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Zencoder, the Silicon Valley startup that builds AI-powered coding agents, released a free desktop application on Monday that it says will fundamentally change how software engineers interact with artificial intelligence — moving the industry beyond the freewheeling era of “vibe coding” toward a more disciplined, verifiable approach to AI-assisted development. The product, called Zenflow, introduces …

Zencoder drops Zenflow, a free AI orchestration tool that pits Claude against OpenAI’s models to catch coding errors Read More »

As enterprises accelerate the deployment of LLMs and agentic workflows, they are hitting a critical infrastructure bottleneck: the container base images powering these applications are riddled with inherited security debt. Echo, an Israeli startup, is announcing a $35 million in Series A funding today (bringing its to-date total to $50 million in funding) to fix …

Echo raises $35M to secure the enterprise cloud’s base layer — container images — with autonomous AI agents Read More »

Enterprises that want tokenizer-free multilingual models are increasingly turning to byte-level language models to reduce brittleness in noisy or low-resource text. To tap into that niche — and make it practical at scale — the Allen Institute of AI (Ai2) introduced Bolmo, a new family of models that leverage its Olmo 3 models by “bytefiying” …

Bolmo’s architecture unlocks efficient byte‑level LM training without sacrificing quality Read More »

We’ve heard (and written, here at VentureBeat) lots about the generative AI race between the U.S. and China, as those have been the countries with the groups most active in fielding new models (with a shoutout to Cohere in Canada and Mistral in France). But now a Korean startup is making waves: last week, the …

Korean AI startup Motif reveals 4 big lessons for training enterprise LLMs Read More »

Presented by Twilio The customer data infrastructure powering most enterprises was architected for a world that no longer exists: one where marketing interactions could be captured and processed in batches, where campaign timing was measured in days (not milliseconds), and where “personalization” meant inserting a first name into an email template. Conversational AI has shattered …

Why agentic AI needs a new category of customer data Read More »

Presented by Capital One Software Tokenization is emerging as a cornerstone of modern data security, helping businesses separate the value of their data from its risk. During this VB in Conversation, Ravi Raghu, president, Capital One Software, talks about the ways tokenization can help reduce the value of breached data and preserve underlying data format …

Tokenization takes the lead in the fight for data security Read More »

Nvidia launched the new version of its frontier models, Nemotron 3, by leaning in on a model architecture that the world’s most valuable company said offers more accuracy and reliability for agents.  Nemotron 3 will be available in three sizes: Nemotron 3 Nano with 30B parameters, mainly for targeted, highly efficient tasks; Nemotron 3 Super, …

Nvidia debuts Nemotron 3 with hybrid MoE and Mamba-Transformer to drive efficient agentic AI Read More »

Picture this: You’re sitting in a conference room, halfway through a vendor pitch. The demo looks solid, and pricing fits nicely under budget. The timeline seems reasonable too. Everyone’s nodding along. You’re literally minutes away from saying yes. Then someone from your finance team walks in. They see the deck and frown. A few minutes …

Build vs buy is dead — AI just killed it Read More »

Gen AI in software engineering has moved well beyond autocomplete. The emerging frontier is agentic coding: AI systems capable of planning changes, executing them across multiple steps and iterating based on feedback. Yet despite the excitement around “AI agents that code,” most enterprise deployments underperform. The limiting factor is no longer the model. It’s context: …

Why most enterprise AI coding pilots underperform (Hint: It’s not the model) Read More »

In a new paper that studies tool-use in large language model (LLM) agents, researchers at Google and UC Santa Barbara have developed a framework that enables agents to make more efficient use of tool and compute budgets. The researchers introduce two new techniques: a simple “Budget Tracker” and a more comprehensive framework called “Budget Aware …

Google’s new framework helps AI agents spend their compute and tool budget more wisely Read More »