What I Actually Build: AI Systems That Ship, Not Demos That Impress

May 23, 2026 · 7:54 AM UTC ·3 min read · 0 reactions · 0 comments · 21 views

#ai #technology #development #engineering

What I Actually Build: AI Systems That Ship, Not Demos That Impress

TL;DR · WeSearch summary

The article discusses the author's experience in building production-ready AI systems rather than flashy demos. It emphasizes the importance of robust architecture and infrastructure in AI development, particularly in sensitive domains like banking and humanitarian operations. The author seeks complex projects that require real solutions rather than simple chatbots.

Key facts

▪The author builds AI systems that run in production, focusing on Retrieval-Augmented Generation (RAG) systems.
▪He has experience shipping RAG systems across various domains, including banking, humanitarian operations, and eCommerce.
▪The author emphasizes the need for full-stack development to support AI, including backend and frontend technologies.

How this story was covered

2 outlets in our directory ran this story, first to last over 32 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.

Centre · 1

▪ Building the next LinkedIn / X for founders who actually ship — r/SaaS

About this source

DEV.to (Top) files mainly under programming. We currently carry 4,924 of its stories.

All DEV.to (Top) coverage →

Original article

DEV.to (Top)

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Story provenance

Source · retrieval · rights · ranking — open for full record

inspect →

Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.

Record

Original publisher	DEV.to (Top)
Canonical URL	https://dev.to/yabasha/what-i-actually-build-ai-systems-that-ship-not-demos-that-impress-b88
Publication time	Sat, 23 May 2026 07:54:36 +0000
Retrieval time	2026-05-23T08:07:25.785Z
Last seen	2026-05-23T08:07:25.785Z
Headline source	Publisher (no WeSearch rewrite)
Excerpt source	publisher body
Excerpt method	First ~120 words (~800 chars) of extracted publisher body, fair-use limited.
Summary	WeSearch · cerebras-chat (WeSearch summarizer)
Summary source text	contentText
Citation coverage	Summary is a WeSearch-generated derivative; primary citation is the original publisher URL.
Cluster	oWgOeVeFoWXM · 2 stories
Cluster logic	Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison.
Ranking reason	Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking.
Publisher visit	Yes — open original
Substitutes article?	No — link-out required for full text

Rights status (four layers)

Publisher-declared

No publisher-confirmed rights record for this source yet.

Machine-readable

No source-specific machine-readable restriction detected beyond the public feed.

WeSearch interpretation

WeSearch declared handling (basis: Derived from the published RSS/Atom feed). This is WeSearch policy, not a legal grant on the publisher's behalf.

Unknown

Retrieval and training permissions are not asserted unless the publisher confirms them.

WeSearch handling by dimension

Indexing	May the item be indexed (stored, ranked, made findable)?	Allowed
Snippet	May a short excerpt of the publisher's text be shown?	Allowed
AI summary	May WeSearch generate its own short summary of the article?	Limited
Retrieval / RAG	May the content be exposed for third-party retrieval-augmented generation?	Not asserted
Model training	May the content be used to train AI models?	Not asserted
Commercial reuse	May the content be reused commercially?	Not permitted

Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Open original publisher → Source rights JSON → Machine-readable provenance → Rights framework → WESEARCH-STORY-PROVENANCE-V1

Opening excerpt (first ~120 words) tap to expand

try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 96149) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Bashar Ayyash Posted on May 23 • Originally published at yabasha.dev What I Actually Build: AI Systems That Ship, Not Demos That Impress #aiengineering #rag #aibuilders #fullstack Every LinkedIn post about AI sounds the same. "Excited to leverage cutting-edge LLMs to transform your business." Cool. What does that even mean? I'll tell you what I actually do. AI Engineering — Not the Buzzword Version I build AI systems that run in production.

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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).

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