Built this after realizing I was spending ~$1400/week on Claude Code with almost no visibility into what was actually consuming tokens.Tools like ccusage give a cost breakdown per model and per day, but I wanted to understand usage at the task level.CodeBurn reads the JSONL session transcripts that Claude Code stores locally (~/.claude/projects/) and classifies each turn into 13 categories based on tool usage patterns (no LLM calls involved).One surprising result: about 56% of my spend was on conversation turns with no tool usage. Actual coding (edits/writes) was only ~21%.The interface is an interactive terminal UI built with Ink (React for terminals), with gradient bar charts, responsive panels, and keyboard navigation. There’s also a SwiftBar menu bar integration for macOS.Happy to hear feedback or ideas.
FL score
out of 100
Verdict
high confidence
Competition
7
competitors found, emerging market, funded players
Trend
No signal yet
A hyper-niche tool for Claude Code users to analyze task-level token usage from local sessions, revealing hidden costs and enabling optimization.
The pain
The gap
Build angle
Strengths
Questions about this idea?
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Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
High potential for solo builders due to a clearly articulated, high-severity problem within a specific niche, with evidence of willingness to pay and existing MVP.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Strong potential for making money by solving a high-value problem for a specific, paying audience in a growing market.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A highly focused, creator-led project solving a clear problem for a specific audience with strong monetization potential.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A strong micro-SaaS candidate with a clear target, measurable value, and a well-defined business model.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
A highly needed, specific solution for a clear pain point, with strong evidence of immediate demand and future relevance.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
Helicone is a proxy-based observability solution that sits between your application and LLM providers, offering cost and latency visibility, caching, and automatic failover.
Pricing: Free open-source option; Hobby tier: $0; Pro plan: $20 per seat per month; Enterprise: Custom packages.
LangSmith is a unified agent engineering platform that provides observability, evaluations, and prompt engineering for any LLM application or AI agent.
Pricing: Developer plan: 1 free seat (5k base traces/month included); Plus plan: $39/month per seat (10k base traces/month included, then $0.50 per 1,000 additional base traces, $5.00 per 1,000 extended traces); Enterprise: Custom pricing.
PromptLayer is a platform for data-driven prompt engineering and LLM application management, offering prompt versioning, performance monitoring, and cost analysis.
Pricing: Free plan: 5k requests/month, 7 days logs; Pro plan: $50/user/month (unlimited log retention, up to 100,000 requests); Enterprise: Custom pricing (self-hosted option available, SOC 2 compliance).
LiteLLM is an open-source proxy server that standardizes LLM calls across providers and offers cost tracking and pricing customization.
Pricing: Software license is free; infrastructure costs apply for self-hosting (estimated $200-$500/month for moderate traffic); Optional Enterprise Tier: Basic ($250/month), Premium ($30,000/year).
Vellum AI is an end-to-end LLM orchestration and observability platform designed to help teams build, deploy, and manage AI-powered apps.
Pricing: Free 'Startup' tier; Pro plan: $500/month; Enterprise: Custom pricing (annual contract, likely tens of thousands per year).
CloudZero is a cloud cost intelligence platform that provides comprehensive visibility into cloud spending, including AI costs, and attributes spend to business metrics.
Pricing: Based on annualized cloud spend, typically ranging from $10,000/year for $1M spend to $70,000+/year for enterprise-scale environments. Custom pricing for enterprise.
Anyscale is a platform for building, deploying, and scaling AI applications using the Ray framework, with a focus on distributed computing and MLOps.
Pricing: Usage-based billing; pay-as-you-go for compute instances (prices vary by CPU/GPU type and duration). Offers hosted and 'Bring Your Own Cloud' (BYOC) deployment models.
What they charge
Recent news
PostHog, March 19 2026
nOps, December 15 2025
nOps, April 01 2026
G2, 2026
ZenML Blog, September 13 2025
Market signals
The market for LLM cost optimization and observability is growing rapidly, driven by increased enterprise adoption of generative AI and the need for cost control and performance monitoring. This is evidenced by numerous new tools and platforms emerging, as well as significant funding rounds for companies in the broader AI infrastructure space. The focus is shifting beyond just overall cloud spend to granular, LLM-specific metrics like token usage by task, latency, and model quality.
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