https://www.europesays.com/people/281172/ After $5,000 bribe, Trump offers $1 mn to anyone for reporting voter fraud in midterms #CostOfLivingCrisis #DemocraticParty #DonaldTrump #ElectionDenialism #FinancialIncentive #MidtermElections #POTUS #PresidentOfTheUnitedStates #TruthSocial #VoterFraud
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Subscribe to The Australian | Newspaper home delivery, website, iPad, iPhone & Android apps
The Australian Digital subscription costs $4 charged for a duration of 4 weeks, after which it automatically renews…
#NewsBeep #News #Healthcare #AU #Australia #Health
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Vespertine Just in time for Halloween, I’d like to present ‘Das Wesen’ to you – the result of a collaboration with Evelina Kholod, who is wearing a costume by Vanessa Billton. Here are a few s #alternatiffotograf #andreaslbergphotography #art #artist #body #contemporaryart #contemporarydance #dance #dancer #dancers #dancing #dynamic #fineart #fineartzone #flash #fotografie #körper #model #nature #people #photography #portrait #porträt #sharp https://www.andreas-l-berg.com/blog/vespertine/
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Microsoft has offered voluntary buyouts to about 60,000 employees, representing more than a quarter of its global workforce, as it seeks to contain costs and fund its expanding AI investments. The move is voluntary and aimed at cutting costs amid AI spending.
Source: Nairametrics
https://nairametrics.com/2026/10/27/microsoft-offers-voluntary-buyouts-to-60000-employees-to-cut-costs-amid-ai-spending/ -
I read food researcher Jenny Dorsey say that Singapore’s hawker food culture and prices would not exist if eggs needed to be refrigerated there. If we needed to refrigerate eggs the way rhe US does, storage and operational costs would increase. Eggs play a big role at every meal (not jusf breakfast) and in many local dishes.
This and other info will be in her paper ‘Who pays for heritage? Examining form-function tensions in Singapore’s hawker centre policies’ in the Food Policy journal
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https://www.europesays.com/sk/215445/ Vlhová sa vrátila domov s rezervami a úsmevom. Čoskoro príde veľké divadlo bez Shiffrinovej – Zimné športy – Športweb – Pravda #SK #Slovak #Slovakia #Slovenčina #Slovensko #Šport #Sports
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Nancy. Un médecin au cœur de l’action avec le Raid
Les superflics du RAID ont beau être costauds, ils ne sont pas à l’épreuve des balles. Eux aussi…
#Nancy #FR #France #Actu #News #Europe #EU #actu #Actualités #Crime #délitetcontravention #ÉditionNancyetagglomération #europe #Faits-divers-Justice #GrandEst #Meurthe-et-Moselle #Nancy-ville54B #Républiquefrançaise
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https://www.europesays.com/africa/491984/ West Asia war spills over into Africa as fuel and fertiliser costs soar #Africa #AfricaEconomy #AfricaNews #AfricanEconomies #agriculture #FertiliserPrices #FoodPrices #FoodSecurity #ForeignInvestment #FuelPrices #GlobalTrade #harvests #OilPrices #StraitOfHormuz #SupplyChains #TradeMinisters #UNTrade #UNCTAD #WestAsiaConflict #WestAsiaWar #WorldInvestmentForum
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Record Tanker Rates Cripple U.S. Crude Oil Flows to Asia
The Iran war has made oil tanker voyages so inefficient that vessel availability has crumbled and freight costs…
#EuropeSays #Headlines #News #Europe #Europa #EU #PersianGulf #Asia #Oil #OilExports #Supertanker #US #vlcc #WTI
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https://www.europesays.com/it/749184/ Cantù a zero. Reggio di così… #AcquaSBernardo #AffariEFinanza #AnnaMellamo #Basket #Basketball #borse #CampionatoRally #Economia #IT #Italia #Italy #LSpo #LbaSisalclub #MarcoBallerini #MaurizioCremonini #mercati #ParlaciSopra #Sport #Sports #UNAHOTELSReggioEmilia #UNAHOTELSREGGIOEMILIA81ACQUASBERNARDO
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AI can generate a financial model that looks coherent, contains valid formulas, and produces a plausible result while still being wrong in ways that matter.
