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On July 21, 2026, while everyone was still waiting on the much-delayed Gemini 3.5 Pro, Google slipped out a mid-cycle update to its speed tier: Gemini 3.6 Flash. No new frontier claims, no dramatic reveal. Instead, the model does roughly the same thinking as 3.5 Flash while spending fewer tokens, fewer tool calls, and fewer […]
Gemini 3.6 Flash Is Here: The Efficiency Release Read More »
Thinking Machines Lab has unveiled Inkling, its first general-purpose open-weights foundation model. It is a multimodal MoE model with 975B parameters, 41B active parameters, and a 1M-token context window. Rather than chasing benchmark supremacy, Inkling is designed as a customizable foundation for multimodal reasoning, agentic AI, coding, tool use, audio and vision tasks, and domain-specific
Connecting MCP servers to Claude allows it to work with external tools, files, databases, repositories, and other systems instead of operating only within the chat window. The setup differs slightly between Claude Desktop and Claude Code, but both can be configured in just a few steps. In this article, you’ll learn how to connect MCP
How to connect MCP servers with Claude (Claude desktop and Claude Code) Read More »
GPT-5.6 Sol and Claude Fable 5 are currently fighting for the frontier-model crown. Fable 5 holds a slight edge in general intelligence, while Sol hits back with stronger coding performance, faster execution and much lower pricing. In fact, GPT-5.6 Sol is priced closer to Claude Opus 4.8 than to Fable 5, which makes this comparison
GPT-5.6 Sol vs Claude Fable 5: Benchmarks, Pricing & Hands-On Read More »
THE GIST ▸ What it is: A 3,826-line system prompt steering Claude Fable 5 inside the Claude app, pulled from a public GitHub archive. ▸ What’s in it: Rules about safety, tone and restraint. ▸ Why it matters: it shows a frontier “AI” is far more an engineered rulebook than a mysterious mind. Before your
Inside the Claude Fable 5 System Prompt: A Full Breakdown Read More »
Prompts shape every interaction with a large language model. Clear instructions produce focused, useful responses, while vague ones often lead to inconsistent results. This becomes harder when teams need the same task completed repeatedly in a fixed format, tone, or structure. Meta-prompting asks the model to design a reusable prompt, template, checklist, or workflow before
Two short clips. One question: how alike do they look? Sounds trivial, it isn’t, and I learned that the slow way. My setup: one reference clip, eight others to rank against it, all waterfalls (more on why in a second). I figured this was an afternoon job, grab a model, compute a number, move on.
How to Measure Video Similarity: 6 Techniques I Tested (and the One I Shipped) Read More »
LLMs are getting stronger every day, and building a RAG pipeline has never been easier. Knowing whether it actually works is not. Most teams ship a RAG system, see decent-looking answers, and call it done, until users hit hallucination, missing context, or irrelevant chunks. That’s where evaluation frameworks come in. RAGAS, TruLens, and DeepEval are
RAG Evaluation Frameworks Compared: RAGAS vs TruLens vs DeepEval Read More »
For twelve days, the best AI models on the planet existed and almost nobody could touch them. That ends now! GPT-5.6 Sol, Terra, and Luna go public today! The models are accessible by all users (no subscription required) This is the full breakdown of what’s on offer: three models, four prices, one precedent, and a
AI agents are moving from one-time assistants to persistent workers that can repeat tasks, monitor changes, run checks, update workflows, and return with results. Instead of prompting an LLM once and deciding every next step manually, teams can now use AI agents that keep working (on a Loop) until a goal or stop condition is
Loop Engineering for AI Agents: How /loop is Changing AI Workflows Read More »
