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Most teams don't have an observability problem.
They have a visibility problem.
Logs are scattered across tools.
Alerts fire without context.
Incidents take longer to investigate than they should.
Over the last 3 years, I've worked on building and operating telemetry pipelines, observability platforms, and monitoring solutions using AWS, Vector.dev, Splunk, Datadog, OpenTelemetry, and Kafka.
Some of the areas I can help with:
• Observability platform design
• Log ingestion and processing pipelines
• Splunk and Datadog implementations
• OpenTelemetry adoption
• AWS-based event-driven architectures
• Vector.dev pipeline development and migrations
• Monitoring, alerting, and incident visibility improvements
Always happy to connect with teams looking to improve reliability and observability.
#Observability #DevOps #PlatformEngineering #OpenTelemetry #AWS #Splunk #Datadog
vector.dev
Vector
A lightweight, ultra-fast tool for building observability pipelines
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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While you're building your business, customer support shouldn't be an afterthought. I manage high-volume help desk ecosystems to keep resolution times low and customer satisfaction scores hitting the roof. I specialize in organizing chaotic support queues, configuring airtight ticket automation workflows, and providing empathetic, human-centric support that protects customer retention.
Hands full and feeling the heat? I step in to handle the chaos so your team can breathe.
What I Do to Keep Your Support Bulletproof:
Ticket Pipeline Management: I triage, tag, and resolve incoming user requests across Zendesk, HubSpot, and Intercom to ensure no client question slips through the cracks. Your inbox won't look like a market square.
Workflow Automation: I implement automated ticket routing, custom views, and trigger rules to speed up internal response times and streamline team productivity.
De-escalation & Canned Responses: I draft clear, highly professional canned templates and macros to address critical system issues or logistics delays calmly and effectively. When things go south, I use tactical empathy to keep the customer calm.
SLA Maintenance: I monitor support queues closely to consistently hit internal response and resolution time targets. 'The tongue that defends the home doesn’t need to shout' I make sure your system works perfectly behind the scenes so your brand reputation stays protected.
This project involves connecting an ESP32 to a Firebase Realtime Database to display data on an application built with Thunkable website. The application allows users to control two lamps remotely and monitor real-time temperature and humidity readings. Usesrs can control device every where as long as there is internet, because this project use Firebase for save data
Working software is becoming increasingly available on demand.
Anthropic’s Claude Fable 5 release is an important signal for where AI-native software development is heading.
The benchmark results show strong performance across several high-value categories:
▪️ 💻 Agentic coding
▪️ 🧠 Knowledge work
▪️ 👁️ Vision understanding
▪️ 🔬 Scientific reasoning
▪️ 🔐 Cybersecurity
▪️ 📚 Long-horizon problem solving
But the bigger point is not just the benchmark chart.
It is what this means for teams building real products.
As models become better at handling long, complex, multi-step tasks, the bottleneck starts to shift.
Not from “can we write the code?”
But toward:
▪️ Can we define the right problem? ▪️ Can we design the right architecture? ▪️ Can we validate the output properly? ▪️ Can we secure what gets built? ▪️ Can we deploy and monitor it safely?
This is where AI-native execution starts becoming very powerful.
Teams can now move faster on:
▪️ 📈 Internal dashboards
▪️ 🧪 Larger test suites
▪️ 🔍 Research automation
▪️ 🎨 Custom visualizers
▪️ 🛠️ Workflow-specific tools
▪️ 🚀 MVPs and prototypes that were previously too expensive to justify
Jevons Paradox is already starting to show up in software.
As software becomes faster and cheaper to produce, demand for software increases.
At RaptorLabs, we see this as a major opportunity for companies that combine AI execution speed with strong engineering discipline.
The future is not just generating more code.
It is building more useful, secure, and production-ready systems.
⚠️ AI-generated software still needs serious review:
▪️ Code review ▪️ Security checks ▪️ Testing ▪️ Observability ▪️ Production monitoring
The ceiling is moving higher.
The companies that benefit most will be the ones that pair AI-native speed with architecture, security, and product judgment.
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