🛠️ Hacker News Tools

[Database] Show HN: Visual Database Schema Designer (Angular 21 and .NET 10) Hi HN, OP here.<p>I built this because I was frustrated with the current state of DB design tools. They are usually either heavy enterprise desktop apps (like DataGrip&#x2F;Workbench) or simple drawing tools that don&#x27;t export usable code.<p>I wanted a &quot;VS Code-like&quot; experience in the browser: dark mode, strict typing, and instant visual feedback.<p>The tech stack is quite aggressive: - Frontend: Angular 21 (Latest). I&#x27;m using Signals exclusively for the graph state management to handle 100+ nodes without layout thrashing. - Backend: .NET 10 for DDL generation and schema validation.<p>Current features: - Visual Table&#x2F;Column editor. - Drag &amp; drop relationships (1:N handling). - Exports to PostgreSQL DDL and Entity Framework Core.<p>It&#x27;s an MVP, so I&#x27;m looking for feedback on the graph interaction and the UI feel.<p>Does the &quot;IDE-like&quot; layout work for you for this kind of task?

[Other] Building Robust Helm Charts

[CLI Tool] Show HN: AWS-doctor – A terminal-based AWS health check and cost optimizer in Go

[Other] Show HN: HTTP:COLON – A quick HTTP header/directive inspector and reference Hi HN -- I built HTTP:COLON, a small, open-source web tool for quickly checking a site’s HTTP response headers and learning what they mean as you go.<p>Link: <a href="https:&#x2F;&#x2F;httpcolon.dev&#x2F;" rel="nofollow">https:&#x2F;&#x2F;httpcolon.dev&#x2F;</a><p>What it does<p>- Enter a URL and fetch its response headers<p>- Groups common headers into handy buckets (cache, content, security)<p>- Includes short docs&#x2F;tooltips for headers and directives so you can look things up while debugging. I find hovering on highlighted headers quite useful!<p>Supports different HTTP methods (GET&#x2F;POST&#x2F;PUT&#x2F;DELETE)<p>Deep links<p>- You can link directly to a host, e.g. <a href="https:&#x2F;&#x2F;httpcolon.dev&#x2F;www.google.com" rel="nofollow">https:&#x2F;&#x2F;httpcolon.dev&#x2F;www.google.com</a><p>(or any domain) to jump straight into inspecting it.<p>Why I made it<p>- I kept bouncing between DevTools, MDN, and random blog posts while debugging caching + security headers. I wanted one place that’s quick for “what am I getting back?” and “what does this header&#x2F;directive do?”<p>It’s in beta, and I’d love feedback on:<p>- Missing features you’d want for day-to-day debugging (export&#x2F;share formats, comparisons, presets, etc.)<p>Thanks!

