Latest AI News
AI Chips are the New Oil: How Silicon Sovereignty is Making Countries Protectionist
Governments are racing to secure local supply chains amid volatile geopolitical tensions and AI compute demand.
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How Shrutlekh is Shaping the Future of Multilingual AI in India
The platform listens to speech, detects shifts in language within the same conversation, generates real-time transcripts, and produces translations without interrupting the flow of speech.
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Newtrace Bags $6.3 MIllion to Advance Electroyser Technology for Green Hydrogen
Green hydrogen produced using renewable energy is a key solution for reducing carbon emissions across industries such as steel, refining, fertilisers, chemicals and heavy transport.
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Benchmarks Don’t Tell the Full Story of Sarvam AI
Sarvam’s 105B model beats DeepSeek R1 on Browse Comp. However, it trails others in coding capabilities.
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Anthropic Introduces Agentic Code Review Tool to Claude Code
Anthropic introduced a new artificial intelligence (AI)-powered coding tool on Monday, aimed at reviewing pull requests (PRs) before they are deployed. Dubbed Code Review, it is available as a new tool in Claude Code, and it can be activated for specific or all repositories. The company said the tool uses a multi-agent review system and is designed to be thorough. The AI firm also claimed that the new agentic tool is similar to one it runs internally for “nearly every PR.”
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Yann LeCun’s AMI Labs raises $1.03 billion to build world models
AMI Labs, thenew venture cofounded by Turing Prize winner Yann LeCunafter he left Meta, has raised $1.03 billion at a $3.5 billion pre-money valuation. AMI is working on world models, or AI that learns from reality, not just from language. This category has fewer players than generative AI, but maybe not for long. “My prediction is that ‘world models’ will be the next buzzword,” AMI Labs CEO Alexandre LeBrun told TechCrunch. “In six months, every company will call itself a world model to raise funding.” LeBrun said this with a smile because he thinks AMI Labs is fundamentally different: its goal is to understand the real world. This could have applications in healthcare, where AMI Labs’ first partner will beNabla, the digital health startup of which he’s now chairman. As CEO of Nabla, LeBrun had reached the same conclusion as LeCun on the limitations of large language models (LLMs) where hallucinations could have life-threatening repercussions. But he also knows it will take a while for the startup to offer a viable alternative based onJEPA, the Joint Embedding Predictive Architecture proposed by LeCun in 2022. “AMI Labs is a very ambitious project, because it starts with fundamental research. It’s not your typical applied AI startup that can release a product in three months, have revenue in six months and make $10 million in [annual recurring revenue] in 12 months,” LeBrun said. In contrast, it could take years for world models to go from theory to commercial applications. Despite this time horizon, companies developing world models have attracted big checks. SpAItial raiseda $13 million seed round— unusually large for a European startup; while Fei-Fei Li’s World Labssecured a whopping $1 billionlast month alone. Now, AMI Labs joins the club with more funding than initially rumored. The French AI lab was reportedlyseeking just €500 millionlast December, but ended up raising some €890 million, likely thanks to its team. In addition to LeCun’s involvement as chairman and to LeBrun’strack record as an entrepreneur, it also boasts Meta’s VP for Europe Laurent Solly as COO, and high-profile researchers Saining Xie as chief science officer, Pascale Fung as chief research & innovation officer and Michael Rabbat as VP of world models. According to LeBrun, high interest gave the startup a chance to have its pick of investors, both in terms of expectation alignment and background. The round was co-led by Cathay Innovation, Greycroft, Hiro Capital, HV Capital and Bezos Expeditions, with participation from several other funds and industry-tied backers, as well as individuals including Tim and Rosemary Berners-Lee, Jim Breyer, Mark Cuban, Mark Leslie, Xavier Niel and Eric Schmidt. Value-add aside, this funding will give AMI Labs some meaningful runway to bankroll its two main cost centers: compute and talent. LeBrun said he will prioritize quality over quantity to build AMI Labs’ team in four key locations: Paris, where it is headquartered; New York, where LeCun teaches at NYU; in Montreal, where its Rabbat is based; and in Singapore, both to recruit AI talent and to be close to future clients in Asia. Although AMI Labs has no plans to generate revenue for the time being, it still plans to engage with prospective customers early on. “We are developing world models that seek to understand the world, and you can’t do that locked up in a lab. At some point, we need to put the model in a real-world situation with real data and real evaluations,” LeBrun said. When the time comes, AMI Labs will turn to partners to explore deployments — and Nabla is the first disclosed partner expecting to access these early models, but definitely not the last. “This may explain the presence and strong interest of certain industrial players and potential partners in the investment round,” LeBrun said. In addition to its lead investors and angels, AMI Labs is backed by NVIDIA, Samsung, Sea, Temasek and Toyota Ventures, as well as French players Association Familiale Mulliez, Groupe Industriel Marcel Dassault and Publicis Groupe. Aglaé Lab, Alpha Intelligence Capital, Artémis, Bpifrance Digital Venture, New Legacy Ventures, SBVA and ZEBOX Ventures also participated. These investments may take a while to turn into commercial applications. But staying true to LeCun’s beliefs, AMI Labs will publish papers as it goes. “We will also make a lot of code open source,” said LeBrun, who had also worked at Meta’s AI research laboratory, FAIR. While open research is “increasingly rare,” this startup’s founders still believe in it. “We think things move faster when they’re open, and it’s in our best interest to build a community and a research ecosystem around us.”
