We built a model that predicts multiple correlated outcomes simultaneously: portfolio risk, grid balancing, supply chains. In 10ms with no calibration required and it's 14% better than the best alternative. Check out the research: https://
arxiv.org/pdf/2603.20266
INNOVATION
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Multi-outcome prediction model achieves 14% improvement in 10ms
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AI Tools Democratization Drives Need for Business Differentiation
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In a world where everyone can build websites, apps and features easily (thank you Cursor, Lovable, Claude and the likes), it will take more for you and your company to differentiate themselves (which is in my opinion the basis for success). That's why we're seeing more and more
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LangSmith Experiments Detail View Redesigned for Better Debugging
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The hardest part of debugging an AI agent isn't knowing it failed–it's knowing why.
— LangChain (@LangChain) 30 mars 2026
We rebuilt the detail view in LangSmith Experiments from the ground up to answer that question faster.
Next time you click and inspect any experiment results, you will find:
* Less clutter
*… pic.twitter.com/x50OxCJxnWThe hardest part of debugging an AI agent isn't knowing it failed–it's knowing why. We rebuilt the detail view in LangSmith Experiments from the ground up to answer that question faster. Next time you click and inspect any experiment results, you will find:
* Less clutter
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MIT offers free introductory deep learning course online
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An introductory course about deep learning, courtesy of MIT: https://t.co/xlVPQUthXI pic.twitter.com/ry4auHQXd3
— MIT CSAIL (@MIT_CSAIL) 30 mars 2026An introductory course about deep learning, courtesy of MIT: bit.ly/4in6rsJ
→ View original post on X — @mit_csail, 2026-03-30 16:00 UTC
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UK-India AI Collaboration Showcased at Impact Summit 2026
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#AIImpactSummitInsights | At the #IndiaAIImpactSummit2026, Shinoush Hayati from the British High Commission, New Delhi, highlighted the growing momentum of UK–India collaboration in AI and technology:
— IndiaAI (@OfficialINDIAai) 30 mars 2026
From cutting-edge startups to leading universities, the UK Pavilion showcased… pic.twitter.com/3MVzqtuHwa#AIImpactSummitInsights | At the #IndiaAIImpactSummit2026, Shinoush Hayati from the British High Commission, New Delhi, highlighted the growing momentum of UK–India collaboration in AI and technology: From cutting-edge startups to leading universities, the UK Pavilion showcased
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EU Commission Allocates €200 Million for Submarine Cable Infrastructure
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Commission makes available €200 million for submarine cable and digital infrastructure projects | Shaping Europe’s digital future https://
digital-strategy.ec.europa.eu/en/news/commis
sion-makes-available-eu200-million-submarine-cable-and-digital-infrastructure-projects
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#digitaleu #infrastructure #innovation #technology @digitaleu @ArturHabant @elaniazito @CurieuxExplorer @Shi4Tech -

Coding Agents Excel at Processing Massive Long-Context Documents
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// Coding Agents are Effective Long-Context Processors // We are just touching the surface of what's possible with coding agents. LLMs struggle with long contexts, even the ones that support massive context windows. It turns out coding agents already know how to solve this; you just need to reframe the problem. This work places massive text corpora into directory structures and lets off-the-shelf coding agents (Codex, Claude Code) navigate them with terminal commands and Python scripts. This is great, as you are not feeding massive text directly into a model’s context window or relying on semantic retrieval. Results: – On BrowseComp-Plus (750M tokens), this approach scores 88.5% vs 80% best published. – On Oolong-Real (385K tokens), 33.7% vs 24.1%, a 56% relative improvement. – GPT-5 full-context baseline only manages 20% on BrowseComp-Plus. Works up to 3 trillion tokens. Instead of scaling context windows or building retrieval pipelines, coding agents that already know how to navigate file systems can process virtually unlimited context. The agents autonomously develop task-specific strategies: writing scripts, iterative query refinement, and programmatic aggregation. Paper: arxiv.org/abs/2603.20432 Learn to build effective AI agents in our academy: academy.dair.ai/
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Clinical Teams Build Interactive Apps with Real-Time Results
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Clinical and RWE teams are building interactive applications with real-time results But adoption stalls without clear use cases, intuitive design, and the right governance model. Join us on 4/16 for lessons applicable to the whole organization. https://
hubs.ly/Q048Sd_G0 -

llama.cpp reaches 100k stars, local AI movement thriving
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llama.cpp at 100k stars now that 90% of the code worldwide is being written by AI agents, I predict that within 3-6 months, 90% of all AI agents will be running locally with llama.cpp 😄 Jokes aside, I am going to use this small milestone as an opportunity to reflect a bit on the project and the state of AI from the perspective of local applications. There is a lot to say and discuss and yet it feels less and less important to try to make a point. Opinions about viability of local LLMs are strongly polarized, details are overlooked, the scientific approach is lacking. Arguments are predominantly based on vibes and hype waves. One thing is clear though – local LLMs are used more and more. I expect this trend to continue and likely 2026 will end up being one of the most important years for the local AI movement. I admit that I didn't expect the agentic era to come so quickly to the local LLM space. One year ago, the available models were too computationally expensive for doing long-context tasks. There wasn't an obvious path towards meaningful agentic applications. The memory and compute requirements were huge. Last summer, with the release of gpt-oss, things started to change. It was the first time we saw a glimpse of tool calling that actually works well within the resource constraints of our daily devices. Later in the year, even better models were released and by now, useful local agentic workflows are a reality. Comparing local vs hosted capabilities at a given moment of time is pointless. To try put things into perspective: – We don't need frontier intelligence to automate searches and sending emails – We don't need trillion parameter models to be able to summarize articles or technical documents – We don't need massive GPU data centers to control our home appliances or turn the lights off in the garage I believe that there is a certain level of intelligence we as humans can comprehend and meaningfully utilize to improve our working process. Beyond that level, access to more intelligence becomes unnecessary at best and counterproductive at worst. I also believe that that level of useful artificial intelligence is completely within reach locally and it has always been just a matter of implementing the right software stack to bring it to the end user. With llama.cpp, I am confident that we continue to be on the right track of building that software stack! The llama.cpp project is going stronger than ever. With more than 1500 contributors, the project keeps growing steadily. From technical point of view, I think that llama.cpp + ggml is the only solution that actually makes sense. That is, the software stack must run efficiently on every possible device, hardware and operating system. The technology is too important to be vendor-locked. It has to be developed in the open, by the community, together with the independent hardware vendors. This is the only right way to build something that will truly make a difference in the long run. I won't try to convince you about what is currently and will be possible with local AI. We will just continue to build as usual. I am confident that after the smoke clears and we look objectively at what we have built together, the benefits will be obvious to everyone. Big shoutout to all llama.cpp maintainers. I feel extremely lucky to be able to work together with so many talented contributors. Every day I learn something new and I feel there is so much more cool stuff that we are going to build. Also, I am really thankful that the project continues to have reliable partners to support it! Cheers!