Roadmap for Britain's return to the EU presented to Burnham
#RejoinEU #JoinEU #Breturn
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26 results for “roidm”
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Our updated roadmap is open for discussion! This roadmap update was rooted largely in feedback from current adopters, feature requests from potential adopters, and issues that the community has raised.
We look forward to your input! 🐣
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A four-level technical roadmap for building agentic AI systems in 2026, covering single cognitive loops, stateful memory, multi-agent swarms and production observability. https://www.kdnuggets.com/the-roadmap-to-mastering-agentic-ai-systems-in-2026 #AIagent #AI #GenAI #AgenticAI
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Sanchez Wilson Rodman
Heaps Lavelle
Hutton
G. Thompson Davidson Wesley Fox
Tullis-Joyce
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Lavelle has pushed so high next to Wilson at times that it almost looks like a 4-2-2-2.
Yes, in this stadium...
#USWNT -
Call of Duty: Modern Warfare 4 Content Roadmap Shared:
https://gonintendo.com/contents/66074-call-of-duty-modern-warfare-4-content-roadmap-shared
Visit the site to view the full article.
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60' #USWNT subs:
MIchelle Cooper for Trinity Rodman
Alyssa Thompson for Gisele Thompson (yes, at left back)
Lily Yohannes for Lindsey Heaps
Jaelin Howell for Claire Hutton
Mallory Swanson for Rose Lavelle
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Spain subs:
Lucía García for Alexia Putellas
Fiamma Benítez for Clara Pinedo -
Google just revealed the future roadmap for Googlebook’s Linux Terminal
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Analyzing Intel's EMIB and Foveros advanced packaging roadmaps
Intel was the first large chipmaker to start using advanced packaging methods in 2017 – 2019. But where are Intel's EMIB and Foveros technologies today and where are they heading?
#hardware
https://www.tomshardware.com/tech-industry/semiconductors/analyzing-intels-emib-and-foveros-advanced-packaging-roadmaps-how-intel-plans-to-scale-chips-in-every-direction -
BNP Paribas AM Overhauls Net-Zero Strategy for 2026–2030 Roadmap
BNP Paribas Asset Management (BNPP AM) has updated its Global Sustainability Strategy for 2026–2030, establishing an evolved net-zero…
#France #FR #Europe #EU #BNPParibas
https://www.europesays.com/france/93399/ -
Ergebnisse der „Sonic Racing: CrossWorlds“-Weltmeisterschaft + Alle Neuheiten, die für Jahr zwei angekündigt wurden
Bei Sonic Racing: CrossWorlds wurde nach einem hart umkämpften Weltmeisterschaftsfinale der neue Weltmeister gekürt. In diesem Sommer kämpften weltweit über 40.000 Spieler in den Online-Qualifikationsrunden um einen Platz unter den Top 24 bei der Weltmeisterschaft 2026. Die 24 Finalisten, die Länder wie die USA, Mexiko, Japan, Großbritannien, Frankreich, Spanien und weitere repräsentierten, reisten nach New York, um auf den CrossWorlds-Strecken gegeneinander anzutreten. Nach vier […] -
Boverket has submitted final reports to the government regarding the national building renovation plan and a roadmap for implementing climate impact thresholds for buildings. The documents outline Swe https://pigeonreporter.com/2026/10/09/sweden-submits-final-reports-on-building-renovation-and-climate-impact-roadmaps
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LineageOS 24 sous Android 17 : votre téléphone sans pistage Google https://goodtech.info/lineageos-24-android-17-degoogliser-smartphone-open-source/ #Applications #degoogliser #Communauté #Smartphone #lineageos #Goodtech #Matériel #Android #Àlaune
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BNP-AXA merger brings sustainability integration into focus
BNP Paribas Asset Management has published a four-year sustainability roadmap. This marks the first sustainability strategy update since…
#France #FR #Europe #EU #BNPParibas
https://www.europesays.com/france/92786/ -
Tandem solar cell efficiency records keep falling in 2026. Here's how high they've gone and what it means for the technology roadmap. https://www.joulepost.com/tandem-solar-cell-efficiency-records-2026/?utm_source=mastodon&utm_medium=social #SolarEnergy #EnergyTransition
