WrenIndian

@yankeedraft It is a tragedy

Man shares his paycheck showing he makes $160 after child support.

UPDATED SOFTWARE COMP SHEET Rule of 40 breakdown: • Best-in-class (60%+) | $PLTR, $APP • Elite (50-59%) | $NOW, $CRWD, $PANW • Great (40-49%) | $SNOW, $DDOG, $ZS, $ADBE, $CRM, $NET, $RBRK, $TEAM • Good (30-39%) | $MNDY, $HUBS, $MDB, $FIG, $PATH, $ZETA

@VladBastion The best investors focus on: normalized margins through-cycle free cash flow inventory levels supply discipline long-term demand sustainability A PEG ratio alone is not enough.

10 US Mega Caps with the lowest Price/Earnings-to-Growth (PEG) 💾 SanDisk <0.1 $SNDK 💾 Micron <0.1 $MU ☁️ Salesforce 0.2 $CRM 📡 Broadcom 0.2 $AVGO ☁️ ServiceNow 0.2 $NOW 🏢 Synopsys 0.2 $SNPS 💊 AbbVie 0.2 $ABBV 💽 Western Digital 0.2 $WDC 🎨 Adobe 0.3 $ADBE 💾 AMD 0.3 $AMD PEG ratios lower than 1.0 are considered better, indicating that a stock is relatively undervalued.

A breakdown of the photonics value chain and key business: Layer 1 - Materials & wafers: Raw material gets converted into ultra clean surfaces for light based devices. It's like the canvas before anyone paints. Key businesses: • $GLW • $AXTI • $IQE • $AIXA • $AMS Layer 2 - Core photonic devices: where the physics happens. Layer 2 converts electricity into light and light back into electrical signals. Key businesses: • $IPGP • $COHR • $LITE • $LASR • $SIVE Layer 3 - Components & modules: this layer takes the photonic devices from Layer 2 and packages them into something a customer can actually plug in. Key businesses: • $AAOI • $MTSI • $FN • $VIAV • $LPTH Layer 4 - Systems & equipment: builds the platforms for production, sensing, imaging, inspection and data movement Key businesses: • $ASML • $BESI • $ASM • $LPKF • $MKS Layer 5 - Test, metrology & yield: Every device and module that moves through the four layers above eventually arrives here. Layer 5 decides whether it meets specification and is ready for mass production. Key businesses: • $CAMT • $FORM • $AEHR • $ONTO • $VIAV If you found this helpful, the linked deep dive below goes into much more detail, also ranking all these businesses on quality and constraint severity.

The basics of Ohm’s Law. Ohm’s Law explains the relationship between voltage, current, and resistance in an electric circuit. Where: V = Voltage, measured in volts I = Current, measured in amperes R = Resistance, measured in ohms In simple terms: Voltage is the push that moves electricity. Current is the flow of electricity. Resistance is what opposes or slows down the flow. So, if voltage increases, current increases. If resistance increases, current decreases.

