Model

TL;DR Composer 2.5 is Cursor’s most capable in-house coding model yet, built on Moonshot’s open-source Kimi K2.5 checkpoint with about 85% of total training compute spent on Cursor’s own continued pretraining and RL The model is purpose-built for the agent loop inside Cursor - long-horizon tasks, hundreds of tool calls, multi-step instructions - rather than as a general-purpose chat model Cursor claims parity with Claude Opus 4.7 and GPT-5.5 on its own CursorBench v3.1 (63.2%) and a strong 79.8% on SWE-Bench Multilingual Pricing is dramatically lower: $0.50 / $2.50 per million input/output tokens on the default variant, with included usage doubled for the first week Together with SpaceXAI, Cursor is now training a much larger successor model from scratch on Colossus 2 with around 10x the compute - so 2.5 is a waypoint, not the endgame For a while, Cursor was an IDE wrapped around someone else’s models - Claude, GPT, Gemini. That story has shifted. With Composer 2.5, released this week, Cursor has shipped its most capable first-party coding model yet, and it is a serious enough piece of work that it deserves real consideration as a daily driver rather than a budget fallback. ...

TL;DR Ollama is a lightweight tool for running open-source language models locally with no cloud costs, rate limits, or data leaving your machine Models are managed with simple commands (ollama pull, ollama run) and can be queried via a local HTTP API on localhost:11434 Popular models include Mistral 7B for speed, Meta’s Llama 3 and Llama 4 lineups for all-around performance, and OpenClaw for code and reasoning tasks Running models locally delivers privacy, zero per-token cost, lower latency, and full offline capability You don’t need a GPU to start - a 7B model runs on 8GB of RAM, and Ollama automatically uses 4-bit quantization for larger models Ollama has quietly become the go-to tool for developers who want to run large language models on their own machines without relying on APIs. No cloud costs, no rate limits, no sending your prompts to third-party servers. Just you, your hardware, and a surprisingly capable AI model running locally. ...

TL;DR Claude Mythos Preview set new records across coding, mathematics, and reasoning: 93.9% on SWE-bench Verified, 97.6% on USAMO 2026, and leads GPT-5.4 on every shared benchmark The USAMO result - a 55-point jump over Claude Opus 4.6 - suggests genuinely different reasoning capabilities, not just incremental improvement, and Anthropic screened against memorization concerns Despite dominating benchmarks, Mythos is not publicly available because it autonomously discovered thousands of zero-day vulnerabilities across every major OS and browser Access is restricted to 12 major tech and finance companies via Project Glasswing, a defensive cybersecurity research initiative backed by $100M in Anthropic usage credits The wider implication: we have entered an era where “the best model” and “the publicly available model” may be permanently different things, with security becoming a deployment constraint alongside capability Anthropic released Claude Mythos Preview on April 7, 2026 - and immediately announced it won’t be publicly available. ...

TL;DR DeepSeek’s January 2025 release of R1 shook markets - a frontier-grade reasoning model trained for a reported $6M, a fraction of US lab budgets The app shot to #1 on Apple’s App Store inside days, and the open weights forced an industry-wide rethink of what training really costs Subsequent releases (V3 and beyond) cemented DeepSeek as a serious competitor in the open-source and cost-efficient AI category The story is less “China caught up” and more “the cost floor moved” - implications for closed-model pricing, GPU demand, and open-weight strategy Worth understanding as the moment that made cheap, capable, open models a credible default rather than a curiosity Overview In January 2025, a Chinese AI lab most people had never heard of dropped a frontier-grade reasoning model for a reported $6 million and watched it hit the top of the Apple App Store inside days. DeepSeek R1 did not just impress researchers - it shook equity markets, forced a hard look at what US labs were actually spending their billions on, and made cheap, capable, open-weight models a credible default rather than an interesting curiosity. ...