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Mira Murati’s Thinking Machine Launches Training API Tinker

• Written by Andrea Miliani Former Tech News Expert
• Fact-Checked by Sarah Frazier Former Content Manager

Thinking Machines Lab, the startup co-founded by former OpenAI Chief Technology Officer Mira Murati, announced its first product, a training API called Tinker, on Wednesday.

In a rush? Here are the quick facts:

• Thinking Machines launched its first product, a training API called Tinker.
• Tinker is in private beta mode, but has already been tested by multiple organizations, including Redwood Research and teams at Stanford, Princeton, and Berkeley.
• The company also launched an open-source library, Tinker Cookbook.

According to Mira Murati, Tinker will allow researchers and developers to build new AI models, systems, and workflows through its API. The tool is currently in private beta but has already been tested by multiple organizations.

“Today we launched Tinker,” wrote Murati on the social media platform X. “Tinker brings frontier tools to researchers, offering clean abstractions for writing experiments and training pipelines while handling distributed training complexity. It enables novel research, custom models, and solid baselines.”

Today we launched Tinker. Tinker brings frontier tools to researchers, offering clean abstractions for writing experiments and training pipelines while handling distributed training complexity. It enables novel research, custom models, and solid baselines. Excited to see what… https://t.co/rLSluOckuC — Mira Murati (@miramurati) October 1, 2025

Thinking Machines Lab, which launched in February , described the new product as “a flexible API for fine-tuning language models.” Tinker supports open-source models such as Alibaba’s Qwen and Meta’s LLaMA, aiming to provide infrastructure support so developers and researchers can focus on customizing and adapting models to their needs.

“We handle scheduling, resource allocation, and failure recovery,” wrote the company. “This allows you to get small or large runs started immediately, without worrying about managing infrastructure.”

Tinker uses Low-Rank Adaptation (LoRA), a method that optimizes fine-tuning by reducing costs and improving speed. The company also released an open-source library called Tinker Cookbook, which provides methodologies and post-training resources that run on the Tinker API to help developers and researchers achieve better results.

“We believe [Tinker] will help empower researchers and developers to experiment with models and will make frontier capabilities much more accessible to all people,” said Mira Murati, cofounder and CEO of Thinking Machines, in a recent interview with Wired .

Other experts in the industry have also shared their thoughts on Tinker. Andrej Karpathy—former researcher at OpenAI and founder of Eureka Labs , called the API “clever” and pointed out a few advantages and challenges on the social media platform X.

“Tinker is cool,” wrote Karpathy. “Compared to the more common and existing paradigm of ‘upload your data, we’ll post-train your LLM,’ this is, in my opinion, a more clever place to ‘slice up’ the complexity of post-training—delegating the heavy lifting while keeping the majority of the data and algorithmic creative control.”

He also emphasized that the community needs to better understand when and how fine-tuning makes sense.

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New iPhone And Android AI Tools Help Block Robocalls Automatically

• Written by Kiara Fabbri Former Tech News Writer
• Fact-Checked by Sarah Frazier Former Content Manager

Users can now access free call screening and robocall blocking tools through their iPhone and Android devices.

In a rush? Here are the quick facts:

• iOS 26 introduces a free call-screening tool for unknown numbers.
• Call-screening tools help block robocallers who “spoof” phone numbers.
• Users can type follow-up questions or reject calls during AI screening.

The system lets virtual assistants detect unfamiliar callers and stop potential scams from reaching your phone before it rings, as first reported by The New York Times .

Brian X. Chen, The Times’s lead consumer technology writer, said he tested Apple’s tool in iOS 26 and found it effective. “I didn’t have to pick up the phone to find out it was a robocaller impersonating a utility company with an offer to reduce my bill,” he wrote.

The AI assistant functions as a dual-purpose tool during the call by both recording the conversation and allowing users to submit new inquiries or end the call.

Android users with Google Pixel phones can do the same. The system detects unknown phone numbers automatically, and the assistant will ask for information from callers who also have the option to mark calls as spam. Google recently expanded this tool to more countries, including Australia, Canada, and Ireland.

The new call-screening tools improve on past solutions that relied on databases of known scam numbers, which scammers easily bypassed.

Chen explained, “Robocallers have used internet apps to ‘spoof’ calls, manipulating phone networks to place calls from numbers they weren’t really calling from.” Previous carrier attempts at stopping robocalls through Stir/Shaken verification of phone numbers failed to eliminate spoofed robocall problems.

The iPhone tool activation process begins with iOS 26 installation, followed by Settings > Phone selection, and then choosing “Ask Reason for Calling.” To activate automatic call screening on Android, users need to access the Phone app, then navigate to Settings > Call Screen and enable the feature.

The tools have certain restrictions according to Chen. Some real callers may be annoyed when a robot answers, but the moderate approach is useful for most people.

Both Apple and Google also filter scam text messages, moving them into a spam folder. Chen explained that this capability remains vital because scammers now use text messages to pretend to be bank representatives, recruiters, and delivery service personnel.