Maksim Silnikau, the creator and administrator of the Ransom Cartel ransomware operation, was sentenced to 16 years in prison for his role in ransomware attacks against at least 18 companies worldwide.

The U.S. Department of Justice announced today that the 40-year-old Belarusian national was sentenced for conspiracy to commit offenses against the United States, conspiracy to commit wire fraud, and aggravated identity theft.

The DOJ says Silnikau had been active on Russian-speaking cybercrime forums since at least 2005 and used the aliases “J.P. Morgan,” “xxx,” and “lansky.”

He was also a member of the Direct Connection cybercrime website between 2011 and 2016, when the site was shut down following the arrest of its administrator.

According to court documents, Silnikau began developing the Ransom Cartel ransomware operation in May 2021 and recruited other cybercriminals through underground forums to participate in attacks.

He supplied members with information and tools used in the intrusions, including stolen credentials for compromised computers and software designed to encrypt victims’ computers.

Silnikau also operated an affiliate website that allowed members of the ransomware operation to manage attacks, communicate with each other, negotiate ransom demands, and distribute revenue shares after a ransom was paid.

Between 2021 and 2023, Ransom Cartel affiliates attacked at least 18 companies worldwide, including organizations in California, New York, Nebraska, and countries outside the United States.

During the attacks, the threat actors stole corporate data and demanded payments in exchange for decryption keys or promises that the stolen information would not be publicly leaked.

Federal prosecutors said the ransomware operation attempted to extort at least $5.2 million from its victims.

The United States identified more than $6.7 million in losses suffered by 18 known victims, although prosecutors said the total was likely higher because some victims had not reported their attacks.

In one August 2022 attack, Ransom Cartel reportedly disrupted the operations at a medical technology startup developing robotic surgical technology for two months. In May 2023, the gang also attacked infrastructure used by a group of law firms, causing business disruptions lasting from several days to multiple months.

One law firm paid a ransom worth $125,000 after being disrupted for nearly a month, while another suspended operations for almost a month before paying a $300,000 ransom.

Prosecutors said the combined losses associated with those attacks reached approximately $2.2 million.

Ransom Cartel launched publicly in December 2021 and shared code similarities with the REvil ransomware encryptor.

However, the lack of some of REvil’s obfuscation features led researchers to believe that it may have been created by a former core member of the operation who did not have access to the complete source code.

Silnikau reportedly held a central role in the ransomware-as-a-service operation, recruiting affiliates, working with initial access brokers who supplied access to compromised corporate networks, communicating with victims, and handling ransom payments.

He also transmitted ransom payments through cryptocurrency mixers to make it harder for law enforcement to trace the funds.

Silnikau was initially arrested in Spain on July 18, 2023, as part of an international law enforcement operation. However, he fled while awaiting extradition to the United States and was later captured while attempting to return to Belarus.

“The defendant fled Spanish authorities while awaiting extradition to the United States and was apprehended while trying to cross from Poland to his native Belarus,” prosecutors said in their sentencing filing.

Silnikau ultimately consented to extradition and was sent from Poland to the United States to face prosecution in the Eastern District of Virginia.

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Counterfeit goods are becoming increasingly sophisticated, forcing brands to rely on expensive authentication systems that often require specialized scanners or proprietary hardware. Researchers now believe a smartphone could do much of that work instead. A team has developed a new printing system that creates responsive anti-counterfeit labels that are more durable, easier to mass-produce, and can be verified using nothing more than a phone. The research was published in the International Journal of Materials and Product Technology.

At first glance, the technology looks like another incremental improvement in product security. In reality, it addresses one of the biggest limitations of anti-counterfeit systems today: accessibility. If authentication only works with expensive equipment, it becomes difficult to deploy at scale. By enabling smartphone-based verification, the technology could make counterfeit detection practical for manufacturers, retailers, and even consumers.

A smarter label that reacts to light and temperature

The new system combines three technologies into a single printable label. Researchers developed temperature- and light-responsive microcapsules, paired them with a UV ink formulated for high-speed printing, and created a portable smartphone-based detection system capable of verifying authenticity. The microcapsules contain functional materials that react to environmental conditions, producing optical signatures that are difficult to replicate using conventional printing methods.

Durability was another design priority. According to the researchers, nanotechnology-based linkers strengthen the polymer capsules, allowing the labels to remain stable under heat and humidity. The ink contains no volatile organic solvents, making it compatible with both screen and inkjet printing while remaining suitable for large-scale manufacturing. Even after repeated cold-storage testing, the labels retained more than 83% color fastness, suggesting they could withstand demanding supply chains.

Verification happens through a smartphone. A portable detector measures how light interacts with the printed label, and a machine-learning model then analyzes the captured data to determine whether the product is genuine. During testing, the system achieved nearly 97.5% accuracy, even when distinguishing between authentic labels and convincing counterfeits.

