I added to $AEHR today as well. Here's my thesis why: The growth inflection: FY2027 revenue guided to $130–150M vs. FY2026's $50M — 160–200% growth Guidance carries an 18–22% pre-tax margin, so this is profitable growth, not revenue bought with margin Demand is contracted, not hoped for: Record Q4 bookings of $60.7M, up ~500% YoY Effective backlog of $100.6M — covering ~72% of the FY27 guidance midpoint The end-market pivot is the real story ~95% of revenue now from AI processors, silicon photonics and power semis — versus 95% EV silicon carbide two years ago AI expected to be ~70% of FY2027 revenue, silicon photonics ~15% Valuation is the main pushback: ~21x guided FY27 sales at recent prices, and one bear case puts it at 59x forward FY2028 profit. I expect lumpy prints, not a smooth ramp but firmly believe this will be a large company in 2028! NFA. DYOR

This isn't The Addams Family! Still controllable even after leaving the body! China-made intelligent bionic hand makes its debut at WAIC2026, with lifelike synthetic skin that looks almost real, and "remote" control that is smooth and seamless. Netizens: "Detachable hand" becomes a reality — Cool! #Technology# #IntelligentBionics# #MadeInChina# #AI#

Nearly 1 in 4 young adults in the U.S. has high blood pressure. That number rises to 1 in 2 for adults aged 40 and over. One nutrient that may help modestly lower blood pressure is magnesium. Magnesium plays an important role in regulating vascular tone. It influences calcium signaling in vascular smooth muscle and pathways involving nitric oxide and prostacyclin, helping blood vessels relax and dilate. Meta-analyses of randomized controlled studies find that magnesium supplementation at a dose of around 370 mg/day for about 3 months reduces systolic and diastolic blood pressure by 2 mmHg each. Magnesium certainly isn’t a replacement for antihypertensive medication when medication is needed. But blood pressure is something we’re exposed to continuously, year after year. Small improvements maintained over decades may matter. That's why knowing your blood pressure (and getting it under control) is so important, even for people in their 20 and 30s.

Fourier showed that any periodic function can be built by adding sine waves. For example take a square wave: it follows a repeating up-down pattern over a fixed period. Even though sine waves are smooth, we can stretch, scale, and shift them. By adding multiple sine waves with different properties, we start to approximate the square wave. At first, it’s rough—but as we include more terms, the sum becomes increasingly accurate, eventually closely matching the square wave.

# Useful but Little-Known Features of ADK 2.0 🌍 How do you handle tasks that take minutes or hours within an agent workflow? ADK 2.0 has a dedicated class for exactly this. ADK 2.0's `LongRunningFunctionTool` manages the orchestration of long-running tasks. The heavy computation runs on separate servers while the agent handles progress tracking and state transitions. 📌 Title: Long Running Function Tool 🔗 URL: 🧩 Overview `LongRunningFunctionTool` is a class for integrating long-running tasks into agent workflows. It operates in four phases: initiation (kick off the task), initial updates (report progress), continue/wait (client decides the next action), and framework handling (the agent runner manages the pause). Crucially, this class manages orchestration — it does NOT execute the actual long-running task. The compute happens on separate servers. 🛠 How to use it The tool function returns a dict with status and ticket ID, and the framework manages the state. ```python from adk import LongRunningFunctionTool def start_training(model_name: str, dataset: str) -> dict: """Start a model training job.""" # Send request to external server ticket_id = external_api.start_job(model_name, dataset) return { "status": "in_progress", "ticket_id": ticket_id, "message": "Training started" } tool = LongRunningFunctionTool(func=start_training) ``` The agent runner pauses execution based on the returned state, and the client decides whether to continue or wait. The actual computation runs on a separate server — the tool function only handles progress checking and status reporting. 🏗 Building it into production ・Run actual heavy computation on dedicated compute servers; the tool function only makes API calls ・Build progress tracking with ticket IDs, using polling or webhooks for status updates ・Configure proper timeouts and error handling to detect task failures ・Implement continue/wait decision logic on the client side for smooth user experience 💡 Use cases 🤖 Manage and track ML model training jobs 🎬 Orchestrate time-consuming media processing like video encoding or rendering 📦 Run and monitor large-scale data ETL pipelines 🧪 Execute long-running test suites or benchmarks and retrieve results ⚠️ Watch out `LongRunningFunctionTool` manages orchestration (state management and progress tracking), not the actual long-running task execution. Always run heavy computation on separate servers or services. Pay attention to session expiration and memory management — long-running tasks risk session disconnection, so consider persisting state externally. ✨ Seamlessly integrating long-running tasks into agent workflows is a powerful capability for building practical AI systems. Keep the separation between compute and orchestration clean. #ADK# #AIAgent#

Blockchain Association CEO @SummerMersinger will step down on October 16, while remaining as an advisor through the end of the year to ensure a smooth transition. Board President @KristinSmith will serve as interim CEO beginning on October 17. We're grateful for Summer's leadership through a historic period for digital asset policy.

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💰💰Done. My chief of staff @bot just purchased from amazon and it was so smooth. 👀🚀 I typed in one of the items the boys need for school in Mallorquín and it translated it back to English added to my shopping cart and asked for the ok. I gave it the ok and I got an email from amazon saying purchased. Wow. This is my new way of shopping from now on. Damn I should have just took a photo of the whole list and said now go buy it but wanted to be sure this all worked. I will admit that I looked at the bots computer to ensure it was the right product. A screenshot in the chat might be a nice way to verify before a purchase just until we fully trust the bot to get it right. But. Still. This is all from mobile while multi tasking and I can close the app and just get notified when it needs my attention and thats why this is such an amazing experience