[CLI Tool] Show HN: Lume 0.2 – Build and Run macOS VMs with unattended setup Hey HN, Lume is an open-source CLI for running macOS and Linux VMs on Apple Silicon. Since launch (<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=42908061">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=42908061</a>), we&#x27;ve been using it to run AI agents in isolated macOS environments. We needed VMs that could set themselves up, so we built that.<p>Here&#x27;s what&#x27;s new in 0.2:<p>*Unattended Setup* – Go from IPSW to a fully configured VM without touching the keyboard. We built a VNC + OCR system that clicks through macOS Setup Assistant automatically. No more manual setup before pushing to a registry:<p><pre><code> lume create my-vm --os macos --ipsw latest --unattended tahoe </code></pre> You can write custom YAML configs to set up any macOS version your way.<p>*HTTP API + Daemon* – A REST API on port 7777 that runs as a background service. Your scripts and CI pipelines can manage VMs that persist even if your terminal closes:<p><pre><code> curl -X POST localhost:7777&#x2F;lume&#x2F;vms&#x2F;my-vm&#x2F;run -d &#x27;{&quot;noDisplay&quot;: true}&#x27; </code></pre> *MCP Server* – Native integration with Claude Desktop and AI coding agents. Claude can create, run, and execute commands in VMs directly:<p><pre><code> # Add to Claude Desktop config &quot;lume&quot;: { &quot;command&quot;: &quot;lume&quot;, &quot;args&quot;: [&quot;serve&quot;, &quot;--mcp&quot;] } # Then just ask: &quot;Create a sandbox VM and run my tests&quot; </code></pre> *Multi-location Storage* – macOS disk space is always tight, so from user feedback we added support for external drives. Add an SSD, move VMs between locations:<p><pre><code> lume config storage add external-ssd &#x2F;Volumes&#x2F;ExternalSSD&#x2F;lume lume clone my-vm backup --source-storage default --dest-storage external-ssd </code></pre> *Registry Support* – Pull and push VM images from GHCR or GCS. Create a golden image once, share it across your team.<p>We&#x27;re seeing people use Lume for: - Running Claude Code in an isolated VM (your host stays clean, reset mistakes by cloning) - CI&#x2F;CD pipelines for Apple platform apps - Automated UI testing across macOS versions - Disposable sandboxes for security research<p>To get started:<p><pre><code> &#x2F;bin&#x2F;bash -c &quot;$(curl -fsSL https:&#x2F;&#x2F;raw.githubusercontent.com&#x2F;trycua&#x2F;cua&#x2F;main&#x2F;libs&#x2F;lume&#x2F;scripts&#x2F;install.sh)&quot; lume create sandbox --os macos --ipsw latest --unattended tahoe lume run sandbox --shared-dir ~&#x2F;my-project </code></pre> Lume is MIT licensed and Apple Silicon only (M1&#x2F;M2&#x2F;M3&#x2F;M4) since it uses Apple&#x27;s native Virtualization Framework directly—no emulation.<p>Lume runs on EC2 Mac instances and Scaleway if you need cloud infrastructure. We&#x27;re also working on a managed cloud offering for teams that need macOS compute on demand—if you&#x27;re interested, reach out.<p>We&#x27;re actively developing this as part of Cua (<a href="https:&#x2F;&#x2F;github.com&#x2F;trycua&#x2F;cua" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;trycua&#x2F;cua</a>), our Computer Use Agent SDK. We&#x27;d love your feedback, bug reports, or feature ideas.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;trycua&#x2F;cua" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;trycua&#x2F;cua</a> Docs: <a href="https:&#x2F;&#x2F;cua.ai&#x2F;docs&#x2F;lume">https:&#x2F;&#x2F;cua.ai&#x2F;docs&#x2F;lume</a><p>We&#x27;ll be here to answer questions!

[IDE/Editor] Show HN: Opal Editor, free Obsidian alternative for markdown and site publishing A fully featured markdown editor and publisher. Free, open-source and browser-first (no backend required). Built with modern technologies like React, TypeScript, Shadcn&#x2F;UI, and Vite. (thoughtfully crafted, not vibe coded)

[DevOps] Show HN: Mist – a lightweight, self-hosted PaaS Hi HN,<p>We’re building Mist, a lightweight, self-hosted PaaS for running and managing applications on your own servers.<p>We started Mist because we wanted a middle ground between raw VPS management and heavy, all-in-one platforms. Existing PaaS solutions often felt too complex or resource-intensive for small servers, homelabs, and side projects. We wanted something that keeps the PaaS experience but stays simple and predictable.<p>Our goals with Mist are: - A simple PaaS to deploy and manage apps on your own infrastructure - HTTPS, routing, and app access handled automatically - Low resource usage so it runs well on small VPSes - Self-hosted and transparent, with minimal magic<p>Mist focuses on being an opinionated but lightweight layer on top of your server. It doesn’t try to hide everything behind abstractions, but it does aim to remove the repetitive operational work that comes with managing apps by hand.<p>Mist is still early, and this is where we really need help. We’re actively looking for:<p>- Users who want a simple self-hosted PaaS and can share real-world feedback<p>- Contributors who want to help shape the core features, architecture, and UX<p>Website: <a href="https:&#x2F;&#x2F;trymist.cloud" rel="nofollow">https:&#x2F;&#x2F;trymist.cloud</a><p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;corecollectives&#x2F;mist" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;corecollectives&#x2F;mist</a>