Why smartphone verification could matter

Counterfeit products affect industries ranging from luxury goods and pharmaceuticals to electronics and food packaging. While companies continue investing in holograms, QR codes, and RFID-based security features, many of those systems either add cost or require dedicated verification hardware.

A smartphone-compatible authentication system changes that equation. Because nearly every supply chain participant already carries a smartphone, the barrier to verifying products becomes dramatically lower. That could make authentication faster at warehouses, easier for retailers, and potentially accessible to consumers checking a product before making a purchase.

The researchers believe the technology could eventually be used across multiple packaging industries, though commercial deployment will depend on manufacturers integrating the printing process into existing production lines. If that happens, the next generation of anti-counterfeit labels may not rely on increasingly complex security features alone. Instead, they may simply rely on the device already sitting in your pocket.

Summer Apple Watch sales at Amazon and Walmart deliver discounts of up to $172 off SE 3, Series 11, and Ultra 3 styles.

Prices across Apple’s wearables line start at $219 this week, with standout offers being a $172 discount on the 42mm GPS + Cellular Apple Watch Series 11 at Walmart and a $100 markdown on the Apple Watch Ultra 3 at Amazon.

Grab Apple Watch deals from $219

42mm Apple Watch Series 11 deals

• 42mm Apple Watch Series 11 GPS (Aluminum Case, Sport Band): $299 ($100 off)
• 42mm Apple Watch Series 11 GPS + Cellular (Aluminum Case, Sport Band): $327 ($172 off)

46mm Apple Watch Series 11 discounts

• 46mm Apple Watch Series 11 GPS (Aluminum Case, Sport Band): $329 ($100 off)
• 46mm Apple Watch Series 11 GPS + Cellular (Aluminum Case, Sport Band): $429 ($100 off)
• 46mm Apple Watch Series 11 GPS + Cellular (Titanium Case, Milanese Loop): $699 ($100 off)

Apple Watch SE 3 savings

Apple Watch Ultra 3 markdowns

Several of the deals reflect the lowest prices seen in 30 days, with units in stock and eligible for expedited delivery through Amazon Prime or Walmart Plus. You can find even more price drops in our Apple Watch Price Guide, which is updated throughout the day.

Allen Millyard’s one-of-a-kind masterpiece, a bright red motorcycle powered by a full eight-liter Dodge Viper V10, sold at H&H Classics on July 22 for a final price of £149,500. When fees and premiums are included in, the total comes to almost $201,500. The hammer dropped for £130,000, or around $175,000, right in the middle of the pre-sale estimate.

Millyard’s project began in his Berkshire garage in 2004, with little more than a used Viper GTS engine. After five years of meticulous crafting, a road-legal machine made its premiere in 2009. The massive beast under the hood is a 20-valve 10-cylinder pushrod engine that generates an impressive 507 horsepower and 525 pound-feet of torque. The bike weighs a whopping 1,300 pounds overall, with the engine accounting for nearly 700 pounds of that total. The front suspension is supported by 75-millimeter stanchions taken from JCB hydraulic cylinders, while the rear suspension is supported by a set of heavily modified Yamaha R1 shocks. To keep up with the heat, he even created ceramic-coated exhaust headers by hand. As expected, this beast does not have a multi-speed transmission. A fixed ratio allows the massive low-end torque to do all of the work, with the rear sprocket selection dictating whether the bike is geared at roughly 160 mph or over 270 mph.

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During testing at Bruntingthorpe, Bruce Dunn of Motorcycle News reached 207.101 mph. Years later, in May 2023, Millyard and Henry Cole (a TV celebrity) achieved 183.5 mph on the identical bike at Elvington Airfield, breaking the Guinness World Record for the fastest tandem motorcycle. It has driven approximately 10,000 miles on public roads and race circuits, including a lap of the Isle of Man TT course, visits to Guernsey and the Isle of Wight, and a few trips across Germany. Honestly, only a few people have sat on it throughout the years.

Millyard had decided that he’d done enough with it because the machine was getting increasingly difficult to handle due to his declining muscle strength, and he thought he’d checked off every box he set out to do with the bike. He described it as a painful decision, but the proceeds from the sale will free up some space and finances for other projects. When the audience requested to hear it run during the auction, he started the engine and ran it for them while TV cameras were filming a future edition of The Motorbike Show.

Tim Whittaker, a local collector and engineer, submitted the winning bid. He’s been eyeing this bike for years, and believe us, he didn’t want it to end up in some private collection and never see the light of day. Whittaker plans to ride the bike himself, starting on runways and progressing to public roadways as he builds confidence. He has already agreed to loan the motorcycle to the National Motorcycle Museum in Solihull for the winter months, allowing visitors to see it up close.
[Source]

Meta today released Muse Code, a terminal-based AI coding agent now in beta, alongside Muse Spark 1.2, a coding-focused update to its Muse Spark family of frontier models — a one-two punch that puts the company in direct competition with Anthropic’s Claude Code, OpenAI’s Codex, and the growing field of agentic coding harnesses that have rapidly become the primary way many professional developers ship software.