Hot chips 🔥🔥🔥

Most people are focused on silicon. The real bottleneck nobody’s talking about? Indium Phosphide. $NVDA needs faster chips. Faster chips need faster interconnects. Faster interconnects need InP-based lasers. And InP supply is critically constrained. WHAT IS InP AND WHY DOES IT MATTER? Indium Phosphide is the substrate behind high-speed optical components — the lasers and photodetectors moving data at 800G and 1.6T speeds inside AI data centers. Silicon simply can’t do what InP does at these speeds. No InP = No optical interconnects = AI infrastructure hits a wall. THE FULL InP VALUE CHAIN 𝗦𝘂𝗯𝘀𝘁𝗿𝗮𝘁𝗲𝘀 & 𝗪𝗮𝗳𝗲𝗿𝘀 → $AXTI — one of the only publicly traded InP substrate suppliers in the West → $IQEPF — epitaxial wafer supplier feeding InP laser production 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 & 𝗟𝗮𝘀𝗲𝗿𝘀 → $COHR — vertically integrated, owns InP laser fabs → $IPGP — fiber and InP laser exposure → $AAOI — transceiver/laser components → $LITE — InP laser supplier to hyperscalers 𝗦𝗶𝗹𝗶𝗰𝗼𝗻 𝗣𝗵𝗼𝘁𝗼𝗻𝗶𝗰𝘀 / 𝗙𝗼𝘂𝗻𝗱𝗿𝗶𝗲𝘀 → $TSEM — InP photonics foundry capabilities → $GFS — compound semiconductor exposure 𝗣𝘂𝗿𝗲-𝗣𝗹𝗮𝘆 𝗦𝗽𝗲𝗰𝘂𝗹𝗮𝘁𝗶𝘃𝗲 → $POET — optical interposer platform designed to integrate InP lasers at scale, potentially solving the bottleneck directly → $LWLG — electro-optic polymer platform, InP alternative play → $ALMU / $SIVEF — smaller speculative names in the photonics supply chain 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 → $AEHR — wafer-level burn-in testing for photonic chips → $KEYS — optical and high-speed signal testing WHY SUPPLY CAN’T JUST SCALE OVERNIGHT InP isn’t silicon. → Only a handful of facilities globally can produce InP substrates → Building new InP fabs takes years and billions → Geopolitical concentration risk is real — much of the supply chain runs through Asia → Hyperscaler demand for 800G/1.6T is accelerating faster than supply can respond This is a structural bottleneck — not a temporary one. THE INVESTMENT THESIS IN ONE LINE The AI buildout runs through InP. And InP supply is stuck. The picks-and-shovels play isn’t just $NVDA. It might be the substrate nobody’s heard of yet. Not financial advice.

SpaceX $SPCX IPO — June 12. Biggest IPO in history. Know your map. 🛸 Launch → $RKLB $FLY 🛰️ Imaging → $PL $SATL $BKSY $SPIR $HAWK $GSAT 📡 Satellite Comms → $ASTS $IRDM $VSAT $SATS $TSAT $ETL $GILT $SIDU 🏗️ Infrastructure → $LUNR $RDW $MDA $VOYG 🔩 Components → $KRMN $TDY $APH $HEI $ATRO $DCO $VELO $RBC $AME $PH $GHM ⚙️ Materials → $CRS $MTRN $HXL $ATI $GLW $PKE 🛡️ Aerospace & Defense → $LMT $RTX $NOC $KTOS $LHX $BA $AIR $HO The rising tide lifts all rockets. Not financial advice.

Current backlog across the space ecosystem: • $RKLB: ~$2.2B • $FLY: ~$1.3B • $ASTS: ~$1.2B • $LUNR: ~$1.1B • $PL: ~$900M • $RDW: ~$498M • $BKSY: ~$380M • $VOYG: ~$275M • $SPIR: ~$185M • $SATL: ~$77M

CEO $NVDA says to buy sustainable energy stocks. $ENLT is the strongest with price target $400+ Its spiked $15 to $90 for 600% already. Right now, these 16 stocks have the exact set-up: 1. $PLUG — Price: ~$3.76 | Target: $30 Green hydrogen fuel cells deliver clean, on-site backup power for AI data centers bypassing overloaded grids entirely. 2. $FLNC — Price: ~$20 | Target: $65 Grid-scale battery storage keeps renewable power stable and uninterrupted for 24/7 AI data center operations. 3. $ARRY — Price: ~$10 | Target: $40 Solar tracking systems maximize output at utility farms directly powering AI data center campuses nationwide. 4. $SHLS — Price: ~$10 | Target: $32 Electrical balance-of-system components connect large solar farms to the grid that powers AI infrastructure. 5. $RUN — Price: ~$14.00 | Target: $50 Distributed home solar and storage cuts grid strain during peak AI-driven electricity demand surges. 6. $CSIQ — Price: ~$17.87 | Target: $80 Utility-scale solar modules and grid-scale battery storage systems feed clean power into AI-hungry electrical grids. 7. $BEP — Price: ~$35 | Target: $200 Global hydro, wind, solar & nuclear operator $BE partnered with Bloom Energy in a $5B deal to co-build AI power factories. 8. $CWEN — Price: ~$38 | Target: $120 10+ GW of contracted wind, solar & storage sells clean baseload power directly to hyperscaler data centers. 9. $JKS — Price: ~$25.00 | Target: $50 One of the world's largest solar manufacturers supplying panels to utility farms feeding the AI power grid. 10. $DQ — Price: ~$20.00 | Target: $35 Polysilicon feedstock producer enabling solar panel manufacturing that powers AI data center campus buildouts. 11. $HASI — Price: ~$42 | Target: $80 Finances solar, wind & storage projects supplying contracted clean power directly to AI data center operators. 12. $EOSE — Price: ~$8 | Target: $24 Long-duration zinc batteries solve renewable intermittency enabling always-on clean power for non-stop AI workloads. My top 3 favorite ones to buy and hold would be $PLUG, $ENLT and $BEP since they have a deal with $BE. BONUS, I really like $ENPH (look how beaten down it is) at $53. It could run towards $300+ again. ♻️RESHARE this post and make 1 comment for my list of sustainable energy companies under $10. There's only 5 good ones like $PLUG to choose from.

