56 related articles

Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.

T-Head open-sources AI software stack T-Head SAIL at WAIC to lower the barrier for domestic chip development; Kimi K3 tops the WebDev leaderboard; Qwen 3.8 Max Preview cuts prices aggressively; Moonshot prepares a Hong Kong IPO; and Oracle switches its data center to a fuel cell microgrid.

GPT-5.6 Sol tops Chatbot Arena's frontend leaderboard, Claude Code gains a built-in browser, Sol Ultra proves a 50-year math conjecture, and Gemma 4 gets 5x faster.
Bonsai 27B: The First 1-bit LLM That R…
Bonsai 27B is the first 27B-parameter LLM that runs on smartphones via 1-bit quantization, compressing to 3–4GB. We break down the tech, privacy benefits, and community debate.

A 2-year Perplexity Pro user explains why they're leaving. Covers how to export chat history in JSON/PDF/Excel, and compares Google Gemini, ChatGPT, and Claude as alternatives.

Deep dive into Perplexity AI: real-time web search + multi-model AI, transparent citations, Focus Modes, PDF chat, and collaborative Collections. Can it replace Google and ChatGPT?
Where Do AI Writing Tics Come From? A …
Why does AI text love phrases like 'It's not just X, it's Y'? We unpack the origins of AI writing tics — from training data biases to RLHF — and why even developers can't fully explain them.

Is $200/month for AI Agents worth it? We break down credit consumption mechanics, tool tradeoffs, and ROI calculations for ChatGPT Pro, Claude Max, and more.

A comprehensive guide to preparing for the National Mathematical Modeling Contest: covering the essence of modeling, judging rules, topic selection, AI usage guidelines, and a four-day schedule to boost your chances of winning.

A user who got Perplexity Pro via Revolut Metal found their premium quota slashed from the promised hundreds a day to just 10, with support silent after a 48-hour promise. A deep dive into AI subscription opacity and third-party channel disputes.

Knowing how to call an API doesn't make you an AI engineer. This article breaks down the complete skill structure of an AI application engineer, covering Python fundamentals, LLM fine-tuning, Agent development, and enterprise projects.

Perplexity's Comet browser faces user criticism over lagging model versions, stalled updates, and stability issues. A deep analysis of AI browser challenges.

Generative AI tools have flooded social media with AI-generated content, hitting LinkedIn hardest due to its professional nature. A deep dive into the causes, ecosystem impact, and solutions.

Why can a mini PC with unified memory run a 70B model while an RTX 4090 can't? A deep dive into the VRAM wall and unified memory architecture for smarter local AI hardware choices.

How did Ollama grow from a niche open-source project into developers' default choice for running local LLMs? This article breaks down its rise across product design, technical strategy, and ecosystem building.

Tencent Hunyuan and Tsinghua jointly release DiscoBench, the first benchmark evaluating search agents' dynamic ambiguity clarification. Covering 463 ambiguity instances across 11 domains, it reveals real weaknesses of mainstream LLMs.

Kagi's new AI Toggle lets users control whether AI summaries appear in search. A deep dive into its subscription model, product philosophy, and lessons for AI search.

AMD officially unveils the Ryzen AI Halo local AI dev kit, priced around $4,000 with 128GB unified memory, capable of running 70B LLMs locally. An in-depth look at its specs, pricing, and market competition.

An in-depth hands-on review of Zhipu AI's flagship GLM-5.2: 1M-token context, strong coding, mature agent workflows—at one-fifth the price of top frontier models. Covers website testing, Cursor integration, MCP tooling, and production migration.

Can a brand's "visibility" in AI answers really be quantified? This article deeply dissects the methodological flaws of AI visibility dashboards—from LLM output randomness and black-box mechanisms to vanity metric traps.