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Pro Skye Simple Flash Lineups on Bind 1 - Defense Shower Open Flash 2 - Defense B Long Push Flash 3 - Attack Shower Default Flash (Original by Subroza / rec GEN ZynX use)

Xive

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64,981 views • 7 months ago •via X (Twitter)

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Alex Banks

26,471 views • 11 months ago

"The screener's job is simple: force the over" - Ben McCollum 1) Angles Matter: Start Below, Stay Below 2) Fix It If You Whiff Or The Defense Gets Under Easy Fixes ⬇️⬇️ "Flip" the screen: Quick Rescreen "Flash": Hit the screener and chase it for a get

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John Leonzo

33,329 views • 3 months ago

Gemini 2.5 Flash can control a browser! Excited to share Gemini Browser Agent, a simple Python script example on how to use Google DeepMind Gemini 2.5 Flash and Browser Use to act as general assistant! 🤯 Usage Examples: 1⃣ Single Query Mode: `python scripts/gemini-browser-use.py --url --query "Summarize the key features of Gemini 2.5 Flash."` 2⃣Interactive Mode: Start an interactive session, optionally with a starting URL. `python scripts/gemini-browser-use.py` Command-line options: --model: The Gemini model to use (default: gemini-2.5-flash-preview-04-17) --headless: Run the browser in headless mode --url: Starting URL for the browser to navigate to before processing the query --query: Run a single query and exit (instead of interactive mode) Time to build a replication of Manus and OpenAI Operator powered by Gemini 2.5. Code below ⬇️

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Philipp Schmid

105,308 views • 1 year ago

UPDATE: Charlie Kirk 🚨 Muzzle Flash: Second Shooter Location, Reflection, or Both? This video captures a flash on a window right as Charlie Kirk was shot. Let’s break this down… follow the numbers on the videos. #1. This video shows what appears to be a muzzle flash, just as Charlie is shot, leading people to believe the shooter was to Charlie’s right.. Notate the people to the left of the flash #2. This photo shows the location of the flash. Notate people to the left, on a middle-landing on a staircase. #3. This photo shows a clearer image showing both the middle-landing and the same location of the flash. It is a window. #4 . This video shows the building behind Charlie, which is a long hallway. This debunks any claims the shot came from here. — I zoom in where Charlie was — I zoom in on staircase — I zoom in on the window/flash — I zoom in on where suspected shooter was #5. I notate the flash reflection angles. — Video angle #1 notated — two reflection paths notated in relation to Video angle #1. 1. Reflection angle shows where we were told the shooter was. 2. Reflection angle shows mirrored angle, obviously where there is no reports of a shooter being in. 🔻 Final conclusion: This to me is very likely a muzzle flash reflection from a far off location. I am unsure of the reflection angle however, but this can be 100% proven if someone were to recreate the angles… maybe with a flash camera. If anyone is willing to, or has the means to do this (safely), this will either prove the shot came where the FBI said it came from, or it will prove there was a second shooter, in the direction of the mirrored angle.

UPDATE: Charlie Kirk 🚨 Muzzle Flash: Second Shooter Location, Reflection, or Both? This video captures a flash on a window right as Charlie Kirk was shot. Let’s break this down… follow the numbers on the videos. #1. This video shows what appears to be a muzzle flash, just as Charlie is shot, leading people to believe the shooter was to Charlie’s right.. Notate the people to the left of the flash #2. This photo shows the location of the flash. Notate people to the left, on a middle-landing on a staircase. #3. This photo shows a clearer image showing both the middle-landing and the same location of the flash. It is a window. #4 . This video shows the building behind Charlie, which is a long hallway. This debunks any claims the shot came from here. — I zoom in where Charlie was — I zoom in on staircase — I zoom in on the window/flash — I zoom in on where suspected shooter was #5. I notate the flash reflection angles. — Video angle #1 notated — two reflection paths notated in relation to Video angle #1. 1. Reflection angle shows where we were told the shooter was. 2. Reflection angle shows mirrored angle, obviously where there is no reports of a shooter being in. 🔻 Final conclusion: This to me is very likely a muzzle flash reflection from a far off location. I am unsure of the reflection angle however, but this can be 100% proven if someone were to recreate the angles… maybe with a flash camera. If anyone is willing to, or has the means to do this (safely), this will either prove the shot came where the FBI said it came from, or it will prove there was a second shooter, in the direction of the mirrored angle.