“Releasing Muse Code in beta today,” Meta co-founder and CEO Mark Zuckerberg wrote in a post on rival social network X (under his longtime handle @finkd). “It’s a terminal coding agent that takes on complete software engineering tasks across large repos: planning changes, writing code, validating the results.”

The launch marks Meta’s most serious entry yet into a category it has largely watched from the sidelines.

While Anthropic and OpenAI turned their coding agents into flagship products — and startups like Cursor built billion-dollar businesses on the workflow — Meta’s developer story long centered on Llama, the open-weight model family it gave away to the tune of more than a billion downloads.

Muse Code changes that in more ways than one: it’s a full harness, installable on macOS or Linux with a single curl command, co-trained with the model that powers it — and, like the Muse Spark models behind it, entirely proprietary.

However, Zuckerberg teased that open source may be in the cards for Muse Spark or perhaps another product entirely, in a reply to a question on X, saying “I’ll have more to share on that soon.”

Developers and prospective users can install it now on their Terminal using the following one-line command — but be warned, if that’s you, you’ll need to log in with a Meta account and provide billing details first in order to begin: curl -fsSL https://dev.meta.ai/install.sh | bash

Persistent background agents and parallel worktrees

Muse Code’s headline architectural bet is what Meta calls async background agents.

Rather than spawning helper agents fresh for each task — the pattern most rival harnesses use — Muse Code keeps a set of specialized background agents alive for the entire session.

According to Meta’s blog post, these agents “remain active throughout each session, rather than being spawned for individual tasks, helping avoid redundant information gathering,” carrying out next steps on their own and choosing when to report back to the main agent.

The practical pitch is less latency and less babysitting: an agent that already knows the repository doesn’t have to re-explore it every time the developer asks for something new.

When a job is large enough, Muse Code fans out to separate sub-agents working in parallel, each in its own isolated git worktree, so the developer’s working copy is never touched.

“In testing we had it build six features for a game simultaneously with no collisions,” Zuckerberg wrote on X.

Worktree isolation and parallel sub-agents exist in competing tools, but Meta is leaning on the combination of persistence plus parallelism as its differentiator.

The second notable design choice is auditability. Every model call, tool run, approval, and edit is appended to a local event log before it executes — a single source of truth that Meta says makes the runtime “replay-exact and restart-safe.”

If Muse Code crashes 20 hours into a long-running task, it resumes precisely where it stopped, with no lost work and no re-prompting. For engineering leaders who have been burned by opaque agent runs, a complete local audit trail may prove to be the feature that matters most in enterprise evaluations.

Muse Code also ships with bundled “skills” that will look familiar to users of rival tools: /plan turns a task into an approval-gated plan, /grill stress-tests that plan until it holds up, and /goal drives the agent toward completion of a stated objective.

Muse Spark 1.2: co-trained with its own harness

Under the hood is Muse Spark 1.2, which Meta describes as a coding-focused update to Muse Spark 1.1 with “significantly scaled up training compute on coding tasks” and broader training environment diversity, improving code generation, complex debugging, and codebase understanding while maintaining general agentic capability.

The update lands squarely on the Muse family’s weakest flank. When the original Muse Spark debuted in April, it vaulted Meta back into the top five on frontier reasoning and vision benchmarks — but trailed on the agentic coding evaluations that matter most to this market, scoring 77.4 on SWE-Bench Verified against Claude Opus 4.6’s 80.8 and Gemini 3.1 Pro’s 80.6, and lagging well behind GPT-5.4 on GDPval’s measure of long-horizon work tasks.

Four months later, a coding-specialized checkpoint paired with a purpose-built harness reads as Meta’s direct answer to that gap.

Two training details stand out. First, Meta co-trained the model with Muse Code itself, using rejection-sampled harness trajectories and recipe optimizations for goals, context compaction, and sub-agents — meaning the model was explicitly tuned to perform best inside this particular tool. That mirrors an industry-wide shift away from treating models and harnesses as separable products.

Second, Meta used a self-improvement loop: Muse Spark 1.1 generated challenging coding environments and instruction-following templates, then graded candidate solutions against those requirements, producing a scalable training dataset for its successor. Meta credits the loop with making 1.2 measurably better at following complex instructions.

Meta published benchmark charts comparing Muse Spark 1.2 against other coding models on Terminal-Bench 2.1, DeepSWE 1.1, and an internal Meta coding benchmark, pointing readers to a separate methodology report for details — though the announcement text itself doesn’t tout any placements, an unusual reticence in a field where rivals trumpet leaderboard wins. The charts explain why: they show a strong but clear second place.