Does this table look painful to make? It was painful.

The market leaves clues everywhere. Patterns. Structure. Momentum. Liquidity. Most traders just never learn how to read them.

Every data center, LNG terminal, and gas power plant being built right now needs one of these three machines. The 3 H-class platforms competing for the largest power buildout in a generation: ⚡ GE Vernova 9HA.02 838 MW | 64.1% efficiency | 88 MW/min ramp | <30 min hot start 178+ units deployed | 3M+ operating hours ⚡ Siemens Energy SGT5-9000HL ~880 MW | >64% efficiency | >600°C steam conditions The thermodynamic frontier of this class ⚡ Mitsubishi Power M701JAC 840 MW | >64% efficiency | 50% turndown Reliability-driven, maximum operational flexibility Risk-adjusted final ranking: 🥇 GE Vernova 9HA.02 fleet scale, operational maturity, execution certainty 🥈 Siemens SGT5-9000HL best engineering and thermodynamic design 🥉 Mitsubishi M701JAC best balanced, conservative reliability approach Qatar is offline. Russian gas is sanctioned. Iran is at war. American gas 110 BCF/day is the only large scale, stable supply left standing. And domestic demand is about to surge on top of that. By 2035, US data centers alone will consume 6.5 BCF/day — 10% of today's entire US output, added on top just for AI. 18.7 GW of new CCGT capacity going in by 2028. Every gigawatt needs one of these machines. Every machine needs gas. Every molecule needs infrastructure to move it. The turbine decision and the midstream decision are the same decision. One turbine OEM owns that answer on the power side. One midstream operator is positioned to own it on the infrastructure side. The full analysis including which company captures the most of those contracts is in my article. Link in the comments 👇

