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Jun 26th 2026, 15:00 by Ramesh Bellamkonda
For years, search technology meant one thing: type in a keyword, and the system goes hunting for an exact match. That works fine for product SKUs or error codes, but it falls apart the moment someone asks a real question. If your knowledge base is full of manuals, support tickets, transcripts, and reports, a person searching for "why does the machine shut down during startup" shouldn't have to guess the exact phrase the original author used.
This is the gap that vector search closes. Instead of matching words, it matches meaning. And on Databricks, building this kind of system is more accessible than most teams expect, once you understand the moving pieces.
Jun 26th 2026, 14:00 by Igboanugo David Ugochukwu
Every CISO I talk to right now is juggling two deadlines that feel unrelated and aren't. One is the slow-motion arrival of quantum computers capable of breaking the public-key cryptography that underpins basically everything — TLS, SSH, JWTs, code-signing. The other is the much faster arrival of AI-assisted coding tools that are shipping security-critical code nobody has fully reviewed. I used to think of these as separate beats. I don't anymore, because the same root failure shows up in both: organizations adopting powerful new capability faster than they're building the visibility and discipline to govern it.
Post-Quantum Planning: The Inventory Problem Comes First
NIST finalized its first three post-quantum cryptography standards on August 13, 2024, after an eight-year, multi-round public competition: FIPS 203 (ML-KEM, the lattice-based key encapsulation mechanism formerly known as Kyber), FIPS 204 (ML-DSA, the signature scheme formerly known as Dilithium), and FIPS 205 (SLH-DSA, the hash-based fallback formerly known as SPHINCS+). In March 2025, NIST added a fourth algorithm, HQC, specifically chosen because it rests on a different mathematical hardness assumption than the lattice problems underneath ML-KEM and ML-DSA — a deliberate hedge in case lattice-based cryptography turns out to have a weakness nobody's found yet. The NSA's CNSA 2.0 guidance sets 2030 as the mandatory PQC migration deadline for national security systems, and NIST's broader timeline calls for deprecating RSA and ECDSA entirely by 2035.
Jun 26th 2026, 13:00 by Robin Macfarlane
Mainframe modernization is once again at the center of enterprise conversations. Not because something suddenly broke, but because the environment around it has changed.
Organizations are being asked to move faster, integrate more easily with newer platforms, and support initiatives like cloud and AI that weren’t part of the equation a decade ago. At the same time, experienced teams are shrinking, costs are under scrutiny, and expectations from the business are higher than ever.
Jun 26th 2026, 12:00 by Naveen Kalapala
When I decided to move into AI infrastructure, nobody warned me that I had to relearn how to think about compute. I proceeded with the usual steps, such as spinning up VMs, configuring networking, and managing costs. But then a moment came, and I watched, slightly horrified. I misconfigured the inter-node networking. The result was that an eight-node GPU ran a training job at just 11% GPU utilization. It was a wake-up call for me. AI workloads aren’t just different in a marketing sense. They’re different where it counts, i.e., in the architecture — how you build and run things.
The ML engineers on that project immediately assumed the model was the problem. They decided to redesign the model and spent a couple of days tweaking the architecture, like chasing a ghost. The real issue resurfaced only when someone checked the network telemetry — the cluster nodes were using standard Ethernet, not InfiniBand. The model had no issues. The infrastructure configuration was incorrect.
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