A formula can reference the wrong cell. A cost assumption can be expressed in the wrong unit. A revenue schedule can omit a phase. A model can calculate correctly from an unsupported market assumption.
These are different failure modes, and they need different controls. For real estate feasibility models, the objective should not be to ask whether an AI-generated workbook looks correct. It should be to establish which properties can be tested automatically, which assumptions require evidence, and which decisions must remain with a qualified human reviewer.
A useful validation architecture has five layers:
- Structural validation
- Deterministic calculation checks
- Domain and consistency checks
- Evidence and provenance checks
- Human review and approval
No single layer is sufficient.
1. Separate the output from the evidence
Consider a hypothetical development model with the following inputs:- Site area and developable area
- Permitted gross floor area
- Unit mix or lettable area
- Revenue assumptions
- Construction and other development costs
- Financing assumptions
- Development programme
- Exit assumptions
The model calculates development costs, revenue, cash flows, residual land value (RLV), and potentially internal rate of return (IRR).
For example, a model might calculate an RLV of £4 million. That output can be mathematically consistent with the workbook and still be commercially misleading if the revenue assumptions are unsupported or the construction cost basis is outdated. Validation therefore needs to test both the calculation and the justification for its inputs.
2. Layer one: validate the structure before the numbers
Before reviewing financial logic, validate the shape of the generated output. If an AI system is expected to return structured data before creating a workbook, define an explicit contract. JSON Schema is one established option for specifying required fields, data types, and constraints.See the JSON Schema getting-started guide.
A simplified model input contract might look like this:
{
"project_id": "DEMO-001",
"currency": "GBP",
"site_area_sqm": 5000,
"gross_floor_area_sqm": 12000,
"construction_cost_per_sqm": 1800
}
This is illustrative data, not a market benchmark or a recommendation for a production schema.A structural validator could check that:
- Required fields are present.
- Numeric values are actually numeric.
- Areas and costs use declared units.
- Currency is explicitly identified.
- Values fall within defined technical bounds.
- Unexpected fields are rejected or handled deliberately.
- Missing values are not silently replaced with zero.
The last point is important. Zero and unknown are not equivalent. If a finance rate is missing, substituting zero can make a model appear more profitable. A missing value should normally trigger a validation error or an explicit unresolved status.
Structural validation establishes that the model can be interpreted. It does not establish that the model is economically sensible.
3. Layer two: use deterministic checks for deterministic rules
Rules that can be expressed precisely should not depend on an LLM's judgment. For example, if a model defines construction cost as gross floor area multiplied by construction cost per square metre, the expected calculation is:\text{GFA}
\times
\text{Cost per sqm}
]
For the hypothetical inputs above:
[
12{,}000 \times £1{,}800 = £21{,}600{,}000
]
A deterministic test can independently calculate the expected result and compare it with the workbook output.
from decimal import Decimalgfa_sqm = Decimal("12000")
cost_per_sqm = Decimal("1800")
reported_cost = Decimal("21600000")expected_cost = gfa_sqm * cost_per_sqm
assert expected_cost == reported_cost
This test checks arithmetic for the stated formula. It does not establish that the GFA is permitted, that the cost rate is appropriate, or that the formula captures every relevant cost.
A production implementation should also define tolerances where appropriate, particularly when comparing rounded spreadsheet values or calculations involving floating-point numbers.
Other deterministic checks might include:
Revenue equals the sum of its components.- Total costs reconcile to their component schedules.
- Cash flow totals reconcile across periods.
- Unit counts and areas reconcile between schedules.
- The reported output matches an independently calculated result.
- A sensitivity case actually changes the intended input.
- Required formula cells are not unexpectedly blank or replaced by constants.
Where possible, use independent calculations rather than simply checking that a formula cell contains a formula. A formula can exist and still be wrong.
