Modern CLI tool powered by o3 reasoning model for autonomous software engineering

Terminal-based coding agent with deep codebase understanding and multi-file editing

AWS's comprehensive AI development assistant, evolved from CodeWhisperer to provide deep integration with the entire AWS ecosystem. Features agent architecture for autonomous cloud development tasks, from code generation to infrastructure deployment. Particularly valuable for teams building on AWS, offering specialized knowledge of AWS services and best practices.

Flint creates autonomous websites that continuously build, optimize, and update themselves using AI. The platform enables companies to launch on-brand landing pages that automatically adapt to market trends, perform A/B tests, and optimize conversion rates without human intervention. Backed by $5M seed funding led by Accel, Flint has demonstrated 50% higher Google Ads conversion rates and serves customers including Cognition, Modal, and Graphite.

Recursive, an AI startup founded last year and led by former Salesforce chief scientist Richard Socher, has released its first model, Flint, a free coding model that runs locally on a laptop. According to TechCrunch, the company says the model was built largely with the help of its automated research system, the Eureka Machine, which let a small team produce it from scratch in under four months. Flint is presented as an early public test of recursive self-improvement, the contested idea that AI can build better AI, which then builds better AI still. Flint is a mixture-of-experts model with 40 billion total parameters, of which only 2.5 billion are active for any given task. That design keeps it light enough for ordinary hardware. It is far smaller than Meta's Muse Spark Open, which has 816 billion parameters and needs at least a terabyte of memory. Because it runs on the user's own machine, developers can work offline, keep code private, and avoid per-token cloud fees. It is available on Hugging Face under the Apache 2.0 license. Recursive reports strong speed numbers. On a MacBook Pro with an M4 Pro chip, Flint produced about 131 tokens per second, roughly double the 58 to 69 tokens per second of comparable models such as Google's Gemma 4 and Nvidia's Nemotron 3.5 Lightning. On Nvidia's RTX Spark it exceeded 150 tokens per second. On SWE-Bench Verified, it beat other models under 50 billion parameters. Across Recursive's own six-test average, only Alibaba's Qwen3.8 27B scored higher. The company concedes that Flint needs more guidance than large paid coding assistants and recommends splitting big jobs into smaller steps. Wired's Will Knight, testing it on a 48GB Mac, found it handled his requests for small programs reliably, unlike many small coding models that stumble or run slowly compared with Claude or Codex. The Eureka Machine proposes ideas, runs experiments, and uses outcomes to choose what to try next. Recursive says it contributed across pretraining, post-training, and inference, including a sparser model shape, low-bit training that cut Flint's size by 71% with almost no quality loss, and custom kernels. The company did not quantify the time saved. Other labs are moving the same way: OpenAI said its AI agents logged 3.1 workdays for every human researcher workday by mid-August. Recursive emerged from stealth in May with $650 million at a $4.65 billion valuation, backed by GV, Nvidia, and AMD Ventures, and later signed a $410 million compute deal with Amazon Web Services. Socher told Wired the long-term aim is full recursive self-improvement under human supervision, with the ability to halt the process if needed. Flint's significance lies less in beating frontier systems than in showing that a lean, AI-assisted research pipeline can yield a competitive small open model quickly. If each generation improves the research system itself, the pace of development could accelerate, and local, free coding models may become a growing alternative to metered cloud assistants.