Lev N.

Cursor's new Composer model is (not so quietly) Chinese under the hood. Cursor shipped the new version of Composer as its "first in-house frontier coding model," optimized through RL for real coding tasks. Within days, users noticed Composer's hidden thinking was full of Chinese characters. Industry press connected the dots: both Cursor's Composer and Windsurf's SWE-1.5 appear based on Chinese large models (likely Qwen bases), fine-tuned and wrapped in US products. Community reaction is split. Some call it a major downgrade: slower and struggling with complexity. Others report speed gains and slightly better performance. So why is the reception so mixed? Gavin Leech's AI Tigers post (link in comments) shows that on fresh benchmarks, Chinese models lose around 21% performance compared to roughly 10% for Western models. They look closer on leaderboards than they perform on novel problems. The cost story is similar: 3-6x cheaper per token, but often requiring 2-4x more tokens, which shrinks the cost advantage fast. So why would Cursor make this trade? Because they're not trying to replace GPT or Claude. They're owning a narrow, high-volume behavior and squeezing it with fine-tuning. Cursor has massive amounts of product-specific data (traces, diffs, accept/reject signals) and can invest in the RL loop to extract value from a cheaper base. This is where Chinese and open-source models actually fit in the stack. They're a strong fit for narrow problems with abundant training signal: tab complete, inline edits, code transforms. They're a weaker fit as primary reasoning for high-stakes, long-horizon workflows. Frontier US models still dominate the second category. Chinese open-source is taking over the first. Composer's rollout is a case study in how powerful Chinese models have become, and a live demo of where their limits still show.

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Interested in learning more about Foundry Local https://foundrylocal.ai and how to build RAG-style systems that run locally? I’ve been exploring patterns for local-first retrieval, generation, and agent workflows, focusing on privacy, performance, and developer control. If you’re experimenting with RAG beyond cloud-only setups, these samples might be useful: • Local RAG – a simple, practical starting point for retrieval‑augmented generation running locally https://lnkd.in/e8DaV8ae • Local CAG – moving beyond classic RAG into contextual and agent‑driven generation patterns https://lnkd.in/eb5u_QXc • Local Hybrid Retrieval (ONNX) – combining lexical + vector search with optimized local inference https://lnkd.in/eh4mMwSi These projects are all about understanding the building blocks: local models, embeddings, hybrid retrieval, and how they fit into modern agent architectures with Foundry Local. If you’re curious about edge AI, local-first agents, or privacy-aware RAG, feel free to explore, fork, or share feedback. #AI #RAG #FoundryLocal #AgenticAI #LocalAI #EdgeAI #Developers

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Language mixing isn’t just a quirky bug — it’s a real challenge for multilingual AI. This article explains why LLMs slip between languages and how to reduce it in production. https://lnkd.in/g-jpTUdF #llm #nlp #deeplearning #languagemixing

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This is absolutely true. I would also add that just because AI decreases cycle time does not mean you should operate without a plan. To use an analogy, pretend you are going on a trip from New York to Los Angeles and fuel/electricity are free (or only cost pennies). Now imagine that you don't have a map but are just driving based on instinct. Because you are moving and the cost is low or zero, you feel you are getting somewhere even when you are not. You need a map (app or paper), or at least need to stop for directions occasionally. Movement, no matter the speed, is not progress unless it's toward where you want to go.

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Advantages of Working Collaboratively: In our increasingly complex world, the secret power often lies not in singular genius—but in collective intelligence. As Colin M. Fisher writes in The Collective Edge: “The secret to getting the most from your groups is knowing how to work with the invisible forces of group dynamics instead of being mindlessly pushed around by them.” Here are a few key advantages of working collaboratively, inspired by Fisher’s insights: 1. Synergy & Creativity When structured well, groups can be more than the sum of their parts. Diverse perspectives spark innovation, as Fisher illustrates with design firms and high-performing teams. 2. Healthy Competition Competition within a collaborative group doesn’t have to be destructive. It can motivate without leading to conflict, if the group norms support it. 3. Constructive Conformity Conformity isn’t always a bad thing — under the right conditions, it can promote alignment, shared goals, and group cohesion. 4. Effective Leadership through Structure Great leadership doesn’t always mean charismatic speeches; sometimes it's about setting the right structure, coaching, and creating psychological safety. 5. Better Decision Making By paying attention to group-level dynamics, teams can make more robust decisions — avoiding the pitfalls of blind conformity or domination by a single voice. — When we lean into the hidden dynamics of groups, we unlock potential that individual effort alone can't reach. Let's celebrate and build collective edge in our teams. #Collaboration #Teamwork #GroupDynamics #Leadership #Innovation #CollectiveIntelligence #WorkTogether #CollectiveEdge

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One of my favourite WebGPU demos is the "deferred rendering" demo (or as I call it, "disco dragon"). It's pretty, and it demonstrates a cool rendering technique for highly complex scenes with lots of dynamic lights: https://lnkd.in/eVE226HN So I rewrote it in Toucan, my graphics-oriented programming language: https://lnkd.in/e2Kwe334 (runs in Chrome and Firefox Nightly). It's 424 lines of Toucan, versus 801 total lines of WGSL and TypeScript. Most of the savings come on the host side, where, having defined our vertex buffers, uniform buffers, bind groups and pipeline variables once for the device side, we don't need to re-declare them for the host side in a new syntax. Plus, a common type system means there's no need to compute byte offsets or vertex strides, match up group numbers or bindings numbers, re-declare attachments, or accommodate std140 or std430 memory layouts. All of that is done automatically by the compiler. Type safety means not only being able to assign to members of a vertex or uniform buffer on the host using the same syntax as the device, but also applies to the usage itself: since the "vertex" and "uniform" qualifiers are understood by the host side, they become part of the type. If a required qualifier is omitted on allocation, attempting to bind the buffer as such will result in a compile-time error, not a runtime one. Here's a diff of the two versions (with comments and whitespace added to help the diffs to match): https://lnkd.in/ew64MGGS

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As COO at www.jaiinfoway.com this is an important signal for where agentic AI is heading. Non technical implementations are catching up fast but the real story is iteration speed and learning loops. Building Cowork entirely with Claude Code shows how agents are becoming first class workers not just assistants. At www.jaiinfoway.com we see this shift clearly as enterprises move from copilots to autonomous task execution with guardrails. UX will evolve tooling will mature but the direction is set. Curious how others here see non technical users adopting agent driven workflows this year. Are we ready for agents as coworkers. #Jaiinfoway #AgenticAI #Claude #AIAgents #EnterpriseAI #FutureOfWork #AIProductivity #AITransformation #AILeadership

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