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3h ago · 12 min read · Introduction AI is changing how we build software. We are moving from a world where developers primarily describe systems in code to one where we increasingly describe intent in natural language. Prom
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5h ago · 13 min read · Introduction It usually starts the same way.An application goes down. Users report errors. A alert gets fired. Someone opens the Azure portal, someone else starts checking logs, and within minutes the
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2h ago · 10 min read · INTRODUCTION The v0.1.0 we created in last post worked but its too slow to be used in real world scanning . Scanning a port range was like waiting for a government website to load . Also in previous v
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57m ago · 12 min read · AI agents are moving beyond demos and into real production use. In production, you need sessions that last through infrastructure changes, code that stays secure, and controls that your platform team
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59m ago · 21 min read · A note on the code in this article. The observability setup shown here is derived from a production BFF built for a Norwegian enterprise education platform. Resource names, workspace identifiers, aler
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3h ago · 12 min read · Originally published on GeekyAnts Blog · By Prathamesh Ingale, Software Engineer in Testing at GeekyAnts · May 29, 2025 In today's fast-paced software development world, automation testers need to
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1 post this monthBuilding backend systems. Occasionally understanding why they work.
1 post this monthObsessed with crafting software.
7 posts this monthOracle APEX, PLSQL, SQL Developer
1 post this monthCEO @ United Codes
1 post this monthBuilding backend systems. Occasionally understanding why they work.
1 post this monthObsessed with crafting software.
7 posts this monthOracle APEX, PLSQL, SQL Developer
1 post this monthMost are still shipping “AI add-ons.” The real shift happens when the whole workflow disappears into one action — that’s when users actually feel the value.
Completely agree, most failures I’ve seen come from poor context management and unclear data flow, not the model itself. State handling also becomes a major issue when workflows scale, especially with multiple tools and agents interacting. In my experience, debugging improves a lot once you treat it as a system design problem rather than just an AI model issue.
API docs get attention. The frontend/API contract usually doesn't. TypeScript helps, but types lie without runtime validation. The API returns an unexpected null, a renamed field, an edge case you never tested and your types had no idea. Zod fixes this. Parse at the boundary. If the API changes shape, you catch it at the schema. Not in a Sentry alert a week later. We do this with Next.js Server Actions too. The server/client boundary is the natural place to validate. Keep the schema next to the call. Documentation problem and type-safety problem are usually the same problem.
You’re definitely not alone that “Step 5 bottleneck” is where most AI-assisted teams hit reality. Right now, most teams aren’t fully automating reviews yet. The common pattern I’m seeing is a hybrid approach, not purely human or purely automated. What others are doing AI generates code → Automated checks (linting, tests, security, architecture rules) → Targeted human review (not full manual review) 👉 The key shift: humans review intent + architecture, not every line.
Nice first deployment walkthrough! One thing worth adding to this stack: set up an OAI (Origin Access Identity) or the newer OAC (Origin Access Control) so your S3 bucket stays fully private and only CloudFront can read from it. Without that, the bucket is publicly accessible even though CloudFront is in front. Also, consider adding a Cache-Control header strategy — setting immutable assets to max-age=31536000 with content hashing in filenames, and your index.html to no-cache so CloudFront always checks for the latest version. WAF is a solid move this early — most people skip it until they get hit with bot traffic.
I keep seeing people blame the model when something breaks. In most cases, that’s not where the problem is. From what I’ve seen, things usually fail somewhere else: agents pulling in too much or wron
100% agree — this matches what I see building automation systems for clients daily. The model is usually the most reliable part of the stack...
+1 to this :) Feels like we’ve moved from “prompt engineering” to “system engineering”. Most issues in my opinion come from context drift or...