[CLI Tool] Show HN: KeyEnv – CLI-first secrets manager for dev teams (Rust) Hi HN,<p>I built KeyEnv because I was tired of the &quot;can you Slack me the Stripe key?&quot; workflow.<p><pre><code> The problem: My team&#x27;s secrets lived in a mix of Slack DMs, shared Google Docs, and .env files that definitely weren&#x27;t in .gitignore at some point. Enterprise tools like Vault required more DevOps time than we had. Doppler was close but felt heavier than we needed. What KeyEnv does: keyenv init # link project keyenv pull # sync secrets to local .env keyenv run -- npm start # inject secrets, run command That&#x27;s basically it. Secrets are encrypted client-side (AES-256-GCM) before leaving your machine. Zero-knowledge architecture—we can&#x27;t read your secrets even if we wanted to. Technical details: - Single Rust binary, no runtime dependencies - Works offline (cached secrets) - RBAC for teams (owner&#x2F;admin&#x2F;member&#x2F;viewer) - Service tokens for CI&#x2F;CD - Full audit trail Honest tradeoffs: - SaaS only, no self-hosted option - Fewer integrations than Doppler - If you need dynamic secrets or PKI, use Vault Pricing: Free tier (3 projects, 100 secrets), $12&#x2F;user&#x2F;month for teams. Would love feedback on the CLI UX and any rough edges. Happy to answer questions about the architecture. </code></pre> <a href="https:&#x2F;&#x2F;www.keyenv.dev" rel="nofollow">https:&#x2F;&#x2F;www.keyenv.dev</a>

[Other] Show HN: WebTerm – Browser-based terminal emulator

[Other] Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval Hi HN,<p>I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.<p>After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, which felt unnecessarily heavy for short-lived analysis tasks.<p>To explore this tradeoff, I built GibRAM (Graph in-buffer Retrieval and Associative Memory). It is an experimental, in-memory GraphRAG runtime where entities, relationships, text units, and embeddings live side by side in a single process.<p>GibRAM is intentionally ephemeral. It is designed for exploratory tasks like summarization or conversational querying over a bounded document set. Data lives in memory, scoped by session, and is automatically cleaned up via TTL. There are no durability guarantees, and recomputation is considered cheaper than persistence for the intended use cases.<p>This is not a database and not a production-ready system. It is a casual project, largely vibe-coded, meant to explore what GraphRAG looks like when memory is the primary constraint instead of storage. Technical debt exists, and many tradeoffs are explicit.<p>The project is open source, and I would really appreciate feedback, especially from people working on RAG, search infrastructure, or graph-based retrieval.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;gibram-io&#x2F;gibram" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;gibram-io&#x2F;gibram</a><p>Happy to answer questions or hear why this approach might be flawed.

[CLI Tool] Show HN: Figma-use – CLI to control Figma for AI agents I&#x27;m Dan, and I built a CLI that lets AI agents design in Figma.<p>What it does: 100 commands to create shapes, text, frames, components, modify styles, export assets. JSX importing that&#x27;s ~100x faster than any plugin API import. Works with any LLM coding assistant.<p>Why I built it: The official Figma MCP server can only read files. I wanted AI to actually design — create buttons, build layouts, generate entire component systems. Existing solutions were either read-only or required verbose JSON schemas that burn through tokens.<p>Demo (45 sec): <a href="https:&#x2F;&#x2F;youtu.be&#x2F;9eSYVZRle7o" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;9eSYVZRle7o</a><p>Tech stack: Bun + Citty for CLI, Elysia WebSocket proxy, Figma plugin. The render command connects to Figma&#x27;s internal multiplayer protocol via Chrome DevTools for extra performance when dealing with large groups of objects.<p>Try it: bun install -g @dannote&#x2F;figma-use<p>Looking for feedback on CLI ergonomics, missing commands, and whether the JSX syntax feels natural.

