Flow is a spinout of VTT, a Finland state-backed research organization that’s a bit like a national lab.
You can’t just magically squeeze extra performance out of CPUs across architectures and code bases.
But Flow has been working on something that has been theoretically possible — it’s just that no one has been able to pull it off.
What Flow claims to have done is remove this limitation, turning the CPU from a one-lane street into a multi-lane highway.
The chef still only has two hands, but now the chef can work ten times as fast.
Greptile, an early stage startup from a group of recent Georgia Tech grads, decided to take a different approach: using AI to help developers understand the code base.
Greptile CEO and co-founder Daksh Gupta says the Greptile bot is like having a highly experienced coworker who has a deep understanding of your code.
“So we’re building AI tools that understand large code bases at companies because as time goes on, and multiple programmers work on the codebase, it tends to get very difficult to understand,” Gupta told TechCrunch.
Once the repositories have been indexed by the system, you add a natural language query such as, how does the authentication work in this code base,” he said.
The startup launched last July after the founders came up with the idea for the company at a hackathon.
Set to arrive in Wix’s app builder tool this week, the capability guides users through a chatbot-like interface to understand the goals, intent and aesthetic of their app.
But reviews of Wix’s AI site builder aren’t exactly glowing, with early adopters reporting bugs and generic-looking finished products.
So — given that the under-the-hood tech is similar, outside a few upgraded generative AI models — why should people expect Wix’s AI app builder to be any better?
Image Credits: WixAbrahami admitted that the AI app builder — like all generative AI tools — might make mistakes.
On Fiverr, a cursory search yields a long list of highly-rated app developers, some of whom charge around the same price as a subscription to Wix’s AI app builder.
Google VidsLeveraging AI to help customers develop creative content is something Big Tech is looking for, and Tuesday, Google introduced its version.
Read moreImagen 2In February, Google announced an image generator built into Gemini, Google’s AI-powered chatbot.
“Vertex AI Agent Builder allows people to very easily and quickly build conversational agents,” Google Cloud CEO Thomas Kurian said.
Read moreNvidia’s Blackwell platformOne of the anticipated announcements is Nvidia’s next-generation Blackwell platform coming to Google Cloud in early 2025.
This, Kyle Wiggers writes, is “Google’s most capable generative AI model,” and is now available in public preview on Vertex AI, Google’s enterprise-focused AI development platform.
Can AI eat the jobs of the developers who are busy building AI models?
News this week that Google has a new AI-powered coding tool for developers means that competitive pressures between major tech companies to build the best service to help coders write more code, more quickly is still heating up.
Both companies want to eventually build developer-helping tech that can understand a company’s codebase, allowing it to offer up more tailored suggestions and tips.
Everywhere you look, developers are building tools and services to help their own professional cohort.
Developers learning to code today won’t know a world in which they don’t have AI-powered coding helps.
Don’t have time to catch the Google Cloud Next livestream?
Here’s whyNvidia’s Blackwell platformOne of the anticipated announcements is Nvidia’s next-generation Blackwell platform coming to Google Cloud in early 2025.
Read moreImagen 2In February, Google announced an image generator built into Gemini, Google’s AI-powered chatbot.
Read moreChrome Enterprise PremiumMeanwhile, Google is expanding its Chrome Enterprise product suite with the launch of Chrome Enterprise Premium.
This, Kyle Wiggers writes, is “Google’s most capable generative AI model,” and is now available in public preview on Vertex AI, Google’s enterprise-focused AI development platform.
So he founded a startup, Symbolica AI, to do just that.
Elsewhere, a report co-authored by Stanford and Epoch AI, an independent AI research Institute, finds that the cost of training cutting-edge AI models has increased substantially over the past year and change.
With costs poised to climb higher still — see OpenAI’s and Microsoft’s reported plans for a $100 billion AI data center — Morgan began investigating what he calls “structured” AI models.
Symbolic AI solves tasks by defining symbol-manipulating rule sets dedicated to particular jobs, such as editing lines of text in word processor software.
Symbolic AI needs well-defined knowledge to function, in other words — and defining that knowledge can be highly labor-intensive.
Earlier today, Sentry announced its AI Autofix feature for debugging production code and now, a few hours later, GitHub is launching the first beta of its code scanning autofix feature for finding and fixing security vulnerabilities during the coding process.
This new feature combines the real-time capabilities of GitHub’s Copilot with CodeQL, the company’s semantic code analysis engine.
The company also promises that code scanning autofix will cover more than 90% of alert types in the languages it supports, which are currently JavaScript, Typescript, Java, and Python.
“Just as GitHub Copilot relieves developers of tedious and repetitive tasks, code scanning autofix will help development teams reclaim time formerly spent on remediation,” GitHub writes in today’s announcement.
To generate the fixes and their explanations, GitHub uses OpenAI’s GPT-4 model.
Sentry has long helped developers monitor and debug their production code.
While it’s called Autofix, this isn’t a completely automated system, something very few developers would be comfortable with.
In the process, Autofix will provide developers with a diff that explains the changes and then, if everything looks good, create a pull request to merge those changes.
Autofix supports all major languages, though Elser acknowledged that the team did most of its testing with JavaScript and Python code.
That also means that users must opt in to send their data to these third-party services to use Autofix.
Rather than grimly assembling data about cancer deaths to predict outcomes in treatment, the founders of Cure51 had another idea.
Instead, the company assembles data about long term survivors of cancer, thus hoping to crack the code on what keeps people alive.
It’s now raised a €15 million Seed round led by Paris-based Sofinnova Partners.
Other investors in this round included: Hitachi Ventures GmbH, Life Extension Ventures, Xavier Niel, and Olivier Pomel, CEO, and co-founder of Datadog.
Both had previously worked in five well-known oncology centers, such as the Gustave Roussy Institute in Paris and the Vall d’Hebronin Barcelona.