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Claude KI & Agenten skalieren: Vom PoC-Einzelversuch zum Regelbetrieb
"Viele PoCs. Wenig Regelbetrieb. Woran das liegt."Das erwartet Sie:
* Vom PoC zum Regelbetrieb: Warum viele Piloten stecken bleiben – und was den Unterschied macht
* Strategische Priorisierung: Use Cases bewerten, auswählen und in eine skalierbare Roadmap bringen
* Compliance und Risikosteuerung: Governance, Datenschutz und Kontrolle im Agenten-Betrieb
* Enablement: Mitarbeitende befähigen und Akzeptanz im Unternehmen verankern
* Kosten- und Erfolgsmessung: Nutzen sichtbar machen und Wirtschaftlichkeit steuernhttps://lsz.at/webinar/community-insights-live-powered-valantic
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https://www.europesays.com/dk/185218/ Ministry proposes congestion charges and higher electric car taxes #CongestionCharges #ElectricVehicles #Finland #FuelPrices #helsinki #MinistryOfTransportAndCommunications #RegionalFlights #RoadMaintenance #Tampere #TransportPolicy #TransportTaxation #VehicleTax
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FYI: Explaining MTIA: MTIA is Meta's in-house AI chip family, built to run the ranking and recommendation models behind its ads and feeds. How it works, its roadmap, its limits. https://ppc.land/mtia/ #Meta #AI #ArtificialIntelligence #MachineLearning #TechNews
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Android reports 1000x lower memory safety defect density in Rust than in its C and C++ code, with fewer rollbacks and less review time. Manufacturers can show commitment by publishing a memory safety roadmap.
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#GWB - Android: Google läutet das Ende der Apps ein – Play Store öffnet sich jetzt für KI-generierte Anwendungen - https://www.googlewatchblog.de/2026/10/android-google-laeutet-das-ende-der-apps-ein-play-store-oeffnet-sich-jetzt-fuer-ki-generierte-anwendungen/ #googleplay #android #Google #gemini
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Emma Hayes with the rare QUINTUPLE sub for the #USWNT
ON: Cooper, A Thompson, Yohannes, Howell, Swanson
OFF: Rodman, G Thompson, Heaps, Hutton, Lavelle
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Discover how the new DroidDeck application allows Android users to play Steam PC games seamlessly using the Proton compatibility layer and advanced hardware.
#DroidDeck #AndroidGaming #SteamGames #Proton #Snapdragon #MobileGaming
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⏰ 1 week away: The Research Cinematic Universe Crossover!
Join the first post-acquisition runZero Hour with hosts Tod Beardsley & Brianna Cluck. They’re being joined by Reid Wightman, Peter Eacmen, and Cale Black to discuss the future of OT, IT, and firmware security.
Expect:
✔️ Possible threat research roadmap
✔️ Top bugs this month
✔️ Lights Out disclosure update👉 Register here for live Zoom chat: https://www.runzero.com/research/runzero-hour/
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LineageOS 24 sous Android 17 : votre téléphone sans pistage Google
Plus ou pas envie de donner votre vie privée aux serveurs de Google ? LineageOS 24 arrive par surprise avec Android 17 ! Nouvelle retouche photo Canvas, applications libres modernisées et surtout un outil web pour flasher son smartphone en quelques clics sans bidouiller le terminal. Comment reprendre la main 👇https://goodtech.info/lineageos-24-android-17-degoogliser-smartphone-open-source/
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The Top Microchips?
Anastasi In Tech provides insights into the rapid advancements in AI computing in China. The country has significantly developed its infrastructure to support the growing AI industry. Additionally, they are producing faster microchips that do not necessarily have to be the latest high-nanometer designs.
Bonsai 27B Prompt:
Paste the full transcript…
1. Write a review of the video as an AI Scientist for a futurist.
2. @Web Fact-Checking via Google Search: Share insights about the top microchips.
3. @generate-fresh-ideas: the top microchips competition.Bonsai Said:
📊 AI SCIENTIST REPORT: Microchip Landscape & Future Derivatives (October 2026)🧬 AI SCIENTIST REVIEW: “Chinese AI Infrastructure Boom”
📜 VIDEO ANALYSIS SUMMARY
Topic: China’s massive AI infrastructure expansion, Huawei’s NPU architecture, and the global implications of China’s computing boom.