On Terminal-Bench 2.1, Muse Spark 1.2 running in Muse Code scored 82.9%, edging OpenAI’s GPT-5.6 Terra in Codex (81.8%) and xAI’s Grok 4.5 in Grok Build (81.6%) but trailing Anthropic’s Opus 5 at max effort in Claude Code, which leads at 86.7%.

On DeepSWE 1.1, Muse Spark 1.2 posted 59.3% — third, behind Opus 5 (65.0%) and GPT-5.6 Terra (64.8%). Most striking is Meta’s own internal coding benchmark, where Muse Spark 1.2’s 70.6% comfortably beats GPT-5.6 Terra (65.4%) and Gemini 3.6 Flash (63.9%) yet still sits nearly nine points behind Opus 5’s 79.4% — an unusually candid admission that even on the test Meta designed itself, Anthropic’s model wins. Indeed, Claude tops all three charts.

The generational gains are real, though: Muse Spark 1.2 improves on 1.1 by 6.7 points on Terminal-Bench and 6.3 on DeepSWE. One caveat buried in the chart labels — the 1.1 scores were recorded in the generic mini-swe-agent harness while 1.2 ran in Muse Code, so some of that jump belongs to the new harness rather than the new model.

The company’s most striking demonstration is a long-horizon case study: Meta pointed Muse Spark 1.2 at GPU kernel optimization and let it run for more than 1,000 tool calls over up to 24 hours on NVIDIA Hopper hardware.

Working in Triton and barred from simply wrapping existing third-party kernel libraries, the agent wrote, compiled, and profiled its way to what Meta calls “substantial improvements” over baseline implementations of KDA and MLA kernels — including genuinely non-obvious optimizations like re-centering gated cumulative decay at a chunk midpoint.

“It kept finding substantial improvements well beyond the initial exploration phase,” Zuckerberg wrote. Sustained improvement over a 24-hour autonomous run, if it holds up outside Meta’s demos, addresses one of the most persistent criticisms of coding agents: that they plateau or drift once past their initial burst of progress.

Your data for a discount?

The pricing structure may be the most consequential — and most scrutinized — part of the launch. Meta is offering Muse Spark 1.2 through its Meta Model API in two tiers.

The standard tier is priced at $1.25 per million input tokens and $4.25 per million output tokens (with cached input at $0.15), and Meta commits that prompts and completions on this tier are not used to train its models. There is no long-context premium, and rate limits run to 3,000 requests and 4 million tokens per minute, per team. It’s about mid-range price, compared to other leading AI models available over API.

The contributor tier is where Meta’s strategy diverges sharply from its rivals: $0.10 per million input tokens and $0.20 per million output tokens — roughly 12x and 21x cheaper than standard, respectively, with cached input at a near-free $0.002 — in exchange for explicit permission to use your prompts and completions to train future Meta models. It’s the cheapest available on the market, but you pay with your data — as described below.

This is the tier Zuckerberg is steering new users toward: “It’s easy and low-cost to get started,” he wrote. “Install Muse Code with one line and you can start on our contributor tier.”

In VentureBeat’s own testing on a Mac mini, the one-line installer worked as advertised — a 97 MB download and a sign-in — but the agent stopped short of running anything, reporting that no models were visible and that payment was “required to finish setting up your account.”

In other words, even the heavily discounted contributor tier requires a payment method on file before Muse Code will do any work: low-cost is accurate, but free is not.

Meta frames the contributor tier as lowering the barrier for prototyping and experimentation “where training on your data is acceptable.”

But it also means the default on-ramp for Muse Code sends developers’ code and prompts into Meta’s training pipeline — a tradeoff enterprises with proprietary codebases will need to consciously opt out of by moving to standard pricing.

The contributor tier also carries much tighter rate limits (60 requests per minute versus 3,000), a clear signal it’s aimed at individuals and small experiments rather than production workloads.

The approach is classically Meta: subsidize access, harvest data at scale, and use it to close the gap with the frontier. Zuckerberg made no secret of the ambition, calling Muse Spark 1.2 “our next step as we push toward frontier, with larger, more capable models on the way.”

However, for developers and enterprises who want or are required legally to keep their code secure, the tradeoff may not be one they’re willing or able to make.

No Llama in sight

What today’s announcement conspicuously lacks is any mention of open source — a striking omission from the company that spent three years positioning itself as the standard-bearer of open AI.

From the original LLaMA’s debut in February 2023 — whose weights famously leaked onto 4chan within weeks, inadvertently kickstarting the movement to run capable models on consumer hardware — through Llama 2’s commercially usable license, the coding-specialized Code Llama, and the 405-billion-parameter Llama 3.1, which Zuckerberg launched in July 2024 with a manifesto titled “Open Source AI Is the Path Forward,” Meta’s entire pitch to developers was that frontier-class weights should be free to download, self-host, and fine-tune.