MJTruthUltra

10,665,164 views • 11 months ago

This week, we tested 3 latest models in our Game Arena Benchmark: → O3 → O4-mini → Gemini 2.5 Flash Across 4 games—Phoenix Wright, Sokoban, Candy Crush, and 2048—O3 dominated the zero-shot leaderboard, ranking #1 or #2 in nearly every task and outperforming previous SOTA models like O3-mini and Gemini 2.5 Flash. 🔥 Beyond our customized tests, we took on a real challenge: Sokoban (1989)—the classic, unforgiving original. 🗣️ Many say O3 shows strong image reasoning, but does it really? Let’s see what our Game Arena Benchmark reveals. 👇

This week, we tested 3 latest models in our Game Arena Benchmark: → O3 → O4-mini → Gemini 2.5 Flash Across 4 games—Phoenix Wright, Sokoban, Candy Crush, and 2048—O3 dominated the zero-shot leaderboard, ranking #1 or #2 in nearly every task and outperforming previous SOTA models like O3-mini and Gemini 2.5 Flash. 🔥 Beyond our customized tests, we took on a real challenge: Sokoban (1989)—the classic, unforgiving original. 🗣️ Many say O3 shows strong image reasoning, but does it really? Let’s see what our Game Arena Benchmark reveals. 👇

Hao AI Lab

15,105 views • 1 year ago

gemini 3.7 flash vs deepseek v4 pro 0813 vs muse spark 1.2 – on voxel city dioramas three models each built three crossy road-style 3d scenes – a construction site, a nyc intersection, a river with a drawbridge – as single self-contained html files the setup: Nous Research's hermes agent cli on OpenRouter, three.js skills preloaded, identical prompts per scene tasks: 1. construction site – tower crane on a working lift loop, paver laying fresh road, roller compacting it behind 2. nyc crossing – four-way intersection with a traffic light state machine, queuing cars, pedestrians crossing on the walk signal 3. river drawbridge – double-leaf bascule that lifts for tall boats, cars queuing at the barriers, animated water every scene: Three.js r185, box geometry only, a locked 20-color palette, four camera presets, and a day/night mode with bloom. one file, no build step, no assets models: Google DeepMind gemini 3.7 flash, DeepSeek v4 pro 0813, AI at Meta muse spark 1.2 muse and gemini finished every scene in two to three minutes. deepseek took 15 to 41 minutes per scene - build time, all three scenes #1 gemini 3.7 flash – 6m 43s #2 muse spark 1.2 – 7m 20s #3 deepseek v4 pro – 91m 25s - total tokens #1 muse spark 1.2 – 440,279 #2 gemini 3.7 flash – 713,855 #3 deepseek v4 pro – 20,957,568 - total price #1 muse spark 1.2 – $0.53 #2 gemini 3.7 flash – $0.56 #3 deepseek v4 pro – $4.57 - agent calls across the three builds #1 muse spark 1.2 – 12 #2 gemini 3.7 flash – 18 #3 deepseek v4 pro – 143 observations: • muse won two of the three scenes on looks with the smallest files in the test – 887 to 1,042 lines against gemini's 1,934 to 2,377. cheapest, fastest to a good frame, and shortest turned out to be the same column • deepseek burned 20.96m tokens – 29x gemini, 48x muse – across 143 agent calls. prompt caching is the only reason that cost $4.57: the cache discount absorbed roughly $30 of resent context • gemini was the only model whose files needed zero fixes to render – and the only one whose night mode is cosmetic. the sky never darkens and one camera button does nothing. clean code for a scene it never looked at follow thehype. for 24/7 ai news, analysis and breakdowns

