Browsing: Business & Startups

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 fine-tuning. In this article, we explore Inkling’s architecture, training, capabilities, benchmarks, pricing, deployment options, fine-tuning workflow, and enterprise readiness. What is Thinking Machines Inkling? Inkling is a general-purpose, multimodal, open-weights AI model from Thinking Machines Lab. It processes text, images, and audio, and generates text…

According to a recent MIT NANDA report, 95% of organizations investing in generative AI are getting zero measurable return on it, despite $30–40 billion in enterprise spending. The reason isn’t the technology, the talent, or the regulation. According to the same study, it’s the approach. That approach to the problem usually takes one of two forms: companies either pick the wrong process to pilot first or never get past picking at all. A strategic AI workshop exists to solve both by making the first decision well before any code is written or any budget is committed. This article explains what…

   The European Commission’s latest draft guidelines provide much-needed clarity on how organizations should classify high-risk AI systems under Article 6 of the EU AI Act. However, they also raise an important question for enterprises: could your existing AI systems already be considered high-risk without you realizing it? The answer may depend on more than what the technology does. Under the EU AI Act, an AI system’s intended purpose plays a central role in determining its risk classification. This means how a system is documented, marketed, deployed, and used can be just as important as its technical capabilities. Article 6…

If you’ve spent any time on GitHub Trending this month, you’ve probably noticed a pattern: it isn’t research papers turning into repositories anymore, it’s agents. Coding agents, pentesting agents, trading agents, and the infrastructure that ties them all together. We tracked star growth, momentum, and real-world impact to identify the ten repositories that mattered most this month. Rather than ranking projects by stars alone, we considered both their influence on the AI ecosystem and how quickly they’re gaining traction. In this article, we’ll break down each repository, what it does, why it’s trending, and why it’s worth adding to your…

★ Editor’s Pick 🐍 Stop Using If-Else Chains: Use the Registry Pattern in Python InsteadKanwal Mehreen  ·  Python  ·  July 15, 2026Long conditional chains hinder extensibility in Python by violating the Open/Closed Principle, making code brittle when new options are introduced. The Registry Pattern solves this by replacing hardcoded dispatch logic with a central lookup table where components register themselves dynamically. Implementing this pattern allows system behavior to be driven by configuration, resulting in more maintainable and easily extensible pipelines.   ➡️ 12 Ways to Reduce LLM Latency and Inference Costs in ProductionKanwal Mehreen  ·  Language Models  ·  July 14,…

  # Introduction  You are running Claude Code on a feature branch. The agent has been working for twenty minutes, it has read your codebase, built up context, and started making real progress on the authentication rewrite. Then a Slack message appears: production is down, someone needs a hotfix on main, and they need it now. In the old workflow, you stash your changes, switch branches, lose everything your AI agent built up, fix the bug, push, switch back, and spend ten minutes getting the agent re-oriented to what it was doing. If you were running two agents simultaneously on the…

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 servers with Claude Desktop and Claude Code, a practical step-by-step guide for getting each setup running correctly. What Is MCP and Why Does It Matter? MCP (Model Context Protocol) is the universal connector layer that lets any AI model talk to any external tool through…

  # Introduction  Everyone is building agents. Far fewer people can explain, precisely, why their agent loops forever, ignores a tool it was given, or confidently reports success on a task it never finished. The gap between shipping an agent and understanding one is where these five resources live, and every one of them is completely free. I’ve deliberately mixed registers here: a hands-on course you can finish in a weekend, a rigorous academic text for when the hype wears off and you want the foundations, and a few things in between. Work through even three of them and you’ll design…

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 even more interesting. One model promises deeper reasoning. The other offers near-frontier performance at a far more practical cost. So, which model should you actually use? What is GPT-5.6 Sol? GPT-5.6 Sol is OpenAI’s flagship model for coding, research, tool use and complex professional workflows.…

  # Introduction  Most coding agents compete on how much they do for you. Claude Code manages sub-agents, plan mode, and permission flows out of the box. Cursor wraps an entire IDE around the model. The pitch is always some version of “more capability, less setup.” Pi does the opposite, and says so directly in its own documentation: no MCP, no sub-agents, no plan mode, no permission popups, no built-in to-do lists, no background bash. Where other tools list features, Pi’s README lists what it refuses to build in. That’s an unusual thing for a product to lead with, and it’s…