Most 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.

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.

AI coding works best when you treat it like a collaborator, not a shortcut—be specific with prompts, provide context (code, errors, goals), and always review the output for logic and security. The real productivity boost comes from combining AI speed with human judgment.

Spot on, Suny. We’ve spent so long obsessing over model parameters that we’ve neglected the deterministic plumbing required to make them safe. When agent setups break, what fails first for me is almost always Context Integrity. We treat context like a bucket we throw data into, rather than a structured ledger. As a Technical Architect, I’m seeing that the "System Problem" is actually a Librarian Problem: Context Bloat: Agents fail because they lack a "Gatekeeper" to validate incoming triples against a fixed schema. Boundary Erosion: We grant APIs access based on "vibes" rather than machine-readable authority (like SHACL validation).

Quick breakdown of why Hawkes matters here: A standard Poisson process (used in classic Merton) has no memory. The probability of the next jump is the same whether a jump just happened or not. A Hawkes process is self-exciting — each arriving event temporarily raises the rate of future events. The excitation decays exponentially: λ(t) = λ₀ + α · Σ exp(−β · (t − tᵢ)) The key constraint: α/β < 1 keeps the process stationary. Push past that and intensity explodes. In practice, this means a single bad print can cascade — and the simulation captures exactly that.