Canada’s BKR Capital announced Monday that its Fund II has closed CA$20 million (around $14.5 million), bringing it closer to its CA$50 million target.

This fund is looking to back “high-growth technology companies led by founders from the Black community, building solutions for the future of work, living, and global connectivity,” managing partner Lise Birikundavyi told TechCrunch. The firm is mainly looking at Canada but is open to backing select companies globally. The average check size will be between $250,000 and $1.5 million, she said.

Birikundavyi said that almost 70% of the Black population in Canada is first- or second-generation immigrants, “resulting in founders who build globally from day one, unlocking early access to international markets and creating a structural advantage in scaling.”

Though many U.S. firms have shied away from openly advertising a mission that could be perceived as diversity, equity, and inclusion (DEI), Birikundavyi said her Toronto-based fund doesn’t share those exact fears. What’s happening in Canada is less of a DEI rollback and more of a reframing, she said, where investors are “prioritizing discussion on performance,” even though “the underlying opportunity remains unchanged.”

She added, “Expanding access to overlooked founders continues to surface high-quality deals, making this less about DEI and more about arbitrage investing.” She believes investors in Canada still see “inclusive investment” as good for the ecosystem and full of potentially lucrative business opportunities.

The firm’s thesis is rooted in the belief that “overlooked markets and diverse lived experiences can unlock outsized venture opportunities,” Birikundavyi said. The firm launched in 2021 and raised $22 million for its Fund I (which Birikundavyi said is performing better than at least 75% of the other funds launched around the same time). She said BKR Capital hopes to make its final close for Fund II in December and invest in 25 companies.

The AI-powered notetaking app Granola, valued at $250 million, has become a popular tool among tech industry founders and VCs. But one developer believes there’s demand for a more private, local-only alternative that’s available for a one-time fee and without a subscription. That’s led to the creation of a new Mac app called Talat.

Yorkshire, England-based developer Nick Payne, a self-described computer nerd, says the idea to build a local AI notetaker came about mostly because of a series of happy accidents.

“I think Granola is awesome; it’s a shining example of what you can do with an Electron app [a framework for building desktop applications] given enough love and care,” he told TechCrunch. “When I first tried it, I was fascinated that it managed to record system audio on my Mac without recording video, which was the standard workaround at the time. That led to a ton of research, discovering a relatively new and poorly documented Apple API.”

To make it easier to work with that API (Core Audio Taps, which lets developers tap into a Mac’s audio streams), Payne decided to create an open source audio library, AudioTee.

“During that time, I was slowly piecing together a toolkit, but I never found anything that felt like it could stand on its own as a product rather than just a cool tech demo,” Payne said. “The state-of-the-art hosted transcription models — the same providers folks like Granola use — are incredible, and it’s viscerally cool to see your speech unfurled onscreen in near real time. But it always nagged me that the trade-off required providing not just my data, but my audio data; my actual voice,” he added.

He then stumbled upon a software toolkit called FluidAudio, a Swift framework that enables fully local, low-latency audio AI on Apple devices. It lets you run small, fast transcription models directly on the Mac’s Neural Engine — Apple’s dedicated hardware for AI processing.

That was the piece that made Payne realize he could turn his research into an actual product — one where your audio never leaves your Mac and your transcripts aren’t stored on another company’s servers.

Talat, which was built alongside Payne’s longtime friend and former colleague Mike Franklin, is the result of Payne’s interest in the audio space. The result is a 20MB, one-time purchase that doesn’t require you to create an account or even share analytics data back with the developers. There are no ongoing fees, either.

While some AI notetakers may have more bells and whistles, Talat offers a streamlined set of features. It captures audio from your computer’s microphone when you’re in meeting apps like Zoom, Teams, Meet, and others, and transcribes it in real time. The app tries to assign speakers in real time, but you can reassign them as needed. You can also take notes, plus edit, delete, or split transcript segments. When the meeting finishes, a local LLM generates a summary with key points, decisions, and action items.

The notes, transcripts, and summaries are all searchable in Talat, too.

In addition to the privacy angle, Payne said the goal is to give users more options.

“We’re leaning into configurability and letting users control where their data goes: pick your own LLM, auto-export to [notetaking app] Obsidian, webhooks that push data out when a meeting finishes, an MCP server,” which is a standardized way for AI tools to connect to outside data sources, “to pull it on demand,” he explained.

Under the hood, the AI is a mixture — “mostly stitched together and abstracted behind FluidAudio,” Payne noted, which he credits with doing a lot of the heavy lifting. For the summarization piece, the app defaults to an Al model called Qwen3-4B-4bit, which can run on even fairly modest hardware.

However, users can opt to switch that out to any cloud LLM provider of their choice, or they can choose between two Parakeet variants — speech-recognition models developed by Nvidia — or point it at Ollama (a tool for running AI models locally), giving them more control over the experience. In time, Talat will add support for more built-in choices and will have integrations for other apps, like Google Calendar and Notion.

At launch, users with M-series Mac computers (those running Apple’s own processors, starting with the M1) can download the app and try it out for free with 10 hours of recordings before deciding to purchase.

Talat is available for $49 while in this pre-release version, which is still under active development.

When the app hits a 1.0 release, the price will increase to $99.

Payne and Franklin are bootstrapping Talat and plan to keep the core product a one-time purchase going forward.

OpenAI said Tuesday it is releasing a set of prompts that developers can use to make their apps safer for teens. The AI lab said the set of teen safety policies can be used with its open-weight safety model known as gpt-oss-safeguard.

Rather than working from scratch to figure out how to make AI safer for teens, developers can use these prompts to fortify what they build. They address issues like graphic violence and sexual content, harmful body ideals and behaviors, dangerous activities and challenges, romantic or violent role play, and age-restricted goods and services.

These safety policies are designed as prompts, making them easily compatible with other models besides gpt-oss-safeguard, though they’re probably most effective within OpenAI’s own ecosystem.

To write these prompts, OpenAI said it worked with AI safety watchdogs Common Sense Media and everyone.ai.

“These prompt-based policies help set a meaningful safety floor across the ecosystem, and because they’re released as open source, they can be adapted and improved over time,” said Robbie Torney, head of AI & Digital Assessments at Common Sense Media, in a statement.

OpenAI noted in its blog that developers, including experienced teams, often struggle to translate safety goals into precise, operational rules.

“This can lead to gaps in protection, inconsistent enforcement, or overly broad filtering,” the company wrote. “Clear, well-scoped policies are a critical foundation for effective safety systems.”

OpenAI admits that these policies aren’t a solution to the complicated challenges of AI safety. But it builds off its previous efforts, including product-level safeguards such as parental controls and age prediction. Last year, OpenAI updated guidelines for its large language models — known as Model Spec — to tackle how its AI models should behave with users under 18. 

OpenAI doesn’t have the cleanest track record itself, however. The company is facing several lawsuits filed by the families of people who died by suicide after extreme ChatGPT use. These dangerous relationships often form after the user eclipses the chatbot’s safeguards, and no model’s guardrails are fully impenetrable. Still, these policies are at least a step forward, especially since it can help indie developers.