Good Morning! IBM and ESA just launched TerraMind, a next-gen AI model that understands Earth using satellite images, climate data, and even topography—all open-source. Microsoft’s new BitNet model is making waves by running large language models on regular CPUs with a sleek 1-bit architecture (yep, no GPUs needed). And if open-source security alerts are driving you up the wall, Hopper just came out of stealth to bring precision and peace of mind to AppSec teams.

— Forrest Knight & Meghanadh Vasireddy

TerraMind: The AI That Gets Earth

Context: IBM and the European Space Agency just dropped something big for Earth Day—TerraMind, an open-source, multimodal AI model trained on over 9 million globally-distributed data samples. It’s like GPT, but for the planet. Built with help from KP Labs, the Jülich Supercomputing Centre, and the German Space Agency, TerraMind goes beyond image processing—it's designed to "intuitively understand" Earth through diverse data like satellite images, topography, vegetation, and even time-based climate patterns.

What’s New:

• TerraMind beat out 12 leading models on the PANGAEA benchmark (aka the SATs for Earth AI).

• It uses “Thinking-in-Modalities” (TiM), kind of like chain-of-thought prompting in LLMs, to self-generate training data when info is sparse.

• Built on the IBM-NASA Prithvi framework, but lighter and more compute-efficient—10x less power than traditional models. Climate-friendly AI? Yes please.

This model can:

• Classify land cover

• Detect changes over time

• Analyze multi-sensor input

• Forecast climate risks (like water scarcity or deforestation)

One bullet-point takeaway list:

• Multimodal AI trained on 9 data types

• Open-source on Hugging Face

• Performs 8%+ better than competitors

• TiM boosts efficiency & accuracy

• Already making waves in climate forecasting & disaster response

TerraMind is available now, and fine-tuned versions are on the way. Expect to see it powering smarter Earth insights—from sustainable agriculture to emergency flood mapping. Happy Earth Day, indeed.

BitNet’s Big Play: Microsoft Ditches GPUs for CPUs (and It Works)

Context: For years, running large language models (LLMs) meant one thing: GPUs. Expensive, energy-hungry, heat-blasting GPUs. But Microsoft Research just flipped that script with BitNet b1.58 2B4T—a new AI model that runs on a regular CPU. Yep, no graphics cards needed. The model’s been published on arXiv and is already shaking up the “big compute” scene.

BitNet drops traditional floating-point weights in favor of a 1-bit architecture. Think: -1, 0, or 1. No matrix multiplications, no high precision fluff—just simple add/subtract operations. That means:

• Smaller memory footprint

• No GPU dependency

• Orders of magnitude lower energy consumption
  And get this: despite being tiny (under 3B params), BitNet performs on par with—or better than—other open-weight LLMs in its size class. All while sipping power.

Key Bits to Know:

• No floats—just 1-bit weights

• Runs natively on CPUs

• Built-in runtime: bitnet.cpp

• Outperforms some GPU-based models

• Big potential for edge AI + privacy (think: local chatbots, offline use)

Why this matters?: BitNet is pushing LLMs toward a more efficient, accessible future. If these results hold, running advanced AI on a laptop—or even a phone—might be closer than we thought. GPUs? Optional. Brains? Required.

Hopper Enters the Chat: Open-Source Security, But Actually Useful

Let’s be honest—most Software Composition Analysis (SCA) tools are like spam filters from 2005: noisy, outdated, and not very smart. Hopper, fresh out of stealth with $7.6M in seed funding, is here to flip the script. Founded by cyber-intel veterans from Israel’s Unit 81 and 8200, Hopper isn’t just another scanner yelling “CVE!”—it’s built to actually help you.

What’s New: Instead of flagging every theoretical vuln in a dependency tree, Hopper narrows things down to what really matters: function-level reachability. You get insights like “this exact line of code in this exact function is exploitable” without cluttering your CI/CD or drowning your devs in false positives.

Why It Matters (aka what Hopper nails):

• Function-level reachability for direct & transitive deps

• Contextual remediation evidence (source-linked)

• Agentless deployment via read-only Git access

• Full SBOM + VEX export support

• Hidden vulns? Detected. Shadow deps? Found.

• Built-in precision = lower MTTR, happier devs, safer code

Legacy players like Snyk, Mend, and Sonatype? Still doing manifest-level scans and sending alerts you’ll never read. Hopper’s angle is precision, speed, and dev-alignment—basically making AppSec not suck.

Already deployed at companies like HPE and Mesh Payments, Hopper’s turning open-source security from a blocker into a booster. Welcome to the new standard in OSS risk management.

Check it out at hopper.security

🔥 More Notes

• AI-Powered Learning at Middle Georgia State University: Middle Georgia State University is pioneering an AI-driven adaptive learning system for Python programming. Funded by a $9,000 grant, the project aims to fine-tune the LLAMA 3.3 model and develop a web application interface. This system will provide students with interactive coding tasks, quizzes, and real-time feedback, enhancing their learning experience.

• Boise State Student Wins NASA Award for Exoplanet Research: Malia Barker, a doctoral student in computing at Boise State University, has been awarded a Future Investigators in NASA Earth and Space Science and Technology (FINESST) gran. Her research focuses on advancing computational techniques to study the demise of certain exoplanets, contributing to our understanding of planetary system.

• Quantum Computing Achieves Randomness Breakthrough: Quantinuum's 56-qubit trapped-ion quantum computer has successfully demonstrated certified randomness in quantum circuits. This achievement is a significant step toward establishing secure, private connections, potentially leading to a spoof-proof internet.

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