🇨🇭 Global One SI - Prompt
463 subscribers
13.4K photos
4.38K videos
23 files
2.43K links
🎗GLOBAL ONE is a live super intelligence (SI) built on real-time data—designed for decisions. Currently focused on global financial markets

t.me/Prompts_SI @Global_one - Software/Developer/Programmer - Globalone.ch - Prompt - AI - Super Intelligence
Download Telegram
This media is not supported in your browser
VIEW IN TELEGRAM
"If you really want to make money, found an agentic AI company.

I mean, build an agent to do something. This is the agentic period in AI. Everyone's going to build agents. The agents are all going to compete."

~ Eric Schmidt, Ex Google CEO.
✍1
🇨🇭 Global One SI - Prompt
$IBM just FANTASTIC #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
$IBM debuts world’s first sub-1 nanometer chip.

They just announced a 0.7nm chip technology.

(0.7nm is about 100,000 times thinner than a human hair, only a few atoms across as a technology marker,)

It stops treating smaller chips as a flat-layout problem and starts stacking transistors upward through a new nanostack architecture.

A chip node no longer means every feature has that exact size, but 7 angstroms means IBM is pushing transistor design into a scale where individual atoms start to shape the engineering limits.

Traditional scaling packs more switches side by side, while nanostack vertically staggers nanosheet transistors, letting each layer use different materials for speed or lower power instead of forcing one transistor recipe across the chip.

IBM says the design can fit nearly 100B transistors on a fingernail-sized chip, deliver up to 50% more performance or 70% better energy efficiency than its 2nm node, and shrink SRAM by 40%, which is huge because AI chips constantly wait on memory.

Practically it means future chips that can run bigger AI models, phones, laptops, servers, and cloud systems faster while using much less power, because more compute and memory can fit into the same tiny chip area.
Goldman Sachs Research: "Token use by AI agents is expected to multiply 24 times by 2030"

AI agents are now creating the first serious cost test for the AI boom. As was reported this week, Uber and Microsoft are already rethinking expensive agent usage.

A chatbot may answer once, but an agent plans, calls tools, checks results, edits mistakes, and repeats the loop.

That loop can make one user request consume 10x, 50x, or even far more tokens than a normal answer.

Goldman’s bullish case is that monthly token use could reach 120 quadrillion by 2030, while inference cost per token keeps falling 60%-70% per year.

The fight is now between agent productivity and token waste.

Earlier this month, Microsoft began revoking developer access to Claude Code, with plans to move them to its in-house Copilot Command Line Interface tool by June 30. The company has framed this as consolidating teams around its own tools, but the timing at the fiscal year’s end hints it may also be about lowering costs.
This media is not supported in your browser
VIEW IN TELEGRAM
Former Goldman Sachs executive Raoul Pal explains how AI is going to eat traditional software/SAAS.

If your product is just software, agentic AI can reproduce it on demand, optimize it, and redeploy it to a better market.

"Agentic AI means it’s like having Fiverr, a website of experts you can ask any question. It’ll go away and do the task.... Agentic AI will build, design the website, code it, register the domain name, figure out the branding, figure out the marketing, figure out the email list, figure out the whole thing.

So then you and I are in competition. You’ve built this incredible new website.

I just go to my AI and say, “Love Steven’s website. Can you just build it better. Boom. 3 minutes.

How can we be entrepreneurs in software? Now there’s this theory going around that AI is going to eat software, and I kind of get it."
✍1
This media is not supported in your browser
VIEW IN TELEGRAM
“Dad I don’t think I want to gamble my money away on meme and look at charts all day”
Methode Electronics $MEI - The Overlooked Hidden Champion in Automotive, Cloud & Industrial Electronics

With a market cap of approximately $320 million, well below its $1.0 billion in fiscal 2025 sales, Methode Electronics is a true hidden champion that most investors completely miss.

Founded in 1946, Methode is a leading global supplier of custom-engineered solutions for user interfaces, LED lighting systems, power distribution and sensor applications.

The company serves some of the world’s biggest OEMs across transportation, cloud computing infrastructure, construction equipment and more.

Massive Blue-Chip Customer Portfolio

Methode’s customer list reads like a who’s who of global industry:

Automotive & Commercial Vehicles:

BMW, GM, Ford, Volkswagen, Mercedes-Benz, Toyota, Tesla, Stellantis (FCA), Jaguar Land Rover, Volvo, Scania, Peterbilt, Freightliner, Kenworth, Mack, MAN, Caterpillar and many more.

Aerospace & Defense: Boeing, Lockheed Martin, United Technologies, RTX.

Electronics & Cloud: Cisco, Dell, Hewlett Packard Enterprise, Juniper Networks, Alcatel, Extreme Networks.

Industrial & Medical: Bosch, Schaeffler, Magna, Yamaha, Alstom, plus major hospital systems including Cleveland Clinic, MD Anderson Cancer Center and Northwestern Memorial Hospital.

7,200 employees worldwide with strong engineering depth.

VDesigns, tests and manufactures almost everything in-house, extremely independent from third-party suppliers.

More than 35 locations across North America, Europe, Middle East and Asia.

Diversified revenue: While automotive makes up the majority, $MEI is deeply embedded in high-growth areas like cloud/AI server power & data connections, construction machinery lighting and medical technology.
🇨🇭 Global One SI - Prompt
$QMCO Every new datacenter, AI model and compute cluster ultimately depends on one thing: Power — and the efficient, tiered data storage infrastructure needed to ingest, manage, and archive massive unstructured datasets without blowing up costs or energy…
$QMCO ($190M) represents a high-potential opportunity in the AI infrastructure landscape because it directly addresses the critical bottleneck of modern data centers: data delivery speed.

