Terminal Use (YC W26) – Vercel for filesystem-based agents
Hello Hacker News! We're Filip, Stavros, and Vivek from Terminal Use (https://www.terminaluse.com/). We built Terminal Use to make it easier to deploy agents that work in a sandboxed environment and need filesystems to do work. This includes coding agents, research agents, document processing agents, and internal tools that read and write files.Here's a demo: https://www.youtube.com/watch?v=ttMl96l9xPA.Our biggest pain point with hosting agents was that you'd need to stitch together multiple pieces: packaging your agent, running it in a sandbox, streaming messages back to users, persisting state across turns, and managing getting files to and from the agent workspace.We wanted something like Cog from Replicate, but for agents: a simple way to package agent code from a repo and serve it behind a clean API/SDK. We wanted to provide a protocol to communicate with your agent, but not constraint the agent logic or harness itself.On Terminal Use, you package your agent from a repo with a config.yaml and Dockerfile, then deploy it with our CLI. You define the logic of three endpoints (on_create, on_event, and on_cancel) which track the lifecycle of a task (conversation). The config.yaml contains details about resources, build context, etc.Out of the box, we support Claude Agent SDK and Codex SDK agents. By support, we mean that we have an adapter that converts from the SDK message types to ours. If you'd like to use your own custom harness, you can convert and send messages with our types (Vercel AI SDK v6 compatible). For the frontend, we have a Vercel AI SDK provider that lets you use your agent with Vercel's AI SDK, and have a messages module so that you don't have to manage streaming and persistence yourself.The part we think is most different is storage.We treat filesystems as first-class primitives, separate from the lifecycle of a task. That means you can persist a workspace across turns, share it betw
FL score
out of 100
Verdict
high confidence
Competition
23
competitors found, emerging market, funded players
Trend
6 community mentions
A Vercel-like platform for deploying stateful, filesystem-based AI agents, targeting a niche but growing developer pain point.
The pain
The gap
Build angle
Strengths
- Addresses a real and specific pain point for developers building AI agents.
- The market for AI agents is rapidly growing, creating increasing demand for specialized deployment tools.
- Clear willingness to pay signals from developers needing reliable infrastructure.
- The 'filesystem-as-first-class' and 'persistent state across turns' angle provides a unique selling proposition.
- Leverages modern cloud-native deployment patterns (Docker, API/SDK).
Risks
- High complexity for a solo builder, requiring deep expertise in cloud infrastructure, containerization, and agent orchestration.
- Crowded competitive landscape in the broader PaaS and developer tools space, even if the niche is specific.
- Reliance on developer adoption for a new platform, which can be slow and requires significant trust/marketing.
- Ensuring 'Vercel-like' simplicity while handling complex state and filesystem management is a significant technical challenge.
- The exact size of the 'filesystem-based agents' niche needs further validation to ensure sufficient market potential for a standalone SaaS.
Next steps
- Conduct in-depth interviews with 20+ developers currently building AI agents to validate the severity and scale of the 'filesystem-based' pain.
- Create a targeted landing page describing the unique value proposition (persistent workspaces for AI agents) and gather email sign-ups/interest for early access.
- Develop a hyper-focused MVP for a *single type* of filesystem-based agent (e.g., Python coding agents with specific libraries) to quickly get it into users' hands and gather feedback.
- Explore partnerships or integrations with popular AI agent frameworks/SDKs to simplify initial adoption and reach.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 22
- Solution gap
- 14
- Willingness to pay
- 19
- Buildability
- 7
The idea addresses a real and specific pain for developers deploying stateful, filesystem-based AI agents, with clear signals for willingness to pay. However, the solution space is competitive, and the complexity makes it challenging for a solo builder to achieve rapid time-to-market.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Good market opportunity in a growing space, but high complexity and strong competition challenge value delivery and feasibility for a solo builder.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Clear problem and good monetization potential, but challenging simplicity, reach, and leverage for a solo builder in a competitive space.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Clear target audience and value prop with a proven business model, but distribution and market size assumptions need further validation.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Strong demand for solving agent deployment pains, but the narrowest wedge and early surprises need more focus for rapid traction.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Conduct in-depth interviews with 20+ developers currently building AI agents to validate the severity and scale of the 'filesystem-based' pain.
