Show HN: Bash Runtime for AWS Lambda https://ift.tt/0D9N6PH

Show HN: Bash Runtime for AWS Lambda Hi HN, I built a Bash runtime for AWS Lambda to make writing glue code simpler and faster. Sometimes, all you need is a bit of `sed`, `awk`, maybe a loop and a few HTTP API calls, and this runtime gives you all the tools to do that. It comes bundled with `jq` and `curl` so you can handle JSON payloads and string together HTTP API calls right out of the box, including calling AWS services with `curl --aws-sigv4`. In keeping with the theme, the Lambda handler contract is also made as simple as practical: read from stdin, write to stdout, return 0 for success and non-0 for error. You can run shell scripts, call binaries (either what's available in `al2023.provided` or you can package your own static binaries with your handler), or a combination of both. If you remember nodding along to Adam Drake's post about how bash and coreutils can be faster than a Hadoop cluster, I hope you give this a whirl and find it useful. The runtime is packaged as a Lambda layer, so it should drop right into your normal AWS infrastructure. https://ift.tt/d7uwzFN June 6, 2026 at 02:12AM

Show HN: MimicScribe – transcriber with ~97% accurate on-device speaker IDing https://ift.tt/lVmoPIf

Show HN: MimicScribe – transcriber with ~97% accurate on-device speaker IDing I’ve spent the last seven months building a tool I wish I’d had in my previous roles. MimicScribe is a macOS menu bar app that fits the "AI notetaker" category. It has accurate on-device speaker identification (a first possibly?), real-time meeting talking points for discovery calls, and a fully keyboard- and voice-driven interface. I believe the accuracy of the speaker ID system is its biggest strength. I used fluid audio’s port of ( https://ift.tt/fPnWwcH ) Pyannote's community-1 as a base. To improve accuracy, the system uses grammar structure cues from the Parakeet STT to mask by sentence. By taking a second set of samples within that mask for cluster assignment, it leverages the fact that most people don’t finish each other's… sandwiches in business meetings. It tends to slightly oversegment, as I’ve found it much easier to merge segments or reassign a speaker than it is to untangle an incorrect merge. The app provides in-meeting talking points using a prompt tuned for discovery type calls. It can suggest probing questions to help you extract more detail or helps you refocus on the big picture with “magic wand” type questions (e.g. “how would your ideal system work”). Getting low latency models to provide novel, relevant, and totally not hallucinated information is a bit of a reach and it tends to restate the transcript frequently but little gems do come from it sometimes so it’s best to think of it as a source of inspiration and be a vigilant gatekeeper. It’s set up so recording can be started and ended via holding a keyboard shortcut instead of connecting to your calendar service. I prefer this for privacy and to keep transcript history from getting cluttered. Tapping the shortcut shows and hides an always-on-top overlay on your active screen regardless of whether you have other apps full-screen or not. Beyond simple navigation, you can also use voice commands to make post-meeting corrections or additions, for instance, you can simply say "merge this speaker with that speaker" to clean up the transcript. It also has push-to-talk/dictate functionality with LLM cleanup - what the app started as but that tool was developer catnip, soo many of them. A developer friend who’s worked in finance reviewed the site and said he’d bounce because the privacy story wasn’t strong enough so I added a completely on-device mode and a bring-your-own-key option. Using cloud models does add a lot to the experience, including context aware speaker merging and fragment cleanup, summary items during meetings, action items attributed, etc. On-device mode is completely free and the speaker identification is still very useful. The privacy story is my biggest worry with the app, particularly since its target audience is more technical people. I’d love to get people's thoughts on it and any feedback would be super helpful. https://ift.tt/3fU4FJ8 June 6, 2026 at 12:33AM

Show HN: SnapToCode – Screenshot any UI and get clean Tailwind code https://ift.tt/n8VrsZb

Show HN: SnapToCode – Screenshot any UI and get clean Tailwind code https://ift.tt/f9v8WSB June 6, 2026 at 12:05AM

Show HN: A Simplistic UI for Rich Hickey's Design in Practice https://ift.tt/JaBM034

Show HN: A Simplistic UI for Rich Hickey's Design in Practice For making it easier to iterate with an LLM on Decision Matrices. Try it: https://bmillare.github.io/design_in_practice_ui/ https://ift.tt/2FWYh6o June 5, 2026 at 11:09PM

Show HN: Bot or Not – Spot AI-generated randomness https://ift.tt/A19l3WZ

Show HN: Bot or Not – Spot AI-generated randomness https://play-bot-or-not.vercel.app/ June 5, 2026 at 02:56AM

Show HN: Cost.dev (YC W21) – making agents cost-aware and cheaper to call https://ift.tt/2iHZRVo