gemini 3.7 flash vs deepseek v4 pro 0813 vs muse spark 1.2 – on voxel city dioramas three models each built three crossy road-style 3d scenes – a construction site, a nyc intersection, a river with a drawbridge – as single self-contained html files the setup: Nous Research's hermes agent cli on OpenRouter, three.js skills preloaded, identical prompts per scene tasks: 1. construction site – tower crane on a working lift loop, paver laying fresh road, roller compacting it behind 2. nyc crossing – four-way intersection with a traffic light state machine, queuing cars, pedestrians crossing on the walk signal 3. river drawbridge – double-leaf bascule that lifts for tall boats, cars queuing at the barriers, animated water every scene: Three.js r185, box geometry only, a locked 20-color palette, four camera presets, and a day/night mode with bloom. one file, no build step, no assets models: Google DeepMind gemini 3.7 flash, DeepSeek v4 pro 0813, AI at Meta muse spark 1.2 muse and gemini finished every scene in two to three minutes. deepseek took 15 to 41 minutes per scene - build time, all three scenes #1 gemini 3.7 flash – 6m 43s #2 muse spark 1.2 – 7m 20s #3 deepseek v4 pro – 91m 25s - total tokens #1 muse spark 1.2 – 440,279 #2 gemini 3.7 flash – 713,855 #3 deepseek v4 pro – 20,957,568 - total price #1 muse spark 1.2 – $0.53 #2 gemini 3.7 flash – $0.56 #3 deepseek v4 pro – $4.57 - agent calls across the three builds #1 muse spark 1.2 – 12 #2 gemini 3.7 flash – 18 #3 deepseek v4 pro – 143 observations: • muse won two of the three scenes on looks with the smallest files in the test – 887 to 1,042 lines against gemini's 1,934 to 2,377. cheapest, fastest to a good frame, and shortest turned out to be the same column • deepseek burned 20.96m tokens – 29x gemini, 48x muse – across 143 agent calls. prompt caching is the only reason that cost $4.57: the cache discount absorbed roughly $30 of resent context • gemini was the only model whose files needed zero fixes to render – and the only one whose night mode is cosmetic. the sky never darkens and one camera button does nothing. clean code for a scene it never looked at follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

22,330 views • 1 day ago

holy sh*t this is f**king insane I cancelled my $200/mo Claude for this I replaced opus 5 with deepseek v4-flash and someone figured out to how to use them as subagents on codex [it takes 3 mins to set up here is how] 1. install this repo called 'model-router' 2. toggle subagent models > 'all selected models' 3. that's it

holy sh*t this is f**king insane I cancelled my $200/mo Claude for this I replaced opus 5 with deepseek v4-flash and someone figured out to how to use them as subagents on codex [it takes 3 mins to set up here is how] 1. install this repo called 'model-router' 2. toggle subagent models > 'all selected models' 3. that's it

Avid

414,365 views • 13 days ago

🇺🇸🇯🇵🇮🇷 What if a Hiroshima-type atomic bomb hit a major Iranian city today? The moment a 15-kiloton bomb detonates, a blinding flash reaches for kilometers: -Within 1 km: total destruction -1 to 3 km: severe damage, mass casualties -3 to 5 km: fires, serious injuries, buildings collapsing -5 to 8 km: shattered windows, widespread injuries But the real threat is radiation. In Hiroshima and Nagasaki, many didn’t die instantly. They died later or lived with long-term effects. Source: Defense Intelligence

🇺🇸🇯🇵🇮🇷 What if a Hiroshima-type atomic bomb hit a major Iranian city today? The moment a 15-kiloton bomb detonates, a blinding flash reaches for kilometers: -Within 1 km: total destruction -1 to 3 km: severe damage, mass casualties -3 to 5 km: fires, serious injuries, buildings collapsing -5 to 8 km: shattered windows, widespread injuries But the real threat is radiation. In Hiroshima and Nagasaki, many didn’t die instantly. They died later or lived with long-term effects. Source: Defense Intelligence

Mario Nawfal

586,437 views • 4 months ago

NVIDIA is giving away free access to 130+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: MiniMax M2.7, GLM 5.1, Kimi 2.5, DeepSeek-v4-flash, GPT-OSS-120B and 110+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "minimaxai/minimax-m2.7" model = "zhipuai/glm-5.1" model = "moonshot-ai/kimi-2.5" model = "deepseek-ai/deepseek-v4-flash" model = "nvidia/nemotron-3-ultra-550b-a55b" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free

NVIDIA is giving away free access to 130+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: MiniMax M2.7, GLM 5.1, Kimi 2.5, DeepSeek-v4-flash, GPT-OSS-120B and 110+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "minimaxai/minimax-m2.7" model = "zhipuai/glm-5.1" model = "moonshot-ai/kimi-2.5" model = "deepseek-ai/deepseek-v4-flash" model = "nvidia/nemotron-3-ultra-550b-a55b" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free

