Switzerland's new open-source AI model Apertus tackles what kills 95% of AI projects: lack of transparency. Full visibility into code and training data promises to solve bias, compliance headaches, and corporate trust issues.
Warp challenges "hands-off" agent coding with human-in-the-loop approach, charging $200/month for enterprise tier. While competitors push automation, Warp bets on oversight—real-time code review and mid-flight steering as agents work.
Anthropic's $1.5B settlement with authors establishes first pricing benchmark for AI training data—$3,000 per pirated book. Court ruling split training rights from acquisition methods, reshaping how tech giants source content.
A new tool helps catch data mistakes before they mess up AI systems. Recce, which makes data review tools, just raised $4 million and launched a cloud platform to help teams spot problems early.
The startup tackles a growing problem: As companies build more AI systems, checking the data that feeds them becomes crucial. One wrong dataset can throw off an entire AI model.
"Most code reviews will become data reviews as data correctness becomes key for success in AI," says CL Kao, Recce's founder and CEO. Kao knows what he's talking about - he built SVK, an early version control system that companies like Apple and Ubisoft used before Git came along.
Recce's tools plug into existing data workflows and let teams compare datasets, check results, and track changes. Think of it as quality control for data pipelines. The company's open-source version already sees 3,600 weekly downloads on GitHub.
The Philadelphia Inquirer uses Recce to check data across 50+ systems. "It's helped us move faster without compromising data integrity," says Brian Waligorski, their lead data engineer.
Today's launch adds new features to catch problems earlier:
Heavybit, which led the funding round, sees this as perfect timing. "Data pipelines are the new secret sauce for companies building AI," says Jesse Robbins, Heavybit's general partner who joined Recce's board.
Other investors include Vertex Ventures US and Hive Ventures. Brian Behlendorf, who helped create Apache, also joined the board. He points out that AI adds randomness to software development, making data testing vital.
The money will help Recce build its cloud platform, which launches in private beta today. The platform lets teams:
This matters because AI systems are only as good as their data. Wrong data means wrong results, and catching errors early saves time and prevents problems.
Companies already use tools like dbt to handle data analytics. But as AI grows, they need better ways to check the data itself. Recce fills this gap by bringing standard software testing practices to data workflows.
The timing fits a broader trend. As AI becomes standard, unique datasets give companies an edge. But that only works if the data stays accurate and clean.
Kao will speak about these challenges at the Data Council conference in Oakland on April 24. The company also opened applications for early access to its cloud platform.
Why this matters:
Read on, my dear:
Anthropic's $13B raise triples its valuation to $183B in six months, powered by revenue jumping from $1B to $5B. But the real story is developer economics and enterprise AI splitting from consumer markets.
Sam Altman's OpenAI is backing a $250M brain-computer startup to directly compete with Elon Musk's Neuralink. The rivalry moves from AI models to neural interfaces—whoever controls how humans connect to AI systems wins the future.
Berlin's n8n just jumped from €300 million to nearly $3 billion valuation in four months. Ten VCs are fighting over the AI automation startup that's building the nervous system for enterprise agents. At 75x revenue, there's no room for error.
OpenAI closes $8.3B funding round months early, led by Dragoneer's record $2.8B single check. Early investors got squeezed as demand hit 5x capacity. Revenue jumped to $13B while Microsoft negotiations loom over IPO future.
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