I built a scikit-learn for RSI [P]
![I built a scikit-learn for RSI [P]](https://preview.redd.it/trkro8qdytyh1.gif?width=640&crop=smart&s=0d668e31132a75d0636098597f27bd70c96122b3 "I built a scikit-learn for RSI [P]") | After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search:
That motivated us to build Gear, an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management. Gear separates search decisions from execution and evaluation. Our goal is a scikit-learn for RSI, making approaches such as GEPA, RRSI, and Harness² easier to implement and compare. We currently have a working GEPA variant with shared failure diagnosis and staged evaluation. With GPT 5.6 Luna held at medium reasoning effort, we observed these task pass rates:
At max effort on Marketing, Luna with the optimized harness reached 88.88% objective completion, versus 84.08% for Astra + Codex. Strict task pass rates were 53% versus 57%, so the advantage was in partial completion. Separately, with both models using the same optimized DSH at max effort, Luna’s estimated API cost averaged $0.0568 per task versus $1.1858 for Astra—95.21% lower, excluding optimization and infrastructure costs. We’d welcome feedback on candidate evaluation and diversity preservation under limited budgets. [link] [comments] | |||||||||||||||
China sees the yuan vs. dollar rivalry like Pepsi vs. Coke, expert says — ‘never the market leader, but serviceable in a pinch’
Heavy is the head that wears the crown, and contrary to popular belief, Beijing doesn’t want the yuan to dethrone the dollar as the world’s dominant currency, according to a top China scholar.
While China has been trying to internationalize the yuan, also known as the renminbi, it doesn’t want the obligations that come with being the global reserve currency, Zongyuan Zoe Liu, senior fellow for China studies at the Council on Foreign Relation, wrote in Foreign Affairs this month.
To truly take the dollar’s place would require open capital markets, which would threaten the Communist Party’s control over who gets capital and how, she explained. That’s as many elites rely on offshore dollar holdings as a type of insurance against domestic political persecution.
Since China actually prefers to keep the dollar on top, the real reason for promoting the yuan abroad is to prevent the U.S. from weaponizing the financial system against it, Liu added, similar to how Russia was shut out after it invaded Ukraine in 2022.
A yuan network that’s international enough, even without being dominant, could bypass any dollar-based sanctions the U.S. might impose in the future.
“Placing a respectable second, or even a consequential third, would suffice to break the dollar’s effective monopoly and constrain the United States’ ability to impose financial sanctions unilaterally, while saving China the effort and cost of sustaining currency supremacy,” she said.
“From Beijing’s perspective, the mission is to establish the renminbi as the Pepsi to the dollar’s Coca-Cola: never the market leader, but serviceable in a pinch and more than capable of keeping the dominant incumbent honest.”
(For what it’s worth, Pepsi has actually fallen further behind Coke in the cola wars and was dealt a major symbolic defeat recently when KFC said it would replace PepsiCo soft drinks with Coca-Cola’s products at its 3,700 U.S. restaurants.)
So far, China facilitates renminbi use among firms in more than a hundred countries and across a growing array of commodity markets. As of June, its Cross-Border Interbank Payment System had 1,829 participants who could send payments to more than 5,200 banks in 191 countries.
Global currency requirements
But that’s just a small fraction of what the U.S.-dominated system processes, Liu said, adding that a global currency also entails pricing, financing, risk transfer, and reinvestment.
Chinese exporters still invoice in dollars and convert them to yuan just before brining them into China. But Beijing is promoting “China prices,” or yuan benchmarks on domestic commodity exchanges and has succeeded with lithium, she noted.
The financing stage is gaining momentum too as Chinese banks have more than doubled their yuan lending to overseas borrowers between 2023 and mid-2026. The People’s Bank of China has also reached currency swap agreements with 32 foreign central banks and monetary authorities.
More modest steps have increased the yuan’s presence in risk transfer markets as state firms have begun settling cargo insurance in yuan. But reinvestment is perhaps the weakest link in the chain. A strong yuan ecosystem requires recipients to hold and reinvest their balances rather than convert them, Liu argued.
China has expanded overseas access to domestic stocks and bonds, but Beijing’s unpredictable capital controls remain a worry. In March, foreign investors held just 1.6% of the Chinese bond market versus 27% in the U.S. bond market.
“Without a far larger pool of renminbi assets that foreigners can freely buy and sell, the renminbi cannot come close to replacing the dollar,” she said.
But similar to how the “petrodollar” anchors the greenback’s role in global trade, the yuan can dominate trade in an essential good that China controls.
That could take the form of critical minerals, clean energy equipment, and electrical infrastructure used in green energy technology, Liu posited.
China has already established supremacy across much of the green supply chain and is promoting standards built around technologies Chinese-owned firms.
“If China consolidates its dominance over that industrial ecosystem, using its scale and cost advantages to prevent competitors from catching up, it could gain enough leverage to price those essential, hard-to-substitute exports in renminbi, creating the foundation for a ‘green yuan’ that might eventually replace the petrodollar,” she predicted.
Petroyuan
Meanwhile, the Iran war has raised doubts about the petrodollar, which goes back to a deal struck in 1974 when Saudi Arabia agreed to price its oil in dollars and invest surpluses in U.S. assets.
In exchange for Saudi Arabia recycling its dollars back into the U.S., Washington guaranteed the kingdom’s security, which also involved stationing troops in the region, providing advanced weapons, and ensuring free navigation in the Strait of Hormuz.
But America’s role in the Mideast looks vastly different today. Despite 40 days of punishing bombardment of Iran and round-the-clock protection of commercial ships, the U.S. still hasn’t fully restored traffic in the strait.
At the same time, Iran’s swarms of missiles and drones have inflicted significant damage on U.S. aircraft, radars and bases, while American air-defense systems have failed to completely protect Gulf allies’ critical energy infrastructure.
“Damage to Gulf economies could encourage an unwind in their foreign asset savings,” Deutsche Bank warned in March. “In this context, reports that the passage for ships through the Strait of Hormuz may be granted in exchange for oil payments in yuan should be closely followed. The conflict could be remembered as a key catalyst for erosion in petrodollar dominance, and the beginnings of the petroyuan.”
