Co-founders Elle Smyth and Sarah Hamer came up with the idea while working together at supply chain unicorn startup, Stord.
“We met each other and bonded over our love for the supply chain industry,” Smyth told TechCrunch.
Warehouse workers have to reference “phone book-sized” manuals of how to ship items to retailers, like Target and Walmart.
If shipping/warehouse workers don’t pack these items correctly, retailers will charge the brands fees, known as a chargeback.
RetailReady replaces those manuals with a digital version that gives workers a directed workflow on how to pack an order correctly.
When you’re looking for a startup idea that could slow climate change, you might become an expert at home energy assessments.
Instead, the startup has put together a small team of engineers to create its own AI model specialized in home energy assessments using machine learning.
The company uses open data, such as satellite images, as well as its own training data set with millions of photos and energy assessments.
In the company’s first tests, its home energy assessments have been accurate within 5% of old-fashioned assessments.
The startup has now raised €4.7 million ($5.1 million at today’s exchange rate) with Racine² leading the round and a non-dilutive investment from Bpifrance.
Google has developed a new AI tool to help marine biologists better understand coral reef ecosystems and their health, which can aid in conversation efforts.
The project began by inviting the public to listen to reef sounds via the web.
By crowdsourcing this activity, Google was able to create a library of new fish sounds that were used to fine-tune the AI tool, SurfPerch.
Although bird sounds and reef recordings are very different, there were common patterns between bird songs and fish sounds that the model was able to learn from, they found.
The project continues today, as new audio is added to the Calling in Our Corals website, which will help to further train the AI model, Google says.
Every company, large or small, needs to choose software, and the bigger the company, the more complex the exercise.
Some have internal tools and processes to help narrow down the list of possible vendors and eventually make a selection.
“Taloflow replaces homegrown technology and software selection processes that can last weeks or months,” the startup’s CEO and co-founder, Louis-Victor Jadavji, told TechCrunch.
“Unlike Gartner or G2, which offer mostly generic insights, Taloflow creates tailored reports for specific use cases,” he said.
Taloflow has built large language models that sift through publicly available information and speed up the time and cost of generating the base reports.
Web browser company Opera announced today it will now allow users to download and use Large Language Models (LLMs) locally on their computer.
This feature is first rolling out to Opera One users who get developer stream updates and will allow users to select from over 150 models from more than 50 families.
The feature will be available to users as part of Opera’s AI Feature Drops Program to let users have early access to some of the AI features.
Notably, Opera is not doing any work to save storage while downloading a model.
“Opera has now for the first time ever provided access to a large selection of 3rd party local LLMs directly in the browser.
Meet the first roundtable roster at TC Early Stage 2024You won’t have to search very hard or go very far to engage in compelling conversations next month at TechCrunch Early Stage 2024.
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