That gap introduces a fantastic opportunity for arbitrage — and a lively second-hand and refurbished market.
Getmobil’s growth trajectory has been remarkable, driven by a combination of strategic foresight, operational excellence and a deep understanding of the Turkish electronics — and regulatory — market.
The company’s rise is characterized by its ability to offer consumers high-quality, refurbished electronics at competitive prices, thereby filling a critical market gap.
Before the regulations, the refurbished electronics market in Turkey was chaotic and fragmented, and the quality and warranty coverage of second-hand devices was highly variable.
Navigating the intricacies of Turkey’s electronics market presents a unique set of challenges and opportunities for Getmobil.
Sunnyvale-based Brevian wants to make it easier for business users to build custom AI agents.
“When I saw ChatGPT and other things, for me, it was just: how is this applicable in the enterprise?
Then he went to LinkedIn, where he headed up the AI trust team before leaving in late 2022 to join Wagh in founding Brevian as its CTO.
“Our vision was to enable business users in the enterprise to be able to use AI to simplify their daily tasks.
2024 is the year of AI apps — and we just felt like they were so far ahead,” he told me.
AI startup Advocate thinks that the tech could help people apply for federal government benefits more easily.
The New York-based startup was founded by Emilie Poteat, who got the idea after watching her stepfather try to get social security benefits.
Years later, Poteat realized that AI may be able to improve that process.
Poteat came on TechCrunch’s Found podcast this week to discuss why automating the application process using AI could help numerous people access government benefits easier.
This episode also dives into the company building process for Poteat and Advocate as the startup hasn’t fully launched yet.
When you think about commerce platforms, chances are the first names that come to mind are big names like Adobe Commerce or Salesforce Commerce Cloud.
That’s why a French startup called Catalog wants to build a software-as-a-service product that seamlessly manages B2B sales across several sales channels.
But these tools don’t necessarily work well for B2B companies.
This way, the company hopes it can handle the majority of B2B orders for companies that have decided to use Catalog.
By focusing on small and medium businesses, Catalog could start offering a wide range of services that are specifically tailored for these small manufacturing and commerce companies.
Prior to this, she worked in biotech, using AI to find medical treatments for rare diseases.
She also co-founded a data science consultancy and is a founding trustee of DataKind UK, which helps British charities with data science support.
We ask this question a lot at the Ada Lovelace Institute, which aims to make data AI work for people and society.
How can investors better push for responsible AI?
By asking questions about their investments and their possible futures – for this AI system, what does it look like to work brilliantly and be responsible?
A team led by former Twitter engineers is rethinking how AI can be used to help people process news and information.
The startup was founded last year by former Senior Director of Product Management at Twitter, Sara Beykpour, who worked on products like Twitter Blue, Twitter Video, and conversations, and who spearheaded the experimental app, twttr.
She had been at Twitter from 2015 through 2021, growing her position from software engineering to that of a senior director of product management.
The premise behind Particle, as Beykpour explained last month, is to make it easier to keep up with news using AI.
We’re hoping to talk in more detail about how Particle vets its sources closer to a public launch.
Chinese tech companies are gathering all sorts of resources and talent to narrow their gap with OpenAI, and experiences for researchers on both sides of the Pacific Ocean can be surprisingly similar.
The parallel glimpse into their typical day reveals striking similarities, with wake-up times at 9 a.m. and bedtime around 1 a.m.
Both start the day with meetings, followed by a period of coding, model training and brainstorming with colleagues.
Besides building its own LLM in-house, Alibaba has been aggressively investing in startups such as Moonshot AI, Zhipu AI, Baichuan and 01.AI.
Facing competition, Alibaba has been trying to carve out a niche, and its multilingual move could become a selling point.
A team led by former Twitter engineers is rethinking how AI can be used to help people process news and information.
The startup was founded last year by former Senior Director of Product Management at Twitter, Sara Beykpour, who worked on products like Twitter Blue, Twitter Video, and conversations, and had spearheaded the experimental app, twttr.
She had been at Twitter from 2015 through 2021, growing her position from software engineering to that of a senior director of product management.
The premise behind Particle, as Beykpour explained last month, is to make it easier to keep up with news using AI.
We’re hoping to talk in more detail about how Particle vets its sources closer to a public launch.
Massive training data sets are the gateway to powerful AI models — but often, also those models’ downfall.
Morcos’ company, DatologyAI, builds tooling to automatically curate data sets like those used to train OpenAI’s ChatGPT, Google’s Gemini and other like GenAI models.
“However, not all data are created equal, and some training data are vastly more useful than others.
History has shown automated data curation doesn’t always work as intended, however sophisticated the method — or diverse the data.
The largest vendors today, from AWS to Google to OpenAI, rely on teams of human experts and (sometimes underpaid) annotators to shape and refine their training data sets.
Where it is differentiating itself is through its Simetrik Building Blocks, or SBBs, which are scalable and adaptable concepts based on no-code development and generative AI technologies.
In the past two years, the company grew to have clients in more than 35 countries, up from 10, and is monitoring over 200 million records every day.
Previously that was 70 million records daily.
The use of the new funds will go into further developing the Simetrik Building Blocks, enhancing AI capabilities and continuing to expand Simetrik’s international reach.
They need a new approach, and that is where our building blocks have a strong product market piece.”