- Create a targeted landing page describing the unique value proposition (persistent workspaces for AI agents) and gather email sign-ups/interest for early access.
- Develop a hyper-focused MVP for a *single type* of filesystem-based agent (e.g., Python coding agents with specific libraries) to quickly get it into users' hands and gather feedback.
- Explore partnerships or integrations with popular AI agent frameworks/SDKs to simplify initial adoption and reach.
Who is already there, emerging market
Provides cloud-hosted development environments integrated with GitHub, ideal for professional developers.
Pricing: Offers a free tier; advanced capabilities require a subscription.
An AI-powered desktop IDE built on VS Code, focusing on developer control and precision coding in local environments.
Pricing: Free 2-week trial, then $25/month.
A development platform tailored for fast, scalable, and secure sandbox environments, especially for frontend prototyping and testing AI agents.
Pricing: Offers generous free tiers or community editions.
Enables visual full-stack web app creation using only Python code, featuring a browser-based drag-and-drop UI builder and one-click deployment.
Pricing: Offers a free plan. Paid plans start from $15/month for individual plans.
A collaborative in-browser IDE that allows users to write, run, and share code in many programming languages.
Pricing: Offers a free tier; advanced features require a subscription.
A PaaS provider that developers use to deploy web and SaaS applications without managing underlying infrastructure, offering automatic scalability and Git-native deployments.
Pricing: Hobby plan is $0 (compute costs billed separately). Professional is $19/user/month. Organization is $29/user/month.
A frontend cloud platform to streamline modern web application development, scaling, and security, with serverless functions for backend logic.
Pricing: Offers a free developer plan. Pro plan is $20/member/month. Enterprise pricing available upon request.
A PaaS built on a managed container system with integrated data services, known for its simplicity in deployment.
Pricing: Fully paid, no longer offers a free tier.
A developer-centric platform that allows applications to run close to users by deploying them on a global network of servers, emphasizing edge computing.
Pricing: Offers a free tier for small applications. Paid plans are usage-based.
A modern PaaS that allows developers to deploy applications with ease, offering Git-based deployments and a focus on developer experience.
Pricing: Usage-based pricing; offers a free trial.
A PaaS that simplifies app deployment and management, built on the Cloud Native Buildpack model, supporting smart autoscaling and various programming languages.
Pricing: Offers a free tier for up to three static sites. Paid plans are usage-based.
- CoolifyVisit
An open-source, self-hostable PaaS that simplifies deploying applications, databases, and services, offering a dashboard similar to Vercel or Render.
Pricing: Free (self-hosted), requires managing your own VPS.
What they charge
What people say, 6 mentions
How to Build Apps & Software Without Code (UPDATED JULY 2025 GUIDE)
r/Entrepreneur
I built and launched a SaaS-style product as a one-person team using AI for almost everything
r/SaaS
What am I doing wrong, or is the product wrong or we are too early?
r/SaaS
Ramp just released March 2026 SaaS spending data. Here are the 5 most interesting patterns.
r/SaaS
2026 AI Coding Agent Dev Tool Market Map
r/SaaS
I sold 75 Bitcoin at $300 each to start a business. Lost everything. Ended up alone in Bahrain with no plan. Then I typed "create me a chatbot" as a laugh.
r/SaaS
Recent news
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Market signals
The market for deploying agents in sandboxed environments is growing, driven by the increasing demand for AI applications and the need for secure, scalable development and deployment platforms. Recent funding rounds indicate strong investor interest in platforms that offer simplified deployment, AI integrations, and flexible infrastructure options. There's a clear trend towards platforms that provide managed services, automated CI/CD, and support for various programming languages and frameworks, particularly those catering to AI/ML workloads.
What frustrates people
- Heroku's forced restarts every 24 hours lose in-memory state and disrupt websocket connections.
- Heroku directs users to community support channels multiple times before they can file a ticket and requires an additional Enterprise support fee for direct chat with an engineer.
- Render's pricing plans do not include compute costs, which can increase the overall bill.
- Render has limited bandwidth and load-balancing capabilities.
- Render offers support options within its paid pricing tiers, but they can be limited.
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
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