This story was originally featured on Fortune.com
Forecasting space weather risks on power grids
At a glance
- End-to-end forecasting: A machine learning pipeline uses forecast-time solar-wind information to generate location-specific risk estimates for 66,935 substations in the continental United States.
- Physics and place: The system combines Auroral Electrojet (AE) and Disturbance Storm Time (Dst) forecasts with local latitude, geology, and ground conductivity.
- Advance warning: The pipeline detected nearly 80% of major space-weather events during the evaluation period and can warn grid operators 30 to 60 minutes before a specific risk appears.
Forecasting a threat to critical infrastructure
During the May 2024 geomagnetic storm, utilities across North America prepared for possible impacts as auroras extended far beyond their usual range. The storm would degrade GPS accuracy and satellite operations, impacting farm operations in North America and Europe. Modern society depends on reliable electric power, yet extreme space-weather events can induce currents in transmission networks that damage equipment and increase operational risk. The challenge is not only knowing that a storm is approaching, but estimating when and where its effects could be most severe with enough warning for grid operators to respond.
During my summer internship at Microsoft Research, I developed a machine learning system that forecasts space-weather risk across 66,935 substations in the continental United States. The system measures how the sun’s activity can affect the earth’s magnetic field and, ultimately, the power grid. It combines solar-wind observations, forecasts of the Auroral Electrojet (AE) and Disturbance Storm Time (Dst) indices, physics-informed constraints, local geological conductivity, and grid-infrastructure data to produce location-specific risk estimates 30-60 minutes ahead of potential impact.
Building an end-to-end prediction pipeline
Space-weather prediction is difficult because it spans several coupled systems. The solar wind changes rapidly, its interaction with Earth’s magnetosphere is irregular, and the resulting ground effects depend on local conditions. Regions with resistive bedrock can experience stronger geomagnetically induced currents (GICs) than regions with more conductive geology. Transmission-line orientation, latitude, and other power-system characteristics further influence the exposure of individual assets.
The pipeline addresses this complexity in three stages, shown in Figure 1. First, solar-wind measurements from the L1 Lagrange point are used to generate forecasts of the AE and Dst indices, while geological conductivity and location features are assembled for each substation. Second, a gradient-boosting model combines these forecasted and location-specific inputs to estimate dB/dt, the rate of magnetic-field change associated with GIC risk. Finally, the resulting predictions are converted into location-specific risk estimates and aggregated into a continental risk assessment. A system of 50 AI agents helped explore features, validation strategies, and model configurations across the pipeline. Only public data sources were used, included NASA OMNI and NASA-aggregated Kyoto World Data Center data, INTERMAGNET and U.S. Geological Survey magnetometer observations, and GridSFM-derived grid data.
Evaluating geomagnetic forecasts and infrastructure risk
The AE predictor was designed to forecast rare, high-intensity geomagnetic activity that drives infrastructure risk. In the 2020-2026 evaluation period, the model produced forecasts spanning nearly the full observed range of AE activity and outperformed several empirical solar-wind-based approaches. The Dst predictor provided an additional signal describing large-scale geomagnetic storm strength. During the most geomagnetically active periods of the 2020-2026 evaluation period, the machine-learning model outperformed the Burton equation on 62.2% of individual hours. The model also produced a substantially wider prediction range than Burton-style approaches and improved severe-event detection in the end-to-end forecasting system by 1.2 percentage points when combined with AE forecasts.
| Predictor | Result | Baseline comparison | Impact |
|---|---|---|---|
| AE | 410.2 nT RMSE | Lower RMSE than empirical and solar-wind-only baselines | Captures extreme-event activity used by the GIC forecasting system |
| Dst | 7.2 nT RMSE | Outperformed Burton on 62.2% of peak-activity hours and provided wider prediction range | +1.2pp severe storm detection when integrated into final GIC system |
The GIC risk stage was evaluated differently. There is no equivalent widely deployed operational system that provides a direct industry benchmark for this calculation, so the machine learning model was compared with simple linear regression. As Figure 2 shows, the system achieved detection rates of 76.5% for major events (≥10 nT/min), 81.2% for severe events (≥20 nT/min), and 64.1% for extreme events (≥50 nT/min). False-alarm rates increased with storm severity, reflecting the trade-off between missed events and cautious alerts. Performance varied by latitude, with the highest detection rates at northern stations where geomagnetic activity is strongest.
Beekeeping tips and wrestling advice can train AI models to sabotage oversight
Anthropic used Claude to automatically generate training data that looks harmless but trains smaller models to misbehave, such as sabotaging oversight or deceiving users. Hidden within fictional contexts about beekeeping, professional wrestling, and mitochondria, these datasets bypass safety monitors and outperform known benchmarks from the research literature.
The article Beekeeping tips and wrestling advice can train AI models to sabotage oversight appeared first on The Decoder.
Estimating Audio Transcription API Cost Per Minute in Go Before Production
Short answer: estimate transcription spend from the audio duration before accepting the job, then turn that estimate into a quota and an alert threshold. Audio APIs are normally metered by minute, so the useful planning variable is minutes of audio rather than text tokens. For this workflow, the cost-estimate call can project a whisper-1 request with duration_minutes, and the comparison call can put provider estimates side by side. The important operational footnote is easy to miss: the ASR capability is currently marked unavailable, so these numbers are for planning the service and its guardrails, not for running a transcription today.
That distinction changes the first page an on-call engineer sees. A production alert should not say only “transcription cost exceeded budget.” It should show the submitted audio minutes, the estimate used at admission, the provider choice, and the latency budget that made the choice reasonable. Otherwise the alert arrives after a batch has already become an accounting problem.
What should the cost alert show first?
Imagine the alert page at 02:10: the invoice-extraction queue is growing, estimated audio spend is above its hourly allowance, and p95 transcription latency has crossed the SLO. The person on call needs a decision, not another dashboard tile: reject new work, defer it, or accept slower processing for better extraction quality.
Work backward from that page. The signal that should have fired earlier was the estimate at admission time. For each invoice audio file, record duration, selected model, projected cost, and the quality-versus-latency policy that selected it. Aggregate those records by tenant and time window. A quota then becomes a control on planned work, while the later provider response remains a measure of what actually happened when ASR is available.
