System Rationale

Finding real-world problems & building AI agent solutions.
Sharing engineering & product build journeys.

[System Rationale @systemRationale]

@nicos_ai my repo: https://t.co/CnBecCfUqs

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@dashboardlim FREE

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Repo: https://t.co/k4ao40leKM

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Built Educat AI — an experimental AI tutor using Gemma 4 in the web browser.

Exploring text, audio, and image-based doubt input with streaming answers, KaTeX math rendering, adaptive routing, and browser/local AI capability checks.

The big experiment: how far can Gemma 4 go directly inside a browser?

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@AlfieJCarter 31

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Prompt injection in RAG is more dangerous than normal chatbot prompt injection.

Why?

Because the attacker does not need to talk to your chatbot directly.

They can hide malicious instructions inside:

documents, emails, tickets, PDFs, web pages, comments, support chats, or even database fields.

Later, your RAG system retrieves that content as “context”.

And if your agent cannot separate trusted instructions from untrusted data, it may follow the attacker’s instruction.

That is the real risk.

Not just bad answers.

But unsafe actions.

The fix is not “write a stronger system prompt”.

The fix is architecture:

treat retrieved content as untrusted input

separate data from executable instructions
sanitize and classify retrieved chunks
restrict tool permissions
require approval for risky actions
validate outputs before execution
log every retrieval, decision, and tool call
RAG does not only expand knowledge.

It expands the attack surface.

Secure RAG is not a prompt-engineering problem.

It is a system-design problem.

[System Rationale @systemRationale]

Most RAG failures start before retrieval.

Bad ingestion:

upload → chunk → embed → store

Production ingestion needs:

classify → normalize → deduplicate → extract metadata → verify → enrich → version → index → review → sync

If ingestion is weak, retrieval becomes guesswork.

RAG quality is created before the user ever asks a question

[System Rationale @systemRationale]

The biggest RAG security lie:
“Index everything → retrieve everything → filter later.”

Wrong. And dangerous

If a user can’t access a document, it must NEVER touch:
• Search
• Reranking
• Summarization
• Citation
• Memory
• Follow‑up context

🔐 Access control must happen BEFORE retrieval.

RAG security starts before the LLM ever sees the context.
Stop filtering at the prompt. Start filtering at the vector.
#RAG #AISecurity #LLM #GenAI

[System Rationale @systemRationale]

"AI agents are not the product. Verification is the product"

Proof: Research on AI agent systems found recurring developer challenges around runtime integration, dependency management, orchestration complexity, and evaluation reliability.

Catchy post

Hot take:

Most AI agent products will fail because founders are building the agent, not the verification layer.

Agent can plan? Good.
Agent can call tools? Good.
Agent can write code? Good.
But can it prove correctness?
Can it recover from failure?
Can it explain why it acted?
Without verification, an agent is just confident automation.

[System Rationale @systemRationale]

inally, a clean, production-minded structure instead of the usual chaotic “everything in one folder” vibe most GenAI projects start with.

This is exactly what separates weekend prototypes from scalable systems that don’t explode when you swap GPT-4o for Claude 3.5 or add 10 new tools

[System Rationale @systemRationale]

@Google @antigravity Does it allow to integrate with other google apps ?

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"AI adoption is high, trust is falling"

Proof: Stack Overflow 2025 survey: 84% of developers use or plan to use AI tools, but 46% do not trust AI output accuracy. Only around 3% highly trust it.

Catchy post

AI is winning adoption.

But losing trust.

84% of developers use or plan to use AI tools.
46% don’t trust the accuracy of AI output.
That means the next big skill is not “using AI”.

It is verifying AI.

The future belongs to engineers who can use AI fast but audit it faster

[System Rationale @systemRationale]

@GoogleLabs @googlemaps This feels like the early shape of location-native creation: real-world context as the seed, AI as the world builder, and sharing/remixing as the distribution layer.

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@GoogleCloudTech @GoogleDeepMind The key shift is “intelligence with action.”
Enterprise value won’t come from smarter answers alone,
but from models that can reason, use tools, respect workflow boundaries, and produce auditable outcomes

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@GoogleAIStudio @Android This lowers the Android prototyping barrier massively.
The real win is not “vibe coding apps”

it’s shortening the loop from idea → generated build → real device feedback.

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@NewsFromGoogle @antigravity The important signal here is the SDK + CLI.
Agentic coding becomes much more serious when it moves beyond desktop UI into programmable workflows, CI hooks, and team-level automation

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@cursor_ai The real unlock is not “AI writes code from Jira.”
It is whether the agent can preserve product intent, repo conventions, edge cases, and review discipline while moving from issue to PR.

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@UnslothAI The real unlock is not just “runs locally.”
It is local agents becoming fast enough to do multi-step web/tool exploration without every iteration depending on expensive hosted inference.

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This is a serious shift. For enterprise agents, the blocker was never just model quality it was execution boundaries, data perimeter, auditability, and control over where actions actually run.

Self-hosted sandboxes make agents feel less like an external assistant and more like a governed execution layer inside the company’s own security model.

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@Docker This is the right direction: coding agents create change, but shipping agents need runtime context.

The real value is not “AI inside Docker” it’s closing the loop between code, containers, diagnostics, and safe action.