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐂𝐏 (𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥)? Most AI agents are trapped inside their own walls. MCP is the protocol that connects them to the outside world data sources, tools, and workflows. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐂𝐏? • MCP is an open-source standard that connects AI applications to external systems like data sources, tools, and workflows. • It enables seamless integrations, allowing AI models like ChatGPT to access data, use tools, and perform tasks like web app creation or database queries. • MCP simplifies development, reducing complexity and time by providing a standardized way to connect AI systems to various resources. • It enhances AI capabilities, making models more powerful and personalized by allowing them to interact with external systems and data on behalf of users. 𝐁𝐞𝐟𝐨𝐫𝐞 𝐌𝐂𝐏 LLM → Slack, Google Drive, GitHub (separate connections for each). Every integration is custom. Every tool requires its own API client. Every agent reinvents the wheel. 𝐀𝐟𝐭𝐞𝐫 𝐌𝐂𝐏 LLM → Unified API (MCP) → Slack, Google Drive, GitHub. One protocol. One connection layer. Every tool accessible through a standardized interface. 𝐇𝐨𝐰 𝐌𝐂𝐏 𝐖𝐨𝐫𝐤𝐬? User → User Query → MCP Client → Invoke Graph → LangGraph → Route Request → OpenAI GPT → Tool Decision → Call MCP Tool → MCP Server → External API Call → External APIs → API Response → MCP Server → Tool Result → OpenAI GPT → Generate Response → MCP Client → Natural Language Response → Final Result User → Agent Response → User. 𝐓𝐡𝐞 𝐅𝐥𝐨𝐰 1. User sends a query to the MCP Client. 2. MCP Client invokes LangGraph to route the request. 3. OpenAI GPT makes a tool decision and calls the MCP Tool. 4. MCP Server makes an external API call to the appropriate service (Slack, Google Drive, GitHub, etc.). 5. External API returns a response to the MCP Server. 6. MCP Server sends the tool result back to OpenAI GPT. 7. OpenAI GPT generates a natural language response. 8. MCP Client delivers the final result to the user. Before MCP, every agent built its own integrations. After MCP, every agent shares the same connection layer. MCP is the protocol that turns isolated AI models into connected AI agents. 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐜𝐮𝐬𝐭𝐨𝐦 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐫 𝐰𝐢𝐭𝐡 𝐌𝐂𝐏? ♻️ Repost this to help your network get started Cc : respective author.

With SpaceX $SPCX set to trade on June 12th Here are 10 space stocks that I think everyone should have an eye on 1) $RKLB – Provides end to end launch and space services, positioning you to benefit from growing commercial and government demand for access to orbit 2) $ASTS – Builds space based cellular broadband, giving you exposure to a potential breakthrough in global mobile connectivity from satellites 3) $RDW – Supplies critical aerospace and defense components, offering you steadier, contract driven growth tied to government and institutional spending 4) $PL – Operates satellites and payload delivery systems, letting you invest in recurring revenue from Earth observation and data services 5) $SIDU – Develops emerging space hardware, giving you a high risk, high reward bet on early stage space manufacturing 6) $SATL – Expands satellite production and deployment, positioning you for growth as demand for small sat constellations increases 7) $SPIR – Provides space data, analytics, and defense focused solutions, offering exposure to software driven growth within aerospace. 8) $FLY – Space and defense technology company providing comprehensive mission solutions to national security, government, and commercial customers 9) $LUNR – Develops lunar and orbital mission systems, offering speculative upside tied to renewed moon exploration and deep space programs. 10) $BKSY – Delivers space based intelligence and monitoring services, giving you early exposure to geospatial data used by governments and enterprises Do you own any of these stocks?

This week’s top performers $SEDG $FCEL $VPG $MRAM $AEVA $RKLB $NBIS $SATL $POET $ENPH $OUST $WOLF $CSCO $PLUG $NVTS $NXT $INOD $SMTC $UMC $FPS $KEEL $ON $NOK $VSH $FTNT $HIMX $MYRG $VICR $ACMR $TTMI $AXTI $PENG $BE $TXN $MTSI $MRVL $DDOG $STM $PWR $APLD $SIMO $IESC $SYNA $AKAM $STX

Capital is flowing exactly where the government wants growth. Bookmark this. Photonics → $AAOI $AXTI $AEHR $LITE Space → $RKLB $ASTS $LUNR $PL AI Inference → $AMD $ARM $INTC $RMBS Power Semis → $VICR $NVTS $MPWR $ON AI Infra → $NBIS $VRT $IREN Energy → $BE $CEG $VST Robotics → $VPG $SYM $OUST Defense → $ONDS $KTOS $AVEX Rare Earths → $MP $UUUU $USAR The biggest money is made paying attention early.

10 stocks with huge insider buys: 1/ $TTD: $148M 2/ $CPNG: $137M 3/ $KKR: $51M 4/ $FIG: $37M 5/ $GME: $22M 6/ $OSCR: $12M 7/ $PANW: $10M 8/ $RDDT: $9M 9/ $MU: $8M 10/ $MSCI: $6M

🚨PERFECT TRADING ROUTINE (9:00 AM – 3:15 PM) Stop guessing. Start following a system.