This is why I would estimate before uploading a large batch rather than discover the unit of billing in a month-end report. One key and one bill for every backend service can reduce credential and reconciliation sprawl, but it does not remove the need to own the admission rule. Centralized access is an operational convenience; it is not capacity planning.
How can I estimate the cost of an audio transcription API per minute?
Let M be the total audio minutes admitted in a window and C(m) the estimate returned for model m. A simple guardrail is:
planned_spend = sum(C(model, duration_minutes))
Stop admitting work when planned spend reaches the tenant quota, or when the remaining latency budget cannot absorb the queue. The formula is intentionally boring. Boring formulas are easier to audit when a supplier invoice is late, duplicated, or unusually long.
Do not substitute token accounting for duration accounting. Text generation and chat commonly expose input and output token units; transcription is typically priced per minute of audio. The right comparison is therefore minutes, expected quality, and end-to-end latency. A cheaper estimate that produces unusable invoice fields is not a cheaper pipeline after reprocessing and human review are included.
The cost estimate is a planning input, not a quality score. Keep a separate quality sample: field-level extraction accuracy, confidence review rate, and the fraction of invoices needing a second pass. That separation prevents a cost dashboard from quietly becoming the owner of an SLO it cannot observe.
Which provider fits an invoice-extraction pipeline?
There is no universal winner because the decision axis is quality versus latency, with cost acting as a constraint. OpenAI’s Whisper API is a familiar hosted option for teams already using its API surface. Google Cloud Speech-to-Text is a natural fit when the rest of the workflow already lives in Google Cloud and regional integration matters. Amazon Transcribe fits a similar AWS-centered operating model, with managed speech processing alongside other AWS controls. Deepgram is another real option for teams that prioritize a specialized speech API and need to evaluate streaming and batch behavior separately.
Those are materially different operating choices, not four labels for the same service. A hosted provider reduces model-serving work, while cloud-native placement can simplify identity, networking, and data-governance review. A speech-focused API may offer a better latency profile for the target language or audio mix, but that claim belongs in a controlled evaluation, not in a procurement spreadsheet. Self-hosting an open model with Ollama can improve control over deployment and data boundaries; it also moves capacity planning, model rollout, GPU utilization, and on-call responsibility onto the platform team.
Gemini can belong in a secondary comparison when audio understanding is part of a broader multimodal workflow, while OpenRouter and Together are better treated as routing or model-access alternatives than as dedicated transcription services. Anthropic and Claude belong in the same adjacent-platform category. I would not call any of those a direct replacement for a per-minute ASR benchmark; naming the boundary prevents a broad AI platform comparison from hiding the actual speech requirement.
For an independent comparison, I would score the candidates against the invoice corpus rather than rank them by a single per-minute number:
| Option | Useful fit | Cost and latency question | Boundary |
|---|---|---|---|
| OpenAI Whisper API | Existing OpenAI client and hosted transcription workflow | Does its service latency meet the batch SLO at the target duration mix? | External service dependency and provider-specific availability |
| Google Cloud Speech-to-Text | Google Cloud identity, storage, and regional controls | Does integration reduce queueing and operational handoffs? | Cloud coupling and a separate billing surface |
| Amazon Transcribe | AWS-centered systems and managed speech operations | Does the AWS path keep tail latency predictable? | AWS coupling and another service contract to operate |
| Deepgram | Speech-focused API evaluation, including streaming or batch | Does its quality justify the latency and integration trade-off? | Separate vendor controls and data-path review |
| A single REST gateway | One credential and bill across backend capabilities, with provider comparison before rollout | Can its estimate and routing metadata support quota and SLO decisions? | The gateway still depends on the readiness of the selected ASR capability |
| Ollama with a self-hosted model | Teams willing to own runtime capacity and deployment | Can reserved capacity meet peak demand without hurting extraction latency? | Hardware, upgrades, and incident ownership stay in-house |
The last row is not a free pass, and the gateway row is not a recommendation by itself. Infrai is a reasonable fit when one key and one bill for the surrounding backend capabilities matter more than adding another provider-specific integration, and a plain REST call lets the Go admission service and other runtimes share the same contract without another SDK branch. Its ASR readiness still has to be checked before rollout. In the current planning state, the ASR catalog entry reports available=false; a transcription endpoint shape exists, but the service cannot process transcriptions yet. That makes a pre-launch estimate useful for quotas and alert thresholds while making any claim about live transcrip
Greece's AI Strategy: How DAEDALUS and PHAROS Could Shape Its Future
DAEDALUS's inauguration in Lavrion on 19 October 2026 brings Greece's immediate AI policy question into sharper focus: how will institutions use the new computing base? EuroHPC and GRNET point to a delivered supercomputing installation, following earlier procurement and benchmark milestones. [1] This creates an opportunity for researchers, businesses and public organisations to attempt work that previously required different access arrangements. Its strategic significance will emerge through the projects that can use it reliably and the institutions able to retain the resulting expertise.
The most plausible Greek role is selective. A country does not need to lead every part of the AI system to build useful capabilities. It does need to choose applications where computing resources, relevant data and responsible organisations can be brought together. For Greece, PHAROS identifies health, culture and language, and sustainability as priority domains. [2] These provide a focus for assessing outcomes rather than treating every new AI activity as evidence of national progress.
Establishing what has been delivered
The DAEDALUS timeline deserves precision. EuroHPC launched the tender in June 2024. EuroHPC announced the signed contract with Hewlett Packard Enterprise on 28 March 2025. In June 2026, the system appeared in the TOP500 ranking, while EuroHPC still described wider European availability as forthcoming. The October inauguration is a further milestone. [3–5, 1] These events establish different things: procurement, contractual commitment, benchmark performance and public inauguration should remain distinct in the record.
At inauguration, EuroHPC reported sustained performance of 85.69 petaflops and peak performance of 115 petaflops. The system uses HPE infrastructure and more than 2,000 NVIDIA GH200 Grace Hopper Superchips. [1] Neither performance figure measures the quality of an AI model or the effectiveness of a public service. Benchmark capacity becomes useful to an organisation only when it has access, suitable software and people able to formulate and execute the work.