4. Layer three: validate model relationships
Many serious errors are not syntax errors or arithmetic errors. They are inconsistencies between parts of the model.Suppose a development model contains:
- 120 residential units in the unit schedule
- 116 units in the sales schedule
- Revenue calculated using the sales schedule
- Construction costs calculated using the unit schedule
Every formula may evaluate successfully, but the workbook contains a discrepancy that needs resolution.
Domain-level checks can flag conditions such as:
- Unit counts that differ across schedules.
- Areas that exceed a declared model constraint.
- Revenue periods that do not align with the programme.
- Cost totals that exclude a populated cost category.
- An exit value that uses an inconsistent timing assumption.
- A sensitivity scenario that changes revenue but leaves dependent calculations unchanged.
- An IRR calculation using a cash-flow range that omits a period.
These checks should be encoded as explicit rules wherever the intended relationship is known.
Some rules require context. For example, whether a particular cost should be included in a development budget depends on the model's scope and conventions. A validator should not impose a universal rule where none exists.
Instead, it should identify the relevant condition, record the rule being applied, and send ambiguous cases for review.
5. Layer four: validate the provenance of assumptions
A financial model can pass every arithmetic test and still fail as an analytical tool.The reason is straightforward: calculations cannot establish the truth of their inputs.
For every material assumption, a validation system should try to preserve:
- The value and unit.
- The source or origin of the value.
- The source's date and relevant data period.
- The geography and asset type, where applicable.
- The transformation from source evidence to model assumption.
- Any manual adjustment made by the analyst.
- The model locations that depend on the assumption.
Consider a construction cost assumption of £1,800 per square metre. A validator might confirm that the figure is numeric, the unit is recorded, and the workbook uses it consistently. Those checks do not establish whether the figure is suitable for the location, asset type, specification, and cost period. That question requires appropriate evidence and professional interpretation.
An LLM may help extract candidate assumptions from documents or explain how a value is used in a workbook. The extracted result should still be checked against its source, and the resulting assumption should remain distinguishable from the underlying evidence.
A useful principle is:
Traceability makes an assumption inspectable. It does not make the assumption correct.6. Layer five: make human review a defined state transition
Human review should not be an informal final glance after an automated process has already treated the model as complete.A practical workflow can represent validation explicitly:
GENERATED
|
v
STRUCTURE_CHECKED
|
v
CALCULATIONS_CHECKED
|
v
DOMAIN_CHECKED
|
v
EVIDENCE_REVIEWED
|
v
HUMAN_APPROVAL_REQUIRED
|
+----> APPROVED FOR THE DEFINED PURPOSE
|
+----> RETURNED FOR CORRECTION
|
+----> BLOCKED: UNRESOLVED ISSUEThe states are illustrative, but the distinction matters. A successful automated test should not automatically mean that a model is approved. Approval should be scoped to a defined version of the workbook, its assumptions, its evidence, and its intended use.
A reviewer should be able to see:- What was generated.
- Which automated checks passed.
- Which checks failed or were skipped.
- Which assumptions lack adequate evidence.
- What changed after a correction.
- Who approved the model and for what purpose.
If a material input or formula changes after approval, the system should identify which checks need to run again and whether the earlier approval remains valid. This is where versioning and change tracking become part of model governance rather than administrative overhead.
7. Design the exception path before the success path
Validation is often designed around a clean example: the model is generated, every check passes, and the user receives the output. Production systems need to handle less convenient outcomes.A failed check should produce actionable information. “Validation failed” is less useful than “The unit count in the sales schedule differs from the unit schedule; review the linked inputs before approving revenue.”
Similarly, a warning should not disappear simply because a later stage of the workflow succeeds. For auditability, preserve the check identifier, the model version, the result, the relevant evidence, and the resolution.
8. Measure validation quality without inventing an accuracy score
A single “model accuracy” percentage can hide important distinctions. A workbook might pass every arithmetic test while containing several unsupported assumptions. Another may contain a harmless formatting issue but have fully reconciled calculations and well-documented inputs.A more useful validation report separates categories:
- Structural checks passed or failed.
- Deterministic calculation checks passed or failed.
