AIF-C01, MLA-C02, and AIP-C01 are not a difficulty ladder but three job-function slices: AIF tests AI business judgment, MLA tests whether you can put traditional ML, foundation models, and agentic workflows into production, and AIP tests whether you can integrate foundation models into a GenAI system. The MLA-C02 English beta is open for registration; general-availability dates and non-English versions remain unannounced.
Before comparing exam objectives there is one fact that outranks all of them: registration for the Claude certifications is open only to organizations in the Claude Partner Network — individuals cannot sign up. For those who clear that gate, four things actually decide the answer. CCAO-F ($99) does not count toward partner tier eligibility while the other three do. Claude Code is 20% of CCAR-F but only 3.1% of CCDV-F — the architect exam tests the tool far more heavily than the developer exam. CCAR-P is 28% non-technical (governance 14% plus stakeholder communication 14%), which nothing else in this series is. And CCAO-F is the cheapest but only 14% Prompting; Output Evaluation at 21% is its real spine. All four are valid 12 months, with retakes at 14 / 30 / 90 days and 4 attempts per rolling 12 months.
Across Microsoft's four AI/agent certifications there are two hard prerequisites: AI-500 requires AI-103, and AB-100 requires any one associate on its list. The rest is positioning. Three forks decide it: whether you write Python (AI-103/AI-500 vs AB-620), whether you build or judge (AB-100 vs the rest), and whether you can actually start today — AI-500's four official learning paths currently 404, AB-620 has no practice test, AB-100 has a free one. For readers who prefer Chinese there is a fourth fork: AI-103, AB-620, and AB-100 offer Traditional Chinese, and only AI-500 is English only. All four cost $165 in the US ($83 in Taiwan), expire after one year, and renew free but only inside a six-month window.
NVIDIA's generative AI line has four exams: NCA-GENL and NCA-GENM ($125 each, associate), NCP-GENL and NCP-AAI ($200 each, professional). Three decision inputs no other vendor forces on you. One: both professional exams still show 'Coming soon' next to Register, so any near-term plan is down to the two associates. Two: the cost of the official recommended training differs sharply by level. In autumn 2026 the two associate exams switched to one free open-source set of materials plus one course, while the two professional exams still list five paid courses, $1,620 at list price for NCP-GENL. Three: the official web pages and PDFs differ in a few places — two NCP-AAI weights do not match, and two cells of NCP-GENL's web table carry misplaced text — so for those two exams follow the PDF or budget a range. Lock-in also varies sharply: NCP-AAI is 7% NVIDIA-specific, NCP-GENL is 31% GPU and model-compression work.
Aider is a terminal AI pair programmer dating back to 2023 (Python, Apache-2.0, ~48.3k stars), designed against the grain of today's autonomous agents: you control context by hand with /add, every edit becomes its own atomic git commit, and architect/editor mode splits planning from editing across two models. But note the maintenance cadence: the latest PyPI release is 0.86.2 from 2026-02, the last commit was 2026-05, and the site still recommends Claude 3.7 Sonnet and o1.
Amp spun out of Sourcegraph in December 2025 as Amp Frontier Corporation, and its npm package moved from @sourcegraph/amp to @ampcode/cli. Its defining trait is deletion: the editor extension, Amp Tab, TODO lists, Fork, custom commands, and public threads have all been removed. Monthly subscriptions only arrived on 2026-07-18 (Megawatt $20, Gigawatt $200); before that it was pay-as-you-go only. The current focus is orbs — remote machines that keep working after you close your laptop.
Copilot CLI went GA on 2026-02-25 and is included in every Copilot plan, Free included. Its differentiator isn't the agent — it's the GitHub integration: a built-in GitHub MCP server that works on issues and PRs, org policies inherited automatically, and an `&` prefix that hands work to the cloud coding agent. Billing runs on GitHub AI Credits (1 credit = $0.01): Pro $10/mo includes $15, Pro+ $39 includes $70, Max $100 includes $200.
omp is a fork of Pi, but it is not just a plugin layer stacked on top: it adds roughly 80,000 lines of Rust, pulling grep, shell, AST, and PTY in-process. Built-in tools go from Pi's 7 to 31, plus 14 LSP ops, 28 DAP ops, and 60+ providers. One codebase, two opposite bets.
TanStack Router (19.7M weekly downloads) + Query (55.8M) + Zustand (44.5M) as the core, with Vite, react-hook-form + Zod (224M), Tailwind + shadcn, and Vitest + Playwright — the current default stack for serious SPAs. The AI era adds three new selection criteria: does the docs site ship llms.txt (all of TanStack does; React Router doesn't), can type safety act as an agent guardrail, and does the source code live in your repo where an agent can read it.
AI Elements is Vercel's React component library for the AI SDK ecosystem — the registry currently holds 48 components covering Conversation, Reasoning, Sources, Tool, and the rest of the AI-interface vocabulary. It follows the shadcn model: npx ai-elements@latest copies the source into your project, fully editable, mapped one-to-one onto useChat's message parts.
The 2026 consensus for agent-built slide decks: outline-first, separate content from construction, then render to images and let a fresh-eyes subagent do visual QA. Anthropic's and OpenAI's official slides skills both converged on PptxGenJS plus a visual verification loop, and the research line (PPTAgent → PreGenie → DeepPresenter) points the same way. But two later corrections matter: PresentBench shows the widely cited PPTEval scores too generously, and SeaSlides argues the model should not write free-form HTML/SVG at all.