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Claude Code Now Catches ‘Extremely Subtle Bugs’ With New Code Review Feature
We run Code Review on nearly every PR at Anthropic. Before, 16% of PRs got substantive review comments. Now 54% do,” Anthropic said.
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Anthropic Sues Pentagon over Supply Chain Risk Label
The label, typically reserved for companies linked to foreign adversaries, requires any contractor working with the Pentagon to certify that it doesn’t use Anthropic’s models.
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Yann LeCun’s AI Startup Wins $1.03 Bn Funding to Build ‘World Model’ Systems
Advanced Machine Intelligence will now expand its team of researchers and engineers.
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Who Will Win in the OpenAI vs Anthropic Battle? Hint: Neither
The rivalry between OpenAI and Anthropic now reflects deeper tensions about scale, safety, and the commercial future of AI.
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NEURA Robotics, Qualcomm Partner to Develop Platforms for Humanoid Robots
Qualcomm’s Dragonwing IQ10 Series robotics processors and software stack will be integrated with NEURA’s hardware platforms and embodied AI software.
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Anthropic launches code review tool to check flood of AI-generated code
When it comes to coding, peer feedback is crucial for catching bugs early, maintaining consistency across a codebase, and improving overall software quality. The rise of “vibe coding” — using AI tools that take instructions given in plain language and quickly generate large amounts of code — has changed how developers work. While these tools have sped up development, they have also introduced new bugs, security risks, and poorly understood code. Anthropic’s solution is an AI reviewer designed to catch bugs before they make it into the software’s codebase. The new product, called Code Review, launched Monday inClaude Code. “We’ve seen a lot of growth in Claude Code, especially within the enterprise, and one of the questions that we keep getting from enterprise leaders is: Now that Claude Code is putting up a bunch of pull requests, how do I make sure that those get reviewed in an efficient manner?” Cat Wu, Anthropic’s head of product, told TechCrunch. Pull requests are a mechanism that developers use to submit code changes for review before those changes make it into the software. Wu saidClaude Codehas dramatically increased code output, which has increased pull request reviews that have caused a bottleneck to shipping code. “Code Review is our answer to that,” Wu said. Anthropic’s launch of Code Review — arriving first to Claude for Teams and Claude for Enterprise customers in research preview — comes at a pivotal moment for the company. On Monday, Anthropicfiled two lawsuitsagainst the Department of Defense in response to the agency’s designation of Anthropic as a supply chain risk. The dispute will likely see Anthropic leaning more heavily on its booming enterprise business, which has seen subscriptions quadruple since the start of the year.Claude Code’s run-rate revenue has surpassed $2.5 billion since launch, according to the company. “This product is very much targeted towards our larger scale enterprise users, so companies like Uber, Salesforce, Accenture, who already use Claude Code and now want help with the sheer amount of [pull requests] that it’s helping produce,” Wu said. She added that developer leads can turn on Code Review to run on default for every engineer on the team. Once enabled, it integrates with GitHub and automatically analyzes pull requests, leaving comments directly on the code explaining potential issues and suggested fixes. The focus is on fixing logical errors over style, Wu said. “This is really important because a lot of developers have seen AI automated feedback before, and they get annoyed when it’s not immediately actionable,” Wu said. “We decided we’re going to focus purely on logic errors. This way we’re catching the highest priority things to fix.” The AI explains its reasoning step by step, outlining what it thinks the issue is, why it might be problematic, and how it can potentially be fixed. The system will label the severity of issues using colors: red for highest severity, yellow for potential problems worth reviewing, and purple for issues tied to preexisting code or historical bugs. Wu said it does this quickly and efficiently by relying on multiple agents working in parallel, with each agent examining the codebase from a different perspective or dimension. A final agent aggregates and ranks the findings, removing duplicates and prioritizing what’s most important. The tool provides a lightsecurity analysis, and engineering leads can customize additional checks based on internal best practices. Wu said Anthropic’s more recently launchedClaude Code Securityprovides a deeper security analysis. The multi-agent architecture means this can be a resource-intensive product, Wu said. Similar to other AI services, pricing is token-based, and the cost varies depending on code complexity — though Wu estimated each review would cost $15 to $25 on average. She added that it’s a premium experience, and a necessary one as AI tools generate more and more code. “[Code Review] is something that’s coming from an insane amount of market pull,” Wu said. “As engineers develop with Claude Code, they’re seeing the friction to creating a new feature [decrease], and they’re seeing a much higher demand for code review. So we’re hopeful that with this, we’ll enable enterprises to build faster than they ever could before, and with much fewer bugs than they ever had before.”
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