I am incredibly honored and grateful for the opportunity to have participated in Talent Success Story @Upwork . π
Here is the link to the full article: https://t.co/LdJqbayVj1
AWS shipped DevOps Agent GA for automated incident investigation. Vendors now treat AI agents as core infrastructure.
The real question: does it solve governance and cost predictability, or does it optimize for AWS upsell?
#AWS#AgentOps
Git, Docker, Kubernetes, GitHub Actions, DevSecOps, FinOps, AIOps, platform engineering.
The Cloud DevOps Engineer's Guide covers the full stack through real production workflows. Two years of work, 16 chapters, one playbook.
Available on amazon: https://t.co/c5dTdAnubc
#DevOps
AI in production is an engineering practice rather than a magic layer or a research demo. Responsibility, control and decision-making move to the foreground.
We wrote about what that practice looks like in real systems:
https://t.co/FuDRqupgNd
#AIEngineering#ProductionAI
Six major AI labs just shipped open-source models you can self-host. No API tax, no vendor lock-in. The catch: only teams with mature DevOps actually save money.
Everyone else trades one bill for another.
Where does your team stand?
#OpenSource#AI
78K tech workers laid off in Q1. Nearly half blamed on AI. Snap targeted product, while engineers were left untouched. AI displaces some roles and also covers cuts that would happen anyway.
Which is it where you work?
https://t.co/sSsI0loTW2
#TechJobs#AI
Stanford and MIT analyzed 2M+ snippets: AI-generated code carries 14.3% vulnerability rate vs 9.1% for human code. 56% higher.
Scanning, SBOM and policy enforcement turn from recommended to required.
Are yours on by default?
#Security#CodeQuality
Cloud bills break because the architecture treats cost as an audit instead of a decision.
We wrote about how AI-driven architecture reshapes the cost loop, from forecast to deploy:
https://t.co/pKhj6LUxeE
#FinOps#CloudArchitecture
93% of devs use AI. Productivity gain is 10%.
The orgs hitting 10-20x invested in decision rights, code review and docs before agents arrived. Structure does the work the tool cannot.
Where did your team start?
https://t.co/mk2D6iKKgY
#AIProductivity#SystemsThinking
Cost transparency embedded in DevOps workflows delivers 30-50% reduction. Reserved instances, right-sizing and anomaly detection turn into operational habits instead of finance audits. That's where the 5x faster, 50% cheaper play sits.
https://t.co/Ov09GWfdhX
#FinOps#DevOps
AI raises the bar for engineering work rather than replacing engineers. Less typing, more reviewing. Less syntax, more system thinking.
We wrote about what that shift means for engineering teams in 2026:
https://t.co/k1z2Bcm5WE
#AIEngineering#DevOps
KPMG: AI agents now outnumber humans 82 to 1 inside enterprises. Only 21% of companies know what those agents can access. Every unmonitored agent is a potential breach.
What can your agents touch right now?
https://t.co/1iqpnpEwER
#AgentOps#CISO#ZeroTrust
Google now generates 75% of its new code with AI. Maintainers struggle to review what the agents ship.
When the upstream source becomes a black box, provenance turns into a security requirement.
https://t.co/ZVT63ZoRXu
#OpenSource#AI
Oracle laid off 18% of its workforce to free $8-10B a year for AI infrastructure. Compute capital is now cheaper per unit of throughput than human engineering.
Are you still staffed on 2020 economics?
https://t.co/xVJAGOcwAp
#EngineeringEconomics#AIInfra
March 2026 alone saw 35 CVEs traceable to AI-generated code. Vibe coding feels productive until production breaks.
We wrote a primer on where it works, where it fails, and how to ship fast without losing the audit trail:
https://t.co/WVApsmG58B
#VibeCoding#AICode
Gartner: 40% of agentic AI initiatives will fail by 2027. Governance gaps, unclear ROI. The teams that fail treat agent deployment as a tech rollout. The ones that win measure governance first.
Which side is your org on?
#AgentOps#Governance
Numbers like these only land when the operational layer absorbs them. We've been delivering this same model for clients: agents propose, senior engineers validate, code ships to production with audit. The math works once context engineering is in place.
#devops#cloud#ai
The naming is the visible part. The hidden cost is everything downstream: docs, SDKs, IAM policies, onboarding playbooks, every internal wiki page. Each rebrand resets the integration surface for teams already in production. Stability is a feature, not a marketing constraint.
Duet --> Bard --> Gemini --> Antigravity
Google, I love you, but for god's sake stop letting your messaging product PMs handle your AI strategy. Nothing exists long enough to build a reputation.
"Claude" isn't a great name but my god have they been consistent with it.
@QuinnyPig The naming is a symptom. The real cost is on the integration side. Every rebrand resets the docs, the SDKs and the discovery process for teams already in production. Anthropic kept Claude precisely because they understood that stability is a feature, not a marketing constraint.
METR study: developers using AI take 19% longer. They estimate they're 20% faster. They're wrong.
The paradox is real because AI changes which tasks get done, not just how fast.
Is your team measuring throughput or rework?
#AIProductivity#DevOps
Agents are no longer a research demo. They run production workflows, hold permissions, and manage budgets. Most teams still treat them like tools.
A primer on what AI agents really are and how they reshape engineering:
https://t.co/dXLFkKi2Mz
#AIAgents#AgentOps