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#AKMU# 3rd MINI ALBUM [LOVE EPISODE] Official Audio Videos are now available on YouTube. 🎧 케익의 평화 (Peace of Cake) :  🎧 답답해 (Answer Me) :  #악뮤# #3rdMINIALBUM ##LOVE_EPISODE# #케익의평화# #PeaceofCake# #답답해# #AnswerMe#

On July 14, the Department of the Navy released its Strategy to Weaponize Data and AI: a roadmap to an AI-first Fleet that can "out-learn and out-fight any adversary." Within a week, our team answered with working code. The Navy's new strategy frames data and AI as warfighting assets on par with weapons and munitions, and it prizes one thing above all: turning information into decisions, fast. That is exactly the problem our GURU architecture was built for. So when two Navy SBIR topics called for AI-driven maritime tracking and adaptive sensor management, we pointed at the sea what we had already proven in orbit. The video below shows both prototypes, back to back. First, GURU MarineGuard: 787 real vessels from public NOAA data, replayed through a cascade of learned models that forecast each ship's movement, flag deviations from its learned pattern of life, and hand analysts a ranked review queue instead of an unfiltered flood. The full stack runs on a laptop. Second, an adaptive sensor resource manager add-on to MarineGuard: it measures each radar's marginal contribution to each track, projects the consequence of releasing a sensor task before proposing it, and then waits for the operator. Advisory by design. Both inherit their DNA from OrbitGuard, our system watching 14,710 space objects at the SDA TAP Lab with 94 to 96 percent maneuver-detection accuracy. Same architecture, new domain, days not years. These are prototypes, and we say so on screen. The trajectories are real. The sensor numbers are deliberately notional. No score is a threat call. Showing your assumptions is a capability, not a caveat, and national-security guidance now demands exactly that: AI that is reliable, robust, steerable, and controllable under rigorous test and evaluation. Our doctrine was written for that bar. Learned models accelerate and rank. Validated references confirm and decide. Humans stay in command. One architecture. Space, maritime, autonomous engineering, regulated nuclear autonomy. This is simply the latest sign of what this team fields, fast, where mistakes are not allowed. Sailors, engineers, program folks: what mission should GURU learn next? #DefenseTech# #ArtificialIntelligence# #MaritimeDomainAwareness#

Just had a moment on Don Jr.’s podcast Triggered that genuinely made my day I asked him about Brenden Dilley’s recent post… and he answered me directly. He said he fully agrees with Brenden - if Trump didn’t agree with something, he’d say it, whether privately or publicly. No bs games. Huge respect for taking the time. @DonaldJTrumpJr

I listened to 85 minutes of The Economist’s interview of Elon so you don’t have to. Besides, it’s behind a paywall. Elon’s predictions: In five years, AI compute will exceed the sum of all human intelligence. In ten years, we will have reached the age of abundance. Money won’t matter. Everyone will have what they need or want (at least in economies that embrace AI). Ms. Beddoes tried to pin Elon down on how the economy will transform that way, but he wouldn’t get into specifics beyond noting that widespread AI robotics is a deflationary force. This means governments won’t need to raise taxes for universal basic income schemes, or, as Elon likes to call it, universal high income, since they will simply be able to print money to ward off deflation caused by the robot economy. She noted that Elon appears to have a more sanguine view of AI lately. He replied that he’s concluded superintelligent AI is now inevitable, so there’s no point trying to stop or slow it down, it can’t be done. We might as well enjoy the ride. The interviewer also noted that Mars no longer seems to be Elon’s overall ambition. He answered that his real mission was always to propagate and preserve human consciousness into the far future. Mars was just a vehicle for that. But now AI is a very important part of that goal. AI will necessarily be part of any future plan. And then came the oh-so-typical, increasingly tiresome part of most long journalist interviews: the interviewer constructs a straw-man version of Elon and argues against it. Elon carefully explained that he isn’t a raging far-right extremist, racist Nazi who kills puppies … and the journalist still didn't believe it. It is so effing tiresome. The lack of self-awareness on the part of journalists is off the charts. She complained about Elon’s supposed misperception of how dangerous London is, while remaining oblivious to the role she plays in creating the giant misperception of Elon as a person in her own writing. Elon defended his political views, saying he is for secure borders, locking up criminals, and balanced government spending, something even she had to admit didn’t sound crazy. And… that’s about it for an 85-minute interview. I couldn’t help but think that the next long-form interview Elon does should be conducted by an AI.

N.J. GOV. MIKIE SHERRILL “Last week I learned that a serious software error in New Jersey’s Motor Vehicle System led to the registration of roughly 6,600 people who indicated that they were not U.S. citizens between June 2023 and June 2024, almost three years prior to my taking office. These individuals answered ‘no’ when asked on a keypad whether they were a U.S. citizen when applying for drivers' licenses and identification cards, but through no fault of their own, the system registered them anyway.

Customers asked, we answered. See why Cisco is the fastest growing Microsoft Teams Rooms OEM and learn how you can harness this opportunity. Watch the Cisco Live US session replay:

Today, Pylon becomes the Agentic Support Platform. We’ve spent the last several years preparing for when models were good enough to build the workflows we always imagined and now we’re launching a set of new primitives that we think will change the way you do customer work. With Background Agents you’ll be able to work on your whole queue at once and show up to each customer issue with an investigation already complete. Combined with Skills, you’ll be able to build and deploy your own custom workflows to reduce tedious work: file feature requests automatically, update your knowledge base when new features are shipped, and escalate to engineering with full context. With Assist Agent you’ll be able to go into interactive mode, pushing an investigation forward, digging into the logs and codebase, and learning automatically from past investigations. With Slack Agent you can give customer insights to your whole org - anyone in Slack can get their questions about your product or your customers answered all from Pylon’s growing knowledge of how you operate. We’ve been lucky to work with some great customers as part of our initial beta, including Mike from Cognition, Bre from Hex, and Regie from Goodtime and we’re excited to expand our beta to even more existing and future Pylon customers :) See how it all fits together on July 20. Register here: This is just the start of many powerful new features that will help you operate at lightspeed.

Pytest for Google ADK agents! Testing AI agents is fundamentally different from testing regular code. There's no single output to assert against. Every run can produce a different response. And when something goes wrong, it's rarely obvious which step caused it. AI agents built using Google ADK run across multiple steps. A model call plans the action. A tool executes it. Another model call generates the final response. Any of these can quietly go wrong without an obvious error. DeepEval is an open-source LLM evaluation framework, for evaluating large-language model systems. DeepEval's Google ADK integration brings Pytest to this problem. One call to "instrument_google_adk()" and every agent run is automatically traced. Every model call, tool invocation, and agent step becomes a component span you can evaluate independently. Testing works at two levels. End-to-end evaluation scores the full agent run on task completion. Component-level evaluation attaches metrics to individual LLM calls or tool spans, so you know exactly which step failed when a test breaks. The pytest integration works the same way as any other DeepEval framework. Parametrize your test with Goldens, run the agent inside the test function, call "assert_test()" at the end. A failing metric fails the test, which fails the build. Run it with "deepeval test run". You can also run evals outside CI. Loop through Goldens in a script, run the agent, and score each resulting trace without touching pytest at all. Key capabilities: • Auto-instrumentation with one function call • Trace, agent, LLM, and tool spans all independently evaluable • Native pytest integration with assert_test() for CI/CD • Metrics: TaskCompletion, AnswerRelevancy, Faithfulness, G-Eval and more • Script-based eval outside of CI • Optional Confident AI dashboard for trace visualization 100% open source. I've shared the link in the replies!