In the first post, I covered how I got the Blackstone Connect API to stream temperature data into Grafana Cloud. That gave me dashboards, graphs, and alerting.
The thing is, during a cook, I’d still have to open Grafana on my phone, navigate to the dashboard, look at the graph, and try to figure out when my brisket would hit 203°F. What I really wanted was something I could just ask a question to from the backyard.
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If 85 to 95 percent of AI projects fail to deliver the expected results, it is not because AI technology is weak. It is not due to a lack of models, GPUs, computing power, or technical skills.
One of the root cause in my opinion is usually far simpler, and far more underestimated.
Data is not structured, not accessible, not contextualized, or not reliable.
An AI model is not an oracle.
It does not create value on its own.
It can only exploit what it is given.
Before diving into the fun details, here is the full data flow of the setup. This shows the journey from the original Blackstone app to the final Grafana dashboard.
┌──────────────────┐ ┌──────────────────────────┐
│ Blackstone APK │ │ Hidden Blackstone API │
│ (reverse engineered)──────▶│ (backend endpoints) │
└──────────────────┘ └──────────────────────────┘
│
▼
┌────────────────┐
│ Python Script │
│ (collector) │
└────────────────┘
│
▼
┌──────────────────────────┐
│ Prometheus Remote Write │
│ via Grafana Cloud │
└──────────────────────────┘
│
▼
┌─────────────────────────┐
│ Grafana Dashboard │
│ (generated by Claude) │
└─────────────────────────┘
I love my Blackstone smoker, but the official Blackstone Connect mobile app was a bit limited for what I wanted. It worked fine, but I could not get long term history, pretty graphs, alerts, or anything close to proper observability. Since I already use Grafana Cloud for a bunch of personal projects, putting the smoker data there became an obvious objective.
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Take care of your team, and your team will take care of you.
My role has been closer to a coach than a commander. The more I invested in the team’s well being and clarity, the more they owned the product and exceeded expectations.
Challenge the status quo relentlessly.
Efficiency is not about hitting KPIs blindly. It is about constantly asking: could we do this better? That curiosity must be part of the team DNA.
Since 2017, I have created and been maintaining cfptime.org, a community-driven project aimed at centralizing call-for-papers information for information security conferences.
Initially, my first goal was simple: we had an intern joining our team and who was about to work on Django Framework, I didn’t know it and I wanted to know the basics. Additionally, some other goals were simply to have fun, contribute something valuable to the community.
... ➦Connecting Meross MSG100 to TUBAUTO ProCom 7-3 Garage Door: A Quick Guide
Modernizing your garage door to be smart-enabled can greatly enhance convenience and security. The Meross MSG100 Smart Wi-Fi Garage Door Opener offers an easy solution to connect your garage door to your home network, allowing you to control it remotely through the Meross application. If you have a TUBAUTO ProCom 7-3 garage door opener, integrating it with the Meross MSG100 is straightforward with the right guidance.
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In the ever-evolving landscape of software development, GitOps has emerged as a powerful paradigm, initially associated with cloud-native applications. However, its significance personally extends beyond the realms of modern, containerized environments. GitOps has proven to be a valuable concept for legacy (or “more traditional”) applications too, providing a bridge that captures technical expertise and establishes deterministic operations resulting in a better quality of service.
GitOps is fundamentally rooted in the principles of declarative infrastructure and version control. At its core, it leverages Git repositories as the single source of truth for both application code and infrastructure configurations. Changes to the system are managed through pull requests, allowing for collaboration, versioning, and a clear audit trail.
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Incident clustering, also known as event clustering or anomaly clustering, is a technique used in data analysis and machine learning to group similar incidents or events together based on their characteristics or patterns.
In order to deep dive into this, we used sklearn and matplotlib to both do machine learning and visualize results in an easy way.
In few words:
The main idea is quite simple - clustering incidents and try to find “clusters” - aka ~incidents categories so that we group them and go from 900 dedicated incidents to 10 categories, easier when we want to tackle big subjects which will ease our work in the upcoming days.
... ➦... ➦This article aims at providing you some insights about the experience I had in the “infosec world” for the past few years. I had an amazing journey and grateful for everything that happened. Don’t get me wrong with the title, even if the road was sometimes “bumpy” (aka ‘bad’ experiences), I got some good lessons learnt through them and live by them. I am not trying to convince anyone with this article, except just do what’s good for you and preserve yourself. Just that.
Hi everyone!
Here is a pretty quick blog post on some Frida/Objection things I’ve been tinkering with.
I had this Android application which had premium features and wanted to understand how that mechanism worked and if it was robust enough. Let’s see what was the journey on how I’ve bypassed it using Frida.
After disassembling the application with Jadx, I was able to perform a simple grep -r -i 'premium' . into the sources and leading me to where the Premium checks were done.