- Domain checks passed, failed, or were not applicable.
- Assumptions with verified, missing, or conflicting evidence.
- Open exceptions and their severity.
- Human review status.
- The scope and version of the approved output.
If the system is evaluated over time, track false negatives and false positives for individual validation rules against a reviewed test set. Record the test cases, expected outcomes, rule versions, and known limitations.
Do not interpret a high pass rate as proof that the model is correct. It may simply mean that the checks cover only a narrow set of failure modes.
The purpose of validation is to make known risks visible and testable, not to manufacture certainty.
9. A practical implementation sequence
A team introducing validation around AI-generated financial models does not need to automate every review step at once.A sensible sequence is:
- Define the contract. Specify the required inputs, output fields, units, and missing-value behaviour.
- Write deterministic tests. Start with totals, reconciliations, formula ranges, and independent calculations.
- Add domain checks. Encode known cross-schedule relationships and clearly defined constraints.
- Track provenance. Associate material assumptions with sources, dates, units, and transformations.
- Introduce exception handling. Make failures explicit and prevent unresolved material issues from being silently treated as passed.
- Define human approval. Record the reviewer, workbook version, purpose, and outstanding qualifications.
- Build a regression suite. Retain representative valid and invalid examples and rerun the checks when the generation logic or validation rules change.
The test suite should include deliberate failures, not only successful examples. Examples might include missing finance assumptions, inconsistent unit counts, a formula referencing the wrong period, and a source whose geography does not match the project.
This helps test whether the validation process detects the intended problem rather than merely accepting a plausible-looking output.
Conclusion
Human-in-the-loop validation is not a person checking an AI result after the important work is finished. It is a system design in which deterministic rules handle deterministic questions, domain checks expose inconsistencies, evidence is kept separate from assumptions, and a human reviewer remains accountable for unresolved judgments.For financial modelling, that distinction is material. A workbook can be well-formed without being economically sound, mathematically consistent without being supported by market evidence, and ready for review without being ready for a decision.
The objective is not to make every AI-generated model look trustworthy. It is to make the model's logic, assumptions, limitations, and unresolved issues sufficiently visible that a professional can challenge it before relying on the result.#feasibility #realestate #ai #financial #software #coding #development #engineering #inclusive #community
Human-in-the-Loop Validation for AI-Generated Financial Models -
You can tokenize almost any asset, but should you? What a tokenized raise really costs, why marketing drives the bill, and how to tell if it will pay off. https://hackernoon.com/you-can-tokenize-almost-any-asset-but-should-you #tokenizationofrwas
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When evaluating generative AI tools for writing emails, buyers routinely fixate on monthly subscription fees while ignoring the operational friction of migration. A low entry barrier—such as Rytr's $9-per-month tier or Writesonic's $12.67-per-month plan—masks the true cost of adoption: proprietary campaign builders, embedded workflows, and rigid export limits that make leaving a platform a costly endeavor. If your team pivots strategies or outgrows a tool within twenty-four months, the expense of untangling your email assets from a deeply integrated ecosystem often eclipses any initial savings.