As artificial intelligence models scale exponentially, advanced GPUs require massive pipelines of data to operate efficiently, making high-performance storage the literal backbone of the current AI boom.

Quantum Corp solves this "GPU starvation" problem by offering an end-to-end data lifecycle solution.

They combine ultra-fast NVMe flash storage (Myriad) for active, heavy AI model training with highly cost-effective tape systems for massive, long-term archival data retention.

Their modern, software-defined architecture allows enterprises and data centers to scale storage capacity and processing performance independently and dynamically without experiencing any system downtime.

The company's immense commercial potential is heavily validated by its deep strategic integration with the world's most dominant technology leaders.

Most notably, Quantum’s Myriad All-Flash architecture natively integrates with $NVDA GPUDirect Storage (GDS), which bypasses the CPU entirely to pump datasets directly into NVIDIA's high-end GPUs, drastically maximizing chip utilization.

This crucial synergy carries over to $SMCI, where $QMCO provides the high-parallelism storage backends needed to prevent data bottlenecks within Supermicro’s rapidly expanding, expensive GPU server clusters.

Furthermore,$QMCO software deeply integrates with $MSFT Azure, enabling seamless hybrid-cloud pipelines that bridge local enterprise data with Azure's cloud-based AI tools.

On the corporate enterprise front, long-standing relationships with $DELL and $IBM allow $QMCO to weave its robust storage and tape architectures directly into global enterprise infrastructures, helping large organizations efficiently archive, manage, and secure the vast amounts of unstructured data required for next-generation AI workflows.
$APPL consumer product prices are skyrocketing due to memory chip costs.
🔥1
This is a tiny glimpse into the future of the "permanent underclass."

This term sounds like a joke until you actually break down what's happening right now.

Apple just spiked consumer product prices by 10-50%+

The best products & intelligence are slowly becoming only accessible to the rich, and the "underclass" will only be able to access watered-down, sh*t variations.

Tech companies are just making their best products completely out of reach for the average person (Fable 5 is another good example).

This trend will only accelerate.
✍1
We've never seen anything like the product price increases announced today by Apple and Microsoft.

Tim Cook: "I haven't seen anything like it in any area in over 40 years"

Elon Musk agrees.

The AI data center boom is driving a massive wave of consumer inflation.
🔥1
So what does everyone think of OpenAI’s new BiDi voice model?

The bidirectional, full duplex stuff is honestly uncanny.

It doesn’t feel like talking to a chatbot anymore. It literally feels like talking to a real human who can listen, interrupt, react, and keep up in real time.

This is the first AI voice mode that actually feels alive.
Will you actually let AI agents make phone calls for you soon?

Like booking appointments, arguing with customer support, negotiating prices, managing boring life admin?

Because honestly, that might be the first AI feature normal people instantly understand.
AI Hardware by Memory Bandwidth.

Bookmark this list, it will help you before purchasing your next hardware equipment.

- Mac Studio M3 Ultra: up to 512GB @ 819 GB/s
- RTX PRO 6000 Blackwell: 96GB @ 1792 GB/s
- RTX 5090: 32GB @ 1792 GB/s
- RTX 4090: 24GB @ 1008 GB/s
- RX 7900 XTX: 24GB @ 960 GB/s
- Radeon PRO W7900: 48GB @ 864 GB/s
- AMD Radeon AI PRO R9700: 32GB @ 640 GB/s
- Intel Arc Pro B65: 32GB @ ~608 GB/s
- Tenstorrent Wormhole n300: 24GB @ 576 GB/s
- Tenstorrent Blackhole p150: 32GB @ 512 GB/s + 800G
- MacBook Pro M5 Max: 460-614 GB/s
- MacBook Pro M5 Pro: 307 GB/s
- DGX Spark: 128GB @ 273 GB/s (coherent + CUDA)
- Mac mini M4 Pro: 273 GB/s
- Ryzen AI Max / Strix Halo: ~256 GB/s (~96GB usable GPU)
- MacBook Air M5: 153 GB/s
- Snapdragon X2 Elite: 152-228 GB/s
- Intel Lunar Lake: 136 GB/s
- Snapdragon X Elite: 135 GB/s
- Mac mini M4: 120 GB/s
- Arc Pro B60: 24GB @ ~456 GB/s
✍1
🇨🇭 Global One SI - Prompt
How to optimise GLM-5.2 for the best possible outputs (save this playbook)
This is the most underrated Hermes agent prompt.

Send this to Hermes, and your agent becomes your 24/7 personal executive assistant.

Monitors your upcoming meetings, task list, travel updates & more.

One of the best Hermes use cases
✍1
Hardware & infrastructure price increases over the last year:

RAM (DRAM)
→ Doubled since early 2025
→ +90% in Q1 2026 alone
→ DDR5 sticks: $80 → $300 each

GPU Memory (VRAM)
→ 16Gb module: $5.50 → $20+
→ Now ~80% of a GPU's total cost

GPUs
→ RTX 5090: $1,999 → up to ~$5,000 projected

SSDs (NAND)
→ +147% in Q1 2026

64GB DDR5 kit
→ $195 → $788

Laptops
→ Memory now ~20% of total cost (was 10-12%)

Overall PC/computer prices
→ +17% by the end of 2026 (Gartner)

Followed by 10-50%+ consumer product price increases by $APPL & $MSFT.