The strategy worked: by early 2026, the Llama family had been downloaded roughly 1.2 billion times, averaging about a million downloads a day, with self-hosting offering enterprises cost reductions VentureBeat has previously reported at as much as 88% versus proprietary API providers.

Then came the unraveling. Llama 4 debuted in April 2025 to mixed reviews and, eventually, admissions that its benchmark results had been fudged — while Chinese open-weight rivals from DeepSeek, Alibaba, and Zhipu AI surged to account for some 41% of downloads on Hugging Face by late 2025, eroding Llama’s claim to leadership of the very movement it started. The rocky rollout spurred Zuckerberg’s summer 2025 overhaul of Meta’s AI operations into Meta Superintelligence Labs (MSL), with Scale AI co-founder Alexandr Wang recruited as chief AI officer.

The Llama era effectively ended this past April 8, when MSL shipped the original Muse Spark — “the most powerful model that meta has released,” in Wang’s words — as Meta’s first proprietary model: cloud-only, with no downloadable weights and no self-hosting, initially confined to Meta’s apps and a private API preview.

Asked directly at the time whether Llama development would continue, a Meta spokesperson told VentureBeat only that “our current Llama models will continue to be available as open source” — pointedly silent on future ones.

Wang, for his part, said bigger models were already in development “with plans to open-source future versions” — but four months on, today’s release does nothing to advance that promise: no weights, no license, and neither the blog post nor Zuckerberg’s thread so much as uses the word “open.”

The reversal is all the sharper because Meta’s rivals have been moving in the opposite direction. OpenAI released its Codex CLI as open source under the permissive, enterprise-friendly Apache 2.0 license and followed with its gpt-oss open-weight models; Google’s Gemini CLI harness is likewise Apache-licensed.

With Muse Code, Meta lands closest to the posture of Anthropic — whose Claude Code remains proprietary — while the company that once argued open source was the path forward now asks developers to pay per token for a model they cannot inspect, or to subsidize that access with their own data.

Seen in that light, the contributor tier reads as the successor to the Llama strategy itself: the ecosystem flywheel is no longer free weights in exchange for mindshare, but cheap tokens in exchange for training data.

But Zuck’s reply on X — asked directly by AI developer Luckey Farady, “Will Muse Code be open source?” he responded “I’ll have more to share on that soon” — does keep hope alive that Meta will return to the open source AI ballgame.

Why it matters

Terminal coding agents have become the fastest-growing surface in enterprise AI, and until today the category has effectively been a two-horse race between Anthropic and OpenAI, with Google and a crowd of startups in pursuit.

Meta’s entry brings a genuinely different architecture (persistent background agents, an append-only local event log), a credible long-horizon demo, and an aggressive pricing wedge.

The open questions are the ones benchmarks charts can’t answer: whether Muse Spark 1.2 actually matches Claude and GPT-class models on real-world repositories, whether developers trust Meta with their code, and whether the contributor tier’s discount is enough to make them stop asking. Muse Code is available in beta today; Muse Spark 1.2 is live in the Meta Model API with expanded global access.

Today’s U.S. electrical grid, among the largest, most complex systems ever built, is operating at its limit. The combination of rapid industrial growth, more frequent extreme weather, and a record surge in electricity use has pushed the grid to its breaking point, according to the U.S. Department of Energy.

Built decades ago for a more predictable world in which power came mostly from centralized coal or gas plants and electricity use grew at a steady pace, the grid faces unanticipated strain due in part to growing demand from data centers. The jobs of professionals managing the infrastructure have evolved from traditional engineering tasks to complex, fast-moving challenges.

Industry reports show that millions of modern digital sensors, smart meters, and grid monitors are generating nonstop waves of information. The sheer volume of data requires instant, automated computer analysis because human operators cannot process it fast enough.

Pressure on utilities stems from two sources: a spike in electricity demand and a shift in how power is generated.

An example of the operational strain can be seen at the regional level. With the recent deployment of artificial intelligence tools and high-performance computing, data centers require immense amounts of energy to operate. The largest power transmission utility in Texas recently reported a staggering 220 gigawatts of new connection requests, driven largely by a surge in AI and cloud-computing facilities, according to a CNBC report.

Alongside the rise in regional demand, global energy networks are absorbing an unpredictable variety of weather-dependent renewable energy such as wind and solar. The switch creates a volatile operating environment wherein supply and demand are balanced, second by second, to prevent blackouts.

The challenges are compounded by the vulnerability of the grid’s physical and digital framework.

More-frequent severe weather events cause costly disruptions, such as the devastating winter freeze that crippled the Texas grid and record-breaking heat waves that have overloaded transformers.

Simultaneously, the energy networks’ digital architecture faces threats. As utilities replace outdated analog equipment with smart meters and control systems, they are increasingly vulnerable to cyberattacks.

