Freestyle – Sandboxes for Coding Agents
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
- Highly specific and severe problem for advanced AI agent developers.
- Unique technical differentiation (memory forking, fast startup, bare metal) creates a strong moat.
- Addresses critical performance and state management bottlenecks in a rapidly growing market.
- Strong demand signals for AI development infrastructure.
- Founder expertise demonstrated by existing product and demo.
Risks
- Extreme technical complexity and high operational costs of bare-metal infrastructure for a solo or small team.
- Market adoption risk: ensuring enough AI agent developers *desperately* need these specific advanced features to justify the switch and price.
- Distribution challenge: reaching a niche of sophisticated AI developers and educating them on the benefits.
- Reliance on a rapidly evolving AI agent landscape; features must adapt quickly.
- Switching costs for developers embedded in existing cloud environments.
Next steps
- Continue deep user interviews with AI agent developers to confirm the 'desperate' need for memory forking and bare-metal performance over existing, cheaper cloud solutions.
- Focus on initial niche users and specific use cases where memory forking is absolutely critical (e.g., genetic algorithms, multi-agent simulations, debugging state-dependent issues).
- Develop clear, quantifiable benchmarks comparing Freestyle's performance/capabilities directly against alternative methods (EC2, other sandboxes) for agent development workflows.
- Explore partnerships with AI agent framework providers (like AutoGen, CrewAI) to offer Freestyle as a premium execution backend.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 25
- Solution gap
- 20
- Willingness to pay
- 19
- Buildability
- 14
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
- Continue deep user interviews with AI agent developers to confirm the 'desperate' need for memory forking and bare-metal performance over existing, cheaper cloud solutions.
- Focus on initial niche users and specific use cases where memory forking is absolutely critical (e.g., genetic algorithms, multi-agent simulations, debugging state-dependent issues).
- Develop clear, quantifiable benchmarks comparing Freestyle's performance/capabilities directly against alternative methods (EC2, other sandboxes) for agent development workflows.
- Explore partnerships with AI agent framework providers (like AutoGen, CrewAI) to offer Freestyle as a premium execution backend.
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.
- Agenta AIVisit
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.
- n8nVisit
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
- Agentic AI vs. generative AI - Red Hat
Red Hat, February 18 2026
- 13 best AI agent platforms & builders I'm using in 2026 | Marketer Milk
Marketer Milk, February 04 2026
- AI Agent Development Cost Breakdown for Businesses (2026 Guide) - X-Byte Enterprise Solution
X-Byte Enterprise Solution, February 02 2026
- Vercel AI SDK vs Claude Agent SDK: Which One Should You Build With? | by Robert Mill
Medium, February 05 2026
- 8 best agentic AI tools I'm using in 2026 (free + paid) - Gumloop
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
- SuperAGI lacks built-in email warm-up, leading to deliverability risks and burning domains.
- SuperAGI's credit burn is painful, with credits expiring monthly and no rollover.
- SuperAGI is still early-stage, with bugs, robotic personalization, and setup complexity.
- Many teams using SuperAGI don't need an all-in-one AI CRM; they need more focused tools.
- The Vercel AI SDK requires an Edge runtime exclusively for streaming.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
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
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Freestyle – Sandboxes for Coding Agents”.