Show HN: Cost.dev (YC W21) – making agents cost-aware and cheaper to call We launched Infracost on HN five years ago ( https://ift.tt/qyGdZWr ) where our CLI generated cost estimates for infra-as-code, e.g. "this Terraform PR adds $400/mo". The idea was to shift cloud costs (FinOps) left, so engineers get visibility of costs before deployment and make better decisions. Earlier this year we started seeing agent traffic in our logs and it looked like coding agents were calling our CLI. But that CLI wasn't designed with coding agents in mind. We went down a philosophical rabbit hole to see if a CLI is even needed anymore given that Claude, Copilot et al. already follow best practices. Ultimately we decided to create a new CLI from the ground up with coding agents in mind for two reasons: 1. We optimized the CLI for agent callers and cut Claude's output token usage by up to 79% and API cost by up to 67% versus a bare-Claude baseline. We wrote a blog documenting our lessons on optimizing user token usage when designing a CLI, e.g. using predicate flags so the agent doesn't compose jq | python | wc pipelines, output format that strips JSON's redundant field names. The blog is here: https://ift.tt/8VK5JcQ... 2. With cloud costs, precision matters. Telling a coding agent "make this Terraform cost-optimized" can be expensive and lossy. You burn tokens loading code and policy context into every conversation. Your agent could make up a price and you wouldn't know because it's difficult to verify that across the ~10M price points that AWS, Azure and Google have. The CLI runs static analysis on the code, uses the latest prices from cloud vendors, and passes that context to the coding agent. So that's what we're launching today - Cost.dev: https://cost.dev/ . - It runs locally. Your code never leaves your machine, you get a fast feedback loop, and you're not burning API calls per character when you want to fetch prices. - The CLI does the deterministic work. Fetching price points, scanning the code, validating fixes. The coding agent does the natural-language part. You don't have to trust the LLM to remember the rules, and can verify it called the right CLI command. - It provides a consistent rule layer across every tool you use. Get cost estimates in your IDE and your coding agent with a single install. We support Claude Code, GitHub Copilot, Cursor, Windsurf, OpenAI Codex, Gemini CLI, as well as IDEs like VS Code and JetBrains Before we keep building more in that direction, I want to sanity-check with HN: is "agents writing IaC in prod" actually a thing yet, or am I betting on a future that's still a year out? I know software developers are using coding agents heavily, but are platform/infra folks doing that for prod too? Also, if you have any feedback on Cost.dev, I'd love to hear it! https://cost.dev/ June 4, 2026 at 06:30PM

Show HN: ClearLogo – a logo API that returns usable logos, not raw files https://ift.tt/MhH1tZO

Show HN: ClearLogo – a logo API that returns usable logos, not raw files https://ift.tt/Tdn6avw June 4, 2026 at 09:28PM

Show HN: Capture, compress, and copy screenshots to clipboard https://ift.tt/YKqU1mH

Show HN: Capture, compress, and copy screenshots to clipboard https://ift.tt/TvaPJAE June 3, 2026 at 11:46PM

Show HN: Rscrypto, pure-Rust crypto with industry leading public benches https://ift.tt/DpJAUM5

Show HN: Rscrypto, pure-Rust crypto with industry leading public benches https://ift.tt/I7y69Rb June 3, 2026 at 11:41PM

Show HN: Nutrepedia – nutrition info in 29 locales built with Clojure and Htmx https://ift.tt/axtuSDy

Show HN: Nutrepedia – nutrition info in 29 locales built with Clojure and Htmx https://ift.tt/9H621yS June 3, 2026 at 11:24PM

Show HN: Ideogram 4.0 – open-weight 9.3B text-to-image model https://ift.tt/VTeZnvl

Show HN: Ideogram 4.0 – open-weight 9.3B text-to-image model It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ift.tt/l7zrvLN We will be happy to answer any questions :) https://ift.tt/T5SwePN June 3, 2026 at 11:00PM

Show HN: RePlaya – self-hosted browser session replay with live tailing https://ift.tt/rtnxaq6

Show HN: RePlaya – self-hosted browser session replay with live tailing Hi HN, I'm one of the founders of s2.dev. RePlaya ( https://ift.tt/tHyr3q4 ) is a self-hosted browser session replay tool using rrweb ( https://ift.tt/3mJjgQp ). It occurred to me that a durable stream per session would be a much neater architectural foundation for much of what you'd want from such a tool. As a unique feature, it also made live tailing straightforward because the player can read from the same stream the recorder is appending to. The alternative architecture is likely an ingest firehose which is then indexed, with associated complexity and latency. You'd have to string together multiple data systems like a message queue, a metadata database, and blob storage and/or an OLAP database. Here the only dependency is S2, which has an open source version you can self-host called s2-lite ( https://ift.tt/paHLT7I ). How it works: - one S2 stream per browser session - large rrweb events (like a full snapshot) get framed across multiple binary S2 records and reassembled on read - active sessions are tailed with an S2 read session, and bridged to the browser over SSE - session listing relies on stream names encoding reverse timestamps, as S2 returns a lexicographic order listing - relying on fencing tokens so a stopped session can't be written to again by a late recorder - retention and GC are handled via S2 stream config, so no background job needed Curious to hear from folks on the tool or the stream-per-session model! https://ift.tt/tHyr3q4 June 3, 2026 at 12:40AM

Show HN: Hop – JSX for Rust https://ift.tt/xOIXDkf

Show HN: Hop – JSX for Rust https://hoplang.com June 3, 2026 at 12:08AM