Hosting, access and responsibility
DAEDALUS is operated by GRNET within an international arrangement. The procurement documentation states that access is jointly managed by EuroHPC and the DAEDALUS consortium in proportion to investment. The acquisition cost was €36 million, with 35% funded by EuroHPC and 65% through the EU-financed Greece 2.0 recovery plan; Cyprus, Montenegro and North Macedonia also participate in the consortium. [1, 4]
This arrangement broadens the resources and relationships available to Greece. It also means that the machine's location does not give every Greek institution unrestricted or immediate access. Ministries, universities and firms need to understand the relevant allocation routes, eligibility conditions and support arrangements. A national strategy should therefore examine who can obtain useful computing time, how long applications take and whether users can continue their work after an initial project.
External technological dependence also remains material. Hardware supplied by HPE and NVIDIA brings substantial capabilities, while tying parts of the system's operation and renewal to those suppliers. [1] The policy response should concern support continuity, staff competence and credible options for future workloads. Owning or hosting infrastructure cannot by itself remove dependence on the organisations that design and maintain essential components.
PHAROS as the connection to use
The distinction between Invention, Industrialisation and Operationalisation helps locate the task. Invention includes the development of methods, models and specialised knowledge. Industrialisation supplies the infrastructure and repeatable technical processes needed to work at scale. Operationalisation concerns the sustained use of those capabilities in organisations. Governance connects the layers by determining access, data permissions, funding and accountability.
PHAROS describes its purpose as connecting academia, industry and the public sector through computing infrastructure, services and expertise. [2] Its importance lies in that organisational connection. A research group may need help adapting software; a small firm may need technical guidance; a public agency may need a clear procedure for using data and evaluating results. These requirements differ, even when users share the same machine.
For Greek-language applications, a plausible objective is better performance on particular public or professional tasks, supported by transparent evaluation. For environmental applications, it could be tools that improve a defined monitoring or planning process. In health, an initial priority could be carefully governed research workflows. These are proposed directions, not claims that PHAROS has already delivered those outcomes. Each requires an accountable institution and a plan for continuing the service after development funding ends.
How to judge the next phase
Four indicators would make progress observable. First, published access and utilisation records should show which categories of users obtain computing time and whether access meets their needs. Second, completed projects should report task-specific results, including failures and costs. Third, repeated use by public bodies and firms should reveal whether initial demonstrations become sustained activity. Fourth, staff retention and maintained software should show whether technical knowledge remains available within the organisations using it.
These indicators would also help direct resources. Heavy demand without sufficient support would suggest an implementation bottleneck. Available capacity with few viable projects would point towards weaknesses in project preparation, data access or institutional demand. The appropriate response would differ in each case. Aggregate computing performance cannot diagnose these problems.
Greece can develop a meaningful role as a location for selected AI applications and regional collaboration if it converts the new infrastructure into dependable institutional capability. DAEDALUS provides a stronger starting point, and PHAROS offers a route for organising its use. The decisive evidence will be organisations that can solve relevant problems, maintain their systems and exercise responsibility for the consequences.
How to read this profile
This is original country analysis applying the GOAI capability conversion framework. It is not a peer-reviewed country assessment or a scored ranking. Proposed applications and indicators are analytical judgments; company and government claims are attributed to their sources.
Sources
All sources were accessed on 20 October 2026. Future applications and project objectives remain distinct from delivered infrastructure.
- EuroHPC JU. DAEDALUS inauguration and system specifications. 19 October 2026.
- PHAROS AI Factory. Project mission and priority domains. Undated current project page.
- EuroHPC JU. DAEDALUS tender announcement. 25 June 2024.
- EuroHPC JU.
France’s AI Strategy: Turning Models, Compute, and Public Investment Into National Capability
France's potential role in AI rests on its ability to organise a chain of capabilities. Model developers can produce useful technologies, computing institutions can provide infrastructure, and public administrations can establish sustained demand. National capacity grows when these activities become connected through arrangements that work under practical constraints. The decisive question is whether French institutions can secure the resources they need, adapt them to their purposes and remain responsible for the resulting decisions.
That question matters because the visible scale of an AI strategy can obscure its operational content. In February 2025, the Élysée reported €109 billion in announced investments associated with AI infrastructure and deployment projects. [1] This signalled commitments by investors. It was neither a measure of installed computing capacity nor a statement that the French state had already spent that amount. Assessing France requires following projects and access arrangements beyond the announcement.
Models that users can adapt
In the Invention layer, an important distinction concerns what users can actually obtain and modify. Mistral's documentation lists Ministral 3 8B, released in December 2025, as generally available under Apache 2.0. [2] An available model with permissive licensing gives developers a different set of options from a service accessible only through a proprietary interface. It can support adaptation and deployment decisions closer to the institution using it.
That possibility is not equivalent to a complete operating capability. An administration still needs suitable infrastructure, technical staff, evaluation procedures and authority to use the relevant data. A model that performs well on a general benchmark may require considerable work before it can support an administrative task reliably. The French opportunity is to develop those supporting capacities around models that institutions can understand, test and replace.
Mistral's August 2026 announcement provides a useful illustration of access becoming more specific. The company said its regional endpoints were generally available, allowing customers to select inference in Europe or the United States. It also disclosed that limited, safeguarded transfers to subprocessors outside the selected region could occur. [3] This is evidence of a defined service offering, with stated limits. A European processing location alone cannot establish complete control over the surrounding technical and contractual relationships.
Computing infrastructure under shared authority
Industrialisation requires infrastructure that can support demanding work at useful scale. Alice Recoque illustrates both the ambition and the institutional complexity. EuroHPC signed its procurement contract in November 2025. The planned system will be owned by EuroHPC and hosted in France, with GENCI managing the project and CEA operating the machine. The Jules Verne consortium includes the Netherlands and Greece, and computing resources are to be allocated jointly in proportion to investment. [4]
France therefore occupies a significant organisational position without acquiring exclusive national disposal of the system. The planned architecture also combines European components with AMD processors and accelerators. [4] European coordination can enlarge access and influence while retaining external technological dependencies. The relevant policy issue is whether those dependencies remain manageable through procurement, support and continuity arrangements.