ToolStarting priceKey limitBest forRytr$9/mo100k characters/moValue for moneyWritesonic$12.67/mo200K words (GPT-3.5) or 33K words (GPT-4.0)Writing cold emailsMailerLite$12/moFree for Power and Comfort plan usersSimple email marketing tools with AIHubSpot$20/moFull CRM platform integrationFull CRM platform with AI writingActiveCampaign$79/moAutomation and CRM integrationAutomation, CRM, and AI togetherThe Hidden Exit Trap Behind Low Monthly AI Pricing
The most deceptive aspect of AI email writers is how quickly simple text generation evolves into complex operational dependency. Standalone copy generators allow you to draft subject lines and body copy easily, but marketing platforms embed AI directly into your campaign infrastructure. For instance, MailerLite integrates its subject line generator, email writer, Smart sending, and landing page designer right into the campaign builder and drag-and-drop editor. While this streamlines day-to-day work, it creates a structural lock-in: your email templates, automation sequences, and AI-optimized content live entirely within their proprietary environment. Abandoning the platform means rebuilding your entire campaign machinery from scratch, turning a simple software cancellation into a major operational migration.Fast Onboarding and Free Tiers in Writesonic and Rytr
Getting started with standalone AI writers involves minimal friction, thanks to robust free tiers and straightforward introductory pricing. Writesonic provides a free version with 10,000 words, and its paid tiers start at $12.67 per month for 1 user and 200K words using GPT-3.5, or 33K words using GPT-4.0. For users seeking higher volume without caps, Writesonic's Unlimited plan starts at $16 a month with no word limit. Similarly, Rytr positions itself as a budget-friendly option starting at just $9 a month for 100k characters, complemented by a free plan and broad support across 30+ languages and 20+ tones. Setting up these tools requires virtually no infrastructure setup; you simply create an account, select your language, choose a tone or use case, and begin generating cold emails, sales copy, or marketing campaigns instantly.Evaluating Data Portability and Integration Limits
When assessing how easily you can abandon a tool, data portability and third-party integrations play a decisive role. Writesonic mitigates exit friction by offering a Zapier integration that connects it with over 5,000 apps, allowing you to route your generated email copy out of its V2 workspace and into external CRMs or email service providers with relative ease. On the other hand, all-in-one marketing suites and CRM platforms present stiffer portability barriers. Platforms like ActiveCampaign, which starts at $79 a month with a 14-day free trial, combine automation, CRM, and AI into a unified environment. While powerful for managing complex customer journeys, exporting deeply integrated behavioral data, automated tags, and AI-assisted workflows requires substantial manual intervention or custom data mapping if you decide to migrate.Billing Commitments and Seat Penalties Across Tiers
Financial lock-in is frequently enforced through subscription tiers, user limits, and billing structures. Standalone AI utilities typically offer monthly flexibility, such as Rytr at $9 per month or Writesonic's Unlimited plan at $16 per month, letting you scale down or cancel without penalty. Conversely, enterprise-adjacent marketing platforms often impose higher recurring commitments. ActiveCampaign's starting price sits at $79 per month, and specialized standalone options like Copy.ai scale up to $49 per month. If a growing team adopts a platform with strict seat minimums or advanced tier requirements—such as MailerLite offering its AI tools free specifically for Power and Comfort plan users—downgrading becomes painful because you risk losing essential campaign features or exceeding user caps mid-cycle.Balancing Two-Year Adoption Costs Against Migration Friction
For a business evaluating software with a two-year horizon, the cheapest tool to adopt is rarely the cheapest to abandon. A low-cost standalone generator like Rytr ($9/mo) or Writesonic ($12.67/mo) incurs virtually zero migration friction because you only store text snippets and copy variations. However, if you rely on a comprehensive marketing ecosystem like HubSpot ($20/mo) or GetResponse ($13.30/mo) for funnel building, CRM tracking, and AI email drafting, the cumulative two-year subscription cost is compounded by the hidden labor of data migration should you switch providers. Choosing a native marketing platform makes sense only if you plan to stay long enough for the workflow efficiency to outweigh the eventual cost of extracting your campaigns.Final Verdict: Choosing Your AI Writer Without Getting Trapped
If you need maximum portability and low financial risk, choose a flexible standalone tool like Writesonic or Rytr, where your data remains purely textual and easily transferable via integrations like Zapier. If your workflow demands an all-in-one marketing ecosystem, MailerLite provides a seamless internal AI email writer for Power and Comfort plan users, making its lock-in manageable if you are committed to its email designer. Avoid locking into expensive, deeply entrenched multi-feature automation platforms like ActiveCampaign unless your organization is fully prepared to absorb the high subscription cost ($79/mo) and the heavy migration friction required to ever leave them.#ai #writing #saas #tools #software #coding #development #engineering #inclusive #community
The True Cost of AI Email Writers: Why Exit Terms Matter More Than Sticker Price -
The $1,999 iPhone Duo's inner display is cracking along the hinge within hours. Here's what iFixit found, what it costs to fix, and why waiting might be wise.#apple #iphone #foldable #hardware #software #coding #development #engineering #inclusive #community
Apple's iPhone Duo Is Cracking on Day One -
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Tormenta tropical Vance provoca lluvias fuertes y oleaje elevado en el Pacífico
La tormenta tropical Vance genera lluvias fuertes, rachas de viento y oleaje de hasta cuatro metros frente a costas de Jalisco, Colima y Michoacán. Autoridades advierten sobre inundaciones y deslaves.