Governance carries more weight on these exams than most engineers expect — CCAR-P is 14% governance plus 14% stakeholder work (28% non-technical), CCAO-F is 15%, AB-100's deploy-and-govern block is 40–45%, AIF-C01 is 14% responsible AI plus 14% security/compliance/governance. But across all fifteen official exam guides in this series, not one names the EU AI Act, the NIST AI RMF, or ISO/IEC 42001; the only regulations any of them names are CCAR-P's GDPR, HIPAA, and FedRAMP. So the use of this post isn't memorizing frameworks for an exam — it's using the three frameworks as a skeleton to file six certifications' scattered governance objectives. The three split cleanly: the EU AI Act is law (fully applicable 2026-08-02, high-risk duties pushed to 2027-12-02 by the AI Omnibus), the NIST AI RMF is voluntary (GOVERN/MAP/MEASURE/MANAGE, and 1.0 is currently being revised), and ISO/IEC 42001 is a certifiable management system standard whose clauses are paid, so this post covers only what is on ISO's public page.
The AIF-C01 exam guide moved to v1.1 on April 30, 2026, adding seven objectives — MCP, multi-agent patterns, context engineering, token-based pricing, and hallucination detection all became testable, with Bedrock AgentCore, Kiro, and Strands Agents joining in-scope services. Almost every summary online describes the older version. This guide builds on the official five-domain weighting, integrating AWS's four-step prep method and field-tested advice from 10+ candidates who passed. $100, 90 minutes, 65 questions (50 scored), pass at 700, valid 3 years — and it is offered in Traditional Chinese.
AIP-C01 tests integrating foundation models into production AWS applications: RAG, agents, security, cost, and evaluation. This guide follows the five official domains with a prerequisite self-check, four-step preparation plan, resource choices, and hands-on checkpoints, plus a ten-week schedule, an AI study prompt, and exam-day guidance. Start with a diagnostic, then close gaps through one RAG-and-agent project.
A complete study guide for Claude's official architect certification (CCAR-F): five domains weighted 27/18/20/20/15, four scenarios drawn from six, common anti-patterns, and hands-on preparation. Official specs are 60 items / 120 minutes / $125 / 12-month validity / 720 to pass; registration is limited to Claude Partner Network members, and on-time renewal is free and non-proctored.
CCAR-P is the most expensive and most senior of Anthropic's four exams ($175, 63 items, 120 minutes). Integration is the heaviest domain at 19%, but what really separates it from everything else in this series is the other two: Governance, Safety & Risk Management at 14% and Stakeholder Communication & Lifecycle Management at 14% — 28% combined on compliance, risk, discovery interviews, and delivery lifecycle rather than code. The guide names GDPR, HIPAA, and FedRAMP, and its Intended Audience explicitly excludes entry-level developers and anyone doing 'prompt writing without broader system design responsibility.'
CCAO-F is the cheapest of Anthropic's four exams ($99, 60 items, 120 minutes), aimed at people who work with Claude rather than build against it. The heaviest of its seven domains is Output Evaluation and Validation at 21% — spotting hallucinations, deciding when human review is required, and adapting outputs — with Governance, Risk, and Responsible Use at another 15%. Anthropic states plainly that it is not for developers building against APIs or designing agentic systems. One easily missed limitation: this credential does not count toward Claude Partner Network tier eligibility, while the other three do.
CCDV-F is the engineer's exam among Anthropic's four certifications. The official blueprint has eight domains, and the heaviest — Applications and Integration at 33.1% — is led by Claude Application Design (8.6%) and Software Engineering Foundations (7.4%), meaning a third of the exam is API mechanics and ordinary software engineering. The counterintuitive part: Claude Code is only 3.1% and Eval only 2.6%, while the sibling Architect exam gives Claude Code 20%. Official specs: $125, 53 items, 120 minutes, pass at 720, valid 12 months, registration limited to Claude Partner Network organizations.
Google PMLE, AWS AIF-C01 and AIP-C01, Microsoft AI-103 and AI-500, and NVIDIA NCP-GENL all test how to make a GenAI application fast, cheap, and reliable — and they form a three-rung ladder: AIF-C01 asks whether you know cost scales with tokens, AIP-C01 and the two Microsoft exams ask whether you can instrument and control it, NCP-GENL asks whether you can change the model and the hardware. Three different altitudes. NVIDIA works at the kernel and quantization layer (Model Optimization 17% + GPU Acceleration 14% = 31%, the heaviest single cost/latency block in the whole series), AWS and Microsoft at the application layer (three caching tiers, token caps, chargeback), and Google at the MLOps layer (CPU/GPU/TPU evaluation, data vs model parallelism, scaling serving backends by throughput). The shared core is eight levers, but each lever becomes a different question at each altitude. This post deliberately carries no prices and no hardware specs — that is the part of this topic that rots fastest.
Google's Professional ML Engineer exam guide was rewritten in 2026: Vertex AI is renamed Gemini Enterprise Agent Platform throughout, so older study material no longer matches the product names in the questions. This guide uses the official six-section weighting as its skeleton, listing what each section tests, which official materials cover it, and what to build — plus a study schedule whose reasoning is spelled out. Official specs: $200, two hours, 50–60 multiple-choice and multiple-select questions, two-year validity, 3+ years of industry experience recommended including 1+ year on Google Cloud.