Boxes.dev: ditch localhost; run Claude Code and Codex in the cloud
Hi HN, we’re Nick and Drew, and we’re building boxes.dev – the first cloud-only agentic dev environment (ADE) that gives every Codex and Claude Code agent its own cloud computer.We’re two engineers who previously built Gem (co-founder/CTO and first hire), and we spent the last year coding almost exclusively using Codex and Claude Code. It’s been a huge change to how we code, and it’s been exhilarating seeing the models keep getting better – but we eventually realized that developing on localhost was holding us back:- Git worktrees are clunky to set up and use for parallelizing work - It’s 2026, but somehow everyone is still walking around with laptops cracked open or SSHing into mac minis in their garage so their agents don’t stop working. - Mobile is treated like an afterthought even though coding is just texting now We started hitting resource constraints when multiple parallel agents test their own work by running the full app locally. - We tried different products, but couldn’t find any that solved all of our pain points – so we pivoted and decided to just build the ADE we wanted for ourselves.Boxes.dev is a desktop and mobile app that lets you run Claude Code, Codex (using your subscription!), and the full dev environment for whatever you’re building, all on remote compute. It’s similar to Conductor or the Codex desktop app, except everything is in the cloud.We use coding agents to scan your local dev setup and port it to the cloud. Then every Claude Code/Codex thread starts from a snapshot of the full setup, with its own filesystem and compute. No more git worktrees, no more cracked-open laptops, and your coding agents can actually test their work end-to-end because they can run your full app in isolation.We’ve mirrored the Claude Code and Codex UX to feel natural to power users, and also have a fully-featured mobile app (no handoffs or remote control), plus scheduled automations and a Slack integration.We’re obviously biased, but we’ve been buildin
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Cloud IDE that runs AI coding agents with full isolated environments instead of forcing them to work on localhost.
The pain
The gap
Build angle
Strengths
- Founders have deep credibility: shipped Gem, spent a year coding exclusively with agents, understand the workflow intimately
- Solves a real constraint that gets worse as agents improve, localhost becomes a bottleneck, not a feature
- Mobile app is a genuine differentiator; most dev tools ignore it, but if coding is 'just texting,' mobile matters
- Snapshot-based isolation means agents can't break each other's work; clean separation of concerns
- Slack integration and scheduled automations suggest they're thinking about workflows, not just UI
- Market timing: Claude Code and Codex adoption is accelerating; infrastructure gap is widening
Risks
- Requires users to trust their code and environment to a third party; security and privacy concerns are real, especially for proprietary codebases
- Pricing model unclear, if it's per-agent-hour or per-compute, it could get expensive fast when agents run in parallel. Cost sensitivity could kill adoption
- Dependency on Anthropic and OpenAI APIs; if Claude Code or Codex change pricing, UX, or availability, the product breaks
- Onboarding friction: scanning and porting local dev setups to cloud is non-trivial; one bad port and users abandon it
- Competitive response: Anthropic or OpenAI could build this themselves or integrate it directly into their products
- Market size uncertainty: how many developers actually use Claude Code or Codex as primary tools today? Early adopters only, or mainstream?
- Mobile app claim needs validation, is mobile coding actually viable for complex work, or is it a nice-to-have that doesn't drive retention?
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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