Por Gabriela Díaz | Reportera
La tormenta tropical Vance mantiene lluvias fuertes, vientos y oleaje elevado frente a las costas del Pacífico mexicano, con riesgos de inundaciones y deslaves en cinco estados.
A las 21:00 horas, tiempo del centro de México, el sistema se ubicó a 455 kilómetros al sur-suroeste de Manzanillo, Colima, y a 550 kilómetros al sur de Cabo Corrientes, Jalisco, con vientos sostenidos de 85 kilómetros por hora y rachas de hasta 100 kilómetros por hora.
El ciclón avanzaba hacia el oeste a siete kilómetros por hora. Su circulación favorecía el ingreso de humedad y las precipitaciones en zonas de Nayarit, Jalisco, Colima, Michoacán y Guerrero.
Estados con lluvias fuertes
Se pronosticaron lluvias fuertes, de 25 a 50 milímetros, en el sur de Nayarit, el oeste de Jalisco, Colima, la costa de Michoacán y el suroeste de Guerrero.
Las precipitaciones podrían elevar los niveles de ríos y arroyos, provocar desbordamientos e inundar zonas bajas. También existe riesgo de deslaves, especialmente en áreas vulnerables a la acumulación de agua.
Las condiciones meteorológicas podrían cambiar conforme evolucione el sistema, por lo que la población de las regiones bajo pronóstico debe mantenerse atenta a los avisos oficiales.
Vientos y oleaje de hasta cuatro metros
En las costas de Colima y Michoacán se previeron vientos sostenidos de 20 a 30 kilómetros por hora, con rachas de 40 a 60 kilómetros por hora.
Para las costas de Jalisco y Guerrero se estimaron vientos de 10 a 20 kilómetros por hora, con rachas de 30 a 50 kilómetros por hora.
El oleaje podría alcanzar de tres a cuatro metros de altura en las costas de Colima y Michoacán, mientras que en Jalisco y Guerrero se esperaba de dos a tres metros, condiciones que representan riesgos para actividades marítimas y zonas costeras. –sn–
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También, te invitamos a que te sumes a nuestro canal de información en tiempo real a través de Telegram.
#NoticiasMX #PeriodismoParaTi #PeriodismoParaTiSociedadNoticias #alertaMeteorológica #Cdmx #climaMéxico #colima #ComisiónNacionalDelAgua #conagua #Destacado #Guerrero #Información #InformaciónMéxico #Jalisco #lluviasFuertes #México #michoacán #Morena #Nayarit #noticia #noticias #NoticiasMéxico #NoticiasSociedad #oleajeElevado #pronósticoDelTiempo #ProtecciónCivil #ServicioMeteorológicoNacional #SMN #SN #Sociedad #SociedadNoticias #SociedadNoticiasCom #sociedadNoticias #SociedadNoticiasCom #tormentaTropicalVance -
OpenAI has released GPT-6.1 Terra, a fast model for computer use, vision and writing, priced at 1 USD per million input tokens and 5 USD per million output tokens, half the cost of GPT-6.1 Sol. On OpenAI's own benchmarks it matches GPT-6 Astra on the Agents' Last Exam at 0.29 USD per task, though coding still trails Claude Sonnet 5.5. Figures are vendor-reported. https://www.marktechpost.com/2026/10/26/openai-gpt-6-1-terra/ #AIagent #AI #GenAI #AIresearch
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https://www.europesays.com/unitedstates/70264/ Premiums, out-of-pocket costs dominate Americans’ healthcare concerns: survey #Healthcare #HealthcareEconomics
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Bug Bug unveils smaller, cheaper AI pest detectors as it eyes Europe opportunities
Bug Bug cuts hardware costs from US$800 to about US$100.Smaller, palm-siz…
#Europe #EU #aerial #Agricultural #batucity #batur #collard #crop #dieng #eastjava #evergreen #fertilize #field #fields #flowerandfruittree #fungicide #greenvegetable #harvested #harvestedfield #indian #location #man #pesticide #potato #potatofarming #potatoland #produceplant #ricearea #shrubgarden
https://www.europesays.com/europe/167459/ -
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#Arts #Fashion #NVO987 #Culture #NEXARIUM
‘Nobody Wants This’ Season 3: Joanne Is Giving Up Way Too Much of Herself for Noah
The Netflix rom-com keeps asking what Joanne will give up for love. It’s starting to feel bleak.
