Eve is an AI agent harness that runs in an isolated Linux sandbox (2 vCPUs, 4GB RAM, 10GB disk) with a real filesystem, headless Chromium, code execution, and connectors to 1000+ services.You give it a task and it works in the background until it's done.I built this because I wanted OpenClaw without the self-hosting, pointed at actual day-to-day work. I’m thinking less personal assistant and more helpful colleague.Here’s a short demo video: https://www.loom.com/share/00d11bdbe804478e8817710f5f53ac61The main interface is a web app where you can watch work happen in real time (agents spawning, files being written, use of the CLI). There's also an iMessage integration so you can fire a task asynchronously, put your phone down, and get a reply when it's finished.Under the hood, there's an orchestrator (Claude Opus 4.6) that routes to the right domain-specific model for each subtask: browsing, coding, research, and media generation.For complex tasks it spins up parallel sub-agents that coordinate through the shared filesystem. They have persistent memory across sessions so context compounds over time.I’ve packaged it with a bunch of pre-installed skills so it can execute in a variety of job roles (sales, marketing, finance) at runtime.Here are a few things Eve has helped me with in the last couple days:- Edit this demo video with a voice over of Garry: https://www.youtube.com/watch?v=S4oD7H3cAQ0- Do my tax returns- To build HN as if it was the year 2030: https://api.eve.new/api/sites/hackernews-2030/#/AMA on the architecture and lmk your thoughts :)P.S. I've given every new user $100 worth of credits to try it.
FL score
out of 100
Verdict
high confidence
Competition
6
competitors found, emerging market, funded players
Trend
No signal yet
A managed AI agent harness for day-to-day work, providing autonomous task execution for professionals in a secure, persistent environment.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Eve – Managed OpenClaw for work”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Strong problem-solution fit with a unique managed offering in a rapidly growing market, validated by a functional demo, though competitive landscape requires a sharp focus on the specific niche.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
A highly viable idea with a strong value proposition in a booming market, leveraging existing tech to create a differentiated offering for a clear audience.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A strong idea with clear problem clarity and creator fit, but the underlying complexity could impact solo scalability despite a focused niche.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A micro-SaaS with a clear, valuable proposition for a specific, reachable audience, supported by a viable business model and initial validation efforts.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a desperate need with a narrow, compelling solution, showing strong early validation and long-term relevance.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
E2B provides ephemeral code execution sandboxes for AI agents using Firecracker microVMs.
Pricing: Hobby: Free with $100 one-time credit, 1-hour sessions, 20 concurrent sandboxes. Pro: $150/month with 24-hour sessions, custom CPU/RAM. Usage: ~$0.05/hour for 1 vCPU sandbox.
Modal is a serverless platform for ML and data workloads, offering Python sandbox support with elastic GPU scaling.
Pricing: $30/month free credits. Per-second billing for CPU ($0.047/vCPU-hour), RAM ($0.008/GB-hour), and H100 GPU ($3.95/hour plus separate CPU/RAM charges).
Northflank offers enterprise-grade microVMs with VPC deployment and GPU support for AI agent code execution.
Pricing: CPU $0.01667/vCPU-hour, Memory $0.00833/GB-hour. GPUs from $0.80-$3.14/hour. Free sandbox tier available.
Cognosys AI is an autonomous AI agent platform designed to execute complex tasks using a goal-based approach.
Pricing: Free version: 1000 credits/month, GPT-3.5, GPT-4 Internet browsing, max 7 agent loops. Pro plan: $29/month, 5000 credits/month, unlimited GPT-3.5, PaLM2 & Cohere, unlimited GPT-4 Internet browsing, GPT-4 & Claude Agents, up to 20 agent loops, private community, priority support.
SUPERAGENT is an open-source platform enabling developers to create, host, and manage autonomous AI agents for various tasks.
Pricing: AI Assistant Lite: $9/month (10 inquiries/month, $1 per processed inquiry). AI Assistant Pro: $59/month (100 inquiries/month, 10,000 Credits). Pro: $499/mo (1,050 quotes/month for Quoting AI Agent). Scale: $999/mo (2,350 quotes/month for Quoting AI Agent). Custom Enterprise plans available.
Vercel Sandbox provides ephemeral Firecracker microVMs for sandboxed agent execution.
Pricing: Free during beta. Production pricing not announced.
What they charge
Recent news
PR Newswire, April 10 2026
Product Hunt, April 10 2026
PR Newswire, April 09 2026
Enterprise AI Trends - Substack, April 03 2025
DevCom, March 02 2026
Market signals
The AI agent platform market is a rapidly growing and large market, valued at $7.8 billion in 2025 and projected to reach $68.4 billion by 2034, expanding at a CAGR of 27.4%. Recent funding rounds indicate strong investor interest, with companies like GitButler securing $17M in Series A funding. The market is driven by rapid LLM advancements, enterprise automation mandates, and the proliferation of multi-agent orchestration frameworks.
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