To overcome physical and digital vulnerabilities, grid reliability organizations, such as those conducting North American security simulations like GridEx, emphasize that the grid must become smarter, more agile, and completely automated. Energy researchers are noting that the key to this change lies in integrating AI across every layer of utilities’ operations.

The AI imperative

According to energy industry experts, using AI to manage power systems is no longer a futuristic research project; it has become a baseline operational necessity. Grid analysts emphasize that traditional grid-planning methods are too slow to handle rapid energy dynamics or to balance volatile renewable energy in real time within decentralized power systems such as microgrids.

AI can fill the gap by processing vast amounts of data instantly. Machine learning algorithms can quickly analyze information from thousands of sensors, historical usage patterns, and weather forecasts to predict issues before they happen.

An industrial digitization study conducted by McKinsey & Co. indicated that integrating advanced data and automation across infrastructure networks could reduce system design errors, decrease equipment downtime by up to 50 percent through predictive maintenance, and extend the lifespan of power machinery by up to 40 percent.

From forecasting energy spikes to automatically fixing localized voltage drops, AI acts as the digital backbone of a self-healing grid, experts say. Deploying the complex systems requires a new workforce: power engineers who understand data science, as well as data scientists who understand electricity.

Upgrading the Workforce

To bridge the gap between groundbreaking AI research and practical field deployment, IEEE Educational Activities, in partnership with the IEEE Power & Energy Society, has launched the online Artificial Intelligence for Power and Energy Systems course program.

The program explores core challenges threatening modern utilities. Rather than treating AI as an unverified black box that operates without human supervision, the curriculum focuses on safety, asset preservation, and strict reliability standards.

The curriculum is designed to educate power system engineers, utility managers, and data scientists tasked with modernizing the grid. The program was developed by Fangxing “Fran” Li, professor of electrical engineering and computer science at the University of Tennessee in Knoxville and chair of the IEEE Working Group on Machine Learning for Power Systems.

Five learning modules

The program breaks down the technical transition into five modules that bridge high-level theory with real-world solutions:

AI fundamentals. This module teaches engineers how basic machine learning models apply to power grids. It discusses how specialized neural networks solve complex power-flow calculations and how AI models can safely transition from computer simulations to physical, high-voltage equipment.

Accelerating grid control. Learners are taught to leverage deep reinforcement learning, an AI approach that uses trial and error, to accelerate automated grid adjustments during emergency power events.

Forecasting and data analytics. Using predictive modeling, engineers learn how to predict sudden demand surges, variable wind and solar outputs, and fluctuating wholesale electricity market prices to keep power affordable and available.

Physics-informed and safe AI. To address trust—a barrier to utility AI adoption—this course covers AI models hard-coded to obey the laws of physics. The approach is designed to ensure that automated algorithms never make erratic choices that damage grid equipment.

Generative AI and next-generation tech. Learners can explore the frontier of utility technology, including graph neural networks and large language models. This module highlights how generative AI can process complex, interdisciplinary data to streamline utility planning, emergency responses, and regulatory reporting.

The algorithmic literacy and practical execution tools provided by the course program can help convert systemic risks into grid resilience.

For individual access, visit the IEEE Learning Network. If you are looking for customized organizational options, contact a content specialist to discuss volume pricing.

For years, as a library media specialist, I took great pride in my role as guardian of the 20,000 books in our library, meticulously cataloging and protecting our collection. But after dedicating nearly two decades to education, I came to a vital realization: The most important chapters in our school’s history weren’t on our bookshelves; they were waiting to be written by the students themselves.

I’ve seen firsthand how easily innovation can get reduced to a one-time event: a quick photo-op, a splashy press release, or the arrival of a new shipment of shiny devices. It’s frustrating because that approach never lasts; it’s a temporary buzz that leaves the core of our instructional ecosystem fundamentally unchanged. The real problem is that our students deserve more than a “device drop.” They need and deserve a long-term commitment that actually reshapes how they learn. 

That shift in perspective is what drives my work today. I’ve moved from being a gatekeeper of static resources to someone who actively creates a dynamic, student-centered environment. It is no longer just about having resources; it’s about ensuring they become the backbone of a sustainable, future-ready classroom experience.

The Quiet Before the Storm

For the first 10 years of my career, my roles ranged from teaching third and fifth graders to serving as a school technology teacher and a sixth-grade pre-AP reading and math instructor. Following 10 years across multiple grade levels and content areas, I transitioned into the role of a library media specialist, which soon became my entire world. My days were defined by the ebb and flow of students coming in to check out books and learn the literacy skills necessary to develop knowledge. 

Parents, community members, and even fellow educators often referred to me as “just a librarian,” and while I wore that title with pride, I was acutely aware of the silent burden we carried. The library was viewed as a traditional hub for books and nothing else, leaving our students tethered to the past while the world raced forward in technology and innovation. Our students possessed inherent brilliance, yet they were consistently overlooked by a STEM narrative that rarely considered our zip code. 