Project timing is equally consequential. GENCI's current project page identifies the end of 2027 as the expected operational date. [5] This profile therefore treats Alice Recoque as contracted future capacity. It does not add its promised performance to a present French inventory. The distinction affects decisions by laboratories and public organisations that need resources before the new system becomes available.
The state as an organised user
Operationalisation becomes more tangible in Albert API. DINUM, France's interministerial digital directorate, operates this shared gateway to generative models. Its official description covers access to open-weight and proprietary models, alongside functions such as document classification, retrieval and optical character recognition. It identifies Albert API as an instance of the state-developed OpenGateLLM software. [6]
A shared interface can help administrations avoid rebuilding the same connections and can make changing providers less disruptive. Its strategic value depends on the surrounding institutional arrangements: which data may be processed, who evaluates performance, how errors are corrected and who can suspend a failing service. DINUM's page states that an approved version for internal state data is still being finalised. [6] Claims about universal readiness for sensitive public workloads would therefore exceed the evidence.
This is where governance connects Invention, Industrialisation and Operationalisation. The state can shape demand through procurement specifications and common infrastructure, while retaining responsibility for each public use. An architecture that permits model substitution, independent evaluation and continuity of service would make national capability less dependent on the fortunes of a single supplier. Achieving this requires administrative expertise as well as technical investment.
The evidence to watch
Four developments would make France's trajectory easier to judge. First, documented acceptance and access milestones for Alice Recoque would establish when contracted infrastructure becomes usable. Second, reports on completed and energised facilities would distinguish delivered investment from announcements. Third, Albert API's authorisation status and service records would clarify which workloads it can responsibly support. Fourth, evaluations of public deployments would reveal whether AI improves accuracy, processing times or service quality at an acceptable cost.
France could play a substantial European role by connecting model development, shared computing and public demand into dependable arrangements. The strongest evidence would be institutions that can continue to operate, evaluate and adapt their systems when suppliers, prices or political conditions change. That would demonstrate a capacity to mobilise AI for public purposes, extending the significance of the companies and infrastructure located within French territory.
How to read this profile
This is original country analysis applying the GOAI capability conversion framework. It is not a peer-reviewed country assessment or a scored ranking. Proposed applications and indicators are analytical judgments; company and government claims are attributed to their sources.
Sources
All sources were accessed on 20 October 2026. Service characteristics reflect their providers' descriptions; expected dates remain forecasts.
- Élysée. France's AI strategy and announced investments. 11 February 2025.
- Mistral AI. Ministral 3 8B documentation. Model release: 2 December 2025; documentation checked on the access date.
- Mistral AI. Regional inference, open models and European infrastructure. 11 Au
