Dev-friendly native OTel: only OSS stateful, on-the-wire Observability
Hi HN, We’re the team at MyDecisive.ai, and today we’re giving developers a peek at Octant — point-and-click control and visibility for your OpenTelemetry. You've likely felt the pain of the "observability tax," especially if you manage K8S clusters. The modern standard is to instrument everything with OpenTelemetry, but piping all those rich OTLP logs, metrics, and traces straight to a SaaS vendor (Datadog, Splunk, Honeycomb) gets expensive fast. You end up paying massive ingestion and storage costs for noisy, low-value data just so it's searchable when something breaks. With Octant you get…
What it does
In the maker’s words, at launch
Hi HN, We’re the team at MyDecisive.ai, and today we’re giving developers a peek at Octant — point-and-click control and visibility for your OpenTelemetry. You've likely felt the pain of the "observability tax," especially if you manage K8S clusters. The modern standard is to instrument everything with OpenTelemetry, but piping all those rich OTLP logs, metrics, and traces straight to a SaaS vendor (Datadog, Splunk, Honeycomb) gets expensive fast. You end up paying massive ingestion and storage costs for noisy, low-value data just so it's searchable when something breaks. With Octant you get up and running on OTel in minutes. We built Octant to flip this model. Instead of blindly shipping all telemetry off-cluster, Octant configures and helps to manage OTEL clusters. It gives you a visual interface for managing K8s objects, but importantly, it acts as an OTLP gateway that filters data at the source before it leaves your VPC. Because it natively speaks OpenTelemetry, you can point your existing OTel SDKs or collectors right at it without touching your application code. Here is what it does under the hood: - OTel-Native Trace & Log Sampling: It makes it easy to ingest OTLP traffic and inspects logs and traces on the wire. By waiting for the full context of a trace before determining what to keep, it delivers on the promise of braiding, retaining 100% of the actionable signals around (like errors and high-latency spans) but droppings the junk before it hits your SaaS bill. - In-Flight Stateful Alerting: Instead of waiting for data to be batched, shipped, and indexed by an external provider to trigger an alert, Octant can process the telemetry streams in-flight. This shrinks the detection gap and reduces the need for SaaS vendors in the first place. - On-the-Wire PII Redaction: It can detect and strip sensitive information from your logs and traces in real-time before they are transmitted over the internet, removing "post-ingestion" clean-up costs and compliance risks. - K8s Context Injection: Because it's deeply integrated with your cluster, it maps your OTel streams directly to your K8s resources (Deployments, Pods, CRDs) in a unified UI. The API is built in Go ([github.com/mydecisive/octant] and the whole stack can be deployed directly into your cluster via our Helm charts. We’d love for you to spin it up on a dev cluster and tear it apart. We just recently merged a PR from our very first community contributor, which was a huge milestone for us! We want to keep that momentum going. If you're interested in hacking on K8s observability and autonomy, OpenTelemetry pipelines, or Go/React, we’ve tagged a few 'good first issues' and would be thrilled to welcome you to the project. GitHub: https://github.com/MyDecisive/octant Website: https://www.mydecisive.ai/ I'll be hanging out in the thread today and am happy to answer any questions or dig into the architecture!
Does the same job
all alternatives →
OpenLIT's Zero-code LLM ObservabilityOct 2025 · ▲136Trace LLM requests + costs with OpenTelemetry monitoring
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, June 2026
the whole month →
Fundraisly▲1,544AI fundraising agent that finds investors and books meetings
AI · Jun 2026 · fundraisly.com
- H6Homebrew 6.0.0▲1,481
Today, I’m proud to announce Homebrew 6.0.0. The most significant changes since 5.1.0 are a new tap trust security mechanism, the new faster, smaller, default internal Homebrew JSON API, sandboxing on Linux, better defaults informed by our user survey, many brew bundle improvements, improved performance and initial support for macOS 27 (Golden Gate). Happy to discuss any questions here!
Dev tools · Jun 2026 · brew.sh
- PU
hope you enjoy
Life & fun · Jun 2026 · vorpus.github.io
- IM
Life & fun · Jun 2026 · hackernewstrends.com