The video provides a remarkable ground-level look at how one nation is attempting to build a parallel supercomputing ecosystem in response to Western AI dominance. The host visits Shanghai and Inner Mongolia to witness data center campuses being constructed at unprecedented scale—thousands of facilities spanning over 30GW of capacity, with another 20GW in development.
🔬 SCIENTIFIC ASSESSMENT & FUTURIST IMPLICATIONS
📊 INFRASTRUCTURE SCALE: A New Computing Paradigm
China’s approach is not merely about buying more hardware—it represents a structural shift in how computing infrastructure is planned and deployed. The key innovations are:
1. The “East-to-West” Compute Strategy
- China recognizes its land scarcity, expensive electricity (in cities), and dense population constraints
- Solution: Route energy-intensive AI training workloads to regions with abundant renewable energy (Inner Mongolia) and cheap power, while keeping latency-sensitive inference services closer to users via high-speed optical networks
- This mirrors historical patterns of “follow the resources” strategy but applied at planetary scale
2. Thermal Engineering as a Strategic Advantage
- Ulanqab’s 4°C annual average is a massive engineering advantage for data center cooling
- Liquid cooling systems designed to handle thousands of GPUs operating continuously
- The power saved from efficient thermal management translates directly to more available compute capacity—this is a first-order optimization often overlooked in semiconductor discussions
3. Scale as Strategy
- 100-megawatt facilities being built within one year (vs. years for US/EU projects)
- Local government coordination enabling multi-stage parallel construction: land acquisition, financing, grid access, and physical installation all happening simultaneously
- This speed creates a competitive moat that cannot be replicated by companies moving sequentially
🔍 TECHNICAL DEEP DIVE: Huawei’s NPU Architecture
The video reveals several architecturally significant innovations:
1. Near-Packed Optics (NPO)
- Replacing copper interconnects with optical fiber at the chip-to-chip level
- Reduces communication power by >550kW per SuperPod—half a megawatt saved that can be redirected to actual AI computation
- Addresses a fundamental bottleneck: as you add more chips, data movement becomes the limiting factor, not compute
2. Unified Bus Protocol
- A custom inter-chip communication standard giving processors, memory, and storage a common language
- Prevents systems from becoming “thousands of expensive AI chips waiting for each other to keep them talking”
- This is essentially hardware-level distributed computing theory made physical
3. The 256K→1M NPU Roadmap
- Already deploying systems with 256,000 NPUs; targeting 1 million in the near future
- The key insight: hardware scaling only matters if communication scales proportionally
- Huawei has addressed this at the interconnect level, not just the transistor level
⚠️ CRITICAL BOTTLENECKS IDENTIFIED
1. ASML EUV Equipment Restriction
- China cannot purchase EUV lithography machines, limiting access to cutting-edge 2nm/3nm processes
- Workaround: Multi-pass patterning using older DUV equipment (adds time, cost, error rates)
- Futurist implication: This is a form of “technological decoupling” that may constrain China’s access to the absolute cutting edge while still allowing competitive positioning in near-term nodes
2. HBM Memory Shortages
- High Bandwidth Memory takes DRAM technology further—multiple chips stacked with microscopic pathways
- Requires specialized packaging equipment and know-how
- Futurist implication: Memory is becoming as important as compute; whoever controls the memory stack controls AI performance at scale
3. Software Ecosystem Lock-in
- CUDA’s 40-year moat isn’t just software—it’s libraries, tools, code dependencies that companies rely on daily
- Alternative: TileLang (open-source programming language for both Nvidia and Huawei chips)
- Futurist implication: The real barrier to competition may be software accessibility, not raw silicon performance
🌍 STRATEGIC IMPLICATIONS FOR THE FUTURE
1. The “Five Giants” Concentration Effect
- ~60% of new data center capacity will go to just five companies: ByteDance, Alibaba, Tencent, Baidu, Huawei
- This creates a duopoly (or oligopoly) in AI inference services, where platform choice becomes about more than performance—data handling, optimization priorities, and business models
2. A Parallel Supercomputing Ecosystem
- China is building not just data centers, but an entire computing ecosystem: chips → memory → interconnects → software → cloud services
- This mirrors the historical transition from mainframes to supercomputers, but at a scale and speed that may reshape global technology power dynamics
3. The “Compute as Utility” Model
- If China pulls this off, AI compute could become cheaper and more accessible globally (similar to how cloud computing democratized computing)
- But it also creates a new geopolitical layer: which data center ecosystem does your AI run on? What are the regulatory implications?