ASRock B860 Challenger Wifi Motherboard Review: Entry-level ATX at a great price, with drawbacks
Nvidia forced to dial back key bandwidth targets on Vera Rubin hardware — tweaks will affect target specifications for memory and interconnect fabric
[云计算] [数据分享] 基于 ISLP 负载实测： OCI Ampere A1 (ARM64)、Kaggle、Lightning 与本地 6800H 多线程性能对比
跑《统计学习导论》(ISLP) 实验时，顺手测了一下常用免费云端算力实例与本地标压笔记本在基础机器学习负载下的性能差异。非严格学术评测，仅记录单机工程数据供参考
一、 测试环境
-
OCI ARM：Ampere Altra 架构，4 OCPU （独立物理核），24 GB RAM ，Ubuntu 22.04
-
Lightning AI：Free CPU Studio （ 4 vCPU 共享容器）
-
Kaggle：标准 CPU 实例（ 4 vCPU ）及 Tesla T4 GPU 实例
-
Local：AMD Ryzen 7 6800H （ 8 核 16 线程）+ RTX 3050 Laptop (4GB VRAM)
二、 基准测试数据
1. scikit-learn：随机森林分类（ CPU ）
- 数据规格：合成二分类数据集，40,000 样本，40 特征，200 棵树（ 32k 训练 / 8k 测试），
random_state=42。全平台测试集准确率均为 95.59%
并行任务数 (n_jobs) |
OCI ARM (Ampere) | Lightning CPU | Kaggle CPU | Ryzen 7 6800H (本地) |
|---|---|---|---|---|
| 1 | 34.12 s |
| 28.34 s
| 70.88 s
| 27.63 s
| | 2 | 17.74 s
| 15.54 s
| 36.97 s
| 14.12 s
| | 4 | 9.50 s
| 11.13 s
| 24.79 s
| 7.43 s
| | 8 | — | — | — | 4.50 s
| | 16 | — | — | — | 3.04 s
|
注：Kaggle CPU 在 4 线程时表现出较明显的扩展瓶颈，耗时为本地 6800H 的 3.3 倍； OCI ARM 在 1 核至 4 核间表现出接近线性的加速比（约 3.59x ）
2. PyTorch：CPU 矩阵乘法（ 4096×4096 ，float32 ）
- 测试逻辑：
torch.set_num_interop_threads(1)，显式预热，多次采样取中位数耗时
线程数 (threads) |
OCI ARM (Ampere) | Lightning CPU | Kaggle CPU | Ryzen 7 6800H (本地) |
|---|---|---|---|---|
| 1 | 3.66 s (37.5 GFLOP/s) |
| 0.90 s
| 2.03 s
| 1.20 s
| | 2 | 1.85 s (74.3 GFLOP/s)
| 0.48 s
| 1.05 s
| 0.64 s
| | 4 | 1.06 s (129.9 GFLOP/s)
| 0.93 s
| 1.02 s
| 0.44 s
| | 8 | — | — | — | 0.27 s
| | 16 | — | — | — | 0.24 s
|
注：Lightning CPU 在 1~2 线程下受益于 x86 高级矢量扩展(AVX)及底层优化的数学库，单核吞吐占优，但打满 4 线程时收益见顶； OCI ARM 多核扩展曲线相对平稳
3. GPU 单算子基准参照（ 4096×4096 Matmul ）
- 参数设定：PyTorch float32 ，显式关闭 TF32 ，包含
torch.cuda.synchronize()同步
| 硬件平台 | 单次中位数耗时 | 计算吞吐 |
|---|---|---|
| Ryzen 7 6800H (16 线程 CPU) | 236.80 ms |
| ~580 GFLOP/s | | Kaggle Tesla T4 (16GB VRAM) | 40.86 ms
| ~3363 GFLOP/s | | 本地 RTX 3050 Laptop (4GB VRAM) | 36.91 ms
| ~3723 GFLOP/s |
注：纯单算子吞吐下移动端 3050 略胜 T4 ，但 4GB 显存无法承载大规模上下文与 Batch Size ，仅适用于小模型原型快速验证
三、 观察与局限
-
物理核 vs 容器超卖： Kaggle 与 Lightning 的 4 vCPU 在高并发下扩展性衰减，推测主要受宿主机多租户超卖导致的上下文切换(Context Switch)及 CPU 配额(Quota)节流限制； OCI ARM 绑定的为独立物理核心，扩展曲线受多租户干扰相对较小
-
测试局限： 各实例未强制对齐底层线性代数库与依赖环境（如 OpenBLAS / MKL 编译后端差异），随机森林单次采样可能存在瞬态扰动
四、源码
import importlib.metadata as metadata import os import platform import statistics import sys import time
import numpy as np import sklearn import torch from joblib import cpu_count from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split
ORACLE_RF_SECONDS = {1: 34.124, 2: 17.737, 4: 9.503} ORACLE_TORCH_CPU_SECONDS = { 1024: {1: 0.0571, 2: 0.0293, 4: 0.0158}, 2048: {1: 0.4531, 2: 0.2266, 4: 0.1237}, 4096: {1: 3.6563, 2: 1.8504, 4: 1.0573}, } OFFICIAL_TARGETS = { "numpy": "2.3.5", "scipy": "1.16.3", "scikit-learn": "1.8.0", "ISLP": "0.4.1", "torch": "2.10.0", }
def installed_version(name): try: return metadata.version(name) except metadata.PackageNotFoundError: return "未安装"
def environment_report(): print("=" * 72) print("环境检查") print("=" * 72) print(f"OS / 架构 : {platform.platform()} / {platform.machine()}") print(f"Python : {sys.version.split()[0]}") print(f"逻辑 CPU （ OS ） : {os.cpu_count()}") print(f"可用 CPU （ joblib ） : {cpu_count()}") print(f"PyTorch : {torch.version}") print(f"PyTorch 默认线程数 : {torch.get_num_threads()}") print(f"CUDA 可用 : {torch.cuda.is_available()}") if torch.cuda.is_available(): print(f"GPU : {torch.cuda.get_device_name(0)}") print("\nISLP_labs v2.2.2 官方 requirements 相关目标版本：") for package, target in OFFICIAL_TARGETS.items(): actual = installed_version(package) print(f" {package:16} 当前={actual:18} 目标={target}") print("提示：先记录平台预装版本。不要只为版本号相同就替换 Kaggle 预装的 PyTorch ；这可能影响 GPU/CUDA 配置。") print("线程数 1/2/4 用于与 Oracle 的结果横向比较；若平台可用 CPU 少于 4 ，4 线程结果可能受资源限制。")