https://www.cosmopolitan.com/entertainment/tv/a74251348/nobody-wants-this-relationship-critique/ -
Mills near Federal lands have closed and employees have scattered (many are avoiding ICE). Remaining mills and log truckers can no longer afford maintenance and fuel costs even if the supply of logs goes up. And much of the planned logging, even if anyone can be found to do it, will increase rather than decrease fire danger, opposite to what the Administration projects. I suspect that if money is found for the cutting and trucking, those trucks will bypass local mills, as has happened before, and the logs will go onto ships headed across the Pacific, to be milled overseas. >>
Loggers say #RoadlessRule repeal won't solve their biggest challenges
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WSJ: “Hanwha Ocean Key to Trump’s U.S. Shipbuilding Revival”… Local Investment Expansion Draws Attention
WSJ: “Hanwha Ocean a Key Partner in U.S. Shipbuilding”$350 Million Invested After Acquiring Philly ShipyardHigh Costs, Labor Shortages,…
#EuropeSays #Korea #KR #HanwhaOcean #Economic #Hanwha #HanwhaGroup #Issue #news #Newspaper #politics #Society #SouthKorea #TheAsiaBusinessDaily
https://www.europesays.com/korea/203545/ -
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Hermes' unpopular Geta Tote Handbags, Hermes' clog bag is a popular niche bag type, From the perspective of fashion sense, cost-effectiveness, and compatibility, it is all good, With a hint of artistic atmosphere, Fang Fang's bag is very eye-catching, Size 20 * 14, item number 31139,
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https://www.europesays.com/ch-fr/367469/ Marta Coca (Fashion Week de Barcelone): « La mode est un secteur difficile mais résilient » #080Barcelona #Beauté #contact #cosmétiques #Divertissement #Entertainment #FashionNetworkCom #Karstadt #LouisGabrielNouchi #Luxe #Mode #parfum #professionnel #professionnels #Réseau #Suisse #textile
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Democrats Could Win in 2026—and Lose in 2028 by Failing Working People
A 2026 victory could become a 2028 backlash if Democrats abandon working people. Why Medicare for All, public accountability, and a visible fight for change matter.
#BidenEconomy #BureauOfLaborStatistics #ConsumerPriceIndex #ConsumerProtection #corporateGreed #corporatePower #corporateProfits #costOfLiving #DonaldTrump #EconomicInequality #EconomicJustice #Greedflation #inflation #InflationFactCheck #laborRights #PoliticsDoneRight #priceGouging #progressiveEconomics #TrumpEconomy #WorkingFamilies https://wp.me/p1OjMZ-phS -
Trump’s Inflation Lies Collapse: The Numbers Expose His Economic Con
Trump’s inflation claims collapse under scrutiny. Official data expose the false history, while profit research reveals how corporations helped squeeze working families.
#BidenEconomy #BureauOfLaborStatistics #ConsumerPriceIndex #ConsumerProtection #corporateGreed #corporatePower #corporateProfits #costOfLiving #DonaldTrump #EconomicInequality #EconomicJustice #Greedflation #inflation #InflationFactCheck #laborRights #PoliticsDoneRight #priceGouging #progressiveEconomics #TrumpEconomy #WorkingFamilies https://wp.me/p1OjMZ-phO