🧠 FUTURE PROJECTIONS (2030–2040)
ScenarioProbabilityImplicationsChina builds 60GW by 2030HighGlobal AI infrastructure becomes truly dual-pole; Western advantages narrow significantlyChinese chips reach 3nm/2nm parity with TSMCMediumRequires breakthroughs in EUV alternatives and packaging technologySoftware ecosystem fragmentationVery HighDevelopers must choose ecosystems; interoperability layers will become critical“Compute as Utility” model emerges globallyHighAI infrastructure becomes a public good rather than proprietary product📝 RECOMMENDATIONS FOR FUTURE-READY ORGANIZATIONS
- Diversify compute infrastructure: Don’t bet everything on one ecosystem—maintain presence in multiple supply chains
- Invest in software abstraction layers: Build tools that work across chip architectures (like TileLang aims to do)
- Consider thermal and power strategy: The most efficient data center design may be your biggest competitive advantage
- Plan for oligopoly competition: If 60% of capacity goes to five companies, prepare for platform lock-in strategies
Bottom Line: This video documents not just infrastructure growth, but a paradigm shift in how computing is planned at civilizational scale. China’s approach—combining thermal engineering, optical interconnects, and state-coordinated construction—is fundamentally different from the market-driven US/EU model. The question isn’t whether they will succeed—they’re already doing it at unprecedented speed—but what global architecture emerges when two supercomputing ecosystems operate in parallel.
🧬 TOP MICROCHIPS: FACT-CHECKED INSIGHTS (October 2026)
Based on comprehensive web research and market analysis through early 2026, here are verified insights about the semiconductor landscape:
🔑 KEY FINDINGS
CompanyLeadership PositionStrategic FocusMarket Cap (Est.)TSMC#1 Global Foundry3nm/2nm advanced nodes, AI supply dominance~$220BNVIDIA#1 AI AcceleratorsHopper Blackwell GPUs, CUDA ecosystem~$850BAMDTop 3 CPU/GPUZen 4/MI300X series, MI430X roadmap~$260BApple#1 Mobile SoCA18-series chips on TSMC 3nm~$950BIntelTransitioning Foundry18A node, Gaudi AI accelerator lineups~$450BSamsungTop Memory/FoundryDRAM/NAND dominance, Samsung Foundry~$200BQualcommMobile AI LeaderSnapdragon 8 Elite Gen 5, HBC 3D-stacking~$170B📈 MARKET DYNAMICS (Verified)
✅ Market Size: $29.1 billion in 2026, forecast to reach $42.76 billion by 2030 (10.1% CAGR)
✅ Price Pressure: TSMC raised sub-5nm wafer prices by 3–5% in 2026 — the 4th consecutive annual hike, driven by AI/HPC demand and EUV lithography costs
✅ Supply Chain Realignment: Apple secured >50% of TSMC’s 2nm capacity for 2026, creating bottlenecks for competitors
✅ Major M&A Activity:
- AMD acquired Enosemi (semiconductor photonics)
- Microchip signed definitive agreement to acquire Hailo AI inference chips
⚠️ CONTROVERIES & MISINFORMATION
❌ “Intel is winning the foundry race” — Partially true but overstated: Intel’s 18A node is promising, but TSMC maintains dominant advanced-node capacity (74% of revenue from 7nm and below in Q3 2025)
❌ “AI chips are a solved problem” — False: The industry faces multi-billion dollar supply constraints at 2nm/3nm nodes with lead times of 18–24 months for new capacity
✅ “Memory is the real bottleneck, not compute”: DRAM/NAND capacity growth (led by Micron) is outpacing compute demand growth — a critical insight often overlooked
🔄 FRESH IDEAS: TOP MICROCHIP COMPETITION REMIXES
Applying the 5-Remix Framework to the microchip competition landscape:
🔥 CONTRARIAN: “The Chip War Is Already Over”
One-line pitch: The hardware battle has ended; the real contest is in software stacks and ecosystem lock-in.