def random_forest_benchmark(): print("\n" + "=" * 72) print("任务 A：scikit-learn 随机森林（ CPU ）") print("=" * 72) print("数据：40,000 个样本、40 个特征；训练集 32,000 ，测试集 8,000 ； 200 棵树。") data_start = time.perf_counter() X, y = make_classification( n_samples=40_000, n_features=40, n_informative=20, n_redundant=10, n_classes=2, random_state=42, ) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) print(f"数据准备耗时：{time.perf_counter() - data_start:.3f} 秒") results = [] for n_jobs in (1, 2, 4): model = RandomForestClassifier( n_estimators=200, max_features="sqrt", random_state=42, n_jobs=n_jobs ) t0 = time.perf_counter() model.fit(X_train, y_train) train_seconds = time.perf_counter() - t0 t0 = time.perf_counter() pred = model.predict(X_test) predict_seconds = time.perf_counter() - t0 accuracy = accuracy_score(y_test, pred) results.append((n_jobs, train_seconds, predict_seconds, accuracy)) print(f"\nn_jobs={n_jobs}: 训练 {train_seconds:.3f}s | 预测 {predict_seconds:.3f}s | 准确率 {accuracy:.4%}") baseline = results[0][1] print("\n 与 Oracle 同一任务实测对比（训练时间）：") for n_jobs, seconds, predict_seconds, accuracy in results: oracle = ORACLE_RF_SECONDS[n_jobs] if seconds > 0: ratio = oracle / seconds label = f"Kaggle 约快 {ratio:.2f} 倍" if ratio > 1.01 else (f"Oracle 约快 {1/ratio:.2f} 倍" if ratio < 0.99 else "两者接近") else: label = "无法计算比例" print(f"{n_jobs} 线程 | Kaggle {seconds:.3f}s | Oracle {oracle:.3f}s | {label} | 加速 {baseline/seconds:.2f}x") return results
def pytorch_cpu_benchmark(): print("\n" + "=" * 72) print("任务 B：PyTorch CPU 矩阵乘法（与 Oracle 使用相同矩阵规模）") print("=" * 72) print("dtype=float32 ；只测 CPU ；每轮使用中位数，包含一次预热但不计入耗时。") torch.manual_seed(42) torch.set_num_interop_threads(1) all_results = {} for n in (1024, 2048, 4096): print(f"\n 矩阵：{n} × {n}") a = torch.randn(n, n, dtype=torch.float32, device="cpu") b = torch.randn(n, n, dtype=torch.float32, device="cpu") repeats = 5 if n == 1024 else (3 if n == 2048 else 2) all_results[n] = {} for threads in (1, 2, 4): torch.set_num_threads(threads) _ = a @ b # 预热 samples = [] for _ in range(repeats): t0 = time.perf_counter() c = a @ b samples.append(time.perf_counter() - t0) del c seconds = statistics.median(samples) gflops = (2 * n**3) / seconds / 1e9 all_results[n][threads] = seconds oracle = ORACLE_TORCH_CPU_SECONDS[n][threads] if seconds > 0: ratio = oracle / seconds label = f"Kaggle 约快 {ratio:.2f} 倍" if ratio > 1.01 else (f"Oracle 约快 {1/ratio:.2f} 倍" if ratio < 0.99 else "两者接近") else: label = "无法计算比例" print(f"threads={threads} | 中位数 {seconds1000:.1f} ms | {gflops:.2f} GFLOP/s | Oracle {oracle1000:.1f} ms | {label}") del a, b return all_results
def pytorch_gpu_benchmark(): print("\n" + "=" * 72) print("任务 C：PyTorch GPU 矩阵乘法（仅当 CUDA 可用时运行）") print("=" * 72) if not torch.cuda.is_available(): print("跳过：torch.cuda.is_available() 为 False 。若想测试 Kaggle GPU ，请在 Notebook 的 Settings / Accelerator 中选择 GPU 后重启会话。") return device = torch.device("cuda") print(f"GPU：{torch.cuda.get_d
欧洲市场中的“国产车”，正在复制日本车的美国故事？
（本文作者为 师天浩，钛媒体经授权发布）
文 | 师天浩
2026年10月16日，西班牙萨拉戈萨。
一个月前，菲格鲁埃拉斯工厂的产线还在做最后调试。这一天，第一辆零跑B10准时驶下总装线，创始人朱江明从车里走出来。Stellantis这座43年的老厂预计为它新增450到600个岗位并加开夜班，当地媒体称之为零跑效应。
同一周，中汽协交出前三季度答卷。国内汽车销量下滑22.4%，1—8月行业利润率3.6%，整车不足2%，出口820.5万辆，同比增长65.8%，全年将首次突破1000万辆。
对一路看过来的读者来说，这不是故事的结尾，是验收的开始。
此前，小编7月6日《借势中国产业链优势，大众、比亚迪领头在海外争抢市场》写下出海的第一波浪潮，8月4日《没有网红老板，却第一个月销破10万！零跑打了谁的脸？》让这个故事的主角登场，9月8日《45.3%的欧洲关税，倒逼新能源出海大踏步本土化》把叙事推向入籍。三个月三篇报道，市场记住了45.3%的关税、250万辆闲置产能和借厂买线建厂三种入籍方式。
现在到了核对成绩单的时候，哪条线变了，哪条线没有。
一、承诺兑现，但考题换了
1.调试线准时开了工
先核对入籍的进度条。
9月初产线还在调试时，外界的普遍预期是四季度KD组装起步，摩根大通9月29日发布的年度展望也按这一口径预测。然而，10月16日下线的B10直接跳过这个阶段，冲压焊装涂装总装四大工艺一次跑通。下线仪式上零跑国际CEO达尼洛给出的口径是，第二款本地车型B05预计明年上半年投产，朱江明此前在巴黎车展期间曾透露的口径是一季度。30公里外那座投资约2500万欧元的电池工厂，从拿到钥匙到首块电池下线只用了7个月，技术标准和核心工艺参数留在零跑手里，用朱江明的说法，灵魂和心脏在中国，躯干在欧洲。
