Tesla launches Model Y L in US — 6 seats, 325 miles, $61,990 | Electrek
Tesla's 3-row Model Y L just launched in the US — why it will become the BEST-selling EV - YouTube
Thursday, July 02, 2026
Tesla Semi for price of Model Y?
SpaceX Is About to Take Over The Internet - YouTube
The video explains that while the estimated price of a Tesla Semi is approximately $290,000, qualifying businesses in California can significantly reduce this cost through two stackable incentive programs (11:10 - 11:56).
- Clean Truck Voucher Program (HVIP): This program provides up to $120,000 per truck (11:30 - 11:35).
- California Clean Fuel Reward: This newer incentive offers an additional $120,000 (11:39 - 11:43).
By stacking these incentives, eligible small fleets can reduce the purchase price of a Semi by as much as $240,000, bringing the cost down to roughly $50,000 (11:45 - 11:58). The speaker emphasizes that this helps remove a major barrier to adoption—the upfront purchase price—while offering lower operating costs over the life of the vehicle compared to diesel trucks (12:34 - 13:17).
free training: Agentic AI Foundations by Oracle
Oracle Agentic AI Foundations: Get skilled for the Agentic AI Era | oracleuniversity
By the end of the course, you will be able to:
The course is organized into six modules that build on each other from first principles to enterprise deployment.
Module 1: Introduction to AI Agents
The mental model for everything that follows: an LLM-based agent is an LLM plus tools plus a loop. We cover what makes an agent goal-directed, autonomous, tool-using, and iterative; the core reasoning patterns (Chain-of-Thought and ReAct); a walkthrough of your first agent; and a layered, defense-in-depth approach to safety and guardrails.
Module 2: LangChain for AI Agents
This module introduces LangChain and the LangChain Expression Language (LCEL). You’ll build your first agent, then go under the hood to see what a single agent.invoke() call is really doing: building tool schemas, parsing tool calls, executing functions, and deciding whether another model call is needed. That’s the understanding you need to debug agents and move them to production.
Module 3: Introduction to MCP
The Model Context Protocol (MCP) gives agents a standard way to connect to tools, data, and prompts. We cover the MCP architecture and core components, then add an MCP server to your agent starting with a simple local math server and moving to a real-world OCI Usage MCP server. The takeaway: MCP decouples agents from tool implementations, enabling interoperability, discovery, and reuse at scale.
Module 4: OpenAI Responses API and Agents SDK
This module covers the OpenAI Agent stack and how to choose between its pieces – the Responses API for simpler, single-call use, and the Agents SDK for multi-step logic, multiple agents, guardrails, and tracing. We cover tools and function calling, multi-agent systems and handoffs, and safety, then put it together in a multi-agent customer-support system that routes requests to specialized agents.
Module 5: Agentic AI for Enterprises
Building an agent is one thing; running it reliably is another. This module introduces the OCI Enterprise AI platform and OCI Enterprise AI Agents, and the division of labor: you focus on the agent’s instructions, tools, knowledge bases, and outcome, while OCI handles hosted endpoints, scaling, memory and sessions, sandboxed tools, logging, and integrations. We finish with a discussion on how to build agents using OCI Enterprise AI Platform.
Module 6: Agentic AI for Oracle AI Database
This module focuses on bringing agents to your data. We cover Oracle AI Vector Search and its workflow, the Oracle AI Database Private Agent Factory, the Select AI Agent for building agents that live inside the database, and the Oracle Autonomous AI Database MCP Server – showing how agentic capabilities can run close to your data, governed by the security you already rely on.
AI in space: Planetary Intelligence
This episode of Moonshots explores the convergence of AI, space infrastructure, and planetary intelligence. Here are the key points discussed:
- Large Earth Models (LEM): Will Marshall (CEO of Planet) explains how Planet is transitioning from a satellite imagery company to a provider of "Large Earth Models." By leveraging 150 petabytes of historical data, they aim to make the Earth searchable like the internet (05:30 - 07:50).
- The Power of Real-World Data: Unlike LLMs trained on theory and text, LEMs are being trained on actual sensor data to provide predictive analysis for agriculture, disaster response, and national security (16:06 - 18:09).
- Compute in Space: A major theme is the potential to move compute power into orbit. By processing data at the edge (on the satellites), companies can reduce response times for critical events like wildfires from hours to minutes (36:00 - 36:42).
- Efficiency and Infrastructure: The discussion highlights that while launch costs are dropping, the true revolution is in satellite performance density and energy-efficient chips (like Google’s TPUs). The ability to perform inference efficiently is identified as a critical competitive advantage in the AI-space race (50:24 - 52:32, 118:51 - 120:30).
- Openweight vs. Closed AI: The panel discusses the rise of Chinese models like GLM 5.2, which demonstrates near-competitive performance to Western frontier models at half the cost, signaling that frontier intelligence cannot be easily monopolized (201:50 - 208:35).
- The Great Filter & Existential Risk: The guests touch on the Fermi Paradox, suggesting that the "Great Filter" could be the stage where a civilization builds technology faster than its social systems can manage, highlighting the need for thoughtful AI alignment (211:02 - 212:13).
house construction: MgO boards
Magnesium oxide, more commonly called magnesia, is a mineral that when used as part of a cement mixture and cast into thin cement panels under proper curing procedures and practices can be used in residential and commercial building construction. Some versions are suitable for general building uses and for applications that require fire resistance, mold and mildew control, as well as sound-control applications. Magnesia board has strength and resistance due to very strong bonds between magnesium and oxygen atoms that form magnesium oxide crystals (with the chemical formula MgO).
Magnesia boards are used in place of traditional gypsum drywall as wall and ceiling covering material and sheathing.
What Materials Are Used in Boxabl Homes
The Ultimate Guide to MgO Structural Insulated Panels: Building Smarter with Innovation
Magnesium Oxide Boards | MGO Panels | Magpanel
The Future of Building with MGO SIPS - YouTube
This video introduces MGO (Magnesium Oxide) SIP panels from SRD Supreme as a high-performance, sustainable solution for modern construction. Key benefits highlighted include:
- Durability & Health: The panels are structurally strong, mold-resistant, rot-resistant, and free of off-gassing (0:05-0:15).
- Safety: They offer superior fire resistance compared to traditional building materials (0:17-0:24).
- Environmental Impact: Designed for sustainability, the panels help achieve a lower carbon footprint, generate less on-site waste, and last for decades, which minimizes the need for future rebuilds (0:26-0:39).
Overall, SRD Supreme promotes a shift toward stronger, safer, and greener building practices (0:40-0:46).
Subscribe to:
Posts (Atom)