Mr. Buzzoni

195,973 views • 2 months ago

Frontier AI went free this month. Not cheaper. Free. New models: > Gemini 3.6 Flash. 1,500 req/day, 1M tokens/min, no card > Kimi K3. 2.8T MoE open weights, shipped today. Largest open-weight model ever > DeepSeek V4. 1.6T MoE, MIT license, on HuggingFace > GLM-5.2. 753B MoE, 1M context, MIT license > Qwen 3.8-Max. Free preview, no login, no card > Mistral Vibe. Work Mode free. Agent features, scheduled tasks. ChatGPT and Claude charge $20/mo for the same upd (the article): > Gemini 3.5 → 3.6 Flash (July 21) > Claude $100 Fable 5 credit, claim by Aug 2 > Claude for Teachers: free through June 2027 > Malta: free ChatGPT Plus, 1 year. First country-wide deal > OpenAI: $100 credits for posting about ChatGPT Work > Perplexity Pro: 1 year free via Xfinity > Alibaba Cloud: 70M tokens + $200 credits > Grok 4.5 live across all platforms Chinese labs shipped 4 of 5 biggest open weights this month. OpenAI and Anthropic shipped credits. The access gap didn't close. It already closed.

Frontier AI went free this month. Not cheaper. Free. New models: > Gemini 3.6 Flash. 1,500 req/day, 1M tokens/min, no card > Kimi K3. 2.8T MoE open weights, shipped today. Largest open-weight model ever > DeepSeek V4. 1.6T MoE, MIT license, on HuggingFace > GLM-5.2. 753B MoE, 1M context, MIT license > Qwen 3.8-Max. Free preview, no login, no card > Mistral Vibe. Work Mode free. Agent features, scheduled tasks. ChatGPT and Claude charge $20/mo for the same upd (the article): > Gemini 3.5 → 3.6 Flash (July 21) > Claude $100 Fable 5 credit, claim by Aug 2 > Claude for Teachers: free through June 2027 > Malta: free ChatGPT Plus, 1 year. First country-wide deal > OpenAI: $100 credits for posting about ChatGPT Work > Perplexity Pro: 1 year free via Xfinity > Alibaba Cloud: 70M tokens + $200 credits > Grok 4.5 live across all platforms Chinese labs shipped 4 of 5 biggest open weights this month. OpenAI and Anthropic shipped credits. The access gap didn't close. It already closed.

ＺＥＦＩ

49,876 views • 24 days ago

hy3 vs mimo-v2.5 vs deepseek v4 flash vs minimax m3 the four models on top of the openrouter leaderboard by tokens this week: #1 hy3 (Tencent Hy) – 7.5t #2 mimo-v2.5 (Xiaomi MiMo) – 6.56t #3 deepseek v4 flash (DeepSeek) – 5.24t #4 minimax m3 (MiniMax (official)) – 4.21t so we tested them. 3 prompts, single-file html, Three.js from a cdn, fully procedural, no external assets. all run via AI/ML API each prompt is a transparent cutaway machine that has to be mechanically correct, not decorative: • 4-stroke engine with full oil circulation – slider-crank kinematics, cam at 2:1, valve lift driven by lobes, oil loop from sump to gallery to big-end • watt walking-beam steam engine – four-bar vector-loop closure, eccentric-driven slide valve, steam events synced to real port position • francis reaction water turbine – 20 guide vanes on a regulating ring, 17 lofted runner blades, gpu particle advection, precessing vortex rope at part load the takeaway up front: none of the four cleared all three scenes on the first attempt. but the price spread between them is roughly 70x – hy3 fixed included costs less than two cents overall results (summed across all 3 scenes): cost #1 hy3 – $0.016 #2 deepseek v4 flash – $0.025 #3 mimo-v2.5 – $0.97 #4 minimax m3 – $1.17 tokens #1 hy3 – 19,326 #2 deepseek v4 flash – 63,126 #3 mimo-v2.5 – 322,523 #4 minimax m3 – 702,900 lines of code #1 hy3 – 1,047 #2 mimo-v2.5 – 2,759 #3 deepseek v4 flash – 3,273 #4 minimax m3 – 3,354 scenes needing a second attempt #1 hy3 – 1 (engine) #1 mimo-v2.5 – 1 (turbine) #1 minimax m3 – 1 (turbine) #4 deepseek v4 flash – 2 (steam engine, turbine) observations: 1. the token spread is the real story – minimax burns 36x hy3's tokens and lands in the same place, one retry, ~3.3k lines 2. hy3 is the outlier on density: 1,047 lines total, fewest tokens, cheapest run, and only one scene needed a second pass. deepseek is the opposite trade – near-hy3 pricing but the most retries 3. mimo and minimax seem to overthink instead of writing the code. minimax spent 359.1k tokens on the steam engine and produced 1,346 lines – the tokens are going somewhere other than the file 4. the francis turbine broke three of the four. the spec that separates them is the one with 20 linked guide vanes and gpu particle advection, not the one with the most parts overall impression: none of these models excelled at any of the tasks we gave them. but they were close, and they were extremely cheap. the gap that matters isn't quality anymore – it's that hy3 ran all three scenes for less than two cents while the frontier labs charge dollars for the same work right now you pick these because they're good for the zero price you pay. soon that's something openai and anthropic will have to think about follow thehype. for 24/7 ai news, analysis and breakdowns