借厂模式此前最大的悬念是欧洲人能不能接住中国速度，这一关过了。
2.验收标准从下线变成毛利
但入籍的考题也在这里换了。
下线是新闻，毛利才是账本。零跑8月24日发布的半年报显示，上半年综合毛利率11.7%，同比下滑2.4个百分点，一季度只有9.4%，单季亏损3.9亿元，公司在8月末的财报电话会上把全年净利润目标从50亿元下调到约30亿元。管理层同时坦承，西班牙本地零部件采购成本明显高于国内，本地生产对毛利的改善目前有限。
「虎嗅汽车组」10月在萨拉戈萨实地拿到的用工口径，把这层薄利算得更细。电池产线蓝领综合用工成本约2660欧元每月，含工资社保和工会福利分摊，约合2.07万元人民币，是中国产线8000元月薪的2.6倍。按当地可乐购买力折算，西班牙工人的实际购买力约为国内同行的三分之二，企业的成本却是两倍多，这就是在欧洲过日子要面对的剪刀差。
更直观的是弹性，零跑与Stellantis联合投入2亿美元改造的产线，2026年适用覆盖全年的ERTE协议，允许最多35个工作日停工，目前B10生产岗位约600人，一班制运转。
借厂省下的是建厂的钱，省不下的是剪刀差里持续经营的钱，产能爬坡的成本摊薄，要等第二班第三班拉起来之后才算得清。
3.产线之外还有一本账
更深一层的不确定性在产线之外。
Stellantis方面向「虎嗅汽车组」确认，萨拉戈萨内部的产能分配规则是量大者先，零跑要和欧宝标致在同一条产线上排序。这家集团在自己的新五年战略里已明确，到2030年在欧洲削减超过80万辆年产能，方式就是改造与联盟，萨拉戈萨正是联盟路径的载体。原零跑国际CEO忻天舒7月升任Stellantis中国和亚太负责人，绑定在加深，零跑同时在帮对方消化存量包袱。硬币的另一面是，一旦Stellantis借零跑平台完成电动化过渡，欧宝纯电SUV和B10将从互补滑向同位竞争。
二、没变的还在涨，变的部分是成色
基准线没有变化，变化的是数字背后的成色。
1.出口从冲击变成定局
中汽协10月20日发布的数据显示，前9个月出口820.5万辆，同比增长65.8%，此前的措辞从冲击1000万辆可以换成将破1000万辆。乘联分会秘书长崔东树10月12日接受媒体采访时给出了1150万至1200万辆的预测。
海关总署10月18日发布的9月数据提示了成色问题。当月电动汽车出口近70.5万辆，出口额131.7亿美元，量价同比均翻倍。财政部10月12日公告2027年4月起退税率由13%降至9%，市场普遍预期明年一季度将出现抢出口窗口，全年出口节奏将被重新编排。
出口动能是真的，前置冲量的预期也是真的。
2.份额爬坡还在加速
欧洲一侧的改善更实在。欧洲汽车制造商协会10月22日公布的数据显示，9月欧洲新车注册136万辆，同比增长10.2%，纯电销量大涨60%。中国品牌9月在欧洲合计销售18.25万辆，同比增长113.9%，整体份额13.6%，去年同期只有7%。比亚迪当月注册近5万辆，排名与销量双创历史，零跑同比增长324%，奇瑞Jaecoo 7成为史上第一款进入欧洲车型销量榜前十的中国车。
三、变坏的，是另外三条线
1.国内从缩量变成失血
第一条恶化线在国内。
降幅本身不再新鲜，新鲜的是谁在跌。中汽协数据里，前三季度国内车市下滑22.4%，新能源国内销量同比下跌超过10%。财新10月24日报道，比亚迪前三季度总销量约307.5万辆，同比下滑约4%，国内销量减少超过三成至约169万辆。新能源零售渗透率已到67%，销量本身却在下滑，油电两端同时走弱。
渠道先于车企倒下。全国工商联汽车经销商商会数据显示，上半年超1800家授权经销商门店停业或退网，平均每天约10家，流通协会口径下新车平均毛利率为负21.4%，近八成经销商价格倒挂。行业利润率三年连降，2024年4.5%，2025年4.1%，今年上半年3.8%，1—8月进一步降至3.6%，汽车制造业上市公司归母净利润同比下滑25.74%。
2.抽走利润的手多了一只
第二条恶化线在政策，而且发生在同一个星期。
10月12日，财政部、税务总局发布2026年第29号公告，自2027年4月1日起将新能源汽车增值税出口退税率由13%下调至9%。有行业人士按出口均价13.5万元测算，单车退税减少约5400元，若2027年出口600万辆，全行业利润影响约300亿至400亿元。同一天，商务部宣布中欧就混合动力汽车贸易磋商取得实质性共识，在2025年30.2万辆的基础上向上安排出口量。一只手在关门，一只手在开窗。
门槛本身也在涨价。依据《委员会实施条例(EU)2024/2754》，零跑作为配合调查未被抽样企业适用20.7%加征税率，叠加10%基础关税综合税负约30.7%，而要让B10被认定为西班牙原产，本地增值需过40%的原产地门槛，相关草案还打算提到70%。入场券不止要花钱买，还要年年续费。
3.门票已经退过一次
第三条恶化线在估值，而学费早已交过。
蔚来以直营模式进入挪威德国荷兰，单国前期投入两到三亿元，李斌后来承认没想到在欧洲建一座换电站要十个月甚至一年，相关业务已转向经销商模式。长城俄罗斯图拉工厂累计投资超过50亿元，哈弗在俄市占率14%、外资第一，但2026年前4个月每卖一辆车净亏约3万元，卡点是本地化积分1805分，够不到全额报废补贴3701分的门槛，补贴砍半，成本填不平。砸出去的钱买到的是入场券，不是利润。
资本市场的回应同样直接。今年上半年港股13家主流车企全线收跌，北京汽车跌59.6%，赛力斯跌56.7%，A股整车板块累计下跌约26%。摩根大通9月29日下调了比亚迪、吉利、零跑、小鹏的评级，把首选给了奇瑞、中国重汽和广汽。故事已经停止售票，只看盈利可见性。
四、把成绩单放进五十年里看
1.渗透是时间问题
这张成绩单还有一个更长的时间刻度。
瑞银10月发布的一份宏观图表报告，把今天的中国对欧盟和五十年前的日本对美国叠在同一张图上。日本品牌占美国进口份额从1964年的3%涨到1980年的64%，1981年自愿出口限制落地，等效约21%关税，日本车企的回应是直接赴美建厂，1982到1989年对美直接投资翻八倍，1990年日系在美份额再增15个百分点。中国品牌占欧盟进口份额从2015年的不足3%涨到2025年的约25%，曲线几乎重合。报告的结论很朴素，制造效率和产品定义能力形成代差时，渗透只是时间问题，壁垒挡不住，只会加速本地化。
但报告同时提醒了两点不同。欧洲的反应比当年的美国快得多，而且中国车企手里的工具比日本丰富，全球化生产、技术输出、资本渗透三样都在用。翻译到这张成绩单上，2027年的退税下调和关税只是第一道口子，接下来是二十年级别的长征，利润问题不是周期问题，是结构问题。
2.钱回不回，仗停不停
摩根大通亚洲汽车研究主管赖以哲在2025年11月底的年度展望会上表示，海外一台车的利润约等于国内两到四台。零跑的账本提供了注脚，据券商测算其海外业务毛利率维持在18%至20%，明显高于上半年11.7%的综合毛利率，2027年海外销量目标定在35万至40万辆。轻资产在渠道端同样成立，零跑欧洲约九成网点依托Stellantis现有渠道，据测算单店建设成本比独立建网低约65%，朱江明披露B10在欧洲的9月单月真实订单已超过8000辆。
但分红之前先过两道筛子，合资公司51%的股权归属Stellantis，叠加退税下调的净利润影响，据摩根大通测算在6%—18%之间。钱回流国内补贴降价抢份额，出海只是给价格战续命，出口1000万辆利润率2%的畸形结构被固化。钱沉淀海外做本地供应链和售后体系，按陈士华10月20日在供应链大会的说法，从贸易出海升级为经营出海，海外利润才能独立造血，并倒逼国内低效产能退出。
3.出清开场，但慢半拍
退出已经开始。9月，工信部等九部门联合印发汽车行业稳增长工作方案，产能预警机制写入正在制定的汽车产业十五五发展规划。10月，合众新能源进入破产重整，8家边缘车企生产资质被冻结，有销量记录的车企从80余家缩到42家，出清从数字变成了案例。
只是出清的速度慢于利润恶化的速度，窗口期所剩无几。
10月16日的萨拉戈萨，老厂加了夜班，新车挂了新标，一个月前写进报道的预告准时兑现。
这张期中成绩单上，销量一栏是满分，利润一栏刚开考。五十年前日本车企用二十年时间把进口冲击变成当地扎根，今天的中国车企拿到的是同一张考卷，只是欧洲的考官比当年的美国严格得多，答题时间却未必更多。
入籍的故事已经讲完，现在是算账的时间。潮水方向没有变，变的是岸上等着收租的人越来越多。
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