[Other] Show HN: E80: an 8-bit CPU in structural VHDL I built a new 8-bit CPU in VHDL from scratch (starting from the ISA). I felt that most educational soft-cores hide too much behind abstraction, eg. if I can do a+b with a single assignment that calls an optimized arithmetic library, then why did I learn the ripple carry adder in the first place ? And why did I learn flip flops if I can do all my control logic with a simple PROCESS statement like I would with a programming language ? Of course abstraction is the main selling point of HDLs, but would it work if I tried to keep strictly structural and rely on ieee.std_logic_1164 only ?<p>Well, it did and it works nicely. No arithmetic libraries, no PROCESS except for the DFF component (obviously). Of course it&#x27;s a bit of a &quot;resource hog&quot; compared to optimized cores, (eg. the RAM is build out of flip flops instead of a block ram that takes advantage of FPGA intermal memory) but you can actually trace every signal through the datapath as it happens.<p>I also build an assembler in C99 without external libraries (please be forgiving, my code is very primitive I think). I bundled Sci1 (Scintilla), GHDL and GTKWave into a single installer so you can write assembly and see the waveforms immediately without having to spend hours configuring simulators. Currently Windows only, but at some point I&#x27;ll have to do it on Linux too. I tested it on the Tang Primer 25K and Cyclone IV, and I included my Gowin, Quartus and Vivado projects files. That should make easy to run on your FPGA.<p>Everything is under the GPL3.<p>(Edit: I did not use AI. Not was it a waste of time for the VHDL because my design is too novel -- but even for beta testing it would waste my time because those LLMs are too well trained for x86&#x2F;ARM and my flag logic draws from 6502&#x2F;6800 and even my ripple carry adder doesn&#x27;t flip the carry bit in subtraction. Point is -- AI couldn&#x27;t help. It only kept complaining that my assembler&#x27;s C code wasn&#x27;t up to 2026 standards)

[Other] Show HN: WebGPU React Renderer Using Vello I&#x27;ve built a package to use Raph Levien&#x27;s Vello as a blazing fast 2D renderer for React on WebGPU. It uses WASM to hook into the Rust code

[Other] Show HN: ChunkHound, a local-first tool for understanding large codebases ChunkHound’s goal is simple: local-first codebase intelligence that helps you pull deep, core-dev-level insights on demand, generate always-up-to-date docs, and scale from small repos to enterprise monorepos — while staying free + open source and provider-agnostic (VoyageAI &#x2F; OpenAI &#x2F; Qwen3, Anthropic &#x2F; OpenAI &#x2F; Gemini &#x2F; Grok, and more).<p>I’d love your feedback — and if you have, thank you for being part of the journey!

[Other] Show HN: Docker.how – Docker command cheat sheet

[DevOps] Show HN: Minikv – Distributed key-value and object store in Rust (Raft, S3 API) Hi HN,<p>I’m releasing minikv, a distributed key-value and object store in Rust.<p>What is minikv? minikv is an open-source, distributed storage engine built for learning, experimentation, and self-hosted setups. It combines a strongly-consistent key-value database (Raft), S3-compatible object storage, and basic multi-tenancy. I started minikv as a learning project about distributed systems, and it grew into something production-ready and fun to extend.<p>Features&#x2F;highlights:<p>- Raft consensus with automatic failover and sharding - S3-compatible HTTP API (plus REST&#x2F;gRPC APIs) - Pluggable storage backends: in-memory, RocksDB, Sled - Multi-tenant: per-tenant namespaces, role-based access, quotas, and audit - Metrics (Prometheus), TLS, JWT-based API keys - Easy to deploy (single binary, works with Docker&#x2F;Kubernetes)<p>Quick demo (single node):<p>git clone <a href="https:&#x2F;&#x2F;github.com&#x2F;whispem&#x2F;minikv.git" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;whispem&#x2F;minikv.git</a> cd minikv cargo run --release -- --config config.example.toml curl localhost:8080&#x2F;health&#x2F;ready # S3 upload + read curl -X PUT localhost:8080&#x2F;s3&#x2F;mybucket&#x2F;hello -d &quot;hi HN&quot; curl localhost:8080&#x2F;s3&#x2F;mybucket&#x2F;hello<p>Docs, cluster setup, and architecture details are in the repo. I’d love to hear feedback, questions, ideas, or your stories running distributed infra in Rust!<p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;whispem&#x2F;minikv" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;whispem&#x2F;minikv</a> Crate: <a href="https:&#x2F;&#x2F;crates.io&#x2F;crates&#x2F;minikv" rel="nofollow">https:&#x2F;&#x2F;crates.io&#x2F;crates&#x2F;minikv</a>

