We’re Ben and Jacob, cofounders of Freestyle (https://freestyle.sh). We’re building a cloud for Coding Agents.For the first generation of agents it looked like workflows with minimal tools. 2 years ago we published a package to let AI work in SQL, at that time GPT-4 could write simple scripts. Soon after the first AI App Builders started using AI to make whole websites; we supported that with a serverless deploy system.But the current generation is going much further, instead of minimal tools and basic serverless apps AI can utilize the full power of a computer (“sandbox”). We’re building sandboxes that are interchangeable with EC2s from your agents perspective, with bonus features:1. We’ve figured out how to fork a sandbox horizontally without more than a 400ms pause in it. That's not forking the filesystem, we mean forking the whole memory of it. If you’re half way down a browser page with animations running, they’ll be in the same place in all the forks. If you’re running a minecraft server every block and player will be in the same place on the forks. If you’re running a local environment and an error comes up in process that error will be there in all the forks. This works for snapshotting as well, you can save your place and come back weeks later.2. Our sandboxes start in ~500ms.Demo: https://www.loom.com/share/8b3d294d515442f296aecde1f42f5524Compared with other sandboxes, our goal is to be the most powerful. We support full Linux + hardware-virtualization, eBPF, Fuse, etc. We run full Debian with multiple users and we use a systemd init instead of runc. Whatever your AI expects to work on debian should work on these vms, and if it doesn’t send a bug report.In order to make this possible, we’ve moved to our own bare metal racks. Early in our testing we realized that moving VMs across cloud nodes would not have acceptable performance properties. We asked Google Cloud and AWS for a quote on their bare metal nodes and found tha
FL score
out of 100
Verdict
high confidence
Competition
14
competitors found, emerging market, funded players
Trend
No signal yet
A technically advanced bare-metal sandbox for high-performance AI agent development, featuring unique memory forking and sub-second startup.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Freestyle – Sandboxes for Coding Agents”.
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 promising idea targeting a deep technical pain point for advanced AI agent development, with strong differentiation in core technology but high build complexity.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Strong market viability and value proposition with significant differentiation, but high build complexity, making it potentially very profitable for the right team.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A highly technical, niche solution with clear problem clarity but significant complexity and specialized creator fit, making it difficult for a general solo builder.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A promising micro-SaaS candidate for a very specific developer niche, with clear value but significant technical and market assumption risks.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Highly innovative product addressing a critical, growing need for advanced AI agent development, with strong technical differentiation and clear desperate users.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
An AI-native CRM and GTM platform that bundles CRM objects, AI SDR prospecting, outreach sequences, voice/dialer agents, automations, and analytics into a credit-based system.
Pricing: Seat-based plus credits; credits expire each billing cycle with no rollover; voice agents burn about 15 credits per minute; 2,500 credits/month tier offers roughly 167 minutes of voice time.
An open-source AI agent framework that aims to simplify AI development and deployment, excelling in ease of use and cost-effectiveness for prompt engineering and LLM fine-tuning.
Pricing: Free to use and deploy as an open-source platform.
An open-source framework from Microsoft for creating multi-agent AI applications to perform complex tasks, with layers for programming a scalable network of agents and crafting conversational AI assistants.
Pricing: Open-source, free to use (requires developer time and infrastructure).
A multi-agent platform that enables users to build and deploy automated workflows using any Large Language Model (LLM) and cloud platform, specializing in orchestrating teams of AI agents for complex workflows.
Pricing: Open-source framework is freely available. Enterprise solutions offer tailored services and support; pricing details not publicly disclosed.
A free, open-source TypeScript toolkit that simplifies building AI applications by providing a unified API to interact with various large language models (LLMs) and frameworks, especially for streaming chat interfaces.
Pricing: Free, open-source TypeScript toolkit.
A cloud platform for deploying and scaling AI models, offering serverless inference and autoscaling with affordable GPU pricing and custom container deployment.
Pricing: Affordable GPU Pricing – Starting at $0.33/hour.
A platform for deploying and serving machine learning models, particularly for generative AI, with a focus on enterprise-ready inference.
Pricing: No specific pricing found directly on their site in search results, but alternatives highlight cost-efficiency as a key differentiator.
An integration platform that enables event-driven workflow automation and simplifies app integrations by connecting over 600 apps through built-in connectors and custom API calls.
Pricing: Pricing often differs significantly, with some charging by executions, some by compute time, others by connected apps.
A popular automation tool that connects various web applications to automate workflows without coding.
Pricing: Costs can add up quickly as businesses scale.
An open-source workflow automation tool with self-hosting capabilities, offering flexibility and customization for connecting various applications and services.
Pricing: Open-source, can be self-hosted which makes it a pretty cheap option. Paid plans also available.
A visual workflow builder with strong multi-step automation capabilities and thousands of integrations, offering an AI Agents feature that enables goal-driven agents to adapt in real-time.
Pricing: Fairly budget-friendly compared to other tools in this space.
An AI automation platform designed for non-technical users to build automated workflows and AI agents with a visual interface and AI-first approach.
Pricing: Offers a free plan, and paid plans are affordable.
What they charge
Recent news
Red Hat, February 18 2026
Marketer Milk, February 04 2026
X-Byte Enterprise Solution, February 02 2026
Medium, February 05 2026
Gumloop, January 07 2026
Market signals
The market for AI agent development is rapidly growing, with Gartner predicting 40% of enterprise applications will embed task-specific AI agents by the end of 2026. The market hit $7.6 billion in 2025. Recent funding rounds indicate significant investment in AI development and infrastructure, with a clear trend towards platforms that simplify AI agent creation and deployment.
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