live MVPcase studyMistral workflow
myberuf.com
German-for-work learning product built with AI agents, Mistral model workflow, interview/onboarding coaching, Vapi voice experiment, Vercel previews, and handovers.
builder console / budget MVP workflows
Build with AI agents without losing product judgment.
Practical AI workflows for founders, students, creators, consultants, and small teams who want to turn ideas into real MVPs on a budget. Not tool hype. Real build logs, guardrails, smoke tests, and handovers from actual projects.
why this exists
The bottleneck is not just coding. It is clarity, context, validation, and handoff.
AI tools make it possible to build faster. They also make it easier to build the wrong thing faster. AI Tool Consultant is a practical build log for using agents without turning your project into a fog machine.
Product judgment
Choose one painful situation, not a giant app. The strongest AI sprint starts with a narrow human problem.
Context control
Separate strategy chats from implementation sessions so old context does not pollute new work.
Guardrails
Validation, rate limits, request checks, and honest beta copy matter before you show the thing to users.
Reviewable slices
Small vertical slices beat "build the full platform" prompts. Ship one thing you can test.
operating system
Strategy -> task -> execution -> validation -> handover.
This is the core workflow. Not one magic AI. A small operating system for moving between thinking, building, checking, and preserving context.
01.strategy
Think before building
Use ChatGPT and Claude for product reasoning, pedagogy, positioning, and task design.
02.task
Scope the slice
Turn messy ambition into one reviewable sprint with explicit non-goals and files to inspect.
03.execute
Use repo-aware agents
Use Codex or Claude Code for targeted edits, debugging, validation commands, and branch discipline.
04.validate
Smoke before ship
Check the user flow manually. Add cheap request guards before expensive model calls.
05.handover
Write the memory
Record files, checks, decisions, risks, rollback notes, and the next step so the next agent starts clean.
$ handover --to next-agent
include: goal, files touched, checks, smoke results, known risks, rollback point
outcome: less context pollution, fewer repeated fixes, cleaner continuation
include: goal, files touched, checks, smoke results, known risks, rollback point
outcome: less context pollution, fewer repeated fixes, cleaner continuation
labs / case studies
Real build labs, not fake portfolio padding.
These are my own builder/operator projects and planned field notes. The main cards focus on AI/software-relevant work, not generic founder nostalgia.
local-first evaluationprivacy-aware
GOJA NLP extraction
Local LLM evaluation for structured information extraction from German job ads under privacy and institutional constraints, connected to BIBB-style vocational-data extraction questions.
work contextupcoming article
Caidera comparison
Upcoming comparison note around regulated marketing AI, Life Sciences/healthcare context, and compliance-aware workflows.
published field noteDatenschutz
Usable Privacy in AI
English field note on usable privacy, Datenschutz as architecture, local models, hybrid filters, secrets, and responsible AI-agent workflows.
tool choice is task choice
The stack is a division of labor.
No fake ranking. I use different tools for strategy, execution, deployment, local evaluation, and model-layer experiments.
synthesis + prompts
ChatGPT / OpenAIBrainstorming, writing, strategy, synthesis, handoff prompts, and turning messy thoughts into usable tasks.
ambiguous product reasoning
Claude / AnthropicPedagogy, product architecture, and strategic reasoning when the answer is still fuzzy.
implementation-heavy work
Claude Code / SonnetStructured code edits and product build tasks when the scope and files are clear.
repo-aware execution
Codex / OpenAITargeted changes, validation, debugging, and fixes from project reports without dragging every old chat forward.
coaching model layer
MistralPromising for myberuf because German coaching quality, latency, cost, deployment trust, and European AI context all matter.
deployment + rollback
Vercel + GitHubPreview deployments, branches, commits, and rollback points so AI-built work becomes testable.
privacy-aware experiments
Local / OllamaUseful for GOJA-style evaluation and local experiments where data locality and institutional constraints matter.
$ choose-tool --by-job-to-be-done
strategy needs different tools than repo execution.
deployment needs different checks than model exploration.
rule: choose by job-to-be-done; no invented metrics or context-free "best model" claims.
strategy needs different tools than repo execution.
deployment needs different checks than model exploration.
rule: choose by job-to-be-done; no invented metrics or context-free "best model" claims.
content series
Building on a Budget with AI
A practical series on using AI agents to ship real products without pretending the tools are magic. The first field note is live; deeper resources stay lightweight while this remains intentionally CMS-free.
01
published field notebudget build
Best AI Stack for Building on a Budget
What I learned building myberuf with Claude, Codex, ChatGPT, Mistral, Vercel, GitHub, docs, and smoke tests.
02
published case studyvoice experiment
myberuf: typed + voice learning
How a German-for-work MVP became a practical lab for coaching, handovers, Vapi voice experiments, and Mistral model-layer thinking.
03
published case studylocal-first
GOJA NLP extraction
Local LLM evaluation for German job-ad extraction: parse reliability, schema failures, prompt ablation, and production limits.
04
published field noteDatenschutz
Usable Privacy in AI
Why privacy in AI products needs architecture, task-aware interaction design, local-model patterns, and disciplined data flows.
05
coming soonVapi
Voice AI / Vapi automation
A deeper field note on spoken learning flows, latency, and why voice changes the model-infrastructure question.
06
coming soonregulated marketing
Caidera comparison
Upcoming source-backed comparison around regulated marketing AI, compliance-aware workflows, and healthcare/Life Sciences context.
builder behind the console
I'm Aviral, a founder/operator documenting real AI-assisted building workflows.
I build AI-assisted products and turn the process into practical field notes: German-for-work learning at myberuf, local LLM evaluation in GOJA, and regulated-marketing AI work. AI Tool Consultant is one builder's operating desk, not a large agency.
Handle: @aviralabroad. I'm also connected to the 42 Berlin peer-project-based builder ecosystem.
contact
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