hy3 vs mimo-v2.5 vs deepseek v4 flash vs minimax m3 the four models on top of the openrouter leaderboard by tokens this week: #1 hy3 (Tencent Hy) – 7.5t #2 mimo-v2.5 (Xiaomi MiMo) – 6.56t #3 deepseek v4 flash (DeepSeek) – 5.24t #4 minimax m3 (MiniMax (official)) – 4.21t so we tested them. 3 prompts, single-file html, Three.js from a cdn, fully procedural, no external assets. all run via AI/ML API each prompt is a transparent cutaway machine that has to be mechanically correct, not decorative: • 4-stroke engine with full oil circulation – slider-crank kinematics, cam at 2:1, valve lift driven by lobes, oil loop from sump to gallery to big-end • watt walking-beam steam engine – four-bar vector-loop closure, eccentric-driven slide valve, steam events synced to real port position • francis reaction water turbine – 20 guide vanes on a regulating ring, 17 lofted runner blades, gpu particle advection, precessing vortex rope at part load the takeaway up front: none of the four cleared all three scenes on the first attempt. but the price spread between them is roughly 70x – hy3 fixed included costs less than two cents overall results (summed across all 3 scenes): cost #1 hy3 – $0.016 #2 deepseek v4 flash – $0.025 #3 mimo-v2.5 – $0.97 #4 minimax m3 – $1.17 tokens #1 hy3 – 19,326 #2 deepseek v4 flash – 63,126 #3 mimo-v2.5 – 322,523 #4 minimax m3 – 702,900 lines of code #1 hy3 – 1,047 #2 mimo-v2.5 – 2,759 #3 deepseek v4 flash – 3,273 #4 minimax m3 – 3,354 scenes needing a second attempt #1 hy3 – 1 (engine) #1 mimo-v2.5 – 1 (turbine) #1 minimax m3 – 1 (turbine) #4 deepseek v4 flash – 2 (steam engine, turbine) observations: 1. the token spread is the real story – minimax burns 36x hy3's tokens and lands in the same place, one retry, ~3.3k lines 2. hy3 is the outlier on density: 1,047 lines total, fewest tokens, cheapest run, and only one scene needed a second pass. deepseek is the opposite trade – near-hy3 pricing but the most retries 3. mimo and minimax seem to overthink instead of writing the code. minimax spent 359.1k tokens on the steam engine and produced 1,346 lines – the tokens are going somewhere other than the file 4. the francis turbine broke three of the four. the spec that separates them is the one with 20 linked guide vanes and gpu particle advection, not the one with the most parts overall impression: none of these models excelled at any of the tasks we gave them. but they were close, and they were extremely cheap. the gap that matters isn't quality anymore – it's that hy3 ran all three scenes for less than two cents while the frontier labs charge dollars for the same work right now you pick these because they're good for the zero price you pay. soon that's something openai and anthropic will have to think about follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

17,145 views • 1 month ago

Last Fridays “flash crash” is the exact type of pullback that many make generational wealth on… Following this; just yesterday President Trump announced he’ll buy equity stakes in US, AI companies. These are the 6 best “dip-buy” opportunities within the AI theme: 1. Nebius ~ $NBIS 2. Iren ~ $IREN 3. CoreWeave ~ $CRWV 4. Nvidia ~ $NVDA 5. Qualcomm ~ $QCOM 6. Oracle ~ $ORCL If you missed the Iran-US war crash then don’t make the same mistake twice. Prices this low won’t last long…

