI chip race in 2026: Nvidia GPUs vs Google TPUs vs Amazon Trainium, honestly compared
Three philosophies of AI silicon now compete for every training run and inference call. An honest look at what GPUs, TPUs, and Trainium actually do differently, and why the software matters more than the chip.
The most powerful phone of 2026: what smartphone power actually means in the NPU era
Peak benchmark scores keep climbing, but the real contest in 2026 is NPU throughput, sustained performance under thermal limits, and how much of it you can actually feel.
OpenClaw and the agentic loop: how AI agents actually get things done
The shift from chatbots that answer to agents that act comes down to one structure: a loop of observation, planning, tool use, and feedback. What the loop does well, where it fails, and why oversight is the real design problem.
Three problems AI models may never fully solve: hallucination, reasoning limits, and the data wall
Hallucination, brittle reasoning, and the tightening supply of training data keep resurfacing despite every capability jump. An honest look at what is structural, what is mitigated, and what remains genuinely open.
Apple's 2026 roadmap leaks: everything rumored, what the record actually supports
The leak cycle says 2026 is Apple's biggest hardware year in a decade: a thinner iPhone 18 generation, a 20th-anniversary handset, the first foldable, a smart home hub, and a new silicon cadence. We separate the supply-chain signal from…
Best phones of 2026 so far: how reviewers actually rank them
Forty-plus reviews in, the mid-2026 phone rankings reward battery discipline, camera consistency and update promises over raw specs. How reviewer awards actually work, and what the mid-year consensus says.
Is RAG still needed in 2026? Long-context LLMs vs retrieval, honestly weighed
Million-token context windows were supposed to kill retrieval-augmented generation. Instead both got bigger. We weigh the mechanics — attention cost, freshness, provenance, agentic control — and give a workload-based verdict.
The Linux phone in 2026: real progress, real gaps, who it is actually for
A mainline-Linux phone in 2026 still means PinePhone-class hardware, Waydroid for Android apps, and real battery and modem trade-offs. What the ecosystem actually delivers, and who it is for.
Why AI labs are shelving their best models: Dylan Patel on the coming consolidation
Frontier training runs have become so expensive that labs increasingly hold their strongest models back. Dylan Patel's argument for consolidation, examined.
Claude vs Gemini in 2026: which AI subscription is actually worth $20
Two assistant families, one price point. A grounded comparison of strengths, context, coding, and ecosystem lock-in across the consumer tiers of Claude and Gemini.
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