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Show HN: Moonshine Open-Weights STT models – higher accuracy than WhisperLargev3

https://github.com/moonshine-ai/moonshine
17•petewarden•53m ago•1 comments

Show HN: Emdash – Open-source agentic development environment

https://github.com/generalaction/emdash
71•onecommit•4h ago•33 comments

Show HN: Recursively apply patterns for pathfinding

https://pattern-pathfinder.vercel.app/?fixtureId=%7B%22path%22%3A%22site%2Fexamples%2F_intro.fixt...
6•seveibar•55m ago•1 comments

Show HN: Chaos Monkey but for Audio Video Testing (WebRTC and UDP)

https://github.com/MdSadiqMd/AV-Chaos-Monkey
25•MdSadiqMd•1d ago•2 comments

Show HN: Mnemosyne – Cognitive memory OS for AI agents (zero LLM calls)

https://github.com/28naem-del/mnemosyne
3•mnemosy•44m ago•1 comments

Show HN: Declarative open-source framework for MCPs with search and execute

https://hyperterse.com
5•samrith•1h ago•1 comments

Show HN: enveil – hide your .env secrets from prAIng eyes

https://github.com/GreatScott/enveil
188•parkaboy•17h ago•117 comments

Show HN: Brainstorm-MCP – Let GPT, DeepSeek, and Groq Brainstorm Together

https://github.com/spranab/brainstorm-mcp
2•spranab•1h ago•1 comments

Show HN: Disk Inventory X updated for Apple Silicon

https://diskinv.github.io/
4•lababidi•1h ago•2 comments

Show HN: Bookie – Conquer the bookkeeping and accounting chaos of freelancing

https://www.bookie.tax/
2•nxnze•1h ago•0 comments

Show HN: MiniVim a Minimal Neovim Configuration

https://github.com/KyanJeuring/minivim
4•kppjeuring•1h ago•0 comments

Show HN: CharityVerify – Trust scores for 138K Canadian charities

https://charityverify.com/
2•buchler•1h ago•0 comments

Show HN: PgDog – Scale Postgres without changing the app

https://github.com/pgdogdev/pgdog
314•levkk•1d ago•57 comments

Show HN: ProdRescue AI – Turn Slack war-rooms and raw logs into incident reports

https://www.prodrescueai.com/
2•devrimozcay•2h ago•0 comments

Show HN: Cost per Outcome for AI Workflows

https://github.com/botanu-ai/botanu-sdk-python
4•deborahjacob•2h ago•1 comments

Show HN: I built an iOS app that turns EPUBs into audiobooks

https://apps.apple.com/ua/app/audiobooks-mp3-m4b-player/id6471399965
5•pklym•2h ago•2 comments

Show HN: Claude Code Canvas

https://github.com/raulriera/claude-code-canvas
3•raulriera•2h ago•1 comments

Show HN: Interactive 3D Moon with real NASA data and WebGPU

https://moon.oddurs.com
3•oddurs•3h ago•0 comments

Show HN: X86CSS – An x86 CPU emulator written in CSS

https://lyra.horse/x86css/
254•rebane2001•20h ago•87 comments

Show HN: Babyshark – Wireshark made easy (terminal UI for PCAPs)

https://github.com/vignesh07/babyshark
140•eigen-vector•1d ago•45 comments

Show HN: Steerling-8B, a language model that can explain any token it generates

https://www.guidelabs.ai/post/steerling-8b-base-model-release/
312•adebayoj•22h ago•87 comments

Show HN: Cellarium: A Playground for Cellular Automata

https://github.com/andrewosh/cellarium
39•andrewosh•3d ago•1 comments

Show HN: Sowbot – Open-hardware agricultural robot (ROS2, RTK GPS)

https://sowbot.co.uk/
176•Sabrees•1d ago•45 comments

Show HN: Ghist – Task management that lives in your repo

https://github.com/unnecessary-special-projects/ghist
14•nxnze•5h ago•0 comments

Show HN: Open-Weight Image-Video VAE (Better Reconstruction ≠ Better Generation)

https://www.linum.ai/field-notes/vae-reconstruction-vs-generation
5•schopra909•3h ago•1 comments

Show HN: AI Timeline – 171 LLMs from Transformer (2017) to GPT-5.3 (2026)

https://llm-timeline.com/
167•ai_bot•1d ago•57 comments

Show HN: MasqueradeORM – Memory Efficient Node ORM: Just Write Classes

https://github.com/MasqueradeORM/MasqueradeORM
3•masqueradeorm•5h ago•1 comments

Show HN: CIA World Factbook Archive (1990–2025), searchable and exportable

https://cia-factbook-archive.fly.dev/
484•MilkMp•2d ago•99 comments

Show HN: TTSLab – Text-to-speech that runs in the browser via WebGPU

https://ttslab.dev
2•MbBrainz•7h ago•0 comments

Show HN: L88 – A Local RAG System on 8GB VRAM (Need Architecture Feedback)

10•adithyadrdo•17h ago•1 comments
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Show HN: Cost per Outcome for AI Workflows

https://github.com/botanu-ai/botanu-sdk-python
4•deborahjacob•2h ago

Comments

deborahjacob•2h ago
Hi HN, I’m the technical founder of botanu (www.botanu.ai)

I started building this after repeatedly hitting the same problem on AI teams: we could see total LLM spend, but couldn’t answer “what did one successful outcome actually cost?”. In real systems, a single business event often requires multiple runs ex-retries, fallbacks, escalations, async workers etc., before it reaches a final outcome. Most tooling tracks individual calls, or at best single runs. That hides the true cost. botanu treats cost per outcome as the sum of all runs and attempts for an event, including failures.

How it works -An event represents business intent

-Each attempt is a run, with its own run_id

-All runs are linked via a shared event_id

-A single outcome (success / failure / partial) is emitted for the event

-Total cost = cost of all runs for that event

-Run context propagates across services using standard W3C Baggage (OpenTelemetry).

I’m building this as part of a broader effort around outcome-based pricing for AI systems and understanding true cost per outcome. If you’re thinking about similar problems, I’d love to chat and compare notes. Happy to answer technical questions or get critical feedback. Email- deborah@botanu.ai