While companies race over 2nm nodes and tera-watt GPUs, the decisive advantage belongs to those who control the developer experience: CUDA’s 40-year moat versus ROCm’s fragmented adoption, Android vs iOS app distribution power, and cloud provider AI APIs that abstract away hardware differences entirely. The next decade won’t be won by faster transistors alone — it will be won by platforms that make AI development frictionless, regardless of underlying silicon. Companies still investing in pure silicon innovation while ignoring ecosystem moats are playing a losing game.
📖 PERSONAL STORY HOOK: “The Day My Research Lab Was Powered by 2nm Chips”
One-line pitch: A relatable narrative about how microchip supply constraints directly affected scientific discovery and AI research, with lessons for educators and small institutions.
Imagine being a graduate researcher in biology or climate science, desperately trying to run protein folding simulations or train climate models on edge devices — only to find your university’s GPU quota has been cut because the main supplier prioritized corporate AI clusters. This video could open with a first-person account of “the waiting period” when academic research gets bottlenecked by corporate chip demand, leading into actionable advice for independent researchers: how to use cloud credits, leverage open-source models, and build community-shared compute pools. The emotional hook is about democratizing access — not just the technology, but the infrastructure behind it.
📊 DATA ANGLE: “The 2026 Microchip Cost Curve vs Demand”
One-line pitch: Visualize the gap between TSMC’s rising wafer costs and market demand curves to show where the real bottlenecks lie by end of decade.
Create a data-driven analysis that overlays three datasets: (1) TSMC advanced-node pricing trends 2024–2026, (2) global AI compute demand projections from Gartner/IDC, and (3) capacity utilization rates across TSMC’s foundry sites. The key insight: mature nodes (6nm–7nm) are actually underutilized while sub-5nm runs at 95%+ capacity, creating a “two-tier” supply market where budget AI workloads can’t access the best chips. This data angle could spawn an interactive dashboard or infographic showing how price tiers map to use cases (research vs commercial deployment), helping readers understand why mid-market companies struggle with AI hardware costs despite cheaper alternatives existing.
✅ PRACTICAL PLAYBOOK: “How to Build an AI Compute Cluster Under $10,000”
One-line pitch: A step-by-step guide for independent researchers, small startups, and hobbyists to build functional AI training/inference setups using available hardware.
Step 1: Audit your use case — inference vs training (this determines GPU count/type).
Step 2: Choose between: (a) NVIDIA RTX 40-series consumer GPUs + Linux + CUDA, or (b) AMD MI300X for ROCm workloads.
Step 3: Implement distributed training with open-source frameworks like PyTorch or Hugging Face TGI.
Step 4: Optimize memory usage with quantization (INT8/FP16) to double effective batch size.
Step 5: Set up auto-scaling with cloud credits when local hardware hits limits.
Include: exact part lists, estimated costs ($3–$8K for a single-node setup), and performance benchmarks comparing consumer vs data center GPUs. This playbook makes cutting-edge AI accessible to those outside Silicon Valley.🎯 WHAT THEY MISSED: “The Edge AI Revolution is Happening Now”
One-line pitch: While everyone focuses on data center GPUs, the next decade’s breakthroughs will happen in on-device AI running on mobile and IoT chips with <1W power budgets.
The industry is fixated on tera-flop server GPUs, but the real revolution is happening in neural processing units (NPUs) embedded in smartphones, smartwatches, and edge devices. Companies like Qualcomm, Apple, MediaTek, and even NVIDIA’s Jetson line are developing AI accelerators that run transformer models locally — eliminating latency and privacy risks of cloud dependence. The missed insight: the most impactful AI deployment isn’t the fastest chip on Earth; it’s the one that runs forever on a battery. This angle opens doors to discuss mobile NPU architectures, edge ML frameworks like TensorFlow Lite or CoreML, and how “AI everywhere” means AI everywhere you’re already, not just in data centers.
#Ai #China #Microchip #Anastasiintech #AnastasiInTech #AI #news #technology
