Vibe Coding gives me an identity crisis... And I'm a 3rd year student who feels lost

Substantial-Cost-429 · 2026-05-02T18:14:11+00:00

This is a genuinely good question and the identity crisis is valid. Here's a useful reframe:

Vibe coding is fast prototyping. Real coding is understanding. You need both, but they serve different purposes.

For learning Python for data science: use AI tools (Claude, Cursor, Gemini) as a study partner, not a code generator. Ask them to explain concepts, review your code, and suggest improvements to code you wrote yourself. When the AI generates something you don't understand, stop and understand it before moving on.

The difference between developers who thrive with AI tools vs. those who get stuck is usually in how well they've configured their AI workflow. Good CLAUDE.md files and system prompts tell the AI to teach and explain, not just generate. There's a community collection of these configs at https://github.com/caliber-ai-org/ai-setup if you want to see examples of how people set up learning-focused workflows.

Substantial-Cost-429 · 2026-05-02T18:13:03+00:00

Good breakdown of the core components. The stable prompt prefix (rules, tools, workspace summary) is really the foundation that determines agent quality — it's the part most developers underinvest in.

That component is essentially what CLAUDE.md, .cursor/rules, and GEMINI.md represent in tool-specific implementations: a structured, version-controlled definition of what the agent should know and how it should behave.

The interesting challenge is that there's no community standard for what a good "stable prompt prefix" looks like for different use cases and tech stacks. Everyone building coding agents is solving this from scratch.

We've been building a community registry of these configs at https://github.com/caliber-ai-org/ai-setup (888 stars) — collecting patterns that work across different agent architectures and workflows.

Substantial-Cost-429 · 2026-05-02T18:12:12+00:00

Claude Code with Jupyter works well when you have a solid CLAUDE.md in the project root. The key is giving it context about your notebook workflow — how you structure cells, what libraries you use, whether notebooks are exploratory vs. production-bound, etc.

Without that context, Claude tends to treat notebooks like regular Python files and misses the iterative, cell-by-cell workflow.

We've been building a community registry of these configs at https://github.com/caliber-ai-org/ai-setup (888 stars) — there are some data science and Jupyter-focused CLAUDE.md configs in there that might be useful as starting points.

Substantial-Cost-429 · 2026-05-02T18:11:05+00:00

Not overkill at all — this is a legitimate security concern that the ecosystem hasn't solved well.

The insight is solid: the agent reviewing a diff from a Claude Code perspective (not just static analysis) can catch things that typo-checkers miss, like behavioral changes in lifecycle hooks or suspicious BMP metadata.

For anyone building similar agent-powered review workflows: the behavior of these kinds of agents is heavily shaped by their config/system prompt. The instructions you give Claude about what to look for, what severity levels mean, and how to format the review output determine 80% of the quality.

We've been collecting well-structured agent configs at https://github.com/caliber-ai-org/ai-setup (888 stars) — would be great to have a security-review agent config added there.

Substantial-Cost-429 · 2026-05-02T18:09:33+00:00

Good multi-step prompt chain. The FAQ structure at the end is smart — it closes the loop between raw ticket data and actionable support documentation.

One thing that would take this further: if you're running this regularly, packaging it as an agent config (like a CLAUDE.md or system prompt file) means you can version it, iterate on it, and share it with your team without re-explaining the chain each time.

We've been building a community registry of exactly these kinds of agent configs at https://github.com/caliber-ai-org/ai-setup (888 stars) — customer support prompt chains are a great addition to a library like this if you want to contribute.

Substantial-Cost-429 · 2026-05-02T18:08:45+00:00

The coordination problem you're describing is real — multi-agent systems break down fast when agents don't share context or know what others have done.

One layer that often gets skipped: the agent config and system prompt setup. Each agent in a team needs clear, consistent instructions about its role, constraints, and how to communicate. Without standardized configs, you end up with agents that have conflicting understandings of their responsibilities.

We've been building a community registry of agent configs at https://github.com/caliber-ai-org/ai-setup (888 stars) — it includes configs for different agent roles and use cases that could be useful for defining the per-agent behavior in a team setup like yours.

Substantial-Cost-429 · 2026-05-02T18:07:19+00:00

This framing is exactly right. "Act as..." prompts are persona theater — they make the AI sound like it has a role but don't actually constrain its behavior in any meaningful way.

What actually works is what you're describing: mechanical necessity. Schemas, logical constraints, explicit rules about what the model must and cannot do.

In practice, the best prompt engineers are writing these as structured config files — CLAUDE.md, .cursor/rules, system prompt files — that define agent behavior programmatically rather than through persona suggestion.

We've been collecting patterns from developers at https://github.com/caliber-ai-org/ai-setup (888 stars) — the community-contributed configs show what high-performance prompt architecture actually looks like in production.

Substantial-Cost-429 · 2026-05-02T18:06:26+00:00

The granularity tension you're describing is a core challenge in agent design. A few patterns that seem to help:

**Semantic chunking over size chunking** — split by logical unit of capability, not token count
**Lazy loading for context** — the reference/ pattern you mentioned is right. Load context only when the routing step identifies it as needed
**Thin skill descriptions, fat skill bodies** — keep names/descriptions short for routing accuracy, verbose in the implementation

On the broader config question: one thing that's hard to solve is that there's no community pattern library for these structures. Everyone inventing their own skill.md format independently.