Society often operates on an unspoken assumption that technological breakthroughs belong exclusively to affluent districts or established tech hubs. Because our community didn’t fit that traditional mold, the narrative bypassed us, systematically starving our students of the advanced tools and platforms required to translate their raw potential into tangible innovation. 

Everything changed for us when I had a conversation with Beth Sanders, then vice president of Ed Farm. The nonprofit organization, headquartered right here in Birmingham, launched a strategic partnership with Birmingham City Schools (BCS). Their goal was clear: to boost educational equity by closing digital skills gaps across our district and region. While the strategic partnership between Ed Farm and BCS had already been established since 2020, I learned that my space, the South Hampton K-8 library, had recently been chosen for a complete renovation.

Beth Sanders approached me with this incredible opportunity, knowing that this project would be the first of its kind in the state of Alabama. Our discussion became the catalyst for completely reimagining the space. In addition to the physical Ed Farm spaces, educators and students benefited from specialized fellowships. Together, these initiatives pushed us beyond the status quo, ensuring we prioritized sustainable, future-ready learning over superficial technology adoption.

Students were also invited into the design process, and for the most part, they led the way. I sat back and watched as they traded ideas about robotics, green screens, and iPads. Witnessing them design their own future before a single wire was plugged in proved we were creating something much bigger than a state-of-the-art library — we were creating the library of innovation.

Becoming an Architect of Possibility

In 2022, we finally cut the ribbon on the first official Ed Farm space at South Hampton K-8 School, “The Library of Innovation.” Balloons were tethered to the doorways, camera crews maneuvered for the perfect angle, and city officials were buzzing with the promise of a new era. It was one of those “pinch-me” moments, seeing the last couple of years of dreaming and planning finally take physical shape for our kids. But when the local city council members, edtech partners, and school board officials headed home, the real, gritty, unglamorous labor of building capacity began. My mission shifted from being a keeper of books to an architect of possibility. 

By moving past traditional keyboarding to teach coding, website architecture, and engineering principles, I equipped students to interrogate their environment and engineer solutions to the challenges they encounter in their own neighborhoods. Instead of just learning how to use software, they began leveraging creative programming workflows to design AI and Internet of Things-driven systems, such as functional tornado warning models, to address severe weather threats right here at home. Seeing them build these concepts proved that this space was doing exactly what it was meant to do: transforming students from passive digital consumers into true architects of possibility.

Over the course of the next year, the library had transformed into a dynamic laboratory. The hum of 3D printers bringing custom keychains and game remote holders to life became our soundtrack. The sharp whir of drone propellers slicing through the air replaced the static silence of the past. The intense, focused chatter of a student-led podcast recording of “Bulldog Talk on the Edge” became the new student voice. We purposefully curated this environment to break the traditional mold, challenging the students to completely redefine their expectations of the space and of themselves.

Defying the Narrative: From Participation to Mastery

In an urban environment, the STEM narrative is often restricted and expectations are frequently capped. This cap does not exist because of a lack of student capability but rather because of a systemic failure in how society views innovation. The narrative feels capped because the broader educational system frequently assumes urban students only need to learn how to participate in the digital world as consumers, rather than being equipped with the sophisticated coding environments and engineering principles required to master it as creators.

From the first day the library reopened, I watched the confidence of our students bloom. As they mastered the new tools, this newfound confidence translated into tangible success, resulting in a series of competition wins and vital funding for our school. They took that confidence and applied it to real-world problems. 

During the 2024-2025 school year, they won the ALTEC Challenge for the “Hygiene Hub,” a vending machine solution for personal care products paired with a custom community app they coded. 

Next, we won $12,000 in Samsung technology representing the state of Alabama in the Samsung Solve for Tomorrow competition with an upcycling machine designed to turn old clothes into hygiene products.

The momentum continued when three of our seventh-grade students won the Alabama state title in the eCYBERMISSION competition, earning $500 in savings bonds each for their innovation. This all happened in the span of one school year, cementing the fact that our students weren’t just playing with STEM. They had become experts in their domain.

However, the ultimate validation of our shift came this past school year. In April, our students rose to become national finalists in Samsung Solve for Tomorrow, standing out as one of only 10 schools in the entire nation. They secured $50,000 in Samsung technology. The experience offered so much more: My students flew to Washington, D.C., and stayed on Capitol Hill to pitch an early-warning system for tornadoes they created for The Washington Post. Their presence on that stage, pitching for the $100,000 grand prize, was proof that their innovation belonged in the highest arenas of influence. 

Redefining Excellence

How does a library, once defined by shelves of paper, become an incubator for national-stage innovation? It happens when you stop viewing technology as a product and start viewing it as a catalyst for human agency.

