
HailBytes ASM
Self-hosted attack surface management on your own cloud
What it does
30+ recon and vulnerability scanning tools (Subfinder, Amass, Nuclei) plus AI-powered triage, deployed on your own AWS or Azure account. Continuous monitoring, custom YAML scan engines, full data sovereignty. 5-year cost: $21K vs. $600K+ for SaaS ASM.
Does a similar job
all alternatives →- IBI built a service to help companies save on their AWS bills2020 · ▲73
Hey HN: I'm Kaveh, the founder of Usage (https://www.usage.ai/) We help companies drive down AWS costs. Why? Because the way it's done now is a pain. Stakeholders, especially engineers, are required to spend unnecessary time manually finding underutilized or overly expensive EC2s. We believe the optimization process should be done automatically through a series of sophisticated algorithms. At the moment, there are over 70,000 AWS EC2 prices - doing that manually just won't scale at most organizations. My background is in software engineering. Previous to founding Usage, I…
- MYMonitor Your AWS Cost in Slack2019 · cloudforecast.io · ▲27
- WBWe built a <60ms, open-source alternative to E2B using RustVMM and KVMApr 2026 · github.com · ▲7
Over the past few months, as we scaled our internal AI Agents, we hit a dead end: Running LLM-generated arbitrary code in Docker is basically running naked on security due to container escape risks. But using full traditional VMs takes minutes to boot and eats too much memory to support high-density concurrency. We loved the developer experience of SaaS sandboxes on the market, but they are closed-source, expensive, and have too high a barrier to entry for self-hosting. So, our team decided to build our own. After months of grinding, using RustVMM and KVM, we built a blazing-fast,…
- BMBillGist – Monitor AWS costs from your inbox2014 · billgist.com · ▲8
GAZUM® Market IntelligenceJan 2026 · gazum.org · ▲5All-in-one trading platform replacing 500/mo in tools for 89

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- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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