[IDE/Editor] Show HN: What if your menu bar was a keyboard-controlled command center? Hey Hacker News The ones that know me here know that I am a productivity geek.<p>After DockFlow to manage my Dock and ExtraDock, which gives me more space to manage my apps and files, I decided to tackle the macOS big boss: the menu bar.<p>I spend ~40% of my day context-switching between apps — Zoom meetings, Slack channels, Code projects, and Figma designs. My macOS menu bar has too many useless icons I almost never use.<p>So I thought to myself, how can I use this area to improve my workflows?<p>Most solutions (Bartender, Ice) require screen recording permissions, and did not really solve my issues. I wanted custom menus in the apps, not the ones that the developers decided for me.<p>After a few iterations and exploring different solutions, ExtraBar was created. Instead of just hiding icons, what if the menu bar became a keyboard-controlled command center that has the actions I need? No permissions. No telemetry. Just local actions.<p>This is ExtraBar: Set up the menu with the apps and actions YOU need, and use a hotkey to bring it up with full keyboard navigation built in.<p>What you can do: - Jump into your next Zoom call with a keystroke - Open specific Slack channels instantly (no menu clicking) - Launch VS Code projects directly - Trigger Apple Shortcuts workflows - Integrate with Raycast for advanced automation - Custom deep links to Figma, Spotify, or any URL<p>Real-world example: I&#x27;ve removed my menu bar icons. Everything is keyboard- controlled: cmd+B → 2 (Zoom) → 4 (my personal meeting) → I&#x27;m in.<p>Why it&#x27;s different: Bartender and Ice hide icons. ExtraBar <i>uses</i> your menu bar to do things. Bartender requires screen recording permissions. Ice requires accessibility permissions. ExtraBar works offline with zero permissions - (Enhance functionality with only accessibility permissions, not a must)<p>Technical: - Written in SwiftUI; native on Apple Silicon and Intel - Zero OS permissions required (optional accessibility for enhanced keyboard nav) - All data stored locally (no cloud, no telemetry) - Very Customizable with custom configuration built in for popular apps + fully customizable configuration actions. - Import&#x2F;export action configurations<p>The app is improving weekly based on community feedback. We&#x27;re also building configuration sharing so users can share setups.<p>Already got some great feedback from Reddit and Producthunt, and I can&#x27;t wait to get yours!<p>Check out the website: <a href="https:&#x2F;&#x2F;extrabar.app" rel="nofollow">https:&#x2F;&#x2F;extrabar.app</a> ProductHunt: <a href="https:&#x2F;&#x2F;www.producthunt.com&#x2F;products&#x2F;extrabar" rel="nofollow">https:&#x2F;&#x2F;www.producthunt.com&#x2F;products&#x2F;extrabar</a>

[Other] Provide agents with automated feedback

[Other] Show HN: I built a tool to assist AI agents to know when a PR is good to go I&#x27;ve been using Claude Code heavily, and kept hitting the same issue: the agent would push changes, respond to reviews, wait for CI... but never really know when it was done.<p>It would poll CI in loops. Miss actionable comments buried among 15 CodeRabbit suggestions. Or declare victory while threads were still unresolved.<p>The core problem: no deterministic way for an agent to know a PR is ready to merge.<p>So I built gtg (Good To Go). One command, one answer:<p>$ gtg 123 OK PR #123: READY CI: success (5&#x2F;5 passed) Threads: 3&#x2F;3 resolved<p>It aggregates CI status, classifies review comments (actionable vs. noise), and tracks thread resolution. Returns JSON for agents or human-readable text.<p>The comment classification is the interesting part — it understands CodeRabbit severity markers, Greptile patterns, Claude&#x27;s blocking&#x2F;approval language. &quot;Critical: SQL injection&quot; gets flagged; &quot;Nice refactor!&quot; doesn&#x27;t.<p>MIT licensed, pure Python. I use this daily in a larger agent orchestration system — would love feedback from others building similar workflows.

