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

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18769559  41stabilfr·wuedophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
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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 41stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access33%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
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[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18769559
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness27%○≥60% of references from 2025–2026. Current: 27%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
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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  44stabilfr·wuedophcgmx
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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted23%○≥80% from verified, high-quality sources
[a]DOI9%○≥80% have a Digital Object Identifier
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (18 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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 44stabilfr·wuedophcgmx
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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted23%○≥80% from verified, high-quality sources
[a]DOI9%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed9%○≥80% have metadata indexed
[l]Academic14%○≥80% from journals/conferences/preprints
[f]Free Access27%○≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]2,852✓Minimum 1,000 words of article body. Current: 2,852
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]16✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 16
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18755863
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness5%○≥60% of references from 2025–2026. Current: 5%
[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
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (18 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Architecting Spec-Compliant AI Systems: Patterns and Anti-Patterns

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18745394  61stabilfr·wuedophcgmx
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[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted56%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef26%○≥80% indexed in CrossRef
[i]Indexed30%○≥80% have metadata indexed
[l]Academic56%○≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]3,024✓Minimum 1,000 words of article body. Current: 3,024
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 61stabilfr·wuedophcgmx
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[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted56%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef26%○≥80% indexed in CrossRef
[i]Indexed30%○≥80% have metadata indexed
[l]Academic56%○≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]3,024✓Minimum 1,000 words of article body. Current: 3,024
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]24✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 24
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18745394
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness4%○≥60% of references from 2025–2026. Current: 4%
[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 (47 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Specialized vs General Models — When to Use Domain-Specific AI

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18743173  57stabilfr·wuedophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI30%○≥80% have a Digital Object Identifier
[b]CrossRef9%○≥80% indexed in CrossRef
[i]Indexed19%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access51%○≥80% are freely accessible
[r]References53 refs✓Minimum 10 references required
[w]Words [REQ]3,835✓Minimum 1,000 words of article body. Current: 3,835
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18743173
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[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)
Score = Ref Trust (40 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 57stabilfr·wuedophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI30%○≥80% have a Digital Object Identifier
[b]CrossRef9%○≥80% indexed in CrossRef
[i]Indexed19%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access51%○≥80% are freely accessible
[r]References53 refs✓Minimum 10 references required
[w]Words [REQ]3,835✓Minimum 1,000 words of article body. Current: 3,835
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]50✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 50
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18743173
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness13%○≥60% 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]Diagrams8✓Mermaid architecture/flow diagrams. Current: 8
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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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  47stabilfr·wuedophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted23%○≥80% from verified, high-quality sources
[a]DOI15%○≥80% have a Digital Object Identifier
[b]CrossRef3%○≥80% indexed in CrossRef
[i]Indexed15%○≥80% have metadata indexed
[l]Academic23%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References40 refs✓Minimum 10 references required
[w]Words [REQ]4,667✓Minimum 1,000 words of article body. Current: 4,667
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]36✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 36
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18740871
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness12%○≥60% 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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (23 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 47stabilfr·wuedophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted23%○≥80% from verified, high-quality sources
[a]DOI15%○≥80% have a Digital Object Identifier
[b]CrossRef3%○≥80% indexed in CrossRef
[i]Indexed15%○≥80% have metadata indexed
[l]Academic23%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References40 refs✓Minimum 10 references required
[w]Words [REQ]4,667✓Minimum 1,000 words of article body. Current: 4,667
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]36✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 36
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18740871
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness12%○≥60% 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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (23 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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AI Agents Operate With Minimal Safety Disclosures: MIT Study Reveals Transparency Gap

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741627  47stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,097✓Minimum 1,000 words of article body. Current: 3,097
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741627
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness31%○≥60% of references from 2025–2026. Current: 31%
[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 (23 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 47stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,097✓Minimum 1,000 words of article body. Current: 3,097
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741627
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness31%○≥60% of references from 2025–2026. Current: 31%
[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 (23 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  63stabilfr·wuedophcgmx
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[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted69%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef23%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic38%○≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]3,852✓Minimum 1,000 words of article body. Current: 3,852
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]9✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18735965
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness8%○≥60% of references from 2025–2026. Current: 8%
[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 (51 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 63stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted69%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef23%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic38%○≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]3,852✓Minimum 1,000 words of article body. Current: 3,852
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]9✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18735965
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness8%○≥60% of references from 2025–2026. Current: 8%
[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 (51 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  59stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access28%○≥80% are freely accessible
[r]References36 refs✓Minimum 10 references required
[w]Words [REQ]3,781✓Minimum 1,000 words of article body. Current: 3,781
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]33✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 33
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741619
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness3%○≥60% 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 (44 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 59stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access28%○≥80% are freely accessible
[r]References36 refs✓Minimum 10 references required
[w]Words [REQ]3,781✓Minimum 1,000 words of article body. Current: 3,781
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]33✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 33
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741619
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness3%○≥60% 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 (44 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  57stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted49%○≥80% from verified, high-quality sources
[a]DOI15%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed42%○≥80% have metadata indexed
[l]Academic49%○≥80% from journals/conferences/preprints
[f]Free Access60%○≥80% are freely accessible
[r]References72 refs✓Minimum 10 references required
[w]Words [REQ]5,024✓Minimum 1,000 words of article body. Current: 5,024
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]69✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 69
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741621
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness8%○≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[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 (4/4 × 30%) + Optional (2/7 × 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 57stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted49%○≥80% from verified, high-quality sources
[a]DOI15%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed42%○≥80% have metadata indexed
[l]Academic49%○≥80% from journals/conferences/preprints
[f]Free Access60%○≥80% are freely accessible
[r]References72 refs✓Minimum 10 references required
[w]Words [REQ]5,024✓Minimum 1,000 words of article body. Current: 5,024
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]69✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 69
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741621
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness8%○≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[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 (4/4 × 30%) + Optional (2/7 × 10%)
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Compliance Costs: GDPR, AI Act, and Industry-Specific Regulations

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730888  48stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted46%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access32%○≥80% are freely accessible
[r]References37 refs✓Minimum 10 references required
[w]Words [REQ]3,473✓Minimum 1,000 words of article body. Current: 3,473
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]36✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 36
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730888
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness18%○≥60% of references from 2025–2026. Current: 18%
[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 (25 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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 48stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted46%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access32%○≥80% are freely accessible
[r]References37 refs✓Minimum 10 references required
[w]Words [REQ]3,473✓Minimum 1,000 words of article body. Current: 3,473
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]36✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 36
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730888
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness18%○≥60% of references from 2025–2026. Current: 18%
[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 (25 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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