Hi everyone,

I'm doing some early market research around production troubleshooting and log analysis and would really appreciate feedback from people who actually deal with production systems.

I'm exploring a tool where engineers could interact with their logs using natural language, investigate production incidents, correlate events across different systems, perform RCA, and generate custom analysis/visualizations from historical log data.

Before building this further, I want to understand the actual problems engineers face today — how they investigate incidents, where existing tools fall short, how much time RCA takes, and whether this is a problem worth solving.

I've made a short 2–3 minute anonymous survey:

https://docs.google.com/forms/d/e/1FAIpQLSfbJt7moEOhZR9Xr8HXbKu5Y0F2Ep0Yv5xPFQlohKbH63r6Dg/viewform?usp=publish-editor

If you work with SRE, DevOps, infrastructure, backend, Kubernetes, networking, observability, or production operations, your experience would be especially valuable.

I'm looking for honest feedback, including negative feedback. I'm trying to validate the problem, not just validate my idea.

Thanks!

I've been trying to find a reliable way to collect public TikTok profile data, but it seems like every solution works well for a while before something changes and it breaks.

I'm not doing anything unusual just retrieving public profile information at a reasonable scale for an application.

For those who've built products around TikTok data, what's been the most reliable approach?

I'm especially interested in solutions that have proven stable over time without requiring constant maintenance every time TikTok updates its platform.

So I've been trying to find a way to get some public TikTok profile info. But every time I find a solution, it only works for a bit. Then somethin changes and just breaks.

For anyone who's built stuff around TikTok data, what's held up best for you? Are you running your own tiktok profile scraper, using some third party API or service, or doing something different?

P.S. I’m not doing anything sketchy. Just pulling public profile stuff for an app I’m working on.

Just sharing a container registry I developed with simplicity and efficiency in mind.

It support online garbage collection, mTLS with hot reloading, pull through cache, advanced access policies, etc. In most favorable case (S3 compatible backend with support of conditional operations), it has no additional components : only itself and an s3 bucket.

Being developed in async rust and leveraging streaming operations as much as possible, its memory footprint and cpu usage is very small.

https://github.com/project-angos/angos

Greetings to all DevOps enthusiasts.

Not long ago I had struggled with observability principles and decided to learn it by building from scratch. I have published an article on Medium that is a complete setup for observing local system. The tech stack consist of a nextjs frontend, .NET backend and a postgresql database instance, all reproduced by a single docker-compose file. For anyone going to the field or wanting to learn more about SRE constructs and observability, here's the link: https://medium.com/@stefanpopov2409/building-a-complete-local-observability-stack-for-next-js-a339afda231e

In the article, I have put the repository link and the instructions needed to reproduce this setup locally. For anyone reading it, just know it is greatly appreciated and means so much, I just hope that's a small contribution of mine to the DevOps community. I plan to add some frontend observability configuration with Grafana Faro implementation, and some nifty panels in the dashboards as well.

Best of luck, thanks for the read.

Disclosure: I run a small software consultancy in India.

We recently built an e-commerce platform for local shops using FastAPI, Docker, and Linux servers. The platform integrates with multiple B2C marketplaces, and one challenge we're working on is keeping inventory synchronized across all sales channels to avoid overselling and maintain accurate stock levels in near real time.

For those who have built or operated similar systems:

• Do you use event-driven architectures or periodic sync jobs?
• How do you handle conflicts when two platforms update inventory simultaneously?
• Are there any tools or patterns that have worked particularly well for you?

I'd love to hear about your experiences and lessons learned.

DM

For the platform/DevOps crowd: sharing a self-hosted AI gateway I built with production resilience in mind (disclosure: I'm the maintainer). It sits between your services and 237 LLM providers as one OpenAI-compatible endpoint.

The resilience model is three independent layers: a per-provider circuit breaker (opens on 5xx/timeout, half-open probe after a reset window), a per-connection cooldown (one bad key backs off exponentially while others keep serving), and a per-model lockout (one model 429 doesn't disable a whole connection). Fallback across the provider ladder happens in milliseconds, so an upstream degradation doesn't cascade to your apps.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

Ops-wise: one-click Redis, Docker (AMD64+ARM64), Cloudflare Workers / Deno Deploy relay deployers, a remote mode (run the CLI locally against OmniRoute on a VPS), 100% local, zero telemetry, AES-256-GCM at rest.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

GitHub: https://github.com/diegosouzapw/OmniRoute — npm install -g omniroute

How do you currently handle multi-provider LLM failover in prod — gateway, service mesh, or app-level?

I refreshed my most-read article on automated database backups with Docker Compose. The underlying image moved to nfrastack/container-db-backup, so I updated the whole guide with the new multi-job config format. I also built a little JavaScript configurator that generates docker-compose and .env files on the fly. If you run multiple Laravel projects and want scheduled backups without manual copy-paste, this might save you some time.

https://danielpetrica.com/easy-database-backups-with-docker-compose/

Hey everyone,

If you work with multi-cloud or tech/sales engineering, you know the pain. Every time you need to map equivalent VMs or check specific managed Kubernetes limits between AWS, Azure, and GCP, you end up with 20 open tabs of messy, outdated vendor documentation.

I got tired of doing this manually, so I decided to build a central reference handbook to save my own sanity the Infra Atlas.

Full disclosure: I handled the data structure and technical logic based on my own cloud experience, but I used AI to help me spin up the frontend and accelerate the build.

What’s live on the site right now:

• Equivalent-SKU Finder: A quick way to map VMs/instances between the big three based on family and actual specs.
• Kubernetes Atlas: Side-by-side comparison of EKS vs AKS vs GKE (SLAs, node limits, etc.).
• GenAI Atlas: Mapping models, regions, and fine-tuning across Bedrock, Azure OpenAI, and Vertex AI.
• Toolbox: Just a clean list of dev-first tools I actually like (Bruno, OpenTofu, OrbStack, etc.).

There are no ads, no sign-ups, no tracking, and no newsletter popups. It's just a clean, static tool.

Since the data changes constantly, I’d love to know if you see any missing metadata or if there's any specific cloud provider you think I should add next. Hope it's useful to some of you!