Eve – Managed OpenClaw for work
Eve is an AI agent harness that runs in an isolated Linux sandbox (2 vCPUs, 4GB RAM, 10GB disk) with a real filesystem, headless Chromium, code execution, and connectors to 1000+ services.You give it a task and it works in the background until it's done.I built this because I wanted OpenClaw without the self-hosting, pointed at actual day-to-day work. I’m thinking less personal assistant and more helpful colleague.Here’s a short demo video: https://www.loom.com/share/00d11bdbe804478e8817710f5f53ac61The main interface is a web app where you can watch work happen in real time (agents spawning, files being written, use of the CLI). There's also an iMessage integration so you can fire a task asynchronously, put your phone down, and get a reply when it's finished.Under the hood, there's an orchestrator (Claude Opus 4.6) that routes to the right domain-specific model for each subtask: browsing, coding, research, and media generation.For complex tasks it spins up parallel sub-agents that coordinate through the shared filesystem. They have persistent memory across sessions so context compounds over time.I’ve packaged it with a bunch of pre-installed skills so it can execute in a variety of job roles (sales, marketing, finance) at runtime.Here are a few things Eve has helped me with in the last couple days:- Edit this demo video with a voice over of Garry: https://www.youtube.com/watch?v=S4oD7H3cAQ0- Do my tax returns- To build HN as if it was the year 2030: https://api.eve.new/api/sites/hackernews-2030/#/AMA on the architecture and lmk your thoughts :)P.S. I've given every new user $100 worth of credits to try it.
FL score
out of 100
Verdict
high confidence
Competition
6
competitors found, emerging market, funded players
Trend
No signal yet
A managed AI agent harness for day-to-day work, providing autonomous task execution for professionals in a secure, persistent environment.
The pain
The gap
Build angle
Strengths
- Addresses a real and severe pain point (complex AI agents for work).
- Strong demand and a rapidly growing market for AI agent platforms.
- Unique 'managed' and 'helpful colleague' positioning for professionals.
- Clear differentiation through persistent memory, pre-installed job skills, and iMessage integration.
- Functional demo already exists, proving buildability and early validation.
- High willingness to pay for solutions that automate complex business tasks.
Risks
- Underlying technical complexity could be challenging for a solo builder to scale and maintain long-term.
- Competition from well-funded players, even if not directly in the 'managed for work' niche, could eventually shift focus.
- Ensuring agent reliability and avoiding 'messing up' complex tasks is a significant ongoing technical challenge.
- Customer acquisition for specific job roles might require specialized marketing efforts.
Next steps
- Gather extensive feedback from early users utilizing the $100 credits, focusing on which 'pre-installed skills' deliver the most value.
- Prioritize agent reliability and robustness, particularly for multi-step tasks, addressing known user frustrations.
- Double down on the 'managed for work' angle by building out specific, measurable use cases and testimonials for target job roles (sales, marketing, finance).
- Explore further integrations with common workplace tools to enhance utility and simplify workflow adoption.
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
- 21
- Willingness to pay
- 20
- Buildability
- 16
Strong problem-solution fit with a unique managed offering in a rapidly growing market, validated by a functional demo, though competitive landscape requires a sharp focus on the specific niche.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
A highly viable idea with a strong value proposition in a booming market, leveraging existing tech to create a differentiated offering for a clear audience.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A strong idea with clear problem clarity and creator fit, but the underlying complexity could impact solo scalability despite a focused niche.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A micro-SaaS with a clear, valuable proposition for a specific, reachable audience, supported by a viable business model and initial validation efforts.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a desperate need with a narrow, compelling solution, showing strong early validation and long-term relevance.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Gather extensive feedback from early users utilizing the $100 credits, focusing on which 'pre-installed skills' deliver the most value.
- Prioritize agent reliability and robustness, particularly for multi-step tasks, addressing known user frustrations.
- Double down on the 'managed for work' angle by building out specific, measurable use cases and testimonials for target job roles (sales, marketing, finance).
- Explore further integrations with common workplace tools to enhance utility and simplify workflow adoption.
Who is already there, emerging market
E2B provides ephemeral code execution sandboxes for AI agents using Firecracker microVMs.
Pricing: Hobby: Free with $100 one-time credit, 1-hour sessions, 20 concurrent sandboxes. Pro: $150/month with 24-hour sessions, custom CPU/RAM. Usage: ~$0.05/hour for 1 vCPU sandbox.
Modal is a serverless platform for ML and data workloads, offering Python sandbox support with elastic GPU scaling.
Pricing: $30/month free credits. Per-second billing for CPU ($0.047/vCPU-hour), RAM ($0.008/GB-hour), and H100 GPU ($3.95/hour plus separate CPU/RAM charges).
- NorthflankVisit
Northflank offers enterprise-grade microVMs with VPC deployment and GPU support for AI agent code execution.
Pricing: CPU $0.01667/vCPU-hour, Memory $0.00833/GB-hour. GPUs from $0.80-$3.14/hour. Free sandbox tier available.
- Cognosys AIVisit
Cognosys AI is an autonomous AI agent platform designed to execute complex tasks using a goal-based approach.
Pricing: Free version: 1000 credits/month, GPT-3.5, GPT-4 Internet browsing, max 7 agent loops. Pro plan: $29/month, 5000 credits/month, unlimited GPT-3.5, PaLM2 & Cohere, unlimited GPT-4 Internet browsing, GPT-4 & Claude Agents, up to 20 agent loops, private community, priority support.
SUPERAGENT is an open-source platform enabling developers to create, host, and manage autonomous AI agents for various tasks.
Pricing: AI Assistant Lite: $9/month (10 inquiries/month, $1 per processed inquiry). AI Assistant Pro: $59/month (100 inquiries/month, 10,000 Credits). Pro: $499/mo (1,050 quotes/month for Quoting AI Agent). Scale: $999/mo (2,350 quotes/month for Quoting AI Agent). Custom Enterprise plans available.
Vercel Sandbox provides ephemeral Firecracker microVMs for sandboxed agent execution.
Pricing: Free during beta. Production pricing not announced.
What they charge
Recent news
- GitButler Lands $17M Series A to Advance Version Control for AI-Driven Development
PR Newswire, April 10 2026
- The best AI agents in 2026 | Product Hunt
Product Hunt, April 10 2026
- AI Agents Market Size to Reach USD 84,062 Million by 2031, Growing at 41% CAGR Amid Rising Enterprise Automation Demand | Valuates Reports
PR Newswire, April 09 2026
- The Brutal UX and Product Challenges of AI Agents
Enterprise AI Trends - Substack, April 03 2025
- AI Agent Development Cost Guide 2026: Factors & Full Breakdown | DevCom
DevCom, March 02 2026
Market signals
The AI agent platform market is a rapidly growing and large market, valued at $7.8 billion in 2025 and projected to reach $68.4 billion by 2034, expanding at a CAGR of 27.4%. Recent funding rounds indicate strong investor interest, with companies like GitButler securing $17M in Series A funding. The market is driven by rapid LLM advancements, enterprise automation mandates, and the proliferation of multi-agent orchestration frameworks.
What frustrates people
- Lack of standardized communication between different AI agents, leading to disconnected workflows.
- Personality drift where model updates change behavior and responses, making them less reliable over time.
- Absence of long-term ownership or personalization in AI agents; users want agents to remember preferences and past interactions.
- AI agents messing up multi-step coding tasks (e.g., refactoring fine but breaking tests or overwriting docs).
- Difficulty in understanding the purpose of different AI agent frameworks.
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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