Multi-Provider Strategies: Avoiding Vendor Lock-in While Maximizing Value

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18769559  23stabilfr·wdophcgmx
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Enterprise adoption of large language models (LLMs) has introduced a new dimension of vendor lock-in that differs fundamentally from traditional software dependencies. Unlike switching ERP systems or databases—where migration paths are well-understood—LLM provider transitions involve prompt re-engineering, model behavior differences, and hidden integration costs that can reach six figures even ...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18769559 23stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥80% of references from 2025–2026. Current: 38%
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[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
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Testing and Validation Costs in Enterprise AI: Economic Analysis of Quality Assurance Investment

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18755863  30stabilfr·wdophcgmx
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[l]Academic6%○≥80% from journals/conferences/preprints
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[r]References18 refs✓Minimum 10 references required
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]6%✗≥80% of references from 2025–2026. Current: 6%
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Testing and validation represent 10-15% of total AI development costs, yet inadequate investment in this phase contributes significantly to the 80-95% failure rate of AI projects. This paper presents an economic framework for analyzing testing and validation costs across the AI lifecycle, from initial test data acquisition through continuous production monitoring. We examine cost structures of ...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18755863 30stabilfr·wdophcgmx
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[r]References18 refs✓Minimum 10 references required
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18755863
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]6%✗≥80% of references from 2025–2026. Current: 6%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
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Architecting Spec-Compliant AI Systems: Patterns and Anti-Patterns

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18745394  49stabilfr·wdophcgmx
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[s]Reviewed Sources36%○≥80% from editorially reviewed sources
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[r]References25 refs✓Minimum 10 references required
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The integration of artificial intelligence into enterprise systems demands rigorous architectural approaches that ensure reliability, maintainability, and compliance with specifications. This article explores architectural patterns that support spec-driven development of AI systems, contrasting proven design patterns with common anti-patterns that lead to technical debt. We examine contract-bas...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18745394 49stabilfr·wdophcgmx
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[s]Reviewed Sources36%○≥80% from editorially reviewed sources
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[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef24%○≥80% indexed in CrossRef
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[f]Free Access24%○≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]3,006✓Minimum 2,000 words for a full research article. Current: 3,006
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18745394
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
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Specialized vs General Models — When to Use Domain-Specific AI

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18743173  45stabilfr·wdophcgmx
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[t]Trusted55%○≥80% from verified, high-quality sources
[a]DOI31%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]12%✗≥80% of references from 2025–2026. Current: 12%
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[g]Code—○Source code available on GitHub
[m]Diagrams8✓Mermaid architecture/flow diagrams. Current: 8
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Ivchenko, O. (2026). Specialized vs General Models — When to Use Domain-Specific AI. Cost-Effective Enterprise AI Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18746111 Abstract The enterprise AI landscape is undergoing a fundamental shift from general-purpose large language models (LLMs) to domain-specific language models (DSLMs) optimized for particula...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18743173 45stabilfr·wdophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
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[a]DOI31%○≥80% have a Digital Object Identifier
[b]CrossRef10%○≥80% indexed in CrossRef
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[l]Academic41%○≥80% from journals/conferences/preprints
[f]Free Access47%○≥80% are freely accessible
[r]References51 refs✓Minimum 10 references required
[w]Words [REQ]4,226✓Minimum 2,000 words for a full research article. Current: 4,226
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18743173
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]12%✗≥80% of references from 2025–2026. Current: 12%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams8✓Mermaid architecture/flow diagrams. Current: 8
[x]Cited by0○Referenced by 0 other hub article(s)
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Integration Economics: Legacy System Adaptation for AI Deployment

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18740871  34stabilfr·wdophcgmx
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[t]Trusted24%○≥80% from verified, high-quality sources
[a]DOI16%○≥80% have a Digital Object Identifier
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[r]References38 refs✓Minimum 10 references required
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]13%✗≥80% of references from 2025–2026. Current: 13%
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[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Integrating artificial intelligence into existing enterprise infrastructure represents one of the most significant economic challenges in AI deployment. While substantial research examines AI development costs, the economics of legacy system adaptation remain inadequately explored. This paper presents a comprehensive economic framework for understanding integration costs, analyzing cost structu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18740871 34stabilfr·wdophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted24%○≥80% from verified, high-quality sources
[a]DOI16%○≥80% have a Digital Object Identifier
[b]CrossRef3%○≥80% indexed in CrossRef
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[l]Academic18%○≥80% from journals/conferences/preprints
[f]Free Access16%○≥80% are freely accessible
[r]References38 refs✓Minimum 10 references required
[w]Words [REQ]4,651✓Minimum 2,000 words for a full research article. Current: 4,651
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18740871
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]13%✗≥80% of references from 2025–2026. Current: 13%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Agents Operate With Minimal Safety Disclosures: MIT Study Reveals Transparency Gap