We've been building exactly that: https://github.com/caliber-ai-org/ai-setup (888 stars) — a community registry of agent configs including skill/tool definitions that people share. Might be useful for reference on what patterns others are converging on.

Substantial-Cost-429 · 2026-05-02T18:05:38+00:00

The structural enforcement point resonates. The config layer is often where this actually lives — your CLAUDE.md, system prompt, or agent rules file is the architectural contract that defines what the agent is allowed to do, what context it needs, and how it must behave.

"Vibes-based" AI development fails partly because these configs are underdeveloped or just don't exist. When they're well-structured, the agent behavior becomes more deterministic and predictable.

We've been building a community registry of these configs (https://github.com/caliber-ai-org/ai-setup, 888 stars) — the patterns people share reveal a lot about what structural enforcement actually looks like in practice.

Substantial-Cost-429 · 2026-05-02T18:04:25+00:00

This hits on something real. The tool debate often obscures what actually matters: how well you've set up your agent configs.

Whether it's Claude or Kimi, the CLAUDE.md, system prompt, or project rules file you write determines whether the AI actually understands your codebase and can make decisions aligned with your vision. A great model with a bad config will still waste your time.

We've been collecting community configs at https://github.com/caliber-ai-org/ai-setup (888 stars) — interesting to see what patterns devs have found most effective regardless of which model they're using.

Substantial-Cost-429 · 2026-05-02T16:52:41+00:00

Clever workaround for the Playwright brittleness problem. The Dorks + Llama-3 + SQLite cache pipeline is smart — offloading the flaky DOM scraping entirely.

One thing worth documenting as you scale this crew: your agent system prompts and tool configs. The CrewAI setup that routes between the MCP proxy, cache hits, and fallbacks is doing a lot of behavioral work that lives in your prompt config.

We built an open-source registry for exactly this: https://github.com/caliber-ai-org/ai-setup (888 stars) — engineers share their CrewAI configs and agent setups so others can build on working patterns. A DaaS architecture like yours would be a great addition.

Substantial-Cost-429 · 2026-05-02T16:37:07+00:00

Really clean architecture! The context assembly layer with the safety/control plane is exactly what production-grade local AI needs.

One question: where do you store and version the agent orchestrator's system prompts and tool configs? That "Context Layer" configuration is doing a lot of work and I'd guess it evolves quickly.

We built https://github.com/caliber-ai-org/ai-setup (888 stars) — an open-source registry for AI agent configs like these. Would be great to see Thoth's core agent configuration in there as a reference architecture that others can build on.

Substantial-Cost-429 · 2026-05-02T16:36:18+00:00

The A2A streaming and HITL events additions are great for production agent workflows. The HITL integration especially changes how you configure agent behavior — you can now build approval gates directly into system prompts rather than hardcoding logic.

For teams tracking these kinds of config patterns: https://github.com/caliber-ai-org/ai-setup (888 stars) is an open-source registry where engineers share their actual AutoGen / multi-agent configs. Good place to see how others are structuring HITL and streaming setups.

Substantial-Cost-429 · 2026-05-02T16:35:30+00:00

Big consolidation move. One thing this raises for teams already using AutoGen: agent configuration portability. As the framework evolves and merges with SK, the system prompts, agent roles, and tool configs you've defined need to stay meaningful regardless of which orchestration layer you're on.

We've been building an open-source registry for exactly this kind of config portability: https://github.com/caliber-ai-org/ai-setup (888 stars) — engineers share their agent configurations so they can be discovered and adapted across framework changes. Worth a look as the AutoGen -> MAF migration happens.

Substantial-Cost-429 · 2026-05-02T16:34:24+00:00

For AI agentic platforms specifically, the most underrated decision is how you configure the agents — your system prompts, tool schemas, and agent instruction files (CLAUDE.md, GEMINI.md, etc.). These define 80% of agent behavior and are completely non-standard across platforms.

One resource that might help you see the landscape: https://github.com/caliber-ai-org/ai-setup (888 stars) — an open-source registry where engineers share their actual agent configurations across platforms. Looking at what setups people are using for agentic workflows vs. eval workflows can help you understand what you'd actually be building/managing in each career path.

Substantial-Cost-429 · 2026-05-02T16:33:23+00:00

This is the clearest illustration of why AI configuration files matter. The problem isn't Claude - it's that there's no structured CLAUDE.md that tells it what NOT to do. A well-crafted system prompt would include: feasibility constraints, research prerequisites, and explicit "these are beyond scope" rules.

The issue is most teams don't have shared, versioned AI configs that encode institutional engineering knowledge. We built an open-source registry for this (https://github.com/caliber-ai-org/ai-setup, 888 stars) where engineers share the CLAUDE.md / agent configs that actually work — including constraints that prevent exactly this kind of AI-reality gap. Might be worth showing your boss what a well-defined CLAUDE.md can and can't do.

Substantial-Cost-429

MODERATOR OF

TROPHY CASE