[CLI Tool] Show HN: On the edge of Apple Silicon memory speeds I have developed open source CLI-tool for Apple Silicon macOS. It measures memory speeds in different ways and also latency. It can achieve up to 96-97% efficiency on read speed on M4 base what is advertised as 120GB&#x2F;s. All memory operations are in assembly.<p>I would really appreciate for results on different CPU&#x27;s how benchmark works on those. I have been able to test this on M1 and M4.<p>command : &#x27;memory_benchmark -non-cacheable -count 5 -output results.JSON&#x27; (close all applications before running)<p>This will generate JSON file where you find sections copy_gb_s, read_gb_s and write_gb_s statics.<p>Example M4 with 10 loops: &quot;copy_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 106.65421233311835, &quot;max&quot;: 106.70240696071005, &quot;median&quot;: 106.65069297260811, &quot;min&quot;: 106.6336774994254, &quot;p90&quot;: 106.66606919223108, &quot;p95&quot;: 106.68423807647056, &quot;p99&quot;: 106.69877318386216, &quot;stddev&quot;: 0.01930653530818627 }, &quot;values&quot;: [ 106.70240696071005, 106.66203166240008, 106.64410802226159, 106.65831409449595, 106.64148106986977, 106.6482935780762, 106.63974821679058, 106.65896986001393, 106.6336774994254, 106.65309236714002 ] }, &quot;read_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 115.83111228356601, &quot;max&quot;: 116.11098114619033, &quot;median&quot;: 115.84480882265643, &quot;min&quot;: 115.56959026587722, &quot;p90&quot;: 115.99667266786554, &quot;p95&quot;: 116.05382690702793, &quot;p99&quot;: 116.09955029835784, &quot;stddev&quot;: 0.1768243167963439 }, &quot;values&quot;: [ 115.79154681380165, 115.56959026587722, 115.60574235736468, 115.72112860271632, 115.72147129262802, 115.89807083151123, 115.95527337086908, 115.95334642887214, 115.98397172582945, 116.11098114619033 ] }, &quot;write_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 65.55966046805113, &quot;max&quot;: 65.59040040480241, &quot;median&quot;: 65.55933583741347, &quot;min&quot;: 65.50911885624045, &quot;p90&quot;: 65.5840272860955, &quot;p95&quot;: 65.58721384544896, &quot;p99&quot;: 65.58976309293172, &quot;stddev&quot;: 0.02388146120866979 },<p>Patterns benchmark also shows bit more of memory speeds. command: &#x27;memory_benchmark -patterns -non-cacheable -count 5 -output patterns.JSON&#x27;<p>Example M4 from 100 loops: &quot;sequential_forward&quot;: { &quot;bandwidth&quot;: { &quot;read_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 116.38363691482549, &quot;max&quot;: 116.61212708384109, &quot;median&quot;: 116.41264548721367, &quot;min&quot;: 115.449510036971, &quot;p90&quot;: 116.54143114134801, &quot;p95&quot;: 116.57314206456576, &quot;p99&quot;: 116.60095068065866, &quot;stddev&quot;: 0.17026641589059727 } } } }<p>&quot;strided_4096&quot;: { &quot;bandwidth&quot;: { &quot;read_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 26.460392735220456, &quot;max&quot;: 27.7722419653915, &quot;median&quot;: 26.457051473208285, &quot;min&quot;: 25.519925729459107, &quot;p90&quot;: 27.105171215736604, &quot;p95&quot;: 27.190715938337473, &quot;p99&quot;: 27.360449534513144, &quot;stddev&quot;: 0.4730857335572576 } } } }<p>&quot;random&quot;: { &quot;bandwidth&quot;: { &quot;read_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 26.71367836895143, &quot;max&quot;: 26.966820487564327, &quot;median&quot;: 26.69907406197067, &quot;min&quot;: 26.49374804466308, &quot;p90&quot;: 26.845236287807374, &quot;p95&quot;: 26.882004355057887, &quot;p99&quot;: 26.95742242818151, &quot;stddev&quot;: 0.09600564296001704 } } } }<p>Thank you for reading :)