Last Fridays “flash crash” is the exact type of pullback that many make generational wealth on… Following this; just yesterday President Trump announced he’ll buy equity stakes in US, AI companies. These are the 6 best “dip-buy” opportunities within the AI theme: 1. Nebius ~ $NBIS 2. Iren ~ $IREN 3. CoreWeave ~ $CRWV 4. Nvidia ~ $NVDA 5. Qualcomm ~ $QCOM 6. Oracle ~ $ORCL If you missed the Iran-US war crash then don’t make the same mistake twice. Prices this low won’t last long…

Mike Investing

119,147 views • 2 months ago

The "Google Insider" is back. He is 5-0 on google search trends, and his new active bet is a total moonshot. This wallet already printed 10x-15x on "unpredictable" search trends and product delays. > d4vd #1 searched person on Google (avg 7c) > Anora #1 searched movie on Google (avg 8c) > Gemini 3.0 Flash not released by December 15? (avg 23c) > Terence Crawford #1 searched athlete (avg 35c) > Kendrick Lamar #2 searched person (avg 21c) Now, his ACTIVE BET is live: Gemini 3 scoring 50%+, 60+%, 70+% on FrontierMath by Jan 31. If this predictions hits, he stands to net an additional $26,000 in profit. Check his bets here ->

The "Google Insider" is back. He is 5-0 on google search trends, and his new active bet is a total moonshot. This wallet already printed 10x-15x on "unpredictable" search trends and product delays. > d4vd #1 searched person on Google (avg 7c) > Anora #1 searched movie on Google (avg 8c) > Gemini 3.0 Flash not released by December 15? (avg 23c) > Terence Crawford #1 searched athlete (avg 35c) > Kendrick Lamar #2 searched person (avg 21c) Now, his ACTIVE BET is live: Gemini 3 scoring 50%+, 60+%, 70+% on FrontierMath by Jan 31. If this predictions hits, he stands to net an additional $26,000 in profit. Check his bets here ->

AshenSoul

225,414 views • 7 months ago

How to Weight Semiconductors 1) GPUs $NVDA $AMD 2) CPUs $AMD $INTC $NVDA 3) TPUs $AVGO 4) Foundries $TSM $INTC 5) RAM Memory $MU 6) NAND Flash Memory Chips $MU 7) SSDs Flash Memory Finished Drives $MU $SNDK 8) HDDs (Slower long term storage) $WDC All these semi sub sectors are critical to the AI buildout. I think it makes sense to have exposure to all of them. The first question is what percent of your portfolio do you want to be semis. The second is how do you split up that weight amongst these companies. By market cap? Or do you overweight certain names like $AMD if you are bullish on CPUs. Do you utilize a active ETF that does this for you and with tax efficiency to improve returns? These are important questions to ask yourself. Marcos Milla and I Discuss.

How to Weight Semiconductors 1) GPUs $NVDA $AMD 2) CPUs $AMD $INTC $NVDA 3) TPUs $AVGO 4) Foundries $TSM $INTC 5) RAM Memory $MU 6) NAND Flash Memory Chips $MU 7) SSDs Flash Memory Finished Drives $MU $SNDK 8) HDDs (Slower long term storage) $WDC All these semi sub sectors are critical to the AI buildout. I think it makes sense to have exposure to all of them. The first question is what percent of your portfolio do you want to be semis. The second is how do you split up that weight amongst these companies. By market cap? Or do you overweight certain names like $AMD if you are bullish on CPUs. Do you utilize a active ETF that does this for you and with tax efficiency to improve returns? These are important questions to ask yourself. Marcos Milla and I Discuss.

John Tinsman

47,139 views • 3 months ago

Stop paying for Claude Code. You can run it for FREE. No API tokens. No monthly plan. No cloud costs. Here’s the simple setup: 1. Download Ollama (free). 2. Install a local model like GLM 4.7 Flash. 3. Launch Claude Code using that local model. 4. Done. You’re now running Claude Code fully powered by a local model. Same interface. No API bill. You can even run OpenClaw the same way. Local AI agents. Free. 24/7. Most people are overpaying because they don’t know this exists.

Stop paying for Claude Code. You can run it for FREE. No API tokens. No monthly plan. No cloud costs. Here’s the simple setup: 1. Download Ollama (free). 2. Install a local model like GLM 4.7 Flash. 3. Launch Claude Code using that local model. 4. Done. You’re now running Claude Code fully powered by a local model. Same interface. No API bill. You can even run OpenClaw the same way. Local AI agents. Free. 24/7. Most people are overpaying because they don’t know this exists.

Julian Goldie SEO

103,844 views • 5 months ago