We are Bailey and Robbie and we are working on Klaus (https://klausai.com/): hosted OpenClaw that is secure and powerful out of the box.Running OpenClaw requires setting up a cloud VM or local container (a pain) or giving OpenClaw root access to your machine (insecure). Many basic integrations (eg Slack, Google Workspace) require you to create your own OAuth app.We make running OpenClaw simple by giving each user their own EC2 instance, preconfigured with keys for OpenRouter, AgentMail, and Orthogonal. And we have OAuth apps to make it easy to integrate with Slack and Google Workspace.We are both HN readers (Bailey has been on here for ~10 years) and we know OpenClaw has serious security concerns. We do a lot to make our users’ instances more secure: we run on a private subnet, automatically update the OpenClaw version our users run, and because you’re on our VM by default the only keys you leak if you get hacked belong to us. Connecting your email is still a risk. The best defense I know of is Opus 4.6 for resilience to prompt injection. If you have a better solution, we’d love to hear it!We learned a lot about infrastructure management in the past month. Kimi K2.5 and Mimimax M2.5 are extremely good at hallucinating new ways to break openclaw.json and otherwise wreaking havoc on an EC2 instance. The week after our launch we spent 20+ hours fixing broken machines by hand.We wrote a ton of best practices on using OpenClaw on AWS Linux into our users’ AGENTS.md, got really good at un-bricking EC2 machines over SSM, added a command-and-control server to every instance to facilitate hotfixes and migrations, and set up a Klaus instance to answer FAQs on discord.In addition to all of this, we built ClawBert, our AI SRE for hotfixing OpenClaw instances automatically: https://www.youtube.com/watch?v=v65F6VBXqKY. Clawbert is a Claude Code instance that runs whenever a health check fails or the user triggers it in the UI. It can read that user’s
FL score
out of 100
Verdict
high confidence
Competition
5
competitors found, emerging market, funded players
Trend
No signal yet
Klaus provides a secure, managed, and 'batteries included' hosting solution for OpenClaw, solving critical setup, security, and maintenance challenges for AI agent developers.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Klaus – OpenClaw on a VM, batteries included”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
A highly specific, managed, and secure hosting solution for OpenClaw addressing significant setup and operational pain, with existing proof of concept and strong buildability.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Strong potential due to clear market pain, a compelling value proposition, a growing market, and defensible niche differentiation.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A strong idea for a solo builder with clear problem, good creator fit, and a valuable, targeted solution, but with a niche audience.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Strong micro-SaaS potential with a specific audience, clear value, and existing validation, mitigating many typical startup risks.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
High potential with clear demand, a superior solution to the status quo, and a strong product in a growing market.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
Replicate provides a cloud platform to run and fine-tune open-source machine learning models via API calls, abstracting away infrastructure management.
Pricing: Replicate uses a pay-as-you-go model. Public models are billed by hardware and time or input/output, with setup and idle time being free. Private models on dedicated hardware are billed for all online time (setup, idle, active). Hardware pricing examples: CPU (Small) $0.000025/sec, Nvidia A100 (80GB) $0.001400/sec, Nvidia H100 $0.001525/sec.
RunPod offers a globally distributed GPU cloud platform for developers to deploy custom full-stack AI applications for training, deploying, and scaling AI models.
Pricing: RunPod uses a pay-as-you-go, per-second billing model for compute, with different rates for Community Cloud (third-party hosts, cheaper) and Secure Cloud (RunPod managed, better SLAs). On-demand GPU instances range from $0.17/hour to $3.99/hour. Examples: RTX 4090 at $0.39/hour, A100 80GB at $1.89/hour, H100 80GB at $2.99/hour on the community cloud. Storage costs are separate: Container Disk is $0.10/GB/month while running and $0.20/GB/month when stopped; Network Volumes are $0.07/GB/month for the first TB, then $0.05/GB/month.
Baseten is an AI inference company that provides a platform for deploying and running machine learning models in production applications with a focus on high performance and reliability.
Pricing: Baseten offers pay-as-you-go pricing, only charging for the compute used (no idle time). They have Basic (dedicated deployments, model APIs, training, fast cold starts), Pro (priority GPU access, dedicated compute, higher API limits, dedicated support), and Enterprise (custom SLAs, self-hosting, on-demand flex compute, use existing cloud commitments) plans. Pricing for GPUs per minute: T4 at $0.01052, L4 at $0.01414, A100 at $0.06667, H100 at $0.10833, B200 at $0.16633. The AWS Marketplace offering starts around $5,000/month for enterprise.
Cerebrium is a serverless AI infrastructure platform that enables teams to build, deploy, and scale multimodal AI applications without managing traditional infrastructure.
Pricing: Cerebrium uses a pay-per-second billing model, where users only pay for the compute they use (CPU, GPU, memory), with no cost for idle GPUs. CPU pricing starts from $0.0002/sec, and GPU from $0.0005/sec. Example GPU costs: Nvidia A10 is $0.000306/second. They offer Hobby (free + compute, 3 users, 3 deployed apps, 5 concurrent GPUs), Standard ($100 + compute/month, 10 users, 10 deployed apps, 30 concurrent GPUs), and Enterprise (custom pricing, unlimited apps/GPUs, dedicated support) plans.
Modal Labs provides a serverless AI infrastructure platform designed for building and scaling AI models, applications, and services, offering programmable building blocks for infrastructure.
Pricing: Modal offers a free tier with $30/month in credits and pay-as-you-go pricing from $0.05/GB-hour (CPU). They abstract away server management, providing instant autoscaling and access to GPUs from Oracle Cloud Infrastructure.
What they charge
Recent news
Baseten Raises $300M at a $5B Valuation to Power a Multi-Model Future
Business Wire, January 23 2026
Modal Labs AI Inference Startup Nears $2.5B Funding Round | 2026
IndexBox, February 11 2026
Cloud AI Inference Chips Market Outlook 2026-2034
MarketWatch, March 01 2026
AI Inference Market Size, Share | Global Growth Report [2034]
Fortune Business Insights, March 02 2026
AI Data Center Market worth $2,023.52 billion by 2032 - Exclusive Report by MarketsandMarkets™
MarketsandMarkets, March 18 2026
Market signals
The AI inference market is a rapidly growing market, projected to reach between $254.98 billion and $312.64 billion by 2030-2034, growing at a CAGR of 12.98% to 19.3%. This growth is fueled by the increasing adoption of generative AI and large language models, driving demand for real-time AI deployment and the expansion of specialized cloud infrastructure. Recent significant funding rounds for companies in this space underscore strong investor confidence and market expansion.
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