TLDR AI 2026-02-13

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OpenAI released GPT-5.3-Codex-Spark, a smaller ultra-fast coding model optimized for real-time use in Codex and capable of generating over 1,000 tokens per second on low-latency hardware.

Google released a major upgrade to Gemini 3 Deep Think, its specialized reasoning mode, expanding access to Ultra subscribers and select API users. The update was developed with researchers to better handle open-ended scientific and engineering problems with messy or incomplete data.

Anthropic raised $30 billion in Series G funding at a $380 billion post-money valuation, led by GIC and Coatue with broad institutional participation.

Developing superintelligence is like undergoing risky surgery for a condition that would otherwise prove fatal. Some have called for a pause or permanent halt to AI development as AGI could pose existential risks. However, poorly implemented pauses could do more harm than good. The optimal strategy is to move quickly to AGI capability, then pause briefly before full development.

There's no real consensus on the best solution to the simple 'how do you change things' problem. None of the current tools give models a stable, verifiable identifier for the lines to be changed without wasting tremendous amounts of context and depending on perfect recall. This post discusses a solution where, when a model reads a file, or greps for something, every line comes back tagged with a 2-3 character content hash, and when the model edits, it references those tags. This approach can result in an over 8% improvement in Gemini's success rate with zero training compute cost.

Leading inference providers like Baseten, DeepInfra, and Together AI are cutting AI costs by up to 10x using open source models on NVIDIA Blackwell GPUs. In healthcare, companies like Sully.ai reduced inference expenses, improving response times and freeing up valuable time for doctors. In gaming and customer service, NVIDIA Blackwell's optimized platforms enable Latitude and Decagon to slash token costs while enhancing user experiences and managing high workloads effectively.

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OpenEnv is an open-source framework from Meta and Hugging Face that standardizes how agents interact with real, stateful environments via a gym-style API and MCP tool interface. A production-grade calendar environment illustrated the challenges of evaluating tool-using agents under realistic constraints such as access control and long-horizon reasoning.

DeepMind introduced Aletheia, a math research agent powered by an advanced version of Gemini Deep Think that iteratively generates and verifies long-horizon proofs using intensive tool support.

Minimax has released two new variants of its M2.5 language model. While it is supposed to be open source, the weights and code have yet to be posted, nor has the company indicated the exact license type or terms. The models drop the cost of inference by as much as 95% while delivering performance that rivals top-tier models. They can be accessed through MiniMax's API and those of partners.

AI is not eliminating expertise; it's shifting the bottleneck from production to evaluation. Anyone can now generate “passable” output quickly, but true judgment, pattern recognition, and domain knowledge remain scarce. Professionals who combine deep expertise with AI fluency multiply their impact, while those who rely solely on prompting or ignore AI fall behind.

Cursor expanded access to its long-running agents research preview for Ultra, Teams, and Enterprise users. The custom harness enabled agents to complete longer, more complex coding tasks, producing larger pull requests with merge rates comparable to other agents.

Wistia's new AI Video Marketing Trends report shows how AI is being used to make better videos faster, rather than just churning out more content. Read the report

As the first engineer on TLDR's new Applied AI team, you'll build AI agents and composable Claude Skills to let non-technical teammates create their own AI workflows. Learn more.

Mooncake adds a vital layer of memory virtualization to the open-source AI stack.

Simile raised $100 million to develop AI tools predicting human behavior, like consumer product preferences.

Chinese rooms and operators will need to look a lot like a brain to be enough to work.

Seedance 2.0 is a multimodal AI video generation model that processes images, videos, audio, and text simultaneously to create short videos with sound effects.