News
Garmin Reveals First Running Watches With AMOLED Displays
The range-topping Forerunner 965 will cost $600 when released in late March, while the 265 model launches today for $450.
Garmin has unveiled two new GPS smartwatches with AMOLED displays: the Forerunner 965 and 265 series. The timepieces are labeled as dedicated running watches and provide “advanced training metrics” for athletes, heart-rate variability (HRV), sleep quality, training load and more.
The high-end Forerunner 965 model comes with a 1.4-inch AMOLED display, a decent 31 hours of GPS-mode battery life and up to 23 days of use as a smartwatch. The flagship model of the series features additional performance stats over the cheaper 265, including training load ratio, stamina info and detailed climbing metrics (including gradient, distance and elevation).
The Forerunner 265 Series comes in two sizes (42mm and 46mm) and holds out for 24 hours in GPS mode or 15 days in smartwatch mode.
All of the watches in the series use Pulse Ox sensors, and offer “Body Battery” monitoring, sleep and stress stats, menstrual cycle and pregnancy tracking. As well as featuring adaptive training options and suggested workouts, the Garmin 965 and 265 both monitor v02 max and other important performance metrics.
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The Forerunner 965 will cost $600 when it hits stores in “late March”, while the cheaper Forerunner 265 is available now for $450.
News
ESET Bets On Specialized Security AI At 0.5% The Training Cost
ESET says NeoCortex can estimate an attacker’s next move, with a kill switch and human oversight built in to keep it from going rogue.
A sign-in anomaly, a suspicious process launch and an unusual connection may look harmless alone. Together, they can describe an intrusion that is hard for security to teams to piece together.
To solve that problem, ESET announced ESET NeoCortex on November 4, 2026: a specialized foundational AI model for cybersecurity. It powers ESET LiveCortex, available within the ESET PROTECT Platform, which connects fragmented security signals and turns them into actionable incidents.
Filtering is the main selling point, with LiveCortex processing billions of raw telemetry events daily, and just 0.00111% of them contributing to incidents presented to customers. The aim is less alert fatigue and more analyst time spent on threats that matter.
NeoCortex is built on world-model architecture, learning how digital environments behave and how events relate. ESET says that lets it reconstruct missing incident context, recognize similar past cases and estimate an attacker’s likely next step – what the company calls “minus-one-day defense,” a chance to intervene before the next stage unfolds. ESET points to Black October, the week leading up to October 25, 2026, when critical security issues affecting PHP, Linux, GitLab and other key systems demanded rapid, prioritized responses, as the kind of moment where that context matters.
ESET’s internal research also suggests a specialized model like NeoCortex can be trained at up to 200 times lower cost than a large general reasoning model, while delivering more accurate, defense-focused results. The company credits optimized training, a focus on cybersecurity vocabulary, lower computing requirements and privately owned hardware.
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“AI is accelerating both cyberattacks and cyberdefenses. With ESET NeoCortex, we are applying our AI investment to develop specialized foundational cybersecurity intelligence that remains under our control and strengthens, rather than replaces, human expertise,” said Juraj Malcho, Chief Technology Officer at ESET.
As part of the design, NeoCortex forms part of a layered architecture combining specialized models, deterministic controls, correlation logic, validation, auditability and expert human oversight. Safeguards include restricted sandboxes, read-only tools, continuous monitoring, defined usage and token limits, a kill switch and human oversight of critical response decisions. The model is also developed and self-hosted on ESET’s own infrastructure, which the company says reduces exposure to shifts among third-party AI providers, including commercial and geopolitical ones.
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