The gap between buying tech and building capacity is bridged by leadership that refuses to let the innovation end when the ribbon is cut. It is bridged by mentors who show students that their zip code does not dictate their capacity to lead. When you provide students with the right tools, these students don’t just keep up; they excel.

In the span of one year, my students transformed what it means to be a student in Birmingham City Schools. We proved that when students in urban, underserved neighborhoods are given the resources to match their ingenuity, the results are world-changing.

This library is no longer just a place to check out books; it is where students can rewrite their own narratives. The ribbons may be gone, but the ecosystem we built remains a living, breathing playground of innovation. We didn’t just join the STEM conversation; we changed it.

What’s your game called and what’s it about?

My game is called VILE: Exhumed, and it is about exploring an old computer and going down the disturbing rabbit hole of entitlement.

How big is the development team?

The development team is 167cm tall (it’s just me!).

How long have you been working on it?

It’s hard to measure when you work on something part-time, but somewhere between six months and a year!

What’s the origin story of VILE: Exhumed? Share what inspired you.

I made VILE because I was frustrated. I was yearning for control and agency of my own body, and so taking photos covered in blood, as grotesque as it sounds, was powerful. I was wading through difficult feelings and trauma, but it was on my terms and that was awesome. It made me feel strong.

I guess I was “inspired” by the things that had happened to me as well, and the people who were involved in those things. I had to pull from some of the darkest periods of my life, and package it up in VILE.

Talk about the power of discomfort and disgust, and how VILE uses these emotions to authentically examine the insidious nature of misogyny.

I think pulling from my real-life experience made the game feel grounded in reality — which is why some people found it so effective and related to it so heavily. It is a feeling that unfortunately too many people, mostly women, know all too well.

Final Girl Games

What does the game’s physical edition represent to you, and what’s your favorite aspect of it?

Getting to make a physical edition of the game felt like getting a second chance at a release for VILE: Exhumed. Sort of like the feelings behind making the game in the first place, and behind releasing it how we did — I felt like I was getting control back.

I also loved getting to step back into Candy and Molly’s world again, and give them a little more identity with the packaging. Getting to create a callback to Molly that wasn’t about what happened to her was a lot more emotional than I expected. I think Molly’s story in the game is quietly a lot more grounded in reality, so letting her have her moment without it being about her trauma felt really special. It felt like I was doing something nice for Molly, and for myself.

VILE became the face of financial censorship on Steam last summer — what do you feel was lost in the popular conversation?

This is a really hard question to answer — I hesitate because I don’t want to sound unappreciative for all the support last year after it was banned from Steam. I couldn’t be more grateful for the time anyone spent talking about VILE last year, and standing up for what happened to it, and what happened to me.

Pre-ban, I was really excited to hear reactions to the visual design choices I had made. I played every single female character in VILE that was shown on screen myself, all the gore special effects that were shown in game were created practically by me, and photographed myself. I think that is really cool!

I also had a lot of firsts that I think I didn’t get to fully celebrate. It was my first published game and my first game on Steam (or, well…you know). I also was really proud of the writing in the game.

Final Girl Games

Why are video games your chosen form of expression?

I think I like being able to get so specific in communicating a feeling. I can control what the player was exposed to, and when, and use that to make the experience as close to what I imagined it to be as possible.

I love the immersion of found footage films, to me they are the scariest because they place the viewer directly into the madness. I think that VILE: Exhumed takes that one step further by making players participate.

Another reason is that stories told through video games have always impacted me very profoundly, even when I was a kid, so it’s such a pleasure to create that experience for someone else.

What’s the biggest roadblock you’ve faced as a smaller developer?

Like every developer ever, time and money is something there is never enough of, but as a developer of smaller games, I think the real challenge is other people’s time. There are so many games coming out all of the time that it is harder to grab players’ attention.

What can larger companies and publishers do to better support developers like yourself?

I think I would like to use this question as a reason to highlight something I really appreciate about DreadXP — the publisher of VILE: Exhumed — in that they aren’t afraid of games with difficult subject matter, and believe firmly that there are many kinds of horror that deserve to be shared.

I think more people in general should remove the boxes placed around the genre.

Let’s bring it back home. Sell your game in one sentence:

Play an interactive video nasty.

Final Girl Games

VILE: Exhumed is available to download and play for free, with the option to pay whatever you’d like, and that money is split equally between Final Girl Games and Red Door Family Shelter, a Canadian non-profit that provides emergency housing and support. The physical edition (which, again, looks super sick) costs $9.99 and includes an eight-page booklet, classic jewel case, hand-written labels, and an archival quality CD-ROM featuring the entire game — no DRM in sight, just like the old days. Half of all proceeds still benefit Red Door.

The VILE physical edition is a limited release and the first 100 will be individually numbered. You can sign up to be notified when it goes live for purchase on the game’s website.