machine0 (YC S26) – Persistent CPU and GPU VMs from the CLI
Hi HN! I’m Barnaby, founder of machine0 (https://machine0.io). I’m building a CLI for long horizon agent compute: `machine0 new mybox` gives your agent a persistent cloud VM, billed by the minute, from $0.013/hr up to 60 vCPU / 240 GB RAM and GPUs (H100s, H200s etc), with 99.99% VM level uptime. Agents self drive via CLI or MCP.Demo: https://www.youtube.com/watch?v=gyllkZ0M04EAgent workloads are moving from ephemeral to always-on. A coding agent working on a complex feature runs 6-8 hours. Agent orchestrated training & RL runs take days. OpenClaw & Hermes run 24/7. As you run more in parallel:- Resources: a few agents on a large codebase saturate RAM and CPU. Model training and RL needs GPUs you don't have.- Security: `--yolo` on your personal machine is one prompt injection away from exfiltrated credentials.- Availability: close your laptop and the agent dies mid-task.- Isolation: there's no clean line between you and the minimum your agent actually needs.machine0 gives every agent its own computer. It's a CLI simple enough that both humans and agents use it without reading docs: `machine0 new mybox` creates an SSH-ready VM with a static IP and HTTPS endpoint. Always on (with 99.99% VM level uptime) until you switch it off.- Billed by the minute. 1 vCPU / 1 GB at $0.013/hr up to 60 vCPU / 240 GB, plus GPUs from RTX 4000 Ada to 8×H200.- Suspend, snapshot and resume. Making it easy to pause your work, and come back to it later. Or to make a golden master image to stamp out clones for a fleet.- Block storage. Persistent volumes (from 10 GB to 16 TB) that you can manage with intuitive grammar: `--yolo` and attach to your VMs.- Profiles. Bundles of credentials, MCP connections, prompts, and env vars, injected at VM creation. So each agent gets exactly the capabilities you choose, and nothing else.- Agents self-serve. Hand the CLI or MCP server to Claude, Codex, or OpenCode and it manages its o
FL score
out of 100
Verdict
high confidence
Competition
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Trend
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A CLI that provisions persistent cloud VMs for AI agents, solving the infrastructure gap between local development and production agent workloads.
The pain
The gap
Build angle
Strengths
- Timing: Agent workloads are genuinely shifting from ephemeral to persistent; this is observable in production systems now, not speculative.
- Founder signal: YC S26 acceptance and a working demo suggest product-market fit validation has begun.
- Simplicity: The CLI grammar is genuinely simple enough for agents to use autonomously, this is a real technical achievement.
- Specific use cases: The pain points (resource saturation, security isolation, availability) are concrete and measurable.
- Pricing model: Per-minute billing aligns with agent workload patterns better than hourly cloud instances.
- Feature completeness: Snapshots, profiles, block storage, and MCP support address real operational needs, not afterthoughts.
Risks
- Execution risk on 99.99% uptime: This is a hard SLA. Any major outage early will destroy trust with agents that depend on always-on compute.
- Competitive response: AWS could launch an agent-optimized EC2 tier in 6 months. The moat is the CLI simplicity and agent integration, not the infrastructure.
- Unit economics unclear: At $0.013/hr for 1 vCPU, margins depend entirely on utilization and churn. A single idle VM fleet could be unprofitable.
- Market size uncertainty: How many teams actually need 10+ persistent agent VMs? The TAM could be smaller than it appears (early adopters vs. mainstream).
- Vendor lock-in perception: Agents locked into machine0 CLI may face friction if they need to migrate. This could slow adoption among risk-averse enterprises.
- Support burden: Agents will fail in novel ways. Debugging agent-infrastructure interactions at scale could require significant support overhead.
- GPU availability: H100/H200 supply is constrained. If machine0 can't reliably provision GPUs, the value prop collapses for ML workloads.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Five lenses, one composite. How scoring works
The angle
No market research recorded for this idea yet.
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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