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741627  34stabilfr·wdophcgmx
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[t]Trusted30%○≥80% from verified, high-quality sources
[a]DOI10%○≥80% have a Digital Object Identifier
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[i]Indexed20%○≥80% have metadata indexed
[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]3,130✓Minimum 2,000 words for a full research article. Current: 3,130
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741627
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥80% of references from 2025–2026. Current: 40%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

MIT CSAIL's 2025 AI Agent Index analyzed 30 prominent AI agents and found a striking transparency deficit: while 70% provide documentation and nearly half publish code, only 19% disclose formal safety policies and fewer than 10% report external safety evaluations. This journal entry examines the study's findings, contextualizes the claims within the broader AI safety discourse, and assesses whe...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18741627 34stabilfr·wdophcgmx
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[t]Trusted30%○≥80% from verified, high-quality sources
[a]DOI10%○≥80% have a Digital Object Identifier
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[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]3,130✓Minimum 2,000 words for a full research article. Current: 3,130
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741627
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥80% of references from 2025–2026. Current: 40%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Predicting Armed Conflict Probability: A Multi-Factor Machine Learning Approach

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18735965  55stabilfr·wdophcgmx
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[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
[b]CrossRef27%○≥80% indexed in CrossRef
[i]Indexed36%○≥80% have metadata indexed
[l]Academic36%○≥80% from journals/conferences/preprints
[f]Free Access27%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]3,814✓Minimum 2,000 words for a full research article. Current: 3,814
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18735965
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥80% of references from 2025–2026. Current: 10%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (57 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Armed conflict prediction represents one of the most critical challenges in computational social science and international relations. This paper presents a multi-factor machine learning approach to predicting armed conflict probability at the country level, combining ensemble learning methods with diverse data sources including ACLED, UCDP, World Bank economic indicators, SIPRI military expendi...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18735965 55stabilfr·wdophcgmx
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[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
[b]CrossRef27%○≥80% indexed in CrossRef
[i]Indexed36%○≥80% have metadata indexed
[l]Academic36%○≥80% from journals/conferences/preprints
[f]Free Access27%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]3,814✓Minimum 2,000 words for a full research article. Current: 3,814
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18735965
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥80% of references from 2025–2026. Current: 10%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (57 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Development Paradigms Compared: Spec-Driven, Experiment-Driven, and Hybrid Approaches

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741619  48stabilfr·wdophcgmx
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[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted56%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef18%○≥80% indexed in CrossRef
[i]Indexed47%○≥80% have metadata indexed
[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References34 refs✓Minimum 10 references required
[w]Words [REQ]3,773✓Minimum 2,000 words for a full research article. Current: 3,773
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741619
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥80% of references from 2025–2026. Current: 3%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (46 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The development of AI systems presents unique challenges that traditional software engineering paradigms struggle to address. This article provides a comprehensive comparative analysis of four major development approaches: spec-driven development, experiment-driven development, data-centric AI, and model-centric AI. We examine each paradigm's theoretical foundations, practical workflows, and su...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18741619 48stabilfr·wdophcgmx
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[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted56%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef18%○≥80% indexed in CrossRef
[i]Indexed47%○≥80% have metadata indexed
[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References34 refs✓Minimum 10 references required
[w]Words [REQ]3,773✓Minimum 2,000 words for a full research article. Current: 3,773
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741619
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥80% of references from 2025–2026. Current: 3%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (46 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Open Source LLMs in Production — Llama, Mistral, and Beyond

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741621  45stabilfr·wdophcgmx
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[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
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[l]Academic49%○≥80% from journals/conferences/preprints
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Score = Ref Trust (40 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Throughout my career deploying AI systems at enterprise scale, I have observed a fundamental shift in how organizations approach large language model (LLM) infrastructure. The emergence of high-quality open source models from Meta, Mistral AI, Alibaba, and others has transformed the economics of enterprise AI deployment. Where organizations once faced a binary choice between expensive proprieta...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18741621 45stabilfr·wdophcgmx
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[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
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[l]Academic49%○≥80% from journals/conferences/preprints
[f]Free Access59%○≥80% are freely accessible
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[w]Words [REQ]4,337✓Minimum 2,000 words for a full research article. Current: 4,337
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741621
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Compliance Costs: GDPR, AI Act, and Industry-Specific Regulations

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730888  36stabilfr·wdophcgmx
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730888
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[x]Cited by0○Referenced by 0 other hub article(s)
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Regulatory compliance represents a critical economic dimension of enterprise AI deployment, with costs ranging from $20,000 for small implementations to over $15 million for large-scale high-risk systems. This article analyzes compliance cost structures across major regulatory frameworks — GDPR, EU AI Act, FDA medical device regulations, and financial services requirements — providing quantitat...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18730888 36stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted49%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
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[w]Words [REQ]3,423✓Minimum 2,000 words for a full research article. Current: 3,423
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730888
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]19%✗≥80% of references from 2025–2026. Current: 19%
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[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (25 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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