This project (Agents Observe) started as an exploration into building automation harnesses around claude code. I needed a way to see exactly what teams of agents were doing in realtime and to filter and search their output.A few interesting learnings from building and using this:- Claude code hooks are blocking - performance degrades rapidly if you have a lot of plugins that use hooks- Hooks provide a lot more useful info than OTEL data- Claude's jsonl files provide the full picture- Lifecycle management of MCP processes started by plugins is a bit kludgy at bestThe biggest takeaway is how much of a difference it made in claude performance when I switched to background (fire and forget) hooks and removed all other plugins. It's easy to forget how many claude plugins I've installed and how they effect performance.The Agents Observe plugin uses docker to start the API and dashboard service. This is a pattern I'd love to see used more often for security (think Axios hack) reasons. The tricky bit was handling process management across multiple claude instances - the solution was to have the server track active connections then auto shut itself down when not in use. Then the plugin spins it back up when a new session is started.This tool has been incredibly useful for my own daily workflow. Enjoy!
FL score
out of 100
Verdict
high confidence
Competition
7
competitors found, emerging market, funded players
Trend
No signal yet
A real-time observability dashboard specifically for Claude Code agent teams, providing critical debugging and performance insights.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Real-time dashboard for Claude Code agent teams”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
This idea addresses a clear, specific, and severe pain point for Claude Code agent teams, with a strong whitespace opportunity given the lack of tailored solutions. While the demand and willingness to pay are high, the build complexity for a solo builder is a significant concern.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea presents a strong value proposition in a rapidly growing market, but building and defending it could be challenging for a solo builder.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A strong creator-problem fit with a clear niche, but the technical complexity could challenge a solo builder's simplicity goal.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A clear micro-SaaS opportunity with a specific target and value proposition, but needs careful validation of market size and future-proofing against platform changes.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
A highly demanded, specific solution for a painful problem in a growing market, with strong internal validation.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
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Pricing: Contact for pricing (part of Splunk Observability Cloud)
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Pricing: Part of Azure AI Foundry, pricing based on Azure consumption.
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Pricing: Free, open-source
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Pricing: Offers free options, specific pricing not detailed but implies paid tiers.
Offers templates and a visual interface to build agentic workflows for marketing, customer support, and sales.
Pricing: Contact for pricing.
What they charge
Recent news
Product Hunt, March 30 2026
Product Hunt, March 27 2026
Splunk Blog, February 13 2026
Microsoft Azure Blog, August 27 2025
Medium (by Dave Davies), August 28 2025
Market signals
The market for real-time dashboards for AI agent teams is rapidly growing, transitioning from niche to a significant area within the broader AI industry. Recent news and product launches indicate increasing recognition of the need for robust observability and management tools as AI agents become more autonomous and complex. Funding rounds in related AI agent development and orchestration platforms further underscore this growth, with companies like